problem solving in adulthood

10 Best Problem-Solving Therapy Worksheets & Activities

Cognitive science tells us that we regularly face not only well-defined problems but, importantly, many that are ill defined (Eysenck & Keane, 2015).

Sometimes, we find ourselves unable to overcome our daily problems or the inevitable (though hopefully infrequent) life traumas we face.

Problem-Solving Therapy aims to reduce the incidence and impact of mental health disorders and improve wellbeing by helping clients face life’s difficulties (Dobson, 2011).

This article introduces Problem-Solving Therapy and offers techniques, activities, and worksheets that mental health professionals can use with clients.

Before you continue, we thought you might like to download our three Positive Psychology Exercises for free . These science-based exercises explore fundamental aspects of positive psychology, including strengths, values, and self-compassion, and will give you the tools to enhance the wellbeing of your clients, students, or employees.

This Article Contains:

What is problem-solving therapy, 14 steps for problem-solving therapy, 3 best interventions and techniques, 7 activities and worksheets for your session, fascinating books on the topic, resources from positivepsychology.com, a take-home message.

Problem-Solving Therapy assumes that mental disorders arise in response to ineffective or maladaptive coping. By adopting a more realistic and optimistic view of coping, individuals can understand the role of emotions and develop actions to reduce distress and maintain mental wellbeing (Nezu & Nezu, 2009).

“Problem-solving therapy (PST) is a psychosocial intervention, generally considered to be under a cognitive-behavioral umbrella” (Nezu, Nezu, & D’Zurilla, 2013, p. ix). It aims to encourage the client to cope better with day-to-day problems and traumatic events and reduce their impact on mental and physical wellbeing.

Clinical research, counseling, and health psychology have shown PST to be highly effective in clients of all ages, ranging from children to the elderly, across multiple clinical settings, including schizophrenia, stress, and anxiety disorders (Dobson, 2011).

Can it help with depression?

PST appears particularly helpful in treating clients with depression. A recent analysis of 30 studies found that PST was an effective treatment with a similar degree of success as other successful therapies targeting depression (Cuijpers, Wit, Kleiboer, Karyotaki, & Ebert, 2020).

Other studies confirm the value of PST and its effectiveness at treating depression in multiple age groups and its capacity to combine with other therapies, including drug treatments (Dobson, 2011).

The major concepts

Effective coping varies depending on the situation, and treatment typically focuses on improving the environment and reducing emotional distress (Dobson, 2011).

PST is based on two overlapping models:

Social problem-solving model

This model focuses on solving the problem “as it occurs in the natural social environment,” combined with a general coping strategy and a method of self-control (Dobson, 2011, p. 198).

The model includes three central concepts:

• Social problem-solving
• The problem
• The solution

The model is a “self-directed cognitive-behavioral process by which an individual, couple, or group attempts to identify or discover effective solutions for specific problems encountered in everyday living” (Dobson, 2011, p. 199).

Relational problem-solving model

The theory of PST is underpinned by a relational problem-solving model, whereby stress is viewed in terms of the relationships between three factors:

• Stressful life events
• Emotional distress and wellbeing
• Problem-solving coping

Therefore, when a significant adverse life event occurs, it may require “sweeping readjustments in a person’s life” (Dobson, 2011, p. 202).

• Enhance positive problem orientation
• Decrease negative orientation
• Foster ability to apply rational problem-solving skills
• Reduce the tendency to avoid problem-solving
• Minimize the tendency to be careless and impulsive

D’Zurilla’s and Nezu’s model includes (modified from Dobson, 2011):

• Initial structuring Establish a positive therapeutic relationship that encourages optimism and explains the PST approach.
• Assessment Formally and informally assess areas of stress in the client’s life and their problem-solving strengths and weaknesses.
• Obstacles to effective problem-solving Explore typically human challenges to problem-solving, such as multitasking and the negative impact of stress. Introduce tools that can help, such as making lists, visualization, and breaking complex problems down.
• Problem orientation – fostering self-efficacy Introduce the importance of a positive problem orientation, adopting tools, such as visualization, to promote self-efficacy.
• Problem orientation – recognizing problems Help clients recognize issues as they occur and use problem checklists to ‘normalize’ the experience.
• Problem orientation – seeing problems as challenges Encourage clients to break free of harmful and restricted ways of thinking while learning how to argue from another point of view.
• Problem orientation – use and control emotions Help clients understand the role of emotions in problem-solving, including using feelings to inform the process and managing disruptive emotions (such as cognitive reframing and relaxation exercises).
• Problem orientation – stop and think Teach clients how to reduce impulsive and avoidance tendencies (visualizing a stop sign or traffic light).
• Problem definition and formulation Encourage an understanding of the nature of problems and set realistic goals and objectives.
• Generation of alternatives Work with clients to help them recognize the wide range of potential solutions to each problem (for example, brainstorming).
• Decision-making Encourage better decision-making through an improved understanding of the consequences of decisions and the value and likelihood of different outcomes.
• Solution implementation and verification Foster the client’s ability to carry out a solution plan, monitor its outcome, evaluate its effectiveness, and use self-reinforcement to increase the chance of success.
• Guided practice Encourage the application of problem-solving skills across multiple domains and future stressful problems.
• Rapid problem-solving Teach clients how to apply problem-solving questions and guidelines quickly in any given situation.

Success in PST depends on the effectiveness of its implementation; using the right approach is crucial (Dobson, 2011).

Problem-solving therapy – Baycrest

The following interventions and techniques are helpful when implementing more effective problem-solving approaches in client’s lives.

First, it is essential to consider if PST is the best approach for the client, based on the problems they present.

Is PPT appropriate?

It is vital to consider whether PST is appropriate for the client’s situation. Therapists new to the approach may require additional guidance (Nezu et al., 2013).

Therapists should consider the following questions before beginning PST with a client (modified from Nezu et al., 2013):

• Has PST proven effective in the past for the problem? For example, research has shown success with depression, generalized anxiety, back pain, Alzheimer’s disease, cancer, and supporting caregivers (Nezu et al., 2013).
• Is PST acceptable to the client?
• Is the individual experiencing a significant mental or physical health problem?

All affirmative answers suggest that PST would be a helpful technique to apply in this instance.

Five problem-solving steps

The following five steps are valuable when working with clients to help them cope with and manage their environment (modified from Dobson, 2011).

Ask the client to consider the following points (forming the acronym ADAPT) when confronted by a problem:

• Attitude Aim to adopt a positive, optimistic attitude to the problem and problem-solving process.
• Define Obtain all required facts and details of potential obstacles to define the problem.
• Alternatives Identify various alternative solutions and actions to overcome the obstacle and achieve the problem-solving goal.
• Predict Predict each alternative’s positive and negative outcomes and choose the one most likely to achieve the goal and maximize the benefits.
• Try out Once selected, try out the solution and monitor its effectiveness while engaging in self-reinforcement.

If the client is not satisfied with their solution, they can return to step ‘A’ and find a more appropriate solution.

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Positive self-statements

When dealing with clients facing negative self-beliefs, it can be helpful for them to use positive self-statements.

Use the following (or add new) self-statements to replace harmful, negative thinking (modified from Dobson, 2011):

• I can solve this problem; I’ve tackled similar ones before.
• I can cope with this.
• I just need to take a breath and relax.
• Once I start, it will be easier.
• It’s okay to look out for myself.
• I can get help if needed.
• Other people feel the same way I do.
• I’ll take one piece of the problem at a time.
• I can keep my fears in check.
• I don’t need to please everyone.

5 Worksheets and workbooks

Problem-solving self-monitoring form.

Answering the questions in the Problem-Solving Self-Monitoring Form provides the therapist with necessary information regarding the client’s overall and specific problem-solving approaches and reactions (Dobson, 2011).

Ask the client to complete the following:

• Describe the problem you are facing.
• What is your goal?
• What have you tried so far to solve the problem?
• What was the outcome?

Reactions to Stress

It can be helpful for the client to recognize their own experiences of stress. Do they react angrily, withdraw, or give up (Dobson, 2011)?

The Reactions to Stress worksheet can be given to the client as homework to capture stressful events and their reactions. By recording how they felt, behaved, and thought, they can recognize repeating patterns.

What Are Your Unique Triggers?

Helping clients capture triggers for their stressful reactions can encourage emotional regulation.

When clients can identify triggers that may lead to a negative response, they can stop the experience or slow down their emotional reaction (Dobson, 2011).

The What Are Your Unique Triggers ? worksheet helps the client identify their triggers (e.g., conflict, relationships, physical environment, etc.).

Problem-Solving worksheet

Imagining an existing or potential problem and working through how to resolve it can be a powerful exercise for the client.

Use the Problem-Solving worksheet to state a problem and goal and consider the obstacles in the way. Then explore options for achieving the goal, along with their pros and cons, to assess the best action plan.

Getting the Facts

Clients can become better equipped to tackle problems and choose the right course of action by recognizing facts versus assumptions and gathering all the necessary information (Dobson, 2011).

Use the Getting the Facts worksheet to answer the following questions clearly and unambiguously:

• Who is involved?
• What did or did not happen, and how did it bother you?
• Where did it happen?
• When did it happen?
• Why did it happen?
• How did you respond?

2 Helpful Group Activities

While therapists can use the worksheets above in group situations, the following two interventions work particularly well with more than one person.

Generating Alternative Solutions and Better Decision-Making

A group setting can provide an ideal opportunity to share a problem and identify potential solutions arising from multiple perspectives.

Use the Generating Alternative Solutions and Better Decision-Making worksheet and ask the client to explain the situation or problem to the group and the obstacles in the way.

Once the approaches are captured and reviewed, the individual can share their decision-making process with the group if they want further feedback.

Visualization

Visualization can be performed with individuals or in a group setting to help clients solve problems in multiple ways, including (Dobson, 2011):

• Clarifying the problem by looking at it from multiple perspectives
• Rehearsing a solution in the mind to improve and get more practice
• Visualizing a ‘safe place’ for relaxation, slowing down, and stress management

Guided imagery is particularly valuable for encouraging the group to take a ‘mental vacation’ and let go of stress.

Ask the group to begin with slow, deep breathing that fills the entire diaphragm. Then ask them to visualize a favorite scene (real or imagined) that makes them feel relaxed, perhaps beside a gently flowing river, a summer meadow, or at the beach.

The more the senses are engaged, the more real the experience. Ask the group to think about what they can hear, see, touch, smell, and even taste.

Encourage them to experience the situation as fully as possible, immersing themselves and enjoying their place of safety.

Such feelings of relaxation may be able to help clients fall asleep, relieve stress, and become more ready to solve problems.

We have included three of our favorite books on the subject of Problem-Solving Therapy below.

1. Problem-Solving Therapy: A Treatment Manual – Arthur Nezu, Christine Maguth Nezu, and Thomas D’Zurilla

This is an incredibly valuable book for anyone wishing to understand the principles and practice behind PST.

Written by the co-developers of PST, the manual provides powerful toolkits to overcome cognitive overload, emotional dysregulation, and the barriers to practical problem-solving.

Find the book on Amazon .

2. Emotion-Centered Problem-Solving Therapy: Treatment Guidelines – Arthur Nezu and Christine Maguth Nezu

Another, more recent, book from the creators of PST, this text includes important advances in neuroscience underpinning the role of emotion in behavioral treatment.

Along with clinical examples, the book also includes crucial toolkits that form part of a stepped model for the application of PST.

3. Handbook of Cognitive-Behavioral Therapies – Keith Dobson and David Dozois

This is the fourth edition of a hugely popular guide to Cognitive-Behavioral Therapies and includes a valuable and insightful section on Problem-Solving Therapy.

This is an important book for students and more experienced therapists wishing to form a high-level and in-depth understanding of the tools and techniques available to Cognitive-Behavioral Therapists.

For even more tools to help strengthen your clients’ problem-solving skills, check out the following free worksheets from our blog.

• Case Formulation Worksheet This worksheet presents a four-step framework to help therapists and their clients come to a shared understanding of the client’s presenting problem.
• Understanding Your Default Problem-Solving Approach This worksheet poses a series of questions helping clients reflect on their typical cognitive, emotional, and behavioral responses to problems.
• Social Problem Solving: Step by Step This worksheet presents a streamlined template to help clients define a problem, generate possible courses of action, and evaluate the effectiveness of an implemented solution.

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While we are born problem-solvers, facing an incredibly diverse set of challenges daily, we sometimes need support.

Problem-Solving Therapy aims to reduce stress and associated mental health disorders and improve wellbeing by improving our ability to cope. PST is valuable in diverse clinical settings, ranging from depression to schizophrenia, with research suggesting it as a highly effective treatment for teaching coping strategies and reducing emotional distress.

Many PST techniques are available to help improve clients’ positive outlook on obstacles while reducing avoidance of problem situations and the tendency to be careless and impulsive.

The PST model typically assesses the client’s strengths, weaknesses, and coping strategies when facing problems before encouraging a healthy experience of and relationship with problem-solving.

Why not use this article to explore the theory behind PST and try out some of our powerful tools and interventions with your clients to help them with their decision-making, coping, and problem-solving?

We hope you enjoyed reading this article. Don’t forget to download our three Positive Psychology Exercises for free .

• Cuijpers, P., Wit, L., Kleiboer, A., Karyotaki, E., & Ebert, D. (2020). Problem-solving therapy for adult depression: An updated meta-analysis. European P sychiatry ,  48 (1), 27–37.
• Dobson, K. S. (2011). Handbook of cognitive-behavioral therapies (3rd ed.). Guilford Press.
• Dobson, K. S., & Dozois, D. J. A. (2021). Handbook of cognitive-behavioral therapies  (4th ed.). Guilford Press.
• Eysenck, M. W., & Keane, M. T. (2015). Cognitive psychology: A student’s handbook . Psychology Press.
• Nezu, A. M., & Nezu, C. M. (2009). Problem-solving therapy DVD . Retrieved September 13, 2021, from https://www.apa.org/pubs/videos/4310852
• Nezu, A. M., & Nezu, C. M. (2018). Emotion-centered problem-solving therapy: Treatment guidelines. Springer.
• Nezu, A. M., Nezu, C. M., & D’Zurilla, T. J. (2013). Problem-solving therapy: A treatment manual . Springer.

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Cognitive Development in Late Adulthood

Diana Lang; Nick Cone; Sonja Ann Miller; Martha Lally; and Suzanne Valentine-French

There are numerous stereotypes regarding older adults as being forgetful and confused, but what does the research on memory and cognition in late adulthood actually reveal? In this section, we wil l focus upon t he impact of aging on memory, how a ge impacts cognitive functioning, and a bnormal memory loss due to Alzheimer’s disease, deliriu m, and dementia. [1]

How does aging affect memory?

The Sensory Register

Aging may create small decrements in the sensitivity of the senses.  And, to the extent that a person has a more difficult time hearing or seeing,   that information will not be stored in memory. This is an important point, because many older people assume that if they cannot remember something, it is because their memory is poor. In fact, it may be that the information was never seen or heard.

The Working Memory

Older people have more difficulty using memory strategies to recall details. [2] Working memory is a cognitive system with a limited capacity responsible for temporarily holding information available for processing . As we age, the working memory loses some of its capacity. This makes it more difficult to concentrate on more than one thing at a time or to remember details of an event.  However, people often compensate for this by writing down information and avoiding situations where there is too much going on at once to focus on a particular cognitive task.

When an elderly person demonstrates difficulty with multi-step verbal information presented quickly, the person is exhibiting problems with working memory. Working memory is among the cognitive functions most sensitive to decline in old age. Several explanations have been offered for this decline in memory functioning; one is the processing speed theory of cognitive aging by Tim Salthouse. Drawing on the findings of general slowing of cognitive processes as people grow older, Salthouse argues that slower processing causes working-memory contents to decay, thus reducing effective capacity. [3] For example, if an elderly person is watching a complicated action movie, they may not process the events quickly enough before the scene changes, or they may processing the events of the second scene, which causes them to forget the first scene. The decline of working-memory capacity cannot be entirely attributed to cognitive slowing, however, because capacity declines more in old age than speed.

Another proposal is the inhibition hypothesis advanced by Lynn Hasher and Rose Zacks [4] . This theory assumes a general deficit in old age in the ability to inhibit irrelevant, or no-longer relevant, information. Therefore, working memory tends to be cluttered with irrelevant contents which reduce the effective capacity for relevant content. The assumption of an inhibition deficit in old age has received much empirical support but, so far, it is not clear whether the decline in inhibitory ability fully explains the decline of working-memory capacity.

An explanation on the neural level of the decline of working memory and other cognitive functions in old age was been proposed by Robert West. He argued that working memory depends to a large degree on the pre-frontal cortex, which deteriorates more than other brain regions as we grow old. [5] Age related decline in working memory can be briefly reversed using low intensity transcranial stimulation, synchronizing rhythms in bilateral frontal and left temporal lobe areas.

The Long-Term Memory

Long-term memory involves the storage of information for long periods of time. Retrieving such information depends on how well it was learned in the first place rather than how long it has been stored. If information is stored effectively, an older person may remember facts, events, names and other types of information stored in long-term memory throughout life. The memory of adults of all ages seems to be similar when they are asked to recall names of teachers or classmates. And older adults remember more about their early adulthood and adolescence than about middle adulthood. [6] Older adults retain semantic memory or the ability to remember vocabulary.

Younger adults rely more on mental rehearsal strategies to store and retrieve information. Older adults focus rely more on external cues such as familiarity and context to recall information. [7] And they are more likely to report the main idea of a story rather than all of the details. [8]

A positive attitude about being able to learn and remember plays an important role in memory. When people are under stress (perhaps feeling stressed about memory loss), they have a more difficult time taking in information because they are preoccupied with anxieties. Many of the laboratory memory tests require comparing the performance of older and younger adults on timed memory tests in which older adults do not perform as well. However, few real life situations require speedy responses to memory tasks. Older adults rely on more meaningful cues to remember facts and events without any impairment to everyday living.

New Research on Aging and Cognition

Can the brain be trained in order to build cognitive reserve to reduce the effects of normal aging? ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly), a study conducted between 1999 and 2001 in which 2,802 individuals age 65 to 94, suggests that the answer is “yes.” These participants received 10 group training sessions and 4 follow up sessions to work on tasks of memory, reasoning, and speed of processing. These mental workouts improved cognitive functioning even 5 years later. Many of the participants believed that this improvement could be seen in everyday tasks as well. [9] Learning new things, engaging in activities that are considered challenging, and being physically active at any age may build a reserve to minimize the effects of primary aging of the brain.

Watch this video from SciShow Psych to learn about ways to keep the mind young and active.

You can view the transcript for “The Best Ways to Keep Your Mind Young” here (opens in new window) .

Changes in Attention in Late Adulthood

Divided attention has usually been associated with significant age-related declines in performing complex tasks. For example, older adults show significant impairments on attentional tasks such as looking at a visual cue at the same time as listening to an auditory cue because it requires dividing or switching of attention among multiple inputs. Deficits found in many tasks, such as the Stroop task which measures selective attention, can be largely attributed to a general slowing of information processing in older adults rather than to selective attention deficits per se. They also are able to maintain concentration for an extended period of time. In general, older adults are not impaired on tasks that test sustained attention, such as watching a screen for an infrequent beep or symbol.

The tasks on which older adults show impairments tend to be those that require flexible control of attention, a cognitive function associated with the frontal lobes. Importantly, these types of tasks appear to improve with training and can be strengthened. [10]

An important conclusion from research on changes in cognitive function as we age is that attentional deficits can have a significant impact on an older person’s ability to function adequately and independently in everyday life. One important aspect of daily functioning impacted by attentional problems is driving. This is an activity that, for many older people, is essential to independence. Driving requires a constant switching of attention in response to environmental contingencies. Attention must be divided between driving, monitoring the environment, and sorting out relevant from irrelevant stimuli in a cluttered visual array. Research has shown that divided attention impairments are significantly associated with increased automobile accidents in older adults [11]   Therefore, practice and extended training on driving simulators under divided attention conditions may be an important remedial activity for older people. [12]

Problem Solving

Problem solving tasks that require processing non-meaningful information quickly (a kind of task which might be part of a laboratory experiment on mental processes) declines with age. However, real life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults are able to resolve everyday problems by relying on input from others such as family and friends. They are also less likely than younger adults to delay making decisions on important matters such as medical care. [13] [14]

Brain Functioning

Research has demonstrated that the brain loses 5% to 10% of its weight between 20 and 90 years of age. [15] This decrease in brain volume appears to be due to the shrinkage of neurons, lower number of synapses, and shorter length of axons. According to Garrett, [16] the normal decline in cognitive ability throughout the lifespan has been associated with brain changes, including reduced activity of genes involved in memory storage, synaptic pruning, plasticity, and glutamate and GABA (neurotransmitters) receptors. There is also a loss in white matter connections between brain areas. Without myelin, neurons demonstrate slower conduction and impede each other’s actions. A loss of synapses occurs in specific brain areas, including the hippocampus (involved in memory) and the basal forebrain region. Older individuals also activate larger regions of their attentional and executive networks, located in the parietal and prefrontal cortex, when they perform complex tasks. This increased activation correlates with a reduced performance on both executive tasks and tests of working memory when compared to those younger. [17]

Despite these changes the brain exhibits considerable plasticity, and through practice and training, the brain can be modified to compensate for age-related changes. [18] Park and Reuter-Lorenz [19] proposed the Scaffolding Theory of Aging and Cognition which states that the brain adapts to neural atrophy (dying of brain cells) by building alternative connections, referred to as scaffolding. This scaffolding allows older brains to retain high levels of performance. Brain compensation is especially noted in the additional neural effort demonstrated by those individuals who are aging well. For example, older adults who performed just as well as younger adults on a memory task used both prefrontal areas, while only the right prefrontal cortex was used in younger participants. [20] Consequently, this decrease in brain lateralization appears to assist older adults with their cognitive skills.

Can we improve brain functioning? Many training programs have been created to improve brain functioning. ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly), a study conducted between 1999 and 2001 in which 2,802 individuals age 65 to 94, suggests that the answer is “yes”. These racially diverse participants received 10 group training sessions and 4 follow up sessions to work on tasks of memory, reasoning, and speed of processing. These mental workouts improved cognitive functioning even 5 years later. Many of the participants believed that this improvement could be seen in everyday tasks as well. [21] However, programs for the elderly on memory, reading, and processing speed training demonstrate that there is improvement on the specific tasks trained, but there is no generalization to other abilities. [22] Further, these programs have not been shown to delay or slow the progression of Alzheimer’s disease. Although these programs are not harmful, “physical exercise, learning new skills, and socializing remain the most effective ways to train your brain” (p. 207). These activities appear to build a reserve to minimize the effects of primary aging of the brain.

Parkinson’s disease

Parkinson’s disease is characterized by motor tremors, loss of balance, poor coordination, rigidity, and difficulty moving . [23] Parkinson’s affects approximately 1% of those over the age of 60, and it appears more frequently in family members in a little less than 10% of cases. Twenty-eight chromosomal areas have been implicated in Parkinson’s disease, but environmental factors have also been identified and include brain injury. Being knocked unconscious once increases the risk by 32%, and being knocked out several times increases the risk by 174%. [24] Other environmental influences include toxins, industrial chemicals, carbon monoxide, herbicides and pesticides. [25] The symptoms are due to the deterioration of the substantia nigra, an area in the midbrain whose neurons send dopamine-releasing axons to the basal ganglia which affects motor activity. Treatment typically includes the medication levodopa (L-dopa), which crosses the blood-brain barrier and is converted into dopamine in the brain. Deep brain stimulation, which involves inserting an electrode into the brain that provides electrical stimulation, has resulted in improved motor functioning. [26]

Similar to other adults, older adults need between 7 to 9 hours of sleep per night, but they tend to go to sleep earlier and get up earlier than those younger. This pattern is called advanced sleep phase syndrome and is based on changes in circadian rhythms. [27] There are sleep problems in older adults, and insomnia is the most common problem in those 60 and older. [28] People with insomnia have trouble falling asleep and staying asleep . There are many reasons why older people may have insomnia, including certain medications, being in pain, having a medical or psychiatric condition, and even worrying before bedtime about not being able to sleep. Using over the counter sleep aids or medication may only work when used for a short time. Consequently, sleep problems should be discussed with a health care professional.

Also, common in older adults are sleep disorders, including sleep apnea, restless legs syndrome, periodic limb movement disorder, and rapid eye movement sleep behavior disorder. [29] Sleep apnea refers to repeated short pauses in breathing, while an individual sleeps, that can lead to reduced oxygen in the blood . Snoring is a common symptom of sleep apnea and it often worsens with age. Untreated sleep apnea can lead to impaired daytime functioning, high blood pressure, headaches, stroke, and memory loss. Restless legs syndrome feels like there is tingling, crawling, or pins and needles in one or both legs, and this feeling is worse at night.  Periodic limb movement disorder causes people to jerk and kick their legs every 20 to 40 seconds during sleep. Rapid eye movement sleep behavior disorder occurs when one’s muscles can move during REM sleep and sleep is disrupted. 

According to the National Sleep Foundation, [30] there are many medical conditions that affect sleep and include gastroesophageal reflux disease, diabetes mellitus, renal failure, respiratory diseases such as asthma, and immune disorders. Diseases such as Parkinson’s disease and multiple sclerosis also commonly cause problems sleeping. Lastly, Alzheimer’s disease can interfere with sleeping patterns. Individuals may wake up many times during the night, wander when up, and yell which can alter the amount of time they sleep. Both minor and significant sleep problems in older adults can lead to increased risk of accidents, falls, chronic fatigue, decreased quality of life, cognitive decline, reduced immune function, and depression. [31]

Because of sleep problems experienced by those in late adulthood, research has looked into whether exercise can improve their quality of sleep. Results show that 150 minutes per week of exercise can improve sleep quality. [32] This amount of exercise is also recommended to improve other health areas including lowering the risk for heart disease, diabetes, and some cancers. Aerobic activity, weight training, and balance programs are all recommended. For those who live in assisted living facilities even light exercise, such as stretching and short walks, can improve sleep. High intensity activity is not necessary to see improvements. Overall, the effects of exercise on sleep may actually be even larger for older adults since their sleep quality may not be ideal to start.

Intelligence and Wisdom

When looking at scores on traditional intelligence tests, tasks measuring verbal skills show minimal or no age-related declines, while scores on performance tests, which measure solving problems quickly decline with age. [33] This profile mirrors crystalized and fluid intelligence. As you recall from last chapter, crystallized intelligence encompasses abilities that draw upon experience and knowledge. Measures of crystallized intelligence include vocabulary tests, solving number problems, and understanding texts. Fluid intelligence refers to information processing abilities, such as logical reasoning, remembering lists, spatial ability, and reaction time. Baltes [34] introduced two additional types of intelligence to reflect cognitive changes in aging. Pragmatics of intelligence are cultural exposure to facts and procedures that are maintained as one ages and are similar to crystalized intelligence . Mechanics of intelligence are dependent on brain functioning and decline with age, similar to fluid intelligence. Baltes indicated that pragmatics of intelligence show little decline and typically increase with age. Additionally, pragmatics of intelligence may compensate for the declines that occur with mechanics of intelligence. In summary, global cognitive declines are not typical as one ages, and individuals compensate for some cognitive declines, especially processing speed.

Wisdom is the ability to use the accumulated knowledge about practical matters that allows for sound judgment and decision making . A wise person is insightful and has knowledge that can be used to overcome obstacles in living. Does aging bring wisdom? While living longer brings experience, it does not always bring wisdom. Paul Baltes and his colleagues [35] [36]   suggest that wisdom is rare. In addition, the emergence of wisdom can be seen in late adolescence and young adulthood, with there being few gains in wisdom over the course of adulthood. [37] This would suggest that factors other than age are stronger determinants of wisdom. Occupations and experiences that emphasize others rather than self, along with personality characteristics, such as openness to experience and generativity, are more likely to provide the building blocks of wisdom. [38] Age combined with a certain types of experience and/or personality brings wisdom.

Attention and Problem Solving

Changes in sensory functioning and speed of processing information in late adulthood often translates into changes in attention. [39] Research has shown that older adults are less able to selectively focus on information while ignoring distractors, [40] [41] although Jefferies and her colleagues found that when given double time, older adults could perform at young adult levels. Other studies have also found that older adults have greater difficulty shifting their attention between objects or locations. [42] Consider the implication of these attentional changes for older adults.

How do changes or maintenance of cognitive ability affect older adults’ everyday lives? Researchers have studied cognition in the context of several different everyday activities. One example is driving. Although older adults often have more years of driving experience, cognitive declines related to reaction time or attentional processes may pose limitations under certain circumstances. [43] In contrast, research on interpersonal problem solving suggested that older adults use more effective strategies than younger adults to navigate through social and emotional problems. [44] In the context of work, researchers rarely find that older individuals perform poorer on the job. [45] Similar to everyday problem solving, older workers may develop more efficient strategies and rely on expertise to compensate for cognitive decline.

Problem solving tasks that require processing non-meaningful information quickly (a kind of task that might be part of a laboratory experiment on mental processes) declines with age. However, many real-life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults resolve everyday problems by relying on input from others, such as family and friends. They are also less likely than younger adults to delay making decisions on important matters, such as medical care. [46] [47]

Deficit theories

The processing speed theory , proposed by Salthouse, [48] [49] suggests that as the nervous system slows with advanced age our ability to process information declines . This slowing of processing speed may explain age differences on many different cognitive tasks. For instance, as we age, working memory becomes less efficient. [50] Older adults also need longer time to complete mental tasks or make decisions. Yet, when given sufficient time older adults perform as competently as do young adults. [51] Thus, when speed is not imperative to the task healthy older adults do not show cognitive declines.

In contrast, inhibition theory argues that older adults have difficulty with inhibitory functioning, or the ability to focus on certain information while suppressing attention to less pertinent information tasks . [52] Evidence comes from directed forgetting research. In directed forgetting people are asked to forget or ignore some information, but not other information. For example, you might be asked to memorize a list of words, but are then told that the researcher made a mistake and gave you the wrong list, and asks you to “forget” this list. You are then given a second list to memorize. While most people do well at forgetting the first list, older adults are more likely to recall more words from the “forget-to-recall” list than are younger adults. [53]

Cognitive losses exaggerated

While there are information processing losses in late adulthood, overall loss has been exaggerated. [54] One explanation is that the type of tasks that people are tested on tend to be meaningless. For example, older individuals are not motivated to remember a random list of words in a study, but they are motivated for more meaningful material related to their life, and consequently perform better on those tests. Another reason is that the research is often cross-sectional. When age comparisons occur longitudinally, however, the amount of loss diminishes. [55] A third reason is that the loss may be due to a lack of opportunity in using various skills. When older adults practiced skills, they performed as well as they had previously. Although diminished performance speed is especially noteworthy in the elderly, Schaie [56] found that statistically removing the effects of speed diminished the individual’s performance declines significantly. In fact, Salthouse and Babcock [57] demonstrated that processing speed accounted for all but 1% of age-related differences in working memory when testing individuals from 18 to 82. Finally, it is well established that our hearing and vision decline as we age. Longitudinal research has proposed that deficits in sensory functioning explain age differences in a variety of cognitive abilities. [58]

Abnormal Loss of Cognitive Functioning During Late Adulthood

Historically, the term dementia was used to refer to an individual experiencing difficulties with memory, language, abstract thinking, reasoning, decision making, and problem-solving. [59] While the term dementia is still in common use, in the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) [60] the term dementia has been replaced by neurocognitive disorder. A Major Neurocognitive Disorder is diagnosed as a significant cognitive decline from a previous level of performance in one or more cognitive domains and interferes with independent functioning, while a Minor Neurocognitive Disorder is diagnosed as a modest cognitive decline from a previous level of performance in one of more cognitive domains and does not interfere with independent functioning. There are several different neurocognitive disorders that are typically demonstrated in late adulthood, and determining the exact type can be difficult because the symptoms may overlap with each other. Diagnosis often includes a medical history, physical exam, laboratory tests, and changes noted in behavior.

Common symptoms of dementia include emotional problems, difficulties with language, and a decrease in motivation. A person’s consciousness is usually not affected. Globally, dementia affected about 46 million people in 2015. About 10% of people develop the disorder at some point in their lives, and it becomes more common with age. About 3% of people between the ages of 65–74 have dementia, 19% between 75 and 84, and nearly half of those over 85 years of age. In 2015, dementia resulted in about 1.9 million deaths, up from 0.8 million in 1990. As more people are living longer, dementia is becoming more common in the population as a whole.

Dementia generally refers to severely impaired judgment, memory or problem-solving ability. It can occur before old age and is not an inevitable development even among the very old. Dementia can be caused by numerous diseases and circumstances, all of which result in similar general symptoms of impaired judgment, etc. Alzheimer’s disease is the most common form of dementia and is incurable, but there are also nonorganic causes of dementia which can be prevented. Malnutrition, alcoholism, depression, and mixing medications can also result in symptoms of dementia. If these causes are properly identified, they can be treated. Cerebral vascular disease can also reduce cognitive functioning.

Delirium , also known as acute confusional state, is an organically caused decline from a previous baseline level of mental function that develops over a short period of time, typically hours to days. It is more common in older adults, but can easily be confused with a number of psychiatric disorders or chronic organic brain syndromes because of many overlapping signs and symptoms in common with dementia, depression, psychosis, etc. Delirium may manifest from a baseline of existing mental illness, baseline intellectual disability, or dementia, without being due to any of these problems.

Delirium is a syndrome encompassing disturbances in attention, consciousness, and cognition. It may also involve other neurological deficits, such as psychomotor disturbances (e.g. hyperactive, hypoactive, or mixed), impaired sleep-wake cycle, emotional disturbances, and perceptual disturbances (e.g. hallucinations and delusions), although these features are not required for diagnosis. Among older adults, delirium occurs in 15-53% of post-surgical patients, 70-87% of those in the ICU, and up to 60% of those in nursing homes or post-acute care settings. Among those requiring critical care, delirium is a risk for death within the next year.

Alzheimer’s Disease

Alzheimer’s disease (AD) , also referred to simply as Alzheimer’s, is the most common cause of dementia, accounting for 60-70% of its cases. Alzheimer’s   is a progressive disease causing problems with memory, thinking and behavior. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks. [61]

Alzheimer’s disease is probably the most well-known and most common neurocognitive disorder for older individuals. In 2016, an estimated 5.4 million Americans were diagnosed with Alzheimer’s disease, [62] which was approximately one in nine aged 65 and over. By 2050, the number of people age 65 and older with Alzheimer’s disease is projected to be 13.8 million if there are no medical breakthroughs to prevent or cure the disease. Alzheimer’s disease is the 6th leading cause of death in the United States, but the 5th leading cause for those 65 and older. Among the top 10 causes of death in America, Alzheimer’s disease is the only one that cannot be prevented, cured, or even slowed. Current estimates indicate that Alzheimer disease affects approximately 50% of those identified with a neurocognitive disorder. [63]

Alzheimer’s disease has a gradual onset with subtle personality changes and memory loss that differs from normal age-related memory problems occurring first. Confusion, difficulty with change, and deterioration in language, problem-solving skills, and personality become evident next. In the later stages, the individual loses physical coordination and is unable to complete everyday tasks, including self-care and personal hygiene. [64] Lastly, individuals lose the ability to respond to their environment, to carry on a conversation, and eventually to control movement (Alzheimer’s Association, 2016). The disease course often depends on the individual’s age and whether they have other health conditions.

Alzheimer’s is the sixth leading cause of death in the United States. On average, a person with Alzheimer’s lives four to eight years after diagnosis, but can live as long as 20 years, depending on other factors. Alzheimer’s is not a normal part of aging. The greatest known risk factor is increasing age, and the majority of people with Alzheimer’s are 65 and older. But Alzheimer’s is not just a disease of old age. Approximately 200,000 Americans under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset Alzheimer’s). [65]

The cause of Alzheimer’s disease is poorly understood. About 70% of the risk is believed to be inherited from a person’s parents with many genes usually involved. Other risk factors include a history of head injuries, depression, and hypertension. The disease process is associated with plaques and neurofibrillary tangles in the brain. A probable diagnosis is based on the history of the illness and cognitive testing with medical imaging and blood tests to rule out other possible causes. Initial symptoms are often mistaken for normal aging, but examination of brain tissue, specifically of structures called plaques and tangles, is needed for a definite diagnosis. Though qualified physicians can be up to 90% certain of a correct diagnosis of Alzheimer’s, currently, the only way to make a 100% definitive diagnosis is by performing an autopsy of the person and examining the brain tissue. In 2015, there were approximately 29.8 million people worldwide with AD. In developed countries, AD is one of the most financially costly diseases.

This Ted-Ed video explains some of the history and biological diagnosis of Alzheimer’s.

You can view the transcript for “What is Alzheimer’s disease? – Ivan Seah Yu Jun” here .

Samuel Cohen researches Alzheimer’s disease and other neurodegenerative disorders. Listen to Cohen’s TED Talk on Alzheimer’s disease to learn more.

• This chapter was adapted from select chapters in Lumen Learning's Lifespan Development , authored by Martha Lally and Suzanne Valentine-French available under a Creative Commons Attribution-NonCommercial-ShareAlike license , and Waymaker Lifespan Development , authored by Sonja Ann Miller for Lumen Learning and available under a Creative Commons Attribution license . Some selections from Lumen Learning were adapted from previously shared content from Laura Overstreet's Lifespan Psychology and Wikipedia. ↵
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Problem-Solving Strategies and Obstacles

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

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• Application
• Improvement

From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cognitive Development in Adulthood

Cognitive development in early adulthood.

We have learned about cognitive development from infancy through adolescence, ending with Piaget’s stage of formal operations. Does that mean that cognitive development stops with adolescence? Couldn’t there be different ways of thinking in adulthood that come after (or “post”) formal operations?

In this section, we will learn about these types of postformal operational thought and consider research done by William Perry related to types of thought and advanced thinking. We will also look at education in early adulthood, the relationship between education and work, and some tools used by young adults to choose their careers.

Beyond Formal Operational Thought: Postformal Thought

Figure 1 . As young adults gain more experience, they think increasingly more in the abstract and are able to understand different perspectives and complexities.

In the adolescence module, we discussed Piaget’s formal operational thought. The hallmark of this type of thinking is the ability to think abstractly or to consider possibilities and ideas about circumstances never directly experienced. Thinking abstractly is only one characteristic of adult thought, however. If you compare a 14-year-old with someone in their late 30s, you would probably find that the later considers not only what is possible, but also what is likely. Why the change? The young adult has gained experience and understands why possibilities do not always become realities. This difference in adult and adolescent thought can spark arguments between the generations.

Here is an example. A student in her late 30s relayed such an argument she was having with her 14-year-old son. The son had saved a considerable amount of money and wanted to buy an old car and store it in the garage until he was old enough to drive. He could sit in it, pretend he was driving, clean it up, and show it to his friends. It sounded like a perfect opportunity. The mother, however, had practical objections. The car would just sit for several years while deteriorating. The son would probably change his mind about the type of car he wanted by the time he was old enough to drive and they would be stuck with a car that would not run. She was also concerned that having a car nearby would be too much temptation and the son might decide to sneak it out for a quick ride before he had a permit or license.

Piaget’s theory of cognitive development ended with formal operations, but it is possible that other ways of thinking may develop after (or “post”) formal operations in adulthood (even if this thinking does not constitute a separate “stage” of development). Postformal thought is practical, realistic and more individualistic, but also characterized by understanding the complexities of various perspectives. As a person approaches the late 30s, chances are they make decisions out of necessity or because of prior experience and are less influenced by what others think. Of course, this is particularly true in individualistic cultures such as the United States. Postformal thought is often described as more flexible, logical, willing to accept moral and intellectual complexities, and dialectical than previous stages in development.

Perry’s Scheme

One of the first theories of cognitive development in early adulthood originated with William Perry (1970), who studied undergraduate students at Harvard University.  Perry noted that over the course of students’ college years, cognition tended to shift from dualism (absolute, black and white, right and wrong type of thinking) to multiplicity (recognizing that some problems are solvable and some answers are not yet known) to relativism (understanding the importance of the specific context of knowledge—it’s all relative to other factors). Similar to Piaget’s formal operational thinking in adolescence, this change in thinking in early adulthood is affected by educational experiences.

Video 1. Perry’s Scheme of Intellectual Development. 

Dialectical Thought

In addition to moving toward more practical considerations, thinking in early adulthood may also become more flexible and balanced. Abstract ideas that the adolescent believes in firmly may become standards by which the individual evaluates reality. As Perry’s research pointed out, adolescents tend to think in dichotomies or absolute terms; ideas are true or false; good or bad; right or wrong and there is no middle ground. However, with education and experience, the young adult comes to recognize that there is some right and some wrong in each position. Such thinking is more realistic because very few positions, ideas, situations, or people are completely right or wrong.

Some adults may move even beyond the relativistic or contextual thinking described by Perry; they may be able to bring together important aspects of two opposing viewpoints or positions, synthesize them, and come up with new ideas. This is referred to as  dialectical thought  and is considered one of the most advanced aspects of postformal thinking (Basseches, 1984). There isn’t just one theory of postformal thought; there are variations, with emphasis on adults’ ability to tolerate ambiguity or to accept contradictions or find new problems, rather than solve problems, etc. (as well as relativism and dialecticism that we just learned about). What they all have in common is the proposition that the way we think may change during adulthood with education and experience.

Schaie and Willis’ Stage Theory of Cognition

Another perspective on post-formal cognitive development focuses less on the development of cognitive skills and instead discerns the changes in the use of intellect. Shaie and Willis’ stage theory of cognition proposed several stages of adult cognitive development.

During childhood and adolescence, cognition is about the acquisition of new knowledge and skills. These young people may not yet know how they will use these acquired skills. In early adulthood, people switch their focus from the acquisition to the application of knowledge, as they use what they know to pursue careers and develop their families. This is called the  achieving stage. It represents most prominently the application of intelligence in situations that have profound consequences for achieving long-term goals. The kind of intelligence exhibited in such situations is similar to that employed in educational tasks, but it requires careful attention to the possible consequences of the problem-solving process.

Adults who have mastered the cognitive skills required for monitoring their own behavior and, as a consequence, have attained a certain degree of personal independence will next move into a stage that requires the application of cognitive skills in situations involving social responsibility. Typically, the responsible stage occurs when a family is established and the needs of a spouse and offspring must be met. Similar extensions of adult cognitive skills are required as responsibilities for others are acquired on the job and in the community.

Some individualsʼ responsibilities become exceedingly complex. Such individuals－presidents of business firms, deans of academic institutions, officials of churches, and a number of other positions－need to understand the structure and the dynamic forces of organizations. They must monitor organizational activities not only on a temporal dimension (past, present, and future), but also up and down the hierarchy that defines the organization. They need to know not only the future plans of the organization but also whether policy decisions are being adequately translated into action at lower levels of responsibility. Attainment of the executive stage, as a variation on the responsibility stage, depends on exposure to opportunities that allow the development and practice of the relevant skills (Avolio, 1991; Smith, Staudinger, & Baltes, 1994).

In the later years of life, beyond the age of 60 or 65, the need to acquire knowledge declines even more, and executive monitoring is less important because frequently the individual has retired from the position that required such an application of intelligence. This stage, reintegration , corresponds in its position in the life course to Eriksonʼs stage of ego integrity. The information that elderly people acquire and the knowledge they apply becomes a function of their interests, attitudes, and values. It requires, in fact, the reintegration of all of these. The elderly are less likely to “waste time” on tasks that are meaningless to them. They are unlikely to expend much effort to solve a problem unless that problem is one that they face frequently in their lives. This stage frequently includes a selective reduction of interpersonal networks in the interest of reintegrating oneʼs concern in a more self-directed and supportive manner (cf. Carstensen, 1993; Carstensen, Gross, & Fung, 1997). In addition, efforts must be directed towards planning how oneʼs resources will last for the remaining 15 to 30 years of post-retirement life that are now characteristic for most individuals in industrialized societies. These efforts include active planning for that time when dependence upon others may be required to maintain a high quality of life in the face of increasing frailty. Such efforts may involve changes in oneʼs housing arrangements, or even oneʼs place of residence, as well as making certain of the eventual availability of both familial and extra-familial support systems. The activities involved in this context include making or changing oneʼs will, drawing up advanced medical directives and durable powers of attorney, as well as creating trusts or other financial arrangements that will protect resources for use during the final years of life or for the needs of other family members.

Although some of these activities involve the same cognitive characteristics of the responsible stage, these objectives involved are far more centered upon current and future needs of the individual rather than the needs of their family or of an organizational entity. Efforts must now be initiated to reorganize oneʼs time and resources to substitute a meaningful environment, often found in leisure activities, volunteerism, and involvement with a larger kinship network. Eventually, however, activities are also engaged in maximizing the quality of life during the final years, often with the additional objective of not becoming a burden for the next generation. The unique objective of these demands upon the individual represent an almost universal process occurring at least in the industrialized societies, and designation of a separate reorganizational stage is therefore warranted. The skills required for the reorganizational stage require the maintenance of reasonably high levels of cognitive competence. In addition, maintenance of flexible cognitive styles are needed to be able to restructure the context and content of life after retirement, to relinquish control of resources to others and to accept the partial surrender of oneʼs independence (Schaie, 1984; 2005).

Many older persons reach advanced old age in relative comfort and often with a clear mind albeit a frail body. Once the reintegrative efforts described above have been successfully completed, yet one other stage is frequently observed. This last stage is concerned with cognitive activities by many of the very old that occur in anticipation of the end of their life. This is a legacy stage t ha t is part of the cognitive development of many, if not all, older persons. This stage often begins by the effort to conduct a life review (Butler, Lewis, & Sunderland, 1998). For the highly literate and those successful in public or professional life this will often include writing or revising an autobiography (Birren, Kenyon, Ruth, Schroots, & Swensson, 1995; Birren & Schroots, 2006). There are also many other more mundane legacies to be left. Women, in particular, often wish to put their remaining effects in order and often distribute many of their prized possessions to friends and relatives, or create elaborate instructions for distributing them. It is not uncommon for many very old people to make a renewed effort at providing an oral history or to explain family pictures and heirloom to the next generation. Last, but not least, directions may be given for funeral arrangements, occasionally including the donation of oneʼs body for scientific research, and there may be a final revision of oneʼs will.

Education and Work

Education in early adulthood.

According to the U.S. Census Bureau (2017), 90 percent of the American population 25 and older have completed high school or higher level of education—compare this to just 24 percent in 1940! Each generation tends to earn (and perhaps need) increased levels of formal education. As we can see in the graph, approximately one-third of the American adult population has a bachelor’s degree or higher, as compared with less than 5 percent in 1940. Educational attainment rates vary by gender and race. All races combined, women are slightly more likely to have graduated from college than men; that gap widens with graduate and professional degrees. However, wide racial disparities still exist. For example, 23 percent of African-Americans have a college degree and only 16.4 percent of Hispanic Americans have a college degree, compared to 37 percent of non-Hispanic white Americans. The college graduation rates of African-Americans and Hispanic Americans have been growing in recent years, however (the rate has doubled since 1991 for African-Americans and it has increased 60 percent in the last two decades for Hispanic-Americans).

Figure 2.  Since 1940, there has been a significant rise in educational attainment for adults over age 25.

Education and the Workplace

With the rising costs of higher education, various news headlines have asked if a college education is worth the cost. One way to address this question is in terms of the earning potential associated with various levels of educational achievement. In 2016, the average earnings for Americans 25 and older with only a high school education was $35,615, compared with $65,482 for those with a bachelor’s degree, compared with $92,525 for those with more advanced degrees. Average earnings vary by gender, race, and geographical location in the United States.

Of concern in recent years is the relationship between higher education and the workplace. In 2005, American educator and then Harvard University President, Derek Bok, called for a closer alignment between the goals of educators and the demands of the economy. Companies outsource much of their work, not only to save costs but to find workers with the skills they need. What is required to do well in today’s economy? Colleges and universities, he argued, need to promote global awareness, critical thinking skills, the ability to communicate, moral reasoning, and responsibility in their students. Regional accrediting agencies and state organizations provide similar guidelines for educators. Workers need skills in listening, reading, writing, speaking, global awareness, critical thinking, civility, and computer literacy—all skills that enhance success in the workplace.

More than a decade later, the question remains: does formal education prepare young adults for the workplace? It depends on whom you ask. In an article referring to information from the National Association of Colleges and Employers’ 2018 Job Outlook Survey, Bauer-Wolf (2018) explains that employers perceive gaps in students’ competencies but many graduating college seniors are overly confident. The biggest difference was in perceived professionalism and work ethic (only 43 percent of employers thought that students are competent in this area compared to 90 percent of the students). Similar differences were also found in terms of oral communication, written communication, and critical thinking skills. Only in terms of digital technology skills were more employers confident about students’ competencies than were the students (66 percent compared to 60 percent).

It appears that students need to learn what some call “soft skills,” as well as the particular knowledge and skills within their college major. As education researcher Loni Bordoloi Pazich (2018) noted, most American college students today are enrolling in business or other pre-professional programs and to be effective and successful workers and leaders, they would benefit from the communication, teamwork, and critical thinking skills, as well as the content knowledge, gained from liberal arts education. In fact, two-thirds of children starting primary school now will be employed in jobs in the future that currently do not exist. Therefore, students cannot learn every single skill or fact that they may need to know, but they can learn how to learn, think, research, and communicate well so that they are prepared to continually learn new things and adapt effectively in their careers and lives since the economy, technology, and global markets will continue to evolve.

Career Choices in Early Adulthood

Hopefully, we are each becoming lifelong learners, particularly since we are living longer and will most likely change jobs multiple times during our lives. However, for many, our job changes will be within the same general occupational field, so our initial career choice is still significant. We’ve seen with Erikson that identity largely involves occupation and, as we will learn in the next section, Levinson found that young adults typically form a dream about work (though females may have to choose to focus relatively more on work or family initially with “split” dreams). The American School Counselor Association recommends that school counselors aid students in their career development beginning as early as kindergarten and continue this development throughout their education.

One of the most well-known theories about career choice is from John Holland (1985), who proposed that there are six personality types (realistic, investigative, artistic, social, enterprising, and conventional), as well as varying types of work environments. The better matched one’s personality is to the workplace characteristics, the more satisfied and successful one is predicted to be with that career or vocational choice. Research support has been mixed and we should note that there is more to satisfaction and success in a career than one’s personality traits or likes and dislikes. For instance, education, training, and abilities need to match the expectations and demands of the job, plus the state of the economy, availability of positions, and salary rates may play practical roles in choices about work.

Link to Learning: What’s Your Right Career?

To complete a free online career questionnaire and identify potential careers based on your preferences, go to:

Career One Stop Questionnaire

Did you find out anything intere sting? Think of this activity as a starting point to your career exploration.  Other great ways for young adults to research careers include informational interviewing, job shadowing, volunteering, practicums, and internships. Once you have a few careers in mind that you want to find out more about, go to the  Occupational Outlook Handbook from the U.S. Bureau of Labor Statistics to learn about job tasks, required education, average pay, and projected outlook for the future.

• Introduction to Cognitive Development in Early Adulthood. Authored by : Margaret Clark-Plaskie for Lumen Learning. Provided by : Lumen Learning. License : CC BY: Attribution
• Modification, adaptation, and original content. Authored by : Margaret Clark-Plaskie. Provided by : Lumen Learning. License : CC BY: Attribution
• Young Woman. Authored by : Karolina Grabowska. Located at : https://pixabay.com/images/id-791849/ . License : CC0: No Rights Reserved
• Psyc 200 Lifespan Psychology. Authored by : Laura Overstreet. Located at : http://opencourselibrary.org/econ-201/ . License : CC BY: Attribution
• Millennials Jam Workshop: Youth and ICTs beyond 2015. Authored by : ITU Pictures. Located at : https://www.flickr.com/photos/itupictures/9024333319 . License : CC BY: Attribution
• Summary of Perry's research. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/William_G._Perry . License : CC BY: Attribution
• Postformal thought. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Postformal_thought . License : CC BY-SA: Attribution-ShareAlike
• Perry's Scheme of Intellectual Development. Authored by : Eric Landrum. Located at : https://www.youtube.com/watch?v=XkEJIXvwROs . License : Other . License Terms : Standard YouTube License
• Highest Educational Levels Reached by Adults in the U.S. Since 1940. Provided by : U.S. Census Bureau. Located at : https://www.census.gov/library/visualizations/2017/comm/cb17-51_educational_attainment.html . License : All Rights Reserved

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Emerging Adulthood & Cognition

Emerging adulthood.

Historically, early adulthood was considered to last from approximately the age of 18 (the end of adolescence) until 40 or 45 (the beginning of middle adulthood). More recently, developmentalists have divided this 25 year age period into two separate stages: Emerging adulthood followed by early adulthood. Although these age periods differ in their physical, cognitive, and social development, overall the age period from 18 to 40 is a time of peak physical capabilities and the emergence of more mature cognitive development, financial independence, and the establishment of intimate relationships.

Emerging Adulthood Defined

Emerging adulthood is the period between the late teens and early twenties ; ages 18-25, although some researchers have included up to age 29 in their definitions (Society for the Study of Emerging Adulthood, 2016). Jeffrey Arnett (2000) argues that emerging adulthood is neither adolescence nor is it young adulthood. Individuals in this age period have left behind the relative dependency of childhood and adolescence but have not yet taken on the responsibilities of adulthood. “Emerging adulthood is a time of life when many different directions remain possible, when little about the future is decided for certain, when the scope of independent exploration of life’s possibilities is greater for most people than it will be at any other period of the life course” (Arnett, 2000, p. 469). Arnett identified five characteristics of emerging adulthood that distinguish it from adolescence and young adulthood (Arnett, 2006).

• It is the age of identity exploration . In 1950, Erik Erikson proposed that it was during adolescence that humans wrestled with the question of identity. Yet, even Erikson (1968) commented on a trend during the 20th century of a “prolonged adolescence” in industrialized societies. Today, most identity development occurs during the late teens and early twenties rather than adolescence. It is during emerging adulthood that people are exploring their career choices and ideas about intimate relationships, setting the foundation for adulthood.
•  Arnett also described this time period as the age of instability (Arnett, 2000; Arnett, 2006). Exploration generates uncertainty and instability. Emerging adults change jobs, relationships, and residences more frequently than other age groups.
• This is also the age of self-focus . Being self-focused is not the same as being “self-centered.” Adolescents are more self-centered than emerging adults. Arnett reports that in his research, he found emerging adults to be very considerate of the feelings of others, especially their parents. They now begin to see their parents as people not just parents, something most adolescents fail to do (Arnett, 2006). Nonetheless, emerging adults focus more on themselves, as they realize that they have few obligations to others and that this is the time where they can do what they want with their life.
• This is also the age of feeling in-between. When asked if they feel like adults, more 18 to 25 year-olds answer “yes and no” than do teens or adults over the age of 25 (Arnett, 2001). Most emerging adults have gone through the changes of puberty, are typically no longer in high school, and many have also moved out of their parents’ home. Thus, they no longer feel as dependent as they did as teenagers. Yet, they may still be financially dependent on their parents to some degree, and they have not completely attained some of the indicators of adulthood, such as finishing their education, obtaining a good full-time job, being in a committed relationship, or being responsible for others. It is not surprising that Arnett found that 60% of 18 to 25 year-olds felt that in some ways they were adults, but in some ways, they were not (Arnett, 2001).
• Emerging adulthood is the age of possibilities . It is a time period of optimism as more 18 to 25 year-olds feel that they will someday get to where they want to be in life. Arnett (2000, 2006) suggests that this optimism is because these dreams have yet to be tested. For example, it is easier to believe that you will eventually find your soul mate when you have yet to have had a serious relationship. It may also be a chance to change directions, for those whose lives up to this point have been difficult. The experiences of children and teens are influenced by the choices and decisions of their parents. If the parents are dysfunctional, there is little a child can do about it. In emerging adulthood, however, people can move out and move on. They have the chance to transform their lives and move away from unhealthy environments. Even those whose lives were happy and fulfilling as children, now have the opportunity in emerging adulthood to become independent and make their own decisions about the direction they would like their lives to take.

Socioeconomic Class and Emerging Adulthood.  The theory of emerging adulthood was initially criticized as only reflecting upper middle-class, college-attending young adults in the United States and not those who were working class or poor (Arnett, 2016). Consequently, Arnett reviewed results from the 2012 Clark University Poll of Emerging Adults, whose participants were demographically similar to the United States population. Results primarily indicated consistencies across aspects of the theory, including positive and negative perceptions of the time-period and views on education, work, love, sex, and marriage. Two significant differences were found, the first being that emerging adults from lower socioeconomic classes identified more negativity in their emotional lives, including higher levels of depression. Secondly, those in the lowest socioeconomic group were more likely to agree that they had not been able to find sufficient financial support to obtain the education they believed they needed. Overall, Arnett concluded that emerging adulthood exists wherever there is a period between the end of adolescence and entry into adult roles, but also acknowledged that social, cultural, and historical contexts were important.

Cross-cultural Variations.  The five features proposed in the theory of emerging adulthood originally were based on research involving Americans between ages 18 and 29 from various ethnic groups, social classes, and geographical regions (Arnett, 2004, 2016). To what extent does the theory of emerging adulthood apply internationally?

The answer to this question depends greatly on what part of the world is considered. Demographers make a useful distinction between the developing countries that comprise the majority of the world’s population and the economically developed countries that are part of the Organization for Economic Co-operation and Development (OECD), including the United States, Canada, Western Europe, Japan, South Korea, Australia, and New Zealand. The current population of OECD countries (also called developed countries) is 1.2 billion, about 18% of the total world population (United Nations Development Programme, 2011). The rest of the population resides in developing countries, which have much lower median incomes, much lower median educational attainment, and much higher incidence of illness, disease, and early death. Let us consider emerging adulthood in other OECD countries as little is known about the experiences of 18-25 year-olds in developing countries.

The same demographic changes as described above for the United States have taken place in other OECD countries as well. This is true of increasing participation in postsecondary education, as well as increases in the median ages for entering marriage and parenthood (UNdata, 2010). However, there is also substantial variability in how emerging adulthood is experienced across OECD countries. Europe is the region where emerging adulthood is longest and most leisurely. The median ages for entering marriage and parenthood are near 30 in most European countries (Douglass, 2007). Europe today is the location of the most affluent, generous, and egalitarian societies in the world, in fact, in human history (Arnett, 2007). Governments pay for tertiary education, assist young people in finding jobs, and provide generous unemployment benefits for those who cannot find work. In northern Europe, many governments also provide housing support. Emerging adults in European societies make the most of these advantages, gradually making their way to adulthood during their twenties while enjoying travel and leisure with friends.

The lives of emerging adults in developed Asian countries, such as Japan and South Korea, are in some ways similar to the lives of emerging adults in Europe and in some ways strikingly different. Like European emerging adults, Asian emerging adults tend to enter marriage and parenthood around age 30 (Arnett, 2011). Like European emerging adults, Asian emerging adults in Japan and South Korea enjoy the benefits of living in affluent societies with generous social welfare systems that provide support for them in making the transition to adulthood, including free university education and substantial unemployment benefits.

However, in other ways, the experience of emerging adulthood in Asian OECD countries is markedly different than in Europe. Europe has a long history of individualism, and today’s emerging adults carry that legacy with them in their focus on self-development and leisure during emerging adulthood. In contrast, Asian cultures have a shared cultural history emphasizing collectivism and family obligations.

Although Asian cultures have become more individualistic in recent decades, as a consequence of globalization, the legacy of collectivism persists in the lives of emerging adults. They pursue identity explorations and self-development during emerging adulthood, like their American and European counterparts, but within narrower boundaries set by their sense of obligations to others, especially their parents (Phinney & Baldelomar, 2011). For example, in their views of the most important criteria for becoming an adult, emerging adults in the United States and Europe consistently rank financial independence among the most important markers of adulthood. In contrast, emerging adults with an Asian cultural background especially emphasize becoming capable of supporting parents financially as among the most important criteria (Arnett, 2003; Nelson, Badger, & Wu, 2004). This sense of family obligation may curtail their identity explorations in emerging adulthood to some extent, and compared to emerging adults in the West, they pay more heed to their parents’ wishes about what they should study, what job they should take, and where they should live  (Rosenberger, 2007).

When Does Adulthood Begin? According to Rankin and Kenyon (2008), in years past the process of becoming an adult was more clearly marked by rites of passage. For many, marriage and parenthood were considered entry into adulthood. However, these role transitions are no longer considered the important markers of adulthood (Arnett, 2001). Economic and social changes have resulted in more young adults attending college (Rankin & Kenyon, 2008) and delaying marriage and having children (Arnett & Taber, 1994; Laursen & Jensen-Campbell, 1999) Consequently, current research has found financial independence and accepting responsibility for oneself to be the most important markers of adulthood in Western culture across age (Arnett, 2001) and ethnic groups (Arnett, 2004).

In looking at college students’ perceptions of adulthood, Rankin and Kenyon (2008) found that some students still view rites of passage as important markers. College students who placed more importance on role transition markers, such as parenthood and marriage, belonged to a fraternity/sorority, were traditionally aged (18–25), belonged to an ethnic minority, were of a traditional marital status (i.e., not cohabitating), or belonged to a religious organization, particularly for men. These findings supported the view that people holding collectivist or more traditional values place more importance on role transitions as markers of adulthood. In contrast, older college students and those cohabitating did not value role transitions as markers of adulthood as strongly.

Young Adults Living Arrangements.  In 2014, for the first time in more than 130 years, adults 18 to 34 were more likely to be living in their parents’ home than they were to be living with a spouse or partner in their own household (Fry, 2016). The current trend is that young Americans are not choosing to settle down romantically before age 35. Since 1880, living with a romantic partner was the most common living arrangement among young adults. In 1960, 62% of America’s 18- to 34-year-olds were living with a spouse or partner in their own household, while only 20% were living with their parents.

By 2014, 31.6% of early adults were living with a spouse or partner in their own household, while 32.1% were living in the home of their parent(s). Another 14% of early adults lived alone, were a single parent, or lived with one or more roommates. The remaining 22% lived in the home of another family member (such as a grandparent, in-law, or sibling), a non-relative, or in group quarters (e.g., college dormitories). Comparing ethnic groups, 36% of black and Hispanic early adults lived at home, while 30% of white young adults lived at home.

As can be seen in Figure 20.2, gender differences in living arrangements were also noted in that young men were living with parents at a higher rate than young women. In 2014, 35% of young men were residing with their parents, while 28% were living with a spouse or partner in their own household. Young women were more likely to be living with a spouse or partner (35%) than living with their parents (29%). Additionally, more young women (16%) than young men (13%) were heading up a household without a spouse or partner, primarily because women are more likely to be single parents living with their children. Lastly, young men (25%) were more likely than young women (19%) to be living in the home of another family member, a non-relative, or in some type of group quarters (Fry, 2016).

What are some factors that help explain these changes in living arrangements? First, early adults are increasingly postponing marriage or choosing not to marry or cohabitate. Lack of employment and lower wages have especially contributed to males residing with their parents. Men who are employed are less likely to live at home. Wages for young men (adjusting for inflation) have been falling since 1970 and correlate with the rise in young men living with their parents. The recent recession and recovery (2007-present) has also contributed to the increase in early adults living at home. College enrollments increased during the recession, which further increased early adults living at home. However, once early adults possess a college degree, they are more likely to establish their own households (Fry, 2016).

Cognitive Development in Early Adulthood

Emerging adulthood brings with it the consolidation of formal operational thought, and the continued integration of the parts of the brain that serve emotion, social processes, and planning and problem solving. As a result, rash decisions and risky behavior decrease rapidly across early adulthood. Increases in epistemic cognition are also seen, as young adults’ meta-cognition, or thinking about thinking, continues to grow, especially young adults who continue with their schooling.

Perry’s Scheme.  One of the first theories of cognitive development in early adulthood originated with William Perry (1970), who studied undergraduate students at Harvard University.  Perry noted that over the course of students’ college years, cognition tended to shift from dualism (absolute, black and white, right and wrong type of thinking) to multiplicity (recognizing that some problems are solvable and some answers are not yet known) to relativism (understanding the importance of the specific context of knowledge—it’s all relative to other factors). Similar to Piaget’s formal operational thinking in adolescence, this change in thinking in early adulthood is affected by educational experiences.

Table 8.1 Stages of Perry's Scheme

Adapted from Lifespan Development by Lumen Learning

Some researchers argue that a qualitative shift in cognitive development tales place for some emerging adults during their mid to late twenties. As evidence, they point to studies documenting continued integration and focalization of brain functioning, and studies suggesting that this developmental period often represents a turning point, when young adults engaging in risky behaviors (e.g., gang involvement, substance abuse) or an unfocused lifestyle (e.g., drifting from job to job or relationship to relationship) seem to “wake up” and take ownership for their own development. It is a common point for young adults to make decisions about completing or returning to school, and making and following through on decisions about vocation, relationships, living arrangements, and lifestyle. Many young adults can actually remember these turning points as a moment when they could suddenly “see” where they were headed (i.e., the likely outcomes of their risky behaviors or apathy) and actively decided to take a more self-determined pathway.

Optional Reading: Current Trends in Post-secondary Education

According to the National Center for Higher Education Management Systems (NCHEMS) (2016a, 2016b, 2016c, 2016d), in the United States:

• 84% of 18 to 24 year olds and 88% of those 25 and older have a high school diploma or its equivalent
• 36% of 18 to 24 year olds and 7% of 25 to 49 year olds attend college
• 59% of those 25 and older have completed some college
• 32.5% of those 25 and older have a bachelor’s degree or higher, with slightly more women (33%) than men (32%) holding a college degree (Ryan & Bauman, 2016).

The rate of college attainment has grown more slowly in the United States than in a number of other nations in recent years (OCED, 2014). This may be due to fact that the cost of attaining a degree is higher in the U.S. than in most other nations.

In 2017, 65% of college seniors who graduated from private and public nonprofit colleges had student loan debt, and nationally owed an average of $28,650, a 1% decline from 2016 (The Institute for College Access & Success (TICAS), 2018).

According to the most recent TICAS annual report, the rate of debt varied widely across states, as well as between colleges. The after graduation debt ranged from $18,850 in Utah to $38,500 in Connecticut. Low-debt states are mainly in the West, and high-debt states in the Northeast. In recent years there has been a concern about students carrying more debt and being more likely to default when attending for-profit institutions. In 2016, students at for-profit schools borrowed an average of $39,900, which was 41% higher than students at non-profit schools that year. In addition, 30% of students attending for-profit colleges default on their federal student loans. In contrast, the default level of those who attended public institutions is only 4% (TICAS, 2018).

College student debt has become a key political issue at both the state and federal level, and some states have been taking steps to increase spending and grants to help students with the cost of college. However, 15% of the Class of 2017’s college debt was owed to private lenders (TICAS, 2018). Such debt has less consumer protection, fewer options for repayment, and is typically negotiated at a higher interest rate. See Table 7.1 for a debt comparison of 6 U.S. States.

Graduate School: Larger amounts of student debt actually occur at the graduate level (Kreighbaum, 2019). In 2019, the highest average debts were concentrated in the medical fields. Average median debt for graduate programs included:

• $42,335 for a master’s degree
• $95,715 for a doctoral degree
• $141,000 for a professional degree

Worldwide, over 80% of college educated adults are employed, compared with just over 70% of those with a high school or equivalent diploma, and only 60% of those with no high school diploma (OECD, 2015). Those with a college degree will earn more over the course of their life time. Moreover, the benefits of college education go beyond employment and finances. The OECD found that around the world, adults with higher educational attainment were more likely to volunteer, felt they had more control over their lives, and thus were more interested in the world around them. Studies of U.S. college students find that they gain a more distinct identity and become more socially competent and less dogmatic and ethnocentric compared to those not in college (Pascarella, 2006).

Is college worth the time and investment? College is certainly a substantial investment each year, with the financial burden falling on students and their families in the U.S., and covered mainly by the government in many other nations. Nonetheless, the benefits both to the individual and the society outweighs the initial costs. As can be seen in Figure 7.18, those in America with the most advanced degrees earn the highest income and have the lowest unemployment.

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Chapter 29: Cognitive Development in Late Adulthood

Chapter 29 learning objectives.

• Describe how memory changes for those in late adulthood
• Describe the theories for why memory changes occur
• Describe how cognitive losses in late adulthood are exaggerated
• Explain the pragmatics and mechanics of intelligence
• Define what is a neurocognitive disorder
• Explain Alzheimer’s disease and other neurocognitive disorders
• Describe work and retirement in late adulthood
• Describe how those in late adulthood spend their leisure time

How Does Aging Affect Information Processing?

There are numerous stereotypes regarding older adults as being forgetful and confused, but what does the research on memory and cognition in late adulthood reveal? Memory comes in many types, such as working, episodic, semantic, implicit, and prospective. There are also many processes involved in memory, thus it should not be a surprise that there are declines in some types of memory and memory processes, while other areas of memory are maintained or even show some improvement with age. In this section, we will focus on changes in memory, attention, problem-solving, intelligence, and wisdom, including the exaggeration of losses stereotyped in the elderly.

Changes in Working Memory:  As discussed in chapter 4, working memory is the more active, effortful part of our memory system. Working memory is composed of three major systems: The phonological loop that maintains information about auditory stimuli , the visuospatial sketchpad , that maintains information about visual stimuli , and the central executive, that oversees working memory, allocating resources where needed and monitoring whether cognitive strategies are being effective (Schwartz, 2011). Schwartz reports that it is the central executive that is most negatively impacted by age. In tasks that require allocation of attention between different stimuli, older adults fair worse than do younger adults. In a study by Göthe, Oberauer, and Kliegl (2007) older and younger adults were asked to learn two tasks simultaneously. Young adults eventually managed to learn and perform each task without any loss in speed and efficiency, although it did take considerable practice. None of the older adults were able to achieve this. Yet, older adults could perform at young adult levels if they had been asked to learn each task individually. Having older adults learn and perform both tasks together was too taxing for the central executive. In contrast, working memory tasks that do not require much input from the central executive, such as the digit span test, which uses predominantly the phonological loop, we find that older adults perform on par with young adults (Dixon & Cohen, 2003).

Changes in Long-term Memory: As you should recall, long-term memory is divided into semantic (knowledge of facts), episodic (events), and implicit (procedural skills, classical conditioning, and priming) memories. Semantic and episodic memory is part of the explicit memory system, which requires conscious effort to create and retrieve. Several studies consistently reveal that episodic memory shows greater age-related declines than semantic memory (Schwartz, 2011; Spaniol, Madden, & Voss, 2006). It has been suggested that episodic memories may be harder to encode and retrieve because they contain at least two different types of memory, the event and when and where the event took place. In contrast, semantic memories are not tied to any particular timeline. Thus, only the knowledge needs to be encoded or retrieved (Schwartz, 2011). Spaniol et al. (2006) found that retrieval of semantic information was considerably faster for both younger and older adults than the retrieval of episodic information, with there being little difference between the two age groups for semantic memory retrieval. They note that older adults’ poorer performance on episodic memory appeared to be related to slower processing of the information and the difficulty of the task. They found that as the task became increasingly difficult, the gap between each age groups’ performance increased for episodic memory more so than for semantic memory.

Studies that test general knowledge (semantic memory), such as politics and history (Dixon, Rust, Feltmate, & See, 2007), or vocabulary/lexical memory (Dahlgren, 1998) often find that older adults outperform younger adults. However, older adults do find that they experience more “blocks” at retrieving information that they know. In other words, they experience more tip-of-the-tongue (TOT) events than do younger adults (Schwartz, 2011).

Implicit memory requires little conscious effort and often involves skills or more habitual patterns of behavior. This type of memory shows few declines with age. Many studies assessing implicit memory measure the effects of priming. Priming refers to changes in behavior as a result of frequent or recent experiences . If you were shown pictures of food and asked to rate their appearance and then later were asked to complete words such as s_ _ p, you may be more likely to write soup than soap, or ship. The images of food “primed” your memory for words connected to food. Does this type of memory and learning change with age? The answer is typically “no” for most older adults (Schacter, Church, & Osowiecki, 1994).

Prospective memory refers to remembering things we need to do in the future , such as remembering a doctor’s appointment next week or to take medication before bedtime. It has been described as “the flip-side of episodic memory” (Schwartz, 2011, p. 119). Episodic memories are the recall of events in our past, while the focus of prospective memories is of events in our future. In general, humans are fairly good at prospective memory if they have little else to do in the meantime. However, when there are competing tasks that are also demanding our attention, this type of memory rapidly declines.  The explanation given for this is that this form of memory draws on the central executive of working memory, and when this component of working memory is absorbed in other tasks, our ability to remember to do something else in the future is more likely to slip out of memory (Schwartz, 2011). However, prospective memories are often divided into time-based prospective memories , such as having to remember to do something at a future time, or event-based prospective memories , such as having to remember to do something when a certain event occurs. When age-related declines are found, they are more likely to be time-based, than event-based, and in laboratory settings rather than in the real-world, where older adults can show comparable or slightly better prospective memory performance (Henry, MacLeod, Phillips & Crawford, 2004; Luo & Craik, 2008). This should not be surprising given the tendency of older adults to be more selective in where they place their physical, mental, and social energy. Having to remember a doctor’s appointment is of greater concern than remembering to hit the space-bar on a computer every time the word “tiger” is displayed.

Recall versus Recognition : Memory performance often depends on whether older adults are asked to simply recognize previously learned material or recall material on their own. Generally, for all humans, recognition tasks are easier because they require less cognitive energy. Older adults show roughly equivalent memory to young adults when assessed with a recognition task (Rhodes, Castel, & Jacoby, 2008). With recall measures, older adults show memory deficits in comparison to younger adults. While the effect is initially not that large, starting at age 40 adults begin to show declines in recall memory compared to younger adults (Schwartz, 2011).

The Age Advantage : Fewer age differences are observed when memory cues are available, such as for recognition memory tasks, or when individuals can draw upon acquired knowledge or experience. For example, older adults often perform as well if not better than young adults on tests of word knowledge or vocabulary. With age often comes expertise, and research has pointed to areas where aging experts perform quite well. For example, older typists were found to compensate for age-related declines in speed by looking farther ahead at the printed text (Salthouse, 1984). Compared to younger players, older chess experts focus on a smaller set of possible moves, leading to greater cognitive efficiency (Charness, 1981). Accrued knowledge of everyday tasks, such as grocery prices, can help older adults to make better decisions than young adults (Tentori, Osheron, Hasher, & May 2001).

Attention and Problem Solving

Changes in Attention in Late Adulthood: Changes in sensory functioning and speed of processing information in late adulthood often translate into changes in attention (Jefferies et al., 2015). Research has shown that older adults are less able to selectively focus on information while ignoring distractors (Jefferies et al., 2015; Wascher, Schneider, Hoffman, Beste, & Sänger, 2012), although Jefferies and her colleagues found that when given double-time, older adults could perform at young adult levels. Other studies have also found that older adults have greater difficulty shifting their attention between objects or locations (Tales, Muir, Bayer, & Snowden, 2002). Consider the implication of these attentional changes for older adults.

How do changes or maintenance of cognitive ability affect older adults’ everyday lives? Researchers have studied cognition in the context of several different everyday activities. One example is driving. Although older adults often have more years of driving experience, cognitive declines related to reaction time or attentional processes may pose limitations under certain circumstances (Park & Gutchess, 2000). In contrast, research on interpersonal problem solving suggested that older adults use more effective strategies than younger adults to navigate through social and emotional problems (Blanchard-Fields, 2007). In the context of work, researchers rarely find that older individuals perform poorer on the job (Park & Gutchess, 2000). Similar to everyday problem solving, older workers may develop more efficient strategies and rely on expertise to compensate for cognitive decline.

Problem Solving : Problem-solving tasks that require processing non-meaningful information quickly (a kind of task that might be part of a laboratory experiment on mental processes) declines with age. However, many real-life challenges facing older adults do not rely on the speed of processing or making choices on one’s own. Older adults resolve everyday problems by relying on input from others, such as family and friends. They are also less likely than younger adults to delay making decisions on important matters, such as medical care (Strough, Hicks, Swenson, Cheng & Barnes, 2003; Meegan & Berg, 2002).

What might explain these deficits as we age? The processing speed theory , proposed by Salthouse (1996, 2004), suggests that as the nervous system slows with advanced age our ability to process information declines . This slowing of processing speed may explain age differences in many different cognitive tasks. For instance, as we age, working memory becomes less efficient (Craik & Bialystok, 2006). Older adults also need a longer time to complete mental tasks or make decisions. Yet, when given sufficient time older adults perform as competently as do young adults (Salthouse, 1996). Thus, when speed is not imperative to the task healthy older adults do not show cognitive declines.

In contrast, inhibition theory argues that older adults have difficulty with inhibitory functioning, or the ability to focus on certain information while suppressing attention to less pertinent information tasks (Hasher & Zacks, 1988). Evidence comes from directed forgetting research. In directed forgetting people are asked to forget or ignore some information, but not other information. For example, you might be asked to memorize a list of words but are then told that the researcher made a mistake and gave you the wrong list and asks you to “forget” this list. You are then given a second list to memorize. While most people do well at forgetting the first list, older adults are more likely to recall more words from the “forget-to-recall” list than are younger adults (Andrés, Van der Linden, & Parmentier, 2004).

Cognitive losses exaggerated: While there are information processing losses in late adulthood, the overall loss has been exaggerated (Garrett, 2015). One explanation is that the type of tasks that people are tested on tend to be meaningless. For example, older individuals are not motivated to remember a random list of words in a study, but they are motivated for more meaningful material related to their life, and consequently perform better on those tests. Another reason is that the research is often cross-sectional. When age comparisons occur longitudinally, however, the amount of loss diminishes (Schaie, 1994). A third reason is that the loss may be due to a lack of opportunity in using various skills. When older adults practiced skills, they performed as well as they had previously. Although diminished performance speed is especially noteworthy in the elderly, Schaie (1994) found that statistically removing the effects of speed diminished the individual’s performance declines significantly. In fact, Salthouse and Babcock (1991) demonstrated that processing speed accounted for all but 1% of age-related differences in working memory when testing individuals from 18 to 82. Finally, it is well established that our hearing and vision decline as we age. Longitudinal research has proposed that deficits in sensory functioning explain age differences in a variety of cognitive abilities (Baltes & Lindenberger, 1997). Not surprisingly, more years of education, and subsequently higher income, are associated with the higher cognitive level and slower cognitive decline (Zahodne, Stern, & Manly, 2015).

Intelligence and Wisdom  

When looking at scores on traditional intelligence tests, tasks measuring verbal skills show minimal or no age-related declines, while scores on performance tests, which measure solving problems quickly, decline with age (Botwinick, 1984). This profile mirrors crystallized and fluid intelligence. As you recall from the last chapter, crystallized intelligence encompasses abilities that draw upon experience and knowledge. Measures of crystallized intelligence include vocabulary tests, solving number problems, and understanding texts. Fluid intelligence refers to information processing abilities, such as logical reasoning, remembering lists, spatial ability, and reaction time. Baltes (1993) introduced two additional types of intelligence to reflect cognitive changes in aging. Pragmatics of intelligence are cultural exposure to facts and procedures that are maintained as one age and are similar to crystallized intelligence. Mechanics of intelligence are dependent on brain functioning and decline with age, similar to fluid intelligence. Baltes indicated that pragmatics of intelligence show a little decline and typically increase with age.

Additionally, the pragmatics of intelligence may compensate for the declines that occur with the mechanics of intelligence. In summary, global cognitive declines are not as typical as one age, and individuals compensate for some cognitive declines, especially processing speed.

Wisdom is the ability to use accumulated knowledge about practical matters that allow for sound judgment and decision making . A wise person is insightful and has knowledge that can be used to overcome obstacles in living. Does aging bring wisdom? While living longer brings experience, it does not always bring wisdom. Paul Baltes and his colleagues (Baltes & Kunzmann, 2004; Baltes & Staudinger, 2000) suggest that wisdom is rare. In addition, the emergence of wisdom can be seen in late adolescence and young adulthood, with there being few gains in wisdom over the course of adulthood (Staudinger & Gluck, 2011). This would suggest that factors other than age are stronger determinants of wisdom. Occupations and experiences that emphasize others rather than self, along with personality characteristics, such as openness to experience and generativity, are more likely to provide the building blocks of wisdom (Baltes & Kunzmann, 2004). Age combined with certain types of experience and/or personality brings wisdom.

Neurocognitive Disorders  

Historically, the term dementia was used to refer to an individual experiencing difficulties with memory, language, abstract thinking, reasoning, decision making, and problem-solving (Erber & Szuchman (2015). However, in the latest edition of the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) (American Psychiatric Association, 2013) the term dementia has been replaced by the neurocognitive disorder. A major neurocognitive disorder is diagnosed as a significant cognitive decline from a previous level of performance in one or more cognitive domains and interferes with independent functioning, while a minor neurocognitive disorder is diagnosed as a modest cognitive decline from a previous level of performance in one or more cognitive domains and does not interfere with independent functioning. There are several different neurocognitive disorders that are typically demonstrated in late adulthood and determining the exact type can be difficult because the symptoms may overlap with each other. Diagnosis often includes a medical history, physical exam, laboratory tests, and changes noted in behavior. Alzheimer’s disease, vascular neurocognitive disorder and neurocognitive disorder with Lewy bodies will be discussed below.

Alzheimer’s Disease: Probably the most well-known and most common neurocognitive disorder for older individuals is Alzheimer’s disease. In 2016 an estimated 5.4 million Americans were diagnosed with Alzheimer’s disease (Alzheimer’s Association, 2016), which was approximately one in nine aged 65 and over. By 2050 the number of people age 65 and older with Alzheimer’s disease is projected to be 13.8 million if there are no medical breakthroughs to prevent or cure the disease. Alzheimer’s disease is the 6th leading cause of death in the United States, but the 5th leading cause for those 65 and older. Among the top 10 causes of death in America, Alzheimer’s disease is the only one that cannot be prevented, cured, or even slowed. Current estimates indicate that Alzheimer’s disease affects approximately 50% of those identified with a neurocognitive disorder (Cohen & Eisdorfer, 2011).

Alzheimer’s disease has a gradual onset with subtle personality changes and memory loss that differs from normal age-related memory problems occurring first. Confusion, difficulty with change, and deterioration in language, problem-solving skills, and personality become evident next. In the later stages, the individual loses physical coordination and is unable to complete everyday tasks, including self-care and personal hygiene (Erber & Szuchman, 2015). Lastly, individuals lose the ability to respond to their environment, to carry on a conversation, and eventually to control movement (Alzheimer’s Association, 2016). On average people with Alzheimer’s survive eight years, but some may live up to 20 years. The disease course often depends on the individual’s age and whether they have other health conditions.

The greatest risk factor for Alzheimer’s disease is age, but there are genetic and environmental factors that can also contribute. Some forms of Alzheimer’s are hereditary, and with the early onset type, several rare genes have been identified that directly cause Alzheimer’s. People who inherit these genes tend to develop symptoms in their 30s, 40s, and 50s. Five percent of those identified with Alzheimer’s disease are younger than age 65. When Alzheimer’s disease is caused by deterministic genes, it is called familial Alzheimer’s disease (Alzheimer’s Association, 2016). Traumatic brain injury is also a risk factor, as well as obesity, hypertension, high cholesterol, and diabetes (Carlson, 2011).

Βeta Amyloid and Tau: According to Erber and Szuchman (2015) the problems that occur with Alzheimer’s disease are due to the “death of neurons, the breakdown of connections between them, and the extensive formation of plaques and tau, which interfere with neuron functioning and neuron survival” (p. 50). Plaques are abnormal formations of protein pieces called beta-amyloid. Beta-amyloid comes from a larger protein found in the fatty membrane surrounding nerve cells. Because beta-amyloid is sticky, it builds up into plaques (Alzheimer’s Association, 2016).  These plaques appear to block cell communication and may also trigger an inflammatory response in the immune system, which leads to further neuronal death.

Tau is an important protein that helps maintain the brain’s transport system. When tau malfunctions, it changes into twisted strands called tangles that disrupt the transport system. Consequently, nutrients and other supplies cannot move through the cells and they eventually die. The death of neurons leads to the brain shrinking and affecting all aspects of brain functioning. For example, the hippocampus is involved in learning and memory, and the brain cells in this region are often the first to be damaged. This is why memory loss is often one of the earliest symptoms of Alzheimer’s disease. Figures 9.32 and 9.33 illustrate the difference between an Alzheimer’s brain and a healthy brain.

Washington University School of Medicine (2019) reported that researchers associated with the School of Medicine discovered that failing immune cells, known as microglia, appear to be the link between amyloid and tau, which are the two damaging proteins of Alzheimer’s disease. Amyloid plaques, which appear first, do not cause Alzheimer’s, but the presence of amyloid leads to the formation of tau tangles, which are responsible for the memory loss and cognitive deficits seen in those with Alzheimer’s disease. It appears that weakening microglia causes the amyloid plaques to injure nearby neurons, thus creating a toxic environment that increases the formation and spread of tau tangles. These findings could lead to a new approach for developing therapies for Alzheimer’s.

Sleep Deprivation and Alzheimer’s: Studies suggest that sleep plays a role in clearing both beta-amyloid and tau out of the brain. Shokri-Kojori et al. (2018) scanned participants’ brains after getting a full night’s rest and after 31 hours without sleep. Beta-amyloid increased by about 5% in the participants’ brains after losing a night of sleep. These changes occurred in brain regions that included the thalamus and hippocampus, which are associated with the early stages of Alzheimer’s disease. Shokri-Kojori et al. also found that participants with the largest increases in beta-amyloid reported the worst mood after sleep deprivation. These findings support other studies that have found that the hippocampus and thalamus are involved in mood disorders.

Additionally, Holth et al. (2019) found that healthy adults who remained awake all day and night had tau levels that were elevated by about 50 percent. Once tau begins to accumulate in brain tissue, the protein can spread from one brain area to the next along with neural connections. Holth et al. also found that older people who had more tau tangles in their brains by PET scanning had a less slow-wave, deep sleep. Holth et al. concluded that good sleep habits and/or treatments designed to encourage plenty of high-quality sleep might play an important role in slowing Alzheimer’s disease. In contrast, poor sleep might worsen the condition and serve as an early warning sign of Alzheimer’s disease.

Healthy Lifestyle Combats Alzheimer’s: Dhana and colleagues with the Rush University Medical Center in Chicago examined how healthy lifestyle mitigates the risk of Alzheimer’s disease (Natanson, 2019). The researchers followed a diverse group of 2765 participants for 9 years and focused on five low-risk lifestyle factors: healthy diet, at least 150 minutes/week of moderate to vigorous physical activity, not smoking, light to moderate alcohol intake, and engaging in cognitively stimulating activities.

Results indicated that those who adopted four or five low-risk lifestyle factors had a 60% lower risk of Alzheimer’s disease when compared with participants who did not follow any or only one of the low-risk factors. The authors concluded that incorporating these lifestyle changes can have a positive effect on one’s brain functioning and lower the risk for Alzheimer’s disease.

Vascular Neurocognitive Disorder is the second most common neurocognitive disorder affecting 0.2% in the 65-70 years age group and 16% of individuals 80 years and older (American Psychiatric Association, 2013). The vascular neurocognitive disorder is associated with a blockage of cerebral blood vessels that affects one part of the brain rather than a general loss of brain cells seen with Alzheimer’s disease. Personality is not as affected in vascular neurocognitive disorder, and more males are diagnosed than females (Erber and Szuchman, 2015). It also comes on more abruptly than Alzheimer’s disease and has a shorter course before death. Risk factors include smoking, diabetes, heart disease, hypertension, or a history of strokes.

Neurocognitive Disorder with Lewy bodies : According to the National Institute on Aging (2015a), Lewy bodies are microscopic protein deposits found in neurons seen postmortem. They affect chemicals in the brain that can lead to difficulties in thinking, movement, behavior, and mood. Neurocognitive Disorder with Lewy bodies is the third most common form and affects more than 1 million Americans. It typically begins at age 50 or older and appears to affect slightly more men than women. The disease lasts approximately 5 to 7 years from the time of diagnosis to death but can range from 2 to 20 years depending on the individual’s age, health, and severity of symptoms. Lewy bodies can occur in both the cortex and brain stem which results in cognitive as well as motor symptoms (Erber & Szuchman, 2015). The movement symptoms are similar to those with Parkinson’s disease and include tremors and muscle rigidity. However, the motor disturbances occur at the same time as the cognitive symptoms, unlike Parkinson’s disease when the cognitive symptoms occur well after the motor symptoms.

Individuals diagnosed with Neurocognitive Disorder with Lewy bodies also experience sleep disturbances, recurrent visual hallucinations, and are at risk for falling.

Work, Retirement, and Leisure

Work: According to the United States Census Bureau, in 1994, approximately 12% of those employed were 65 and over, and by 2016, the percentage had increased to 18% of those employed (McEntarfer, 2019). Looking more closely at the age ranges, more than 40% of Americans in their 60s are still working, while 14% of people in their 70s and just 4% of those 80 and older are currently employed (Livingston, 2019). Even though they make up a smaller number of workers overall, those 65- to 74-year-old and 75-and- older age groups are projected to have the fastest rates of growth in the next decade. See Figure 9.35 for the projected annual growth rate in the labor force by age in percentages, 2014-2024.

Livingston (2019) reported that similar to other age groups, those with higher levels of education are more likely to be employed. Approximately 37% of adults who are 60 and older and have a bachelor’s degree or more are working. In contrast, 31% with some college experience and 21% of those with a high school diploma or less are still working at age 60 and beyond. Additionally, men 60 and older are more likely to be working than women (33% vs. 24%). Not only are older persons working more, but they are also earning more than previously, and their growth in earnings is greater compared to workers of other ages (McEntarfer, 2019).

Older adults are proving just as capable as younger adults at the workplace. In fact, jobs that require social skills, accumulated knowledge, and relevant experiences favor older adults (Erber & Szuchman, 2015). Older adults also demonstrate lower rates of absenteeism and greater investment in their work.

Transitioning into Retirement: For most Americans, retirement is a process and not a one-time event (Quinn & Cahill, 2016). Sixty percent of workers transition straight to bridge jobs, which are often part-time and occur between a career and full retirement. About 15% of workers get another job after being fully retired. This may be due to not having adequate finances after retirement or not enjoying their retirement. Some of these jobs may be in encore careers  or work in a different field from the one in which they retired . Approximately 10% of workers begin phasing into retirement by reducing their hours. However, not all employers will allow this due to pension regulations.

Retirement age changes: Looking at retirement data, the average age of retirement declined from more than 70 in 1910 to age 63 in the early 1980s. However, this trend has reversed and the current average age is now 65. Additionally, 18.5% of those over the age of 65 continue to work (US Department of Health and Human Services, 2012) compared with only 12% in 1990 (U. S. Government Accountability Office, 2011). With individuals living longer, once retired the average amount of time a retired worker collects social security is approximately 17-18 years (James, Matz-Costa, & Smyer, 2016).

When to retire: Laws often influence when someone decides to retire. In 1986 the Age Discrimination in Employment Act (ADEA) was amended, and mandatory retirement was eliminated for most workers (Erber & Szuchman, 2015). Pilots, air traffic controllers, federal law enforcement, national park rangers, and firefighters continue to have enforced retirement ages. Consequently, for most workers, they can continue to work if they choose and are able. Social security benefits also play a role. For those born before 1938, they can receive full social security benefits at age 65. For those born between 1943 and 1954, they must wait until age 66 for full benefits, and for those born after 1959, they must wait until age 67 (Social Security Administration, 2016). Extra months are added to those born in years between. For example, if born in 1957, the person must wait until 66 years and 6 months. The longer one waits to receive social security, the more money will be paid out. Those retiring at age 62, will only receive 75% of their monthly benefits. Medicare health insurance is another entitlement that is not available until one is aged 65.

Delayed Retirement: Older adults primarily choose to delay retirement due to economic reasons (Erber & Szchman, 2015). Financially, continuing to work provides not only added income but also does not dip into retirement savings which may not be sufficient. Historically, there have been three parts to retirement income; that is, social security, a pension plan, and individual savings (Quinn & Cahill, 2016). With the 2008 recession, pension plans lost value for most workers. Consequently, many older workers have had to work later in life to compensate for absent or minimal pension plans and personal savings.  Social security was never intended to replace full income, and the benefits provided may not cover all the expenses, so elders continue to work. Unfortunately, many older individuals are unable to secure later employment, and those especially vulnerable include persons with disabilities, single women, the oldest- old, and individuals with intermittent work histories.

Some older adults delay retirement for psychological reasons, such as health benefits and social contacts.  Recent research indicates that delaying retirement has been associated with helping one live longer. When looking at both healthy and unhealthy retirees, a one-year delay in retiring was associated with a decreased risk of death from all causes (Wu, Odden, Fisher, & Stawski, 2016). When individuals are forced to retire due to health concerns or downsizing, they are more likely to have negative physical and psychological consequences (Erber & Szuchman, 2015).

Retirement Stages: Atchley (1994) identified several phases that individuals go through when they retire:

• Remote pre-retirement phase includes fantasizing about what one wants to do in retirement
• Immediate pre-retirement phase when concrete plans are established
• Actual retirement
• Honeymoon phase when retirees travel and participate in activities they could not do while working
• Disenchantment phase when retirees experience an emotional let-down
• Reorientation phase when the retirees attempt to adjust to retirement by making less hectic plans and getting into a regular routine  

Not everyone goes through every stage, but this model demonstrates that retirement is a process.

Post-retirement: Those who look most forward to retirement and have plans are those who anticipate adequate income (Erber & Szuchman, 2015). This is especially true for males who have worked consistently and have a pension and/or adequate savings. Once retired, staying active and socially engaged is important. Volunteering, caregiving, and informal helping can keep seniors engaged. Kaskie, Imhof, Cavanaugh, and Culp (2008) found that 70% of retirees who are not involved in productive activities spent most of their time watching TV, which is correlated with negative affect. In contrast, being productive improves well-being.

Elder Education : Attending college is not just for the young as discussed in the previous chapter. There are many reasons why someone in late adulthood chooses to attend college.

PNC Financial Services surveyed retirees aged 70 and over and found that 58% indicated that they had retired before they had planned (Holland, 2014). Many of these individuals chose to pursue additional training to improve skills to return to work in a second career. Others may be looking to take their career in a new direction. For some older students who no longer are focus on financial reasons, returning to school is intended to enable them to pursue work that is personally fulfilling. Attending college in late adulthood is also a great way for seniors to stay young and keep their minds sharp. Even if an elder chooses not to attend college for a degree, there are many continuing education programs on topics of interest available. In 1975, a nonprofit educational travel organization called Elderhostel began in New Hampshire with five programs for several hundred retired participants (DiGiacomo, 2015). This program combined college classroom time with travel tours and experiential learning experiences. In 2010 the organization changed its name to Road Scholar, and it now serves 100,000 people per year in the U.S. and in 150 countries. Academic courses, as well as practical skills such as computer classes, foreign languages, budgeting, and holistic medicines, are among the courses offered. Older adults who have higher levels of education are more likely to take continuing education. However, offering more educational experiences to a diverse group of older adults, including those who are institutionalized in nursing homes, can bring enhance the quality of life.

Leisure: During the past 10 years, leisure time for Americans 60 and older has remained at about 7 hours a day. However, the amount of time spent on TVs, computers, tablets or other electronic devices has risen almost 30 minutes per day over the past decade (Livingston, 2019). Those 60 and older now spend more than half of their daily leisure time (4 hours and 16 minutes) in front of screens. Screen time has increased for those in their 60s, 70s, 80s and beyond, and across genders and education levels. This rise in screen time coincides with significant growth in the use of digital technology by older Americans. In 2000, 14% of those aged 65 and older used the Internet, and now 73% are users and 53% own smartphones. Alternatively, the time spent on other recreational activities, such as reading or socializing, has gone down slightly. People with less education spend more of their leisure time on screens and less time reading compared with those with more education. Less-educated adults also spend less time exercising: 12 minutes a day for those with a high school diploma or less, compared with 26 minutes for college graduates.

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Adapted from Chapter 9 from Lifespan Development: A Psychological Perspective Second Edition by Martha Lally and Suzanne Valentine-French under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 unported license.

Human Behavior and the Social Environment I Copyright © 2020 by Susan Tyler is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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12.6: Cognitive Development in Late Adulthood

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Diana Lang; Nick Cone; Sonja Ann Miller; Martha Lally; and Suzanne Valentine-French

There are numerous stereotypes regarding older adults as being forgetful and confused, but what does the research on memory and cognition in late adulthood actually reveal? In this section, we wil l focus upon t he impact of aging on memory, how a ge impacts cognitive functioning, and a bnormal memory loss due to Alzheimer’s disease, deliriu m, and dementia. [1]

How does aging affect memory?

The Sensory Register

Aging may create small decrements in the sensitivity of the senses. And, to the extent that a person has a more difficult time hearing or seeing, that information will not be stored in memory. This is an important point, because many older people assume that if they cannot remember something, it is because their memory is poor. In fact, it may be that the information was never seen or heard.

The Working Memory

Older people have more difficulty using memory strategies to recall details. [2] Working memory is a cognitive system with a limited capacity responsible for temporarily holding information available for processing . As we age, the working memory loses some of its capacity. This makes it more difficult to concentrate on more than one thing at a time or to remember details of an event. However, people often compensate for this by writing down information and avoiding situations where there is too much going on at once to focus on a particular cognitive task.

When an elderly person demonstrates difficulty with multi-step verbal information presented quickly, the person is exhibiting problems with working memory. Working memory is among the cognitive functions most sensitive to decline in old age. Several explanations have been offered for this decline in memory functioning; one is the processing speed theory of cognitive aging by Tim Salthouse. Drawing on the findings of general slowing of cognitive processes as people grow older, Salthouse argues that slower processing causes working-memory contents to decay, thus reducing effective capacity. [3] For example, if an elderly person is watching a complicated action movie, they may not process the events quickly enough before the scene changes, or they may processing the events of the second scene, which causes them to forget the first scene. The decline of working-memory capacity cannot be entirely attributed to cognitive slowing, however, because capacity declines more in old age than speed.

Another proposal is the inhibition hypothesis advanced by Lynn Hasher and Rose Zacks [4] . This theory assumes a general deficit in old age in the ability to inhibit irrelevant, or no-longer relevant, information. Therefore, working memory tends to be cluttered with irrelevant contents which reduce the effective capacity for relevant content. The assumption of an inhibition deficit in old age has received much empirical support but, so far, it is not clear whether the decline in inhibitory ability fully explains the decline of working-memory capacity.

An explanation on the neural level of the decline of working memory and other cognitive functions in old age was been proposed by Robert West. He argued that working memory depends to a large degree on the pre-frontal cortex, which deteriorates more than other brain regions as we grow old. [5] Age related decline in working memory can be briefly reversed using low intensity transcranial stimulation, synchronizing rhythms in bilateral frontal and left temporal lobe areas.

The Long-Term Memory

Long-term memory involves the storage of information for long periods of time. Retrieving such information depends on how well it was learned in the first place rather than how long it has been stored. If information is stored effectively, an older person may remember facts, events, names and other types of information stored in long-term memory throughout life. The memory of adults of all ages seems to be similar when they are asked to recall names of teachers or classmates. And older adults remember more about their early adulthood and adolescence than about middle adulthood. [6] Older adults retain semantic memory or the ability to remember vocabulary.

Younger adults rely more on mental rehearsal strategies to store and retrieve information. Older adults focus rely more on external cues such as familiarity and context to recall information. [7] And they are more likely to report the main idea of a story rather than all of the details. [8]

A positive attitude about being able to learn and remember plays an important role in memory. When people are under stress (perhaps feeling stressed about memory loss), they have a more difficult time taking in information because they are preoccupied with anxieties. Many of the laboratory memory tests require comparing the performance of older and younger adults on timed memory tests in which older adults do not perform as well. However, few real life situations require speedy responses to memory tasks. Older adults rely on more meaningful cues to remember facts and events without any impairment to everyday living.

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New Research on Aging and Cognition

Can the brain be trained in order to build cognitive reserve to reduce the effects of normal aging? ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly), a study conducted between 1999 and 2001 in which 2,802 individuals age 65 to 94, suggests that the answer is “yes.” These participants received 10 group training sessions and 4 follow up sessions to work on tasks of memory, reasoning, and speed of processing. These mental workouts improved cognitive functioning even 5 years later. Many of the participants believed that this improvement could be seen in everyday tasks as well. [9] Learning new things, engaging in activities that are considered challenging, and being physically active at any age may build a reserve to minimize the effects of primary aging of the brain.

Watch this video from SciShow Psych to learn about ways to keep the mind young and active.

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You can view the transcript for “The Best Ways to Keep Your Mind Young” here (opens in new window) .

Changes in Attention in Late Adulthood

Divided attention has usually been associated with significant age-related declines in performing complex tasks. For example, older adults show significant impairments on attentional tasks such as looking at a visual cue at the same time as listening to an auditory cue because it requires dividing or switching of attention among multiple inputs. Deficits found in many tasks, such as the Stroop task which measures selective attention, can be largely attributed to a general slowing of information processing in older adults rather than to selective attention deficits per se. They also are able to maintain concentration for an extended period of time. In general, older adults are not impaired on tasks that test sustained attention, such as watching a screen for an infrequent beep or symbol.

The tasks on which older adults show impairments tend to be those that require flexible control of attention, a cognitive function associated with the frontal lobes. Importantly, these types of tasks appear to improve with training and can be strengthened. [10]

An important conclusion from research on changes in cognitive function as we age is that attentional deficits can have a significant impact on an older person’s ability to function adequately and independently in everyday life. One important aspect of daily functioning impacted by attentional problems is driving. This is an activity that, for many older people, is essential to independence. Driving requires a constant switching of attention in response to environmental contingencies. Attention must be divided between driving, monitoring the environment, and sorting out relevant from irrelevant stimuli in a cluttered visual array. Research has shown that divided attention impairments are significantly associated with increased automobile accidents in older adults [11] Therefore, practice and extended training on driving simulators under divided attention conditions may be an important remedial activity for older people. [12]

Problem Solving

Problem solving tasks that require processing non-meaningful information quickly (a kind of task which might be part of a laboratory experiment on mental processes) declines with age. However, real life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults are able to resolve everyday problems by relying on input from others such as family and friends. They are also less likely than younger adults to delay making decisions on important matters such as medical care. [13] [14]

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Brain Functioning

Research has demonstrated that the brain loses 5% to 10% of its weight between 20 and 90 years of age. [15] This decrease in brain volume appears to be due to the shrinkage of neurons, lower number of synapses, and shorter length of axons. According to Garrett, [16] the normal decline in cognitive ability throughout the lifespan has been associated with brain changes, including reduced activity of genes involved in memory storage, synaptic pruning, plasticity, and glutamate and GABA (neurotransmitters) receptors. There is also a loss in white matter connections between brain areas. Without myelin, neurons demonstrate slower conduction and impede each other’s actions. A loss of synapses occurs in specific brain areas, including the hippocampus (involved in memory) and the basal forebrain region. Older individuals also activate larger regions of their attentional and executive networks, located in the parietal and prefrontal cortex, when they perform complex tasks. This increased activation correlates with a reduced performance on both executive tasks and tests of working memory when compared to those younger. [17]

Despite these changes the brain exhibits considerable plasticity, and through practice and training, the brain can be modified to compensate for age-related changes. [18] Park and Reuter-Lorenz [19] proposed the Scaffolding Theory of Aging and Cognition which states that the brain adapts to neural atrophy (dying of brain cells) by building alternative connections, referred to as scaffolding. This scaffolding allows older brains to retain high levels of performance. Brain compensation is especially noted in the additional neural effort demonstrated by those individuals who are aging well. For example, older adults who performed just as well as younger adults on a memory task used both prefrontal areas, while only the right prefrontal cortex was used in younger participants. [20] Consequently, this decrease in brain lateralization appears to assist older adults with their cognitive skills.

Can we improve brain functioning? Many training programs have been created to improve brain functioning. ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly), a study conducted between 1999 and 2001 in which 2,802 individuals age 65 to 94, suggests that the answer is “yes”. These racially diverse participants received 10 group training sessions and 4 follow up sessions to work on tasks of memory, reasoning, and speed of processing. These mental workouts improved cognitive functioning even 5 years later. Many of the participants believed that this improvement could be seen in everyday tasks as well. [21] However, programs for the elderly on memory, reading, and processing speed training demonstrate that there is improvement on the specific tasks trained, but there is no generalization to other abilities. [22] Further, these programs have not been shown to delay or slow the progression of Alzheimer’s disease. Although these programs are not harmful, “physical exercise, learning new skills, and socializing remain the most effective ways to train your brain” (p. 207). These activities appear to build a reserve to minimize the effects of primary aging of the brain.

Parkinson’s disease

Parkinson’s disease is characterized by motor tremors, loss of balance, poor coordination, rigidity, and difficulty moving . [23] Parkinson’s affects approximately 1% of those over the age of 60, and it appears more frequently in family members in a little less than 10% of cases. Twenty-eight chromosomal areas have been implicated in Parkinson’s disease, but environmental factors have also been identified and include brain injury. Being knocked unconscious once increases the risk by 32%, and being knocked out several times increases the risk by 174%. [24] Other environmental influences include toxins, industrial chemicals, carbon monoxide, herbicides and pesticides. [25] The symptoms are due to the deterioration of the substantia nigra, an area in the midbrain whose neurons send dopamine-releasing axons to the basal ganglia which affects motor activity. Treatment typically includes the medication levodopa (L-dopa), which crosses the blood-brain barrier and is converted into dopamine in the brain. Deep brain stimulation, which involves inserting an electrode into the brain that provides electrical stimulation, has resulted in improved motor functioning. [26]

Similar to other adults, older adults need between 7 to 9 hours of sleep per night, but they tend to go to sleep earlier and get up earlier than those younger. This pattern is called advanced sleep phase syndrome and is based on changes in circadian rhythms. [27] There are sleep problems in older adults, and insomnia is the most common problem in those 60 and older. [28] People with insomnia have trouble falling asleep and staying asleep . There are many reasons why older people may have insomnia, including certain medications, being in pain, having a medical or psychiatric condition, and even worrying before bedtime about not being able to sleep. Using over the counter sleep aids or medication may only work when used for a short time. Consequently, sleep problems should be discussed with a health care professional.

Also, common in older adults are sleep disorders, including sleep apnea, restless legs syndrome, periodic limb movement disorder, and rapid eye movement sleep behavior disorder. [29] Sleep apnea refers to repeated short pauses in breathing, while an individual sleeps, that can lead to reduced oxygen in the blood . Snoring is a common symptom of sleep apnea and it often worsens with age. Untreated sleep apnea can lead to impaired daytime functioning, high blood pressure, headaches, stroke, and memory loss. Restless legs syndrome feels like there is tingling, crawling, or pins and needles in one or both legs, and this feeling is worse at night. Periodic limb movement disorder causes people to jerk and kick their legs every 20 to 40 seconds during sleep. Rapid eye movement sleep behavior disorder occurs when one’s muscles can move during REM sleep and sleep is disrupted.

According to the National Sleep Foundation, [30] there are many medical conditions that affect sleep and include gastroesophageal reflux disease, diabetes mellitus, renal failure, respiratory diseases such as asthma, and immune disorders. Diseases such as Parkinson’s disease and multiple sclerosis also commonly cause problems sleeping. Lastly, Alzheimer’s disease can interfere with sleeping patterns. Individuals may wake up many times during the night, wander when up, and yell which can alter the amount of time they sleep. Both minor and significant sleep problems in older adults can lead to increased risk of accidents, falls, chronic fatigue, decreased quality of life, cognitive decline, reduced immune function, and depression. [31]

Because of sleep problems experienced by those in late adulthood, research has looked into whether exercise can improve their quality of sleep. Results show that 150 minutes per week of exercise can improve sleep quality. [32] This amount of exercise is also recommended to improve other health areas including lowering the risk for heart disease, diabetes, and some cancers. Aerobic activity, weight training, and balance programs are all recommended. For those who live in assisted living facilities even light exercise, such as stretching and short walks, can improve sleep. High intensity activity is not necessary to see improvements. Overall, the effects of exercise on sleep may actually be even larger for older adults since their sleep quality may not be ideal to start.

Intelligence and Wisdom

When looking at scores on traditional intelligence tests, tasks measuring verbal skills show minimal or no age-related declines, while scores on performance tests, which measure solving problems quickly decline with age. [33] This profile mirrors crystalized and fluid intelligence. As you recall from last chapter, crystallized intelligence encompasses abilities that draw upon experience and knowledge. Measures of crystallized intelligence include vocabulary tests, solving number problems, and understanding texts. Fluid intelligence refers to information processing abilities, such as logical reasoning, remembering lists, spatial ability, and reaction time. Baltes [34] introduced two additional types of intelligence to reflect cognitive changes in aging. Pragmatics of intelligence are cultural exposure to facts and procedures that are maintained as one ages and are similar to crystalized intelligence . Mechanics of intelligence are dependent on brain functioning and decline with age, similar to fluid intelligence. Baltes indicated that pragmatics of intelligence show little decline and typically increase with age. Additionally, pragmatics of intelligence may compensate for the declines that occur with mechanics of intelligence. In summary, global cognitive declines are not typical as one ages, and individuals compensate for some cognitive declines, especially processing speed.

Wisdom is the ability to use the accumulated knowledge about practical matters that allows for sound judgment and decision making . A wise person is insightful and has knowledge that can be used to overcome obstacles in living. Does aging bring wisdom? While living longer brings experience, it does not always bring wisdom. Paul Baltes and his colleagues [35] [36] suggest that wisdom is rare. In addition, the emergence of wisdom can be seen in late adolescence and young adulthood, with there being few gains in wisdom over the course of adulthood. [37] This would suggest that factors other than age are stronger determinants of wisdom. Occupations and experiences that emphasize others rather than self, along with personality characteristics, such as openness to experience and generativity, are more likely to provide the building blocks of wisdom. [38] Age combined with a certain types of experience and/or personality brings wisdom.

Attention and Problem Solving

Changes in sensory functioning and speed of processing information in late adulthood often translates into changes in attention. [39] Research has shown that older adults are less able to selectively focus on information while ignoring distractors, [40] [41] although Jefferies and her colleagues found that when given double time, older adults could perform at young adult levels. Other studies have also found that older adults have greater difficulty shifting their attention between objects or locations. [42] Consider the implication of these attentional changes for older adults.

How do changes or maintenance of cognitive ability affect older adults’ everyday lives? Researchers have studied cognition in the context of several different everyday activities. One example is driving. Although older adults often have more years of driving experience, cognitive declines related to reaction time or attentional processes may pose limitations under certain circumstances. [43] In contrast, research on interpersonal problem solving suggested that older adults use more effective strategies than younger adults to navigate through social and emotional problems. [44] In the context of work, researchers rarely find that older individuals perform poorer on the job. [45] Similar to everyday problem solving, older workers may develop more efficient strategies and rely on expertise to compensate for cognitive decline.

Problem solving tasks that require processing non-meaningful information quickly (a kind of task that might be part of a laboratory experiment on mental processes) declines with age. However, many real-life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults resolve everyday problems by relying on input from others, such as family and friends. They are also less likely than younger adults to delay making decisions on important matters, such as medical care. [46] [47]

Deficit theories

The processing speed theory , proposed by Salthouse, [48] [49] suggests that as the nervous system slows with advanced age our ability to process information declines . This slowing of processing speed may explain age differences on many different cognitive tasks. For instance, as we age, working memory becomes less efficient. [50] Older adults also need longer time to complete mental tasks or make decisions. Yet, when given sufficient time older adults perform as competently as do young adults. [51] Thus, when speed is not imperative to the task healthy older adults do not show cognitive declines.

In contrast, inhibition theory argues that older adults have difficulty with inhibitory functioning, or the ability to focus on certain information while suppressing attention to less pertinent information tasks . [52] Evidence comes from directed forgetting research. In directed forgetting people are asked to forget or ignore some information, but not other information. For example, you might be asked to memorize a list of words, but are then told that the researcher made a mistake and gave you the wrong list, and asks you to “forget” this list. You are then given a second list to memorize. While most people do well at forgetting the first list, older adults are more likely to recall more words from the “forget-to-recall” list than are younger adults. [53]

Cognitive losses exaggerated

While there are information processing losses in late adulthood, overall loss has been exaggerated. [54] One explanation is that the type of tasks that people are tested on tend to be meaningless. For example, older individuals are not motivated to remember a random list of words in a study, but they are motivated for more meaningful material related to their life, and consequently perform better on those tests. Another reason is that the research is often cross-sectional. When age comparisons occur longitudinally, however, the amount of loss diminishes. [55] A third reason is that the loss may be due to a lack of opportunity in using various skills. When older adults practiced skills, they performed as well as they had previously. Although diminished performance speed is especially noteworthy in the elderly, Schaie [56] found that statistically removing the effects of speed diminished the individual’s performance declines significantly. In fact, Salthouse and Babcock [57] demonstrated that processing speed accounted for all but 1% of age-related differences in working memory when testing individuals from 18 to 82. Finally, it is well established that our hearing and vision decline as we age. Longitudinal research has proposed that deficits in sensory functioning explain age differences in a variety of cognitive abilities. [58]

Abnormal Loss of Cognitive Functioning During Late Adulthood

Historically, the term dementia was used to refer to an individual experiencing difficulties with memory, language, abstract thinking, reasoning, decision making, and problem-solving. [59] While the term dementia is still in common use, in the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) [60] the term dementia has been replaced by neurocognitive disorder. A Major Neurocognitive Disorder is diagnosed as a significant cognitive decline from a previous level of performance in one or more cognitive domains and interferes with independent functioning, while a Minor Neurocognitive Disorder is diagnosed as a modest cognitive decline from a previous level of performance in one of more cognitive domains and does not interfere with independent functioning. There are several different neurocognitive disorders that are typically demonstrated in late adulthood, and determining the exact type can be difficult because the symptoms may overlap with each other. Diagnosis often includes a medical history, physical exam, laboratory tests, and changes noted in behavior.

Common symptoms of dementia include emotional problems, difficulties with language, and a decrease in motivation. A person’s consciousness is usually not affected. Globally, dementia affected about 46 million people in 2015. About 10% of people develop the disorder at some point in their lives, and it becomes more common with age. About 3% of people between the ages of 65–74 have dementia, 19% between 75 and 84, and nearly half of those over 85 years of age. In 2015, dementia resulted in about 1.9 million deaths, up from 0.8 million in 1990. As more people are living longer, dementia is becoming more common in the population as a whole.

Dementia generally refers to severely impaired judgment, memory or problem-solving ability. It can occur before old age and is not an inevitable development even among the very old. Dementia can be caused by numerous diseases and circumstances, all of which result in similar general symptoms of impaired judgment, etc. Alzheimer’s disease is the most common form of dementia and is incurable, but there are also nonorganic causes of dementia which can be prevented. Malnutrition, alcoholism, depression, and mixing medications can also result in symptoms of dementia. If these causes are properly identified, they can be treated. Cerebral vascular disease can also reduce cognitive functioning.

Delirium , also known as acute confusional state, is an organically caused decline from a previous baseline level of mental function that develops over a short period of time, typically hours to days. It is more common in older adults, but can easily be confused with a number of psychiatric disorders or chronic organic brain syndromes because of many overlapping signs and symptoms in common with dementia, depression, psychosis, etc. Delirium may manifest from a baseline of existing mental illness, baseline intellectual disability, or dementia, without being due to any of these problems.

Delirium is a syndrome encompassing disturbances in attention, consciousness, and cognition. It may also involve other neurological deficits, such as psychomotor disturbances (e.g. hyperactive, hypoactive, or mixed), impaired sleep-wake cycle, emotional disturbances, and perceptual disturbances (e.g. hallucinations and delusions), although these features are not required for diagnosis. Among older adults, delirium occurs in 15-53% of post-surgical patients, 70-87% of those in the ICU, and up to 60% of those in nursing homes or post-acute care settings. Among those requiring critical care, delirium is a risk for death within the next year.

Alzheimer’s Disease

Alzheimer’s disease (AD) , also referred to simply as Alzheimer’s, is the most common cause of dementia, accounting for 60-70% of its cases. Alzheimer’s is a progressive disease causing problems with memory, thinking and behavior. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks. [61]

Alzheimer’s disease is probably the most well-known and most common neurocognitive disorder for older individuals. In 2016, an estimated 5.4 million Americans were diagnosed with Alzheimer’s disease, [62] which was approximately one in nine aged 65 and over. By 2050, the number of people age 65 and older with Alzheimer’s disease is projected to be 13.8 million if there are no medical breakthroughs to prevent or cure the disease. Alzheimer’s disease is the 6th leading cause of death in the United States, but the 5th leading cause for those 65 and older. Among the top 10 causes of death in America, Alzheimer’s disease is the only one that cannot be prevented, cured, or even slowed. Current estimates indicate that Alzheimer disease affects approximately 50% of those identified with a neurocognitive disorder. [63]

Alzheimer’s disease has a gradual onset with subtle personality changes and memory loss that differs from normal age-related memory problems occurring first. Confusion, difficulty with change, and deterioration in language, problem-solving skills, and personality become evident next. In the later stages, the individual loses physical coordination and is unable to complete everyday tasks, including self-care and personal hygiene. [64] Lastly, individuals lose the ability to respond to their environment, to carry on a conversation, and eventually to control movement (Alzheimer’s Association, 2016). The disease course often depends on the individual’s age and whether they have other health conditions.

Alzheimer’s is the sixth leading cause of death in the United States. On average, a person with Alzheimer’s lives four to eight years after diagnosis, but can live as long as 20 years, depending on other factors. Alzheimer’s is not a normal part of aging. The greatest known risk factor is increasing age, and the majority of people with Alzheimer’s are 65 and older. But Alzheimer’s is not just a disease of old age. Approximately 200,000 Americans under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset Alzheimer’s). [65]

The cause of Alzheimer’s disease is poorly understood. About 70% of the risk is believed to be inherited from a person’s parents with many genes usually involved. Other risk factors include a history of head injuries, depression, and hypertension. The disease process is associated with plaques and neurofibrillary tangles in the brain. A probable diagnosis is based on the history of the illness and cognitive testing with medical imaging and blood tests to rule out other possible causes. Initial symptoms are often mistaken for normal aging, but examination of brain tissue, specifically of structures called plaques and tangles, is needed for a definite diagnosis. Though qualified physicians can be up to 90% certain of a correct diagnosis of Alzheimer’s, currently, the only way to make a 100% definitive diagnosis is by performing an autopsy of the person and examining the brain tissue. In 2015, there were approximately 29.8 million people worldwide with AD. In developed countries, AD is one of the most financially costly diseases.

This Ted-Ed video explains some of the history and biological diagnosis of Alzheimer’s.

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You can view the transcript for “What is Alzheimer’s disease? – Ivan Seah Yu Jun” here .

Samuel Cohen researches Alzheimer’s disease and other neurodegenerative disorders. Listen to Cohen’s TED Talk on Alzheimer’s disease to learn more.

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• This chapter was adapted from select chapters in Lumen Learning's Lifespan Development , authored by Martha Lally and Suzanne Valentine-French available under a Creative Commons Attribution-NonCommercial-ShareAlike license , and Waymaker Lifespan Development , authored by Sonja Ann Miller for Lumen Learning and available under a Creative Commons Attribution license . Some selections from Lumen Learning were adapted from previously shared content from Laura Overstreet's Lifespan Psychology and Wikipedia. ↵
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35 problem-solving techniques and methods for solving complex problems

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All teams and organizations encounter challenges as they grow. There are problems that might occur for teams when it comes to miscommunication or resolving business-critical issues . You may face challenges around growth , design , user engagement, and even team culture and happiness. In short, problem-solving techniques should be part of every team’s skillset.

Problem-solving methods are primarily designed to help a group or team through a process of first identifying problems and challenges , ideating possible solutions , and then evaluating the most suitable .

Finding effective solutions to complex problems isn’t easy, but by using the right process and techniques, you can help your team be more efficient in the process.

So how do you develop strategies that are engaging, and empower your team to solve problems effectively?

In this blog post, we share a series of problem-solving tools you can use in your next workshop or team meeting. You’ll also find some tips for facilitating the process and how to enable others to solve complex problems.

Let’s get started! 

How do you identify problems?

How do you identify the right solution.

• Tips for more effective problem-solving

Complete problem-solving methods

• Problem-solving techniques to identify and analyze problems
• Problem-solving techniques for developing solutions

Problem-solving warm-up activities

Closing activities for a problem-solving process.

Before you can move towards finding the right solution for a given problem, you first need to identify and define the problem you wish to solve. 

Here, you want to clearly articulate what the problem is and allow your group to do the same. Remember that everyone in a group is likely to have differing perspectives and alignment is necessary in order to help the group move forward. 

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner. It can be scary for people to stand up and contribute, especially if the problems or challenges are emotive or personal in nature. Be sure to try and create a psychologically safe space for these kinds of discussions.

Remember that problem analysis and further discussion are also important. Not taking the time to fully analyze and discuss a challenge can result in the development of solutions that are not fit for purpose or do not address the underlying issue.

Successfully identifying and then analyzing a problem means facilitating a group through activities designed to help them clearly and honestly articulate their thoughts and produce usable insight.

With this data, you might then produce a problem statement that clearly describes the problem you wish to be addressed and also state the goal of any process you undertake to tackle this issue.  

Finding solutions is the end goal of any process. Complex organizational challenges can only be solved with an appropriate solution but discovering them requires using the right problem-solving tool.

After you’ve explored a problem and discussed ideas, you need to help a team discuss and choose the right solution. Consensus tools and methods such as those below help a group explore possible solutions before then voting for the best. They’re a great way to tap into the collective intelligence of the group for great results!

Remember that the process is often iterative. Great problem solvers often roadtest a viable solution in a measured way to see what works too. While you might not get the right solution on your first try, the methods below help teams land on the most likely to succeed solution while also holding space for improvement.

Every effective problem solving process begins with an agenda . A well-structured workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

In SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Tips for more effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.  

Try different approaches  

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally 

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator 

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

• Six Thinking Hats
• Lightning Decision Jam
• Problem Definition Process
• Discovery & Action Dialogue

Design Sprint 2.0

• Open Space Technology

1. Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

2. Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on. 

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages. 

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

3. Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design. 

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition   #problem solving   #idea generation   #creativity   #online   #remote-friendly   A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

4. The 5 Whys 

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges. 

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results. 

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys   #hyperisland   #innovation   This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

5. World Cafe

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold. 

World Cafe   #hyperisland   #innovation   #issue analysis   World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

6. Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD)   #idea generation   #liberating structures   #action   #issue analysis   #remote-friendly   DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

7. Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

8. Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology   #action plan   #idea generation   #problem solving   #issue analysis   #large group   #online   #remote-friendly   Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

• The Creativity Dice
• Fishbone Analysis
• Problem Tree
• SWOT Analysis
• Agreement-Certainty Matrix
• The Journalistic Six
• LEGO Challenge
• What, So What, Now What?
• Journalists

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed. 

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.  

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It!   #gamestorming   #problem solving   #action   Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

10. The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed. 

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable. 

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

11. Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around. 

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish. 

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis   #problem solving   ##root cause analysis   #decision making   #online facilitation   A process to help identify and understand the origins of problems, issues or observations.

12. Problem Tree 

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them. 

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree   #define intentions   #create   #design   #issue analysis   A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

13. SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward. 

SWOT analysis   #gamestorming   #problem solving   #action   #meeting facilitation   The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

14. Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results. 

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process. 

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID   #gamestorming   #project planning   #issue analysis   #problem solving   When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

16. Speed Boat

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!   

Speed Boat   #gamestorming   #problem solving   #action   Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

17. The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How   #idea generation   #issue analysis   #problem solving   #online   #creative thinking   #remote-friendly   A questioning method for generating, explaining, investigating ideas.

18. LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills. 

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO! 

LEGO Challenge   #hyperisland   #team   A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

19. What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems. 

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken. 

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What?   #issue analysis   #innovation   #liberating structures   You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

20. Journalists  

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists   #vision   #big picture   #issue analysis   #remote-friendly   This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for developing solutions 

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to narrow down to the correct solution.

Use these problem-solving techniques when you want to help your team find consensus, compare possible solutions, and move towards taking action on a particular problem.

• Improved Solutions
• Four-Step Sketch
• 15% Solutions
• How-Now-Wow matrix
• Impact Effort Matrix

21. Mindspin  

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly. 

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation. 

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex. 

MindSpin   #teampedia   #idea generation   #problem solving   #action   A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

22. Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result. 

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution. 

Improved Solutions   #creativity   #thiagi   #problem solving   #action   #team   You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

23. Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged. 

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch   #design sprint   #innovation   #idea generation   #remote-friendly   The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper,  Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

24. 15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change. 

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.   

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change. 

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

25. How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process. 

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud. 

How-Now-Wow Matrix   #gamestorming   #idea generation   #remote-friendly   When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

26. Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice. 

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them. 

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

27. Dotmocracy

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action? 

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus. 

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively. 

Dotmocracy   #action   #decision making   #group prioritization   #hyperisland   #remote-friendly   Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

• Check-in/Check-out
• Doodling Together
• Show and Tell
• Constellations
• Draw a Tree

28. Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process.

Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute. 

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out   #team   #opening   #closing   #hyperisland   #remote-friendly   Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

29. Doodling Together  

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start. 

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems. 

Doodling Together   #collaboration   #creativity   #teamwork   #fun   #team   #visual methods   #energiser   #icebreaker   #remote-friendly   Create wild, weird and often funny postcards together & establish a group’s creative confidence.

30. Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team! 

Show and Tell   #gamestorming   #action   #opening   #meeting facilitation   Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

31. Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible. 

Constellations   #trust   #connection   #opening   #coaching   #patterns   #system   Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

32. Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic. 

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree   #thiagi   #opening   #perspectives   #remote-friendly   With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

• One Breath Feedback
• Who What When Matrix
• Response Cards

How do I conclude a problem-solving process?

All good things must come to an end. With the bulk of the work done, it can be tempting to conclude your workshop swiftly and without a moment to debrief and align. This can be problematic in that it doesn’t allow your team to fully process the results or reflect on the process.

At the end of an effective session, your team will have gone through a process that, while productive, can be exhausting. It’s important to give your group a moment to take a breath, ensure that they are clear on future actions, and provide short feedback before leaving the space. 

The primary purpose of any problem-solving method is to generate solutions and then implement them. Be sure to take the opportunity to ensure everyone is aligned and ready to effectively implement the solutions you produced in the workshop.

Remember that every process can be improved and by giving a short moment to collect feedback in the session, you can further refine your problem-solving methods and see further success in the future too.

33. One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round. 

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them. 

One breath feedback   #closing   #feedback   #action   This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

34. Who What When Matrix 

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward. 

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved. 

Who/What/When Matrix   #gamestorming   #action   #project planning   With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

35. Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out! 

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised. 

Response Cards   #debriefing   #closing   #structured sharing   #questions and answers   #thiagi   #action   It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Save time and effort discovering the right solutions

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks  to build your agenda. When you make changes or update your agenda, your session  timing   adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore  how to use SessionLab  to design effective problem solving workshops or  watch this five minute video  to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of creative exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you! 

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

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Coupled Cognitive Changes in Adulthood: A Meta-analysis

Elliot m. tucker-drob.

1 University of Texas at Austin, Austin, USA

Andreas M. Brandmaier

2 Max Planck Institute for Human Development, Berlin, Germany

3 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, United Kingdom

Ulman Lindenberger

Associated data.

With advancing age, healthy adults typically exhibit decreases in performance across many different cognitive abilities such as memory, processing speed, spatial ability, and abstract reasoning. However, there are marked individual differences in rates of cognitive decline, with some adults declining steeply and others maintaining high levels of functioning. To move toward a comprehensive understanding of cognitive aging, it is critical to know whether individual differences in longitudinal changes interrelate across different cognitive abilities. We identified 89 effect sizes representing shared variance in longitudinal cognitive change from 22 unique datasets composed of over 30,000 unique individuals, which we meta-analyzed using a series of multilevel meta-regression models. Results indicate that an average of 60% of the variation in cognitive changes is shared across cognitive abilities. Shared variation in changes increased with age, from approximately 45% at age 35 years to approximately 70% at age 85 years. There was a moderate-to-strong correspondence (r = .49, congruence coefficient = .98) between the extent to which a variable indicated general intelligence and the extent to which change in that variable indicated a general factor of aging-related change. Shared variation in changes did not differ substantially across cognitive ability domain classifications. In a sensitivity analysis based on studies that carefully controlled for dementia, shared variation in longitudinal cognitive changes remained at upwards of 60%, and age-related increases in shared variation in cognitive change continued to be evident. These results together provide strong evidence for a general factor of cognitive aging that strengthens with advancing adult age.

How Many Causes are there of Aging-Related Decrements in Cognitive Functioning?

( Salthouse, 1994 )

With advancing age, adults typically exhibit decreasing performance across many different domains of cognitive performance. Although it is sometimes assumed that cognitive aging is a phenomenon confined to very late adulthood that only affects a small subset of diseased individuals, there is now strong evidence that aging-related cognitive declines begin to emerge at least as early as middle adulthood, occur fairly continuously with the passage of time, affect individuals without diagnosed pathologies, and occur throughout the entire distribution of psychological and physical health ( Salthouse, 2004a ; Salthouse 2009 ). Normative aging-related decrements are large. Cross-sectional studies estimate correlations between adult age and abstract reasoning, visuospatial ability, episodic memory, and processing speed at between approximately r = −.40 and r = −.60 ( Salthouse, 2004b ). Longitudinal studies indicate shallower decline in earlier adulthood and are more consistent with cross-sectional estimates for later adulthood ( Schaie, 1994 ). When practice effects associated with repeated assessments of individuals followed over time, selective attrition, and cohort effects are taken into account, the apparent gap between longitudinal and cross-sectional estimates of aging-related declines narrows substantially ( Lindenberger et al., 2002 ; Lövdén et al., 2004 ; McArdle et al., 2002 ; Rönnlund et al, 2005 ; Salthouse, 2015 ).

Crucial to a complete account of cognitive aging is its dimensionality, that is, the structure and magnitude of correlations among changes in cognitive abilities ( Lindenberger, von Oertzen, Ghisletta, & Hertzog, 2011 ; Rabbitt, 1993 ). Whereas research approaches stemming from cognitive-experimental traditions often seek to uncover specific psychological mechanisms for aging-related declines in individual cognitive tasks, approaches stemming from differential psychology have taken seriously the hypothesis that aging-induced changes in general psychological factors, or even a single psychological factor, may largely account for aging-related declines across many different cognitive abilities ( Salthouse, 1991 ). Such common factor hypotheses of cognitive aging have been popular for several decades. For instance, according to Salthouse (1988) , the hypothesis that an “age-related reduction in some type of general-purpose processing resource contributes to impaired cognitive performance appears to be the only explanation with sufficient generality to account for the age differences observed across a variety of cognitive tasks” (p. 238). Verhaeghen and Salthouse (1997) concluded that “age-related influences on a wide range of cognitive variables are shared” and that “age-related changes in the cognitive system are associated with a decline in some general and fundamental mechanism.” Salthouse (2016) more recently commented that, “if the contribution of general influences is at least moderate, explanations of domain-specific age relations will need to be supplemented with explanations of general age relations to fully account for cognitive aging phenomena.” Relatedly, Birren (1964) , Craik (1983) , and Welford (1965) have argued that aging-sensitive psychological resources may limit performance in a large variety of cognitive domains.

Importantly, the question of “how many causes are there” of cognitive aging ( Salthouse, 1994 ) can be addressed at multiple levels of analysis, including psychological, social, and biological. The key question that has been the topic of much research, and which is the focus of the current meta-analysis, is the dimensionality of cognitive aging at the psychological level of analysis. As Tucker-Drob (2011a) has clarified, a single general psychological cause could “be the outcome of multiple independent biological mechanisms, each broadly affecting cognition.” Relatedly, Deary, Cox, and Ritchie (2016 , p. 198) have proposed a model of multiple “formative… biological elements giving rise to a reflective, psychometric general” psychological dimension, and Lindenberger et al. (2006) hypothesized that “changes in behavioral repertoires are accompanied by continuous changes in multiple brain-behavior mappings” (p. 713-714). In other words, rather than directly seeking to identify the many biological and experiential causes that likely exist for cognitive aging, we seek to reveal the extent to which aging-related changes in different cognitive abilities occur along a common statistical dimension. Even though we cannot presently identify the totality of specific causal processes that underlie aging-related cognitive declines, or directly enumerate the number of such specific causal mechanisms, analyses that characterize the dimensionality of aging-related cognitive changes are an important descriptive step that may prove invaluable for guiding ongoing research into specific mechanisms of cognitive aging, and the cognitive dimensions on which they act.

What’s change got to do with it?

( Lindenberger, von Oertzen, Ghisletta, & Hertzog, 2011 )

Historically, approaches to testing for shared aging-related effects across multiple cognitive domains have relied on cross-sectional mediation approaches. For instance, early work tested the extent to which cross-sectional age differences in cognitive abilities such as reasoning, visuospatial ability, and episodic memory were mediated by hypothesized “processing resources,” such as information processing speed ( Lindenberger, Mayr, & Kliegl, 1993 ; Salthouse, 1996 ) and working memory capacity (Salthouse, 1990). More recent work of this sort (e.g. Salthouse, 2004b ) has tested the extent to which cross-sectional age differences in a range of cognitive abilities are mediated by a common higher order general intelligence factor. Such cross-sectional shared influence approaches ( Tucker-Drob & Salthouse, 2011 ) have generally indicated that substantial proportions of age-related effects on different cognitive abilities are mediated by a general intelligence factor, although some residual age effects on individual abilities typically remain.

As has been pointed out by several scholars, cross-sectional mediation approaches reflect, to a large extent, patterns of mean age differences across domains but are unable to directly test whether individual differences in rates of cognitive change are shared across domains ( Hofer & Sliwinski, 2001 ; Horn, 1970 ; Kalveram, 1965 ; Lindenberger & Pötter, 1998 ). When the goal is to test for mediation of aging-related differences in cognitive abilities, either by a processing resource (e.g. processing speed) or by a general factor, cross-sectional approaches are quite limited. As put by Lindenberger and Pötter (1998) , cross-sectional mediation “does not offer a test of the basic mediation assumption. All it does is tell us how the world may look if that assumption were true” (p. 227). Hofer, Flaherty, and Hoffman (2006) similarly wrote that “high levels of association between time-dependent processes can result simply from average population age differences and not necessarily from associations between individual ‘rates of aging’” (p. 165), and Maxwell and Cole (2007) referred to cross-sectional mediation approaches as “often highly misleading” (p. 23). More recently, Lindenberger, et al. (2011) formally demonstrated that high levels of “explained age-related variance” obtained using cross-sectional mediation approaches may stem from either similar average mean age trends, from within-time (but not longitudinal) correlations between the putative mediator and outcome variable, or some mixture of the two. They characterized the link between cross-sectional mediation approaches and developmental co-dependencies over time that researchers seek to make inferences about, as “brittle and volatile” (p. 40). Indeed, examples of strong cross-sectional overlap but much weaker associations among longitudinal changes have been reported for the associations between cognitive abilities and sensory functions (e.g. Anstey, Hofer, & Luszcz, 2003 ; Lindenberger & Ghisletta, 2009 ), and cognitive abilities and physical functions ( Ritchie, Tucker-Drob, Starr, & Deary, 2016 ). Note that the opposite also appears to occur: Abilities that dissociate cross-sectionally, such as verbal knowledge and perceptual speed, have been found to show correlated change when probed longitudinally (e.g, Ghisletta et al., 2012 ). Thus, the weaknesses of cross-sectional data for indexing the interrelations among time-based developmental associations are not only a logical possibility, but –in at least some substantively important empirical circumstances– a reality.

Does it all go together when it goes?

( Rabbitt, 1993 )

Fundamental to accurately assessing the dimensionality of cognitive aging are longitudinal approaches that index within-person changes in multiple cognitive abilities over time. Such approaches can be used to test whether individual differences in rates of intraindividual longitudinal changes across different cognitive abilities are interrelated, as would be predicted by common factor theories of cognitive aging. Given that individual differences in cognitive abilities are moderately stable from middle childhood forward (e.g., Deary, 2014 ; Humphreys & Davey, 1988 ; Tucker-Drob & Briley, 2014 ), correlated individual differences in static levels of different cognitive abilities in adulthood could be a vestige of an interdependence that came into existence earlier in life (e.g. effects of schooling on multiple cognitive functions during childhood) but no longer exists in adulthood. In contrast, correlated (i.e. “coupled”) individual differences in longitudinal rates of adult cognitive change are more likely to reflect systems of influence that are unfolding during adulthood ( Tucker-Drob, 2011b ). Examining such associations among individual differences in longitudinal cognitive changes allows one to directly ask whether individuals who are declining particularly rapidly relative to their peers in one ability are more likely to be declining rapidly, (or to be improving less) relative to their peers in a different cognitive ability, and whether those remaining relatively intact in one ability are also likely to remain relatively intact in another ability. In other words, longitudinal data allow researchers to ask the question “does it all go together when it goes?” ( Rabbitt, 1993 ) at the level of correlated interindividual differences in intraindividual change ( Baltes, Reese, & Nesselroade, 1977 ). This precise question is foundational for addressing common factor theories of cognitive aging. As Deater-Deckard and Mayr (2010) wrote, “The ultimate answer to the question of whether cognitive aging is a general factor or a multifaceted phenomenon will come from careful longitudinal data…that allow uncovering the dimensionality of change across a wide range of cognitive abilities” (p. 25).

Factor Analysis Since Spearman: Where do we Stand? What do we Know?

( Carroll, 1989 )

One reason to suspect that longitudinal aging-related declines might be correlated across cognitive abilities is what Deary (2000 , p. 6) has described as “arguably the most replicated result in all psychology,” namely, that individual differences in cognitive abilities, measured at a single point in time, are positively correlated with one another. This positive manifold of correlations was originally discovered by Spearman (1904) , and served as the basis for the hypothesis that a common statistical dimension, or what Spearman termed general intelligence ( g ), underlies substantial proportions of variation in different cognitive abilities. Spearman formalized this hypothesis using factor analysis, which tests whether an observed matrix of variable intercorrelations can be closely approximated by a model in which all variables interrelate by way of their mutual relations with an unobserved latent factor. In the time since Spearman (1904) , factor analytic methods have established that individual differences in cognitive abilities fit a hierarchical structure in which narrow abilities (often indexed by individual tests) load on broader cognitive ability domains (e.g. abstract reasoning, spatial ability, verbal ability, episodic memory, working memory, and processing speed), which in turn load on a single higher order g factor ( Carroll, 1993 ). Typically, g accounts for upwards of 50% of the variance in the first-order ability domains ( Carroll, 1993 ; Tucker-Drob, 2009 ). Whether g should be treated as a veridical psychological entity or simply as a statistical shorthand for conveniently summarizing an otherwise high dimensional matrix of correlations has been a topic of tremendous theoretical interest ( Bartholomew, Deary, & Lawn, 2009 ; Dickens, 2007 ; Kievit et al., 2017 ; Kovacs & Conway, 2016 ; Thurstone, 1938 ; van der Maas et al., 2006 ) and ideological consternation and debate (Gould, 1981) over the past century. One view is that higher-order factors such as g are “defining a working reference frame, located in a convenient manner in the ‘space’ defined by all behaviors of a given type” ( Cronbach & Mehl, 1955 , p. 277-278). A different view is that general intelligence is a “genuine construct” ( Gignac, 2016 , p. 69) that causally influences the behaviors through which it is expressed (e.g. Spearman, 1904 , Panizzon et al., 2014 ).

The goal of this article is to provide meta-analytic, descriptive evidence on the dimensionality of cognitive change in adulthood. This goal is compatible with both above-described views of general intelligence, though the two views will diverge in the interpretation of the results. That a moderately strong general factor underlies individual differences in different cognitive abilities at a single point in time suggests the possibility, but does not guarantee, that a general factor may underlie individual differences in rates of change in different abilities over time. In other words, the statistical dimensions along which individual differences in cognitive abilities emerge over the course of development may correspond to the dimensions along which individual differences in cognitive aging occur. Salthouse (1988) proposed that declines in general processing resources may underlie aging-related declines in different cognitive abilities, specifically noting strong “parallels between processing resources and intellectual g” (p. 251). Juan-Espinosa et al. (2002) provided an anatomic metaphor for lifespan growth and decline of cognitive abilities (see also Baltes et al., 1980 ; Schaie, 1962 ; Tetens, 1777 ; Werner, 1948 ). They proposed that, in the same way that age-related growth and shrinkage of the human bones is organized by the anatomical structure of the human skeleton, individual differences in human cognitive abilities may have an inherent structure along which growth and decline naturally occur (see Baltes et al., 2006, for a summary of this line of thought).

Of course, the factor structure of cognitive aging could barely, if at all, resemble the structure of individual differences in cognitive abilities measured at a static point in time. For instance, the structure of static individual differences in cognitive ability levels in early adulthood may primarily be reflective of how heterogeneity in environmental experience is structured over childhood (e.g., experiences that foster growth in one ability tend to co-occur with other experiences that foster growth in other abilities), from the broad effects of cognitively enriching experiences on many different cognitive abilities over child development, or from the broad effects of intellectual engagement and achievement motivation on many different cognitive abilities over child development ( Dickens, 2007 ; Tucker-Drob, 2013 ; Tucker-Drob, Briley, & Harden, 2013 ; for an early exploration of these ideas, see Baltes et al, 1978 ). In contrast, heterogeneity in aging-related cognitive declines may stem from specific neurodegenerative processes in different neural structures and functions that each subserves a different ability. Indeed, the correlates of levels of cognitive abilities in both childhood and adulthood, including indices of socioeconomic status and physical health have typically failed to significantly predict individual differences in aging-related cognitive declines ( Tucker-Drob, Johnson, & Jones, 2009 ; Ritchie et al., 2016 ). Moreover, there is little consistent evidence that individual differences in levels of cognitive abilities are systematically correlated with individual differences in changes in those abilities ( Verhaegen, 2013 ). Thus, although individual differences in cognitive aging may conform to a similarly low dimensional structure as that underlying static individual differences in cognitive abilities, individual differences in cognitive aging do not appear to simply reconsistute static individual differences present during earlier periods of life.

The factor structure of individual differences in cognitive changes over adulthood may drive transformations in the factor structure of individual differences in cognitive abilities with advancing age. This becomes clear when one considers that individual differences in a trait at a particular adult age represent a mixture of individual differences in the levels in that trait that have existed since early adulthood and individual differences in changes in that trait thereafter. As per Hofer and Sliwinski (2001 ; cf. Hertzog, 1985 ), the covariance between abilities x and y at time t is function of the covariance between the abilities at time baseline, the level-change covariances, and the covariance between changes from baseline to time t. If individual differences in levels of different abilities covary moderately in early adulthood, and individual differences in subsequent changes in those abilities are uncorrelated or correlate very weakly, then we would expect the correlation between ability levels to decline with advancing age. If individual differences in levels of different abilities covary moderately in early adulthood, and individual differences in subsequent changes in those abilities are strongly correlated, then we would expect the correlation between ability levels to increase with advancing age. Finally, if the magnitude of the correlation between individual differences in levels of different abilities in early adulthood is similar to the magnitude of the correlation between individual differences in subsequent changes in those abilities, then we would expect the correlation between ability levels to remain relatively constant with advancing age. As Hofer and Sliwinksi (2001) write “as time elapses, the magnitude of the covariance becomes increasingly due to the covariance associated with rates of change relative to the other sources of covariance. Therefore, in older samples of individuals, more time will have transpired and this will increase the contribution… that reflects individual differences in rates of ageing” (p. 346). Indeed, this rationale has been the motivation behind several investigations of the dedifferentiation hypothesis that abilities become increasingly correlated with adult age (see Baltes et al., 1980 and Reinert, 1970 for early investigations of the dedifferentiation hypothesis; see Cox et al., 2016 , for an investigation of the dedifferentiation hypothesis with respect to neurostructural connectivity). In a factor analytic model, the prediction is that a common g factor should account for increasing variance in abilities with age. Evidence for aging-related dedifferentiation of cognitive abilities has been mixed (see Tucker-Drob, 2009 , for a review and negative evidence from a large cross-sectional sample).

Building on earlier work by Baltes, Nesselroade, Reinert, and others, de Frias et al. (2007) extended the dedifferentiation hypothesis to predict transformations in the factor structure of aging-related changes over time. Theorizing that “an ensemble of common sources increasingly dominates development of intellectual abilities” ( de Frias et al., 2007 , p. 382, italics in original) in adulthood, de Frias et al. (2007) predicted that there are age-related increases in “the degree to which changes in a single cognitive ability are associated with changes in other abilities,” ( de Frias et al., 2007 , p. 382) which they termed the dynamic dedifferentiation hypothesis .

How should we measure “change” – Or should we?

( Cronbach & Furby, 1970 )

Historically, major impediments to progress in longitudinal research on individual differences in change over time stemmed from the unavailability of suitable methods for analyzing longitudinal data. Perhaps the most intuitive approach to analyzing change is to calculate observed difference scores between test scores at baseline and follow-up measurement occasions. However when such “raw change scores” are calculated from measures that themselves have less than perfect reliability, issues surrounding unreliability and regression to the mean compound to such a degree that the true signal of interest −individual differences in cognitive change− becomes highly obscured. Cronbach and Furby (1970) for example wrote that “‘raw change’ or ‘raw gain’ scores formed by subtracting pretest scores from posttest scores lead to fallacious conclusions, primarily because such scores are systematically related to any random error of measurement” (p. 68), and that “investigators who ask questions regarding gain scores would ordinarily be better advised to frame their questions in other ways” (p. 80).

Sophisticated methods now exist for analyzing longitudinal data that avoid the many pitfalls associated with raw change scores. These include growth curve models (which are typically specified as structural equation models, hierarchical linear models, mixed effects models, or random coefficient models; McArdle & Nesselroade, 2003 ; Raudenbush & Bryk, 2002 ) and latent difference score models (which are typically specified as structural equation models; McArdle & Nesselroade, 1994 ; Kievit et al., 2017 ). Both growth curve and latent difference score models estimate latent factors representing change in systematic variance over time. Growth curve models form latent slope factors (random coefficients) that represent systematic individual differences in longitudinal change that conform to a specified functional form, such that random error (which is, by definition, unsystematic over time) is captured by time-specific residuals. Latent difference score models form latent change factors from occasion-specific latent factors that use psychometric measurement models to confine random measurement error to test-specific uniquenesses. Thus, by modeling systematic variance in longitudinal change, both growth curve models and latent difference score models are in principle able to limit biases that would otherwise result from random error, such as regression-to-the-mean, variance-inflation, and correlation attenuation for which “raw change” score approaches are infamous. 1 We provide a formal treatment of multivariate growth curve and latent difference score models in Appendix A .

Over the past roughly fifteen years, several studies have capitalized on multivariate growth curve and latent difference score models of interrelations among individual differences in changes over time. For instance, noting that the processing speed theory of cognitive aging had been rarely examined in longitudinal data, Zimprich and Martin (2002) reasoned that “if processing speed constitutes an important limiting factor for cognitive functioning, then a person with a specific longitudinal change in processing speed should show a comparable change in other intellectual abilities” (p. 690). Applying latent difference score models to four year longitudinal data from older adults, they reported that individual differences in changes in processing speed were correlated with individual differences in changes in fluid intelligence at r = .53. Wilson et al. (2002) extended this work from the bivariate to the multivariate context. They used growth curve modeling to estimate correlations among individual differences in seven different cognitive variables, including measures of working memory, visual spatial ability, perceptual speed, fluency, episodic memory, and verbal knowledge. When they submitted this correlation matrix to principal components analysis, they found that a single component accounted for 61.6% of the variance in individual differences in cognitive changes. Several more recent studies have combined factor analytic models and growth curve approaches in the form of “factors of curves” models ( McArdle, 1988 ) to estimate common variance in individual differences in cognitive changes. Lindenberger and Ghisletta (2009) reported that a single common factor accounted for 60% of the variance in 13-year longitudinal declines in multiple cognitive variables from the Berlin Aging Study. Tucker-Drob (2011a) reported that a single common factor accounted for 63% of the variance in longitudinal changes in abstract reasoning, spatial visualization, episodic memory, and processing speed composites in participants from the Virginia Cognitive Aging Project over up to 7 years. Using 20 year longitudinal data from middle-aged to very old adults from the UK, Ghisletta, Rabbitt, Lunn, and Lindenberger (2012) reported that a single common factor accounted for two thirds of the variance in longitudinal changes in fluid and crystallized intelligence, perceptual speed, and memory.

Several outstanding questions remain. First, while the studies highlighted above have indicated that approximately 60% of the variance in aging-related cognitive declines is shared across domains, other reports have reported much lower estimates of shared variance. For instance, in longitudinal data from a subset of participants from the Einstein Aging Studies, Sliwinski and Buschke (2004) reported correlations between individual differences in longitudinal changes in memory, speed, and fluency ranging between r = .16 and r = .33. Thus, a meta-analytic estimate of the magnitude of shared variance across aging-related changes in cognitive abilities is necessary in order to distinguish whether the true effect is in the range of 50-60% or more, as suggested by some studies, or in the range of 15-30%, as suggested by others. It is particularly informative to compare the magnitude of shared variance in longitudinal change to shared variance in levels from the same studies, in order to test whether cognitive aging is to a greater, lesser, or comparable extent domain general as has been established for static individual differences in cognitive abilities ( Spearman, 1904 ; Carroll, 1993 ). Meta-analysis also provides the opportunity to test whether shared variance differs according to the type of cognitive ability, and according to other moderators, such as the age range within adulthood under study. We therefore conducted a meta-analysis to answer these questions. We also conduct the first formal test of the congruence of factor loading patterns of levels and slopes, allowing us to determine the extent to which the common factor of cognitive aging represents a similar dimension to the general intelligence factor ( Spearman, 1904 ; Carroll, 1993 ).

Literature Search

Our goal was to collate a comprehensive meta-analytic database containing estimates of shared variation in normal-range aging-related longitudinal changes in two or more cognitive abilities from the corpus of published research. In order for a study to be considered for inclusion in our meta-analysis, it needed to report an estimate of shared variation in normative aging-related longitudinal changes in measures of two or more different cognitive abilities. Shared variation in change could take the form of correlations or covariances between longitudinal growth curve slopes or latent difference scores, or loadings of longitudinal growth curve slopes or latent difference scores on a common factor (see Appendix A for an overview of the statistical basis for such multivariate models of longitudinal changes). We compiled an initial set of articles based on Table 1 from Tucker-Drob, Briley, Starr, and Deary (2014) , which listed (but did not meta-analyze) past major studies reporting relations among rates of change in two or more cognitive variables using a statistical method (e.g. growth curve modeling or latent difference score modeling) for modeling systematic change over time, as separate from random error. We then sought to expand this set in a number of ways. First, we examined the reference sections for each of the papers that met our inclusion criteria to identify further papers that might warrant inclusion, and performed this process iteratively for every new paper included. Second, we used Google Scholar to search for papers citing the included papers to identify further papers that might warrant inclusion, and performed this process iteratively for every new paper included. Third, we performed searches using Google Scholar with combinations of at least one search term from each of the following categories longitudinal change (longitudinal, change, slope, growth curve, difference score), cognitive (cognitive, cognition, ability, intelligence), and aging (aging, ageing, adult, adulthood).

Characteristics of studies contributing to the meta-analytic dataset.

We excluded studies that primarily focused on clinical populations, studies of child and adolescent populations (individuals under 18 years of age), studies that solely employed dementia screening instruments (e.g. the Mini Mental State Exam) to index cognitive abilities, studies that only examined shared variation in aging-related longitudinal changes in different markers of the same cognitive ability, studies that did not correct (or provide information that could be used to correct) estimates for unreliability (e.g. not using growth curve modeling, latent difference score modeling, or disattenuated correlations among raw change scores), studies that only reported change in shared variation (e.g. a curve of factors model) but not shared variation in change (e.g. a factor of curves model, a bivariate or multivariate [parallel process] growth model), publications that examined within-person correlations in abilities over time but did not examine between-person correlations in wave-, time-, or age- based longitudinal change, and publications in languages other than English.

When more than one publication based on the same sample met our inclusion criteria, we retained the study that reported the longest longitudinal timespan or changes in the largest number of abilities measured. In instances in which two different publications based on the same sample each contained unique information (e.g. one publication reported results for more abilities, but the other publication analyzed data from a longer longitudinal timespan), we entered results from both studies, and included appropriate downweights and clustering terms for each, as further described below.

Recording Effect Sizes

The key effect sizes that we recorded for the current meta-analysis were estimates of communality from a factor model fit to longitudinal changes in indicators of two or more ability domains. These communality estimates can intuitively be conceptualized as indices of shared variation in cognitive changes across ability domains, or as the proportion of variation in change in an ability that is accounted for by a common factor of changes in multiple abilities (see the end of Appendix A for a formal treatment of communality). When a factor model is fit directly to longitudinal slopes, the communality is computed as the standardized factor loading squared. When a single correlation is reported between longitudinal changes in only two variables, that correlation is a direct estimate of communality. This is because, when a factor model is fit to two-variables, the standardized loading of each of the two variables on that factor is calculated as the square root of their correlation. When the loading is squared to compute proportion of variance accounted for, this returns the original correlation. Thus, (1) when a correlation was reported between rates of longitudinal changes in only two cognitive variables, we recorded this correlation as the communality; (2) when standardized loadings on a common factor of longitudinal changes in three or more cognitive variables was reported, we recorded the squared standardized loading as the communalities; (3) when correlations or covariances were reported between rates of longitudinal changes in three or more cognitive variables were reported, we fit a factor model to the matrix so as to derive standardized factor loadings, which we then squared and recorded as the communalities. We also recorded the proportions of shared variance in levels (i.e. communalities for growth curve and latent difference score levels) for each variable, such that we could make direct comparisons of shared variance in change to shared variance in levels.

We recorded supplemental information, including information necessary for calculating meta-analytic precision weights, as described below. For growth curve models, this included the variance in latent levels and slopes of the respective cognitive variables, the time-specific residual variances, and the within-variable level-slope correlation. For latent difference score models, this included the variance in latent levels and changes of the respective cognitive ability factors, the within-factor level-change correlation, and the loadings and unique variances for each for each individual indicator of the latent factors. To facilitate interpretability, all parameters were rescaled to reflect latent level variances of 1.0. We additionally recorded the time intervals between each assessment wave, and the sample size at each assessment wave.

We made a number of additional decisions according to the following guidelines. When results were broken down by age group, we entered in the parameters from the age groups, rather than parameters from the pooled analysis. When results were available for both individual tests and composites based on multiple tests representing the same ability, we entered the results for the composites. In cases in which an article only provided sample sizes for number of complete waves (e.g. 400 individuals completed 4 waves, 350 individuals completed only 3 waves, 250 completed only 3 waves), but sample sizes per assessment wave were not provided, we treated the missingness as if it was entirely due to dropout (as opposed to, e.g., enrolling new participants at later waves, or participants skipping waves). Standardized estimates greater than 1.0 were top-coded to 1.0. All parameters were coded to reflect scaling in which higher scores indicated better performance. For instance, if a reaction time measure had a negative loading on a factor, the sign was changed to a positive loading. In situations in which the entire set of parameters required was not provided in the article, but full longitudinal multivariate covariance matrices were provided, we analyzed the covariance matrices with multivariate growth curve or latent difference score models to derive the full set of parameters. In situations in which the complete set of pairwise correlations between latent slopes was provided for three or cognitive ability domains, we derived factor loadings on a common slope factor for use in the meta-analysis. Further information about specific coding decisions made for individual studies is provided in the online supplement .

Coding Moderators

We additionally coded a variety of characteristics of the effect sizes, samples, and studies as potential moderators of effect size magnitudes.

Mean Age at Baseline Assessment was recorded for all studies. When the mean age at baseline was not provided, but an age-range at baseline was provided, we coded the mid-point of that age range.

Mean Age at Latent Level was calculated for all studies based on the best available information. The latent level represents individual differences at the age or point in time at which the growth curve basis coefficient is set to zero. Thus, the choice of how to center time or age in a growth curve model affects the interpretation of the growth curve level. If, for example, age is centered at 70, then the level in an age-based growth model should be interpreted as representing individual differences at age 70, even if the average age at baseline is different (e.g., 60). For age-based modelling, the age at latent level is the age at which the growth curve slope is equal to 0. For time-based modelling (including latent difference score modeling), the age at latent level is the average age of the participants at time 0 (i.e. baseline).

Longitudinal Time-Lag was recorded as the average amount of time that passed between the first and last occasion of measurement for those individuals who completed the final wave of the study.

Broad vs. Narrow Ability was recorded for each outcome measure under study. An outcome measure was coded as a broad ability (1) if it was a latent factor or composite index formed from multiple different measures of the same cognitive domain. An outcome measure was coded as a narrow ability (0) if it was measured with a single test. Latent factors derived from alternate forms of the same test were considered indices of narrow abilities.

Cognitive Ability Domains were coded for each outcome under study, regardless of whether the domain was measured with a single test or a broader composite or factor. The following domains were coded: Processing Speed, Episodic Memory, Working Memory, Spatial Ability, Reasoning, Verbal knowledge, and Prospective Memory.

Mean Rate of Longitudinal Change was coded for each outcome under study, when available. This information is reflected in the mean of the growth curve slope (also commonly referred to as a fixed effect) or the mean of the latent difference score. All recorded means were scaled in units of level standard deviations per year.

Deriving Meta-Analytic Precision-Weights

Initially, we sought to obtain standard errors for the key effect size estimates of interest (the communalities), or information from which we could derive such standard errors, such as 95% confidence intervals or exact p values. This would enable us to weight the contribution of each effect size to the meta-analytic estimate by the precision of that effect size, as is considered best practice in meta-analysis ( Cheung, 2015 ). However, upon reviewing the studies that met inclusion criteria for our meta-analysis, only a minority reported the necessary information for the communalities (standard errors were more consistently available for the mean rates of longitudinal change). Typically, when standard errors are not available, a meta-analysis is conducted using sample size weighting. However, in the context of longitudinal research, weighting by sample size alone is problematic, as several characteristics of the dataset beyond sample size determine the precision of the estimates. For instance, the number of occasions, the amount of time-specific measurement error variance relative to the amount of level and slope variance, the time intervals between occasions, and the degree of attrition across waves all effect the precision of parameter estimates from longitudinal growth curve models ( Brandmaier et al., 2018a ; Brandmaier et al., 2015 ; von Oertzen & Brandmaier, 2013 ). As described in Appendix B , we developed algorithms designed to capitalize on formal mathematical theorems of effective error in growth curve modeling and latent difference score modeling ( Brandmaier et al., 2018a ; also see Brandmaier et al., 2018b ) to derive meta-analytic weights proportional to the asymptotic precision of the communality effect size estimates.

Our meta-analytic focus was on the magnitude of shared variance between the individual differences in longitudinal changes in two or more cognitive variables (i.e. the communalities). We therefore developed our weighting algorithm to produce weights proportional to the inverse sampling variances (i.e. inverse of the squared standard error) of the level and slope communalities (equivalent to the correlation, as described earlier) in bivariate growth curve models and latent difference score models. In calculating precision weights, we made a number of simplifying assumptions. First, we assumed that change occurred linearly over time, and that the growth curve model was specified in terms of time since baseline (as opposed to, for example, occasion number or age). We also assumed that level-slope covariances were negligible (a more complex algorithm that included information about these covariances performed no better at approximating sampling-variance-based weights in a simulation analysis), and that variable-specific autocorrelations in the latent difference score model were negligible. As our algorithm assumed that shared variance was inferred from bivariate “parallel process” growth curve or latent difference score models, we took the following approach to consolidating information from results of multivariate “factor of curves” models, so as to calculate weights for ability-specific loadings when more than one other ability was being modeled. For information pertaining to the first of the two variables, we inputted the level variance, slope/change variance, time-specific/residual variance, and indicator loading information for the variable for which we were deriving the weight. For information pertaining to the second of the two variables, we calculated average estimates for level variance, slope/change variance, time-specific/residual variance, and indicator loading taken across the remaining abilities modeled after first standardizing to a common metric. 2 This approach is somewhat conservative, as it does not give extra weight to estimates derived from more complex multivariate models, compared to less complex bivariate models (all effects size estimates are treated as if derived from bivariate models).

Constructing Downweights to Account for Multiple Effect Sizes Per Study

In addition to constructing precision weights proportional to the inverse sampling variances, we constructed a downweighting scheme to correct for the fact that many of the studies contributed multiple effect sizes for shared slope variance and shared level variance. For instance a study in which one factor is fit to the levels and a separate factor is fit to the slopes of indices of five different abilities would produce five estimates of shared level variance (i.e. five squared loadings on the first factor) and five estimates of shared slope variance (i.e. five squared loadings on the second factor). All of these estimates contain unique and important information. However, they clearly are not independent. To ensure that studies that measured more variables were not given disproportional leverage on meta-analytic estimates by virtue of contributing a greater number of effect size estimates, we constructed additional “downweights” proportional to the reciprocal number of effect sizes contributed for a given effect size type. Thus, in the above example of a multivariate model of changes in five abilities, the downweights for both shared level variance and shared slope variance would be equal to one fifth (.20). If not for this downweighting, all else being equal, a study with N =300 and 20 variables could have greater leverage on the meta-analytic estimate of shared change variance than a study with N =1000 and only 2 variables. Downweighting corrects for this potential bias. Moreover, as described in further detail below, we implemented a multilevel modeling approach that accounts for the statistical dependencies that arise when multiple effect sizes are derived from the same sample.

Constructing a More Restrictive Meta-Analytic Dataset for Dementia-Controlled Sensitivity Analyses

We also conducted sensitivity analyses to examine the extent to which results were driven the presence of substantial subsamples of individuals with dementia in the primary studies(cf. Sliwinski, Hofer, & Hall, 2003 ). To this end, we constructed an additional meta-analytic dataset based on the above-described procedures using only those studies that met at least one of the following criteria: a) data from an individual were excluded from any wave at which that individual met criteria for dementia diagnosis or scored in the cognitive impairment range on a dementia screening instrument (e.g. Mini Mental Status Exam [MMSE] scores of 23 and below), b) the reported rate of dementia in the sample was less than 1% over the entire study period, or c) dementia status was included as a time-varying covariate. In practice, this often involved entering different results from those entered for the main meta-analytic dataset. For instance, Sliwinski, Hofer, and Hall (2003) reported separate results for their complete sample (“All Participants”) and a dementia-free (“Noncases”) subsample. In this case, we entered the results from the dementia-free subsample into this more restrictive dataset in place of those from the complete sample that was entered into the primary meta-analytic dataset. Similarly, Lindenberger and Ghisletta (2009) reported results from models with and without controls for age, time to death, and a dichotomous marker of likely dementia. In this case, we entered the results from the model with controls into this more restrictive dataset in place of those from the model without controls that was entered into the primary meta-analytic dataset. Tucker-Drob (2011a) and Ritchie et al. (2016) did not report full results excluding participants with likely dementia (both reported that sensitivity analyses that excluded likely dementia cases did not substantively change results), but because we had access to the raw data, which contained MMSE scores, we reanalyzed those two datasets excluding data from any wave at which an individual scored in the cognitive impairment range on the MMSE (i.e. scores of 23 and below). Tucker-Drob (2011b) and Ghisletta et al. (2012) did not remove data from participants with dementia or control for dementia status. Unverzagt et al. (2012; their Table 3 ) reported a dementia event rate of 7.2% (and an incidence rate of 19.2/1000 person years) in the sample analyzed by Tucker-Drob (2011b) during the longitudinal period analyzed. We therefore excluded Tucker-Drob (2011b) from this more restrictive dataset. In contrast, Ghisletta et al. (2012) estimated a dementia prevalence of 20 out of 6203 in their sample. As this estimated dementia prevalence was less than 1%, we retained results from Ghisletta et al. (2012) in this more restrictive dataset. Finally, Tucker-Drob et al. (2014) analyzed data from a sample of 857 individuals, 48 of whom (5.6%) were diagnosed with dementia and provided cognitive scores during the study period. Results were reported for a model that controlled for dementia as a time-varying covariate, and were therefore included in both the primary meta-analytic dataset and the more restricted meta-analytic dataset. Studies included in the primary meta-analytic dataset that did not indicate the dementia rate or report a method for dealing with participants with dementia (e.g. all results reported by Rast & Hofer, 2014 ), along with those reporting nontrivial rates of dementia (e.g. Lemke & Zimprich, 2005 , as reported in Sattler et al., 2015 ) were excluded from this more restrictive dataset.

Parameter estimates for additional moderators of meta-analytic effect sizes.

Note. Moderators were tested individually. Two-level meta-regressions were weighted by the respective precision of the individual communality estimates and by the inverse number of effect sizes contributed by the associated dataset, with random intercepts estimated. Because of substantial variability within datasets for Mean Rate of Change, we also allowed for a random regression slope and an intercept-slope covariance. SE = standard error. LDS = Latent Difference Score Model. LGM = Latent Growth Curve Model.

Analytic Approach: Multilevel Meta-Regression Models

We meta-analyzed effects sizes using a meta-regression framework in which the effect sizes of interest were regressed onto hypothesized moderators of the effect sizes, using a weighted fit function that incorporates precision weights and downweights of the outcome variables. As many of the individual studies included in the meta-analytic dataset contributed effect sizes for multiple variables, we specified meta-regression models as two-level models, in which total effects were decomposed into within- and between- study components.

An unconditional multilevel meta-regression model of effect size ES ij for outcome i in study j can be written as:

where ES j represents an inferred study-specific effect size that is allowed to have a mean and a (between-study) variance σ E S j 2 (a so-called random effect), and u i,j is a within-study deviation from the study-specific mean that is assumed to have a mean of zero and a (within-study) variance σ u i , j 2 . In this unconditional model the total variance of ES ij is therefore specified as the sum of between-study and within-study variation, i.e.:

The unconditional meta-regression model can be expanded to allow for predictors (so-called moderators) at the within-study level, the between-study level, or a combination of the two. Such a conditional multilevel meta-regression model can be written as:

where b 0 is a regression intercept, b k j is a regression coefficient of the effect size on within-study variable x k i,j is a regression coefficient of the effect size on between-study variable x l j , u i,j is a within-study residual, and u j is a between-study residual. Within-study and between-study residuals are specified to have means of zero and freely estiamted varianes. Variances may also be estimated for selected within-study regression coefficents ( b k j ), such that they constitute random slopes representing between-study variation in the magnitude of the within-study regression effect. Random slopes may be allowed to covary with between-study residuals.

When relevant, we included precision weights by specifying them at the level of the individual effect sizes (i.e. as within -cluster weights). Downweights were specifed at the level of the contributing samples (i.e. as between -cluster weights). Weights were rescaled such that the products of the within-cluster and between-cluster weights sum to the total number of effect sizes in the meta-regression model ( Asparouhov, 2008 ). Models were estimated in M plus ( Muthén & Muthén, 1998–2017 ). Data cleaning, derivation of weights, and plotting were conducted in R ( R Core Team, 2016 ).

Description of Dataset

Descriptive information for each of the studies that contributed effect size estimates to the meta-analysis is provided in Table 1 . In total, we identified 89 effect sizes representing shared variance in cognitive change in 98 cognitive outcomes from 22 unique datasets composed of over 30,000 unique individuals in total. Note that multivariate analyses in which a factor model is fit to the slopes provide individual estimates for the slope variance of each of the variables analyzed and individual estimates for shared variance for each of the variables analyzed. However, bivariate analyses provide individual estimates for the slope variance for each of the two variables but only provide a single estimate for shared variance between the two variables. As some studies employed bivariate approaches, the number of total shared variance estimates is slightly lower than the number of total variables.

Of the 89 estimates of shared variance, 74 were derived from growth curve models and 15 were derived from latent difference score models. Across the 89 shared variance estimates, number of waves ranged from 2 to 12, with a median of 5.00, a mean of 5.45, and a standard deviation of 2.40. Across the 89 shared variance estimates, the total time elapsed from beginning to end of the study ranged from 2.81 to 21 years, with a median of 8.41, a mean of 10.33 years and a standard deviation of 4.59 years. Across the 89 estimates of shared variance, the average age at baseline wave ranged from 35.42 years to 84.92 years, with a median of 64.90, a mean of 66.27 years and a standard deviation of 11.32 years. The average age at latent level ranged from 35.42 years to 85.00 years, with a median of 69.53, a mean of 66.72 years and a SD of 11.64 years. The correlation between the average age at baseline and the average age at latent level was .91.

Of the 98 outcomes analyzed, 26 indexed Processing Speed, 35 indexed Episodic Memory, 3 indexed Working Memory, 9 indexed Spatial Ability, 12 indexed Reasoning, 12 indexed Verbal Knowledge, and 1 indexed Prospective Memory. Forty three outcomes were classified as broad ability composites or factors and 55 were classified as specific measures. Rast & Hofer (2014) did not report mean rate of longitudinal change for any outcome in their paper, and Lemke & Zimprich (2005) did not report mean rate of longitudinal change for memory. Of the remaining 75 outcomes, 20 indexed Processing Speed, 27 indexed Episodic Memory, 1 indexed Working Memory, 8 indexed Spatial Ability, 10 indexed Reasoning, and 9 indexed Verbal Knowledge. Of these, 40 outcomes were classified as broad ability composites or factors and 35 were classified as specific measures.

Mean Change

To produce a meta-analytic estimate of mean cognitive change, we fit a two level unconditional meta-regression model, with the individual effect size estimates of mean change weighted by their inverse sampling variance and the inverse number of effect sizes contributed by the associated dataset to the complete meta-analytic dataset. The mean change was –.051 (SE = .007, p < .0005). 3 This indicates that, on average, cognitive performance decreased by approximately one twentieth of a standard deviation per year, i.e. half a standard deviation per decade. In this two-level model that decomposed effect size variation into within-sample and between-sample components, the within-sample standard deviation of mean change estimates was .022 (SE = .002, p < .0005; variance = .022 2 =.00048) and the between-sample standard deviation of mean change estimates was .027 (SE = .004, p < .0005, variance = .027 2 = .00073). This indicates that mean rates of longitudinal change varied substantially across outcomes and across samples.

Level-Slope Correlations

The variable-specific level-slope correlations ranged from −.67 to .84. In a two level unconditional meta-regression model, with the individual effect sizes weighted by the inverse number of effect sizes contributed by the associated dataset, the mean level-slope correlation was −.042 (SE = .047, p = .362). Five level-slope correlations were exactly zero. Two of these came from Zimprich and Martin (2002) , and two came from Lemke and Zimprich (2005) , who appear to have fixed these parameters to 0 rather than estimating them. One came from Sliwinski and Buschke (2004) , who appeared to have freely estimated the association to be exactly zero. When these five effect sizes were excluded, the weighted mean remained at very close to zero (−.047 SE = .049, p = .347). Finally, to facilitate comparisons with results pertaining to slope communalities, which were the primary focus of the current meta-analysis, we calculated the within-variable level-slope correlations, weighting by the corresponding slope communality precisions and the inverse number of effect sizes contributed by the associated dataset. This estimate was .001 (SE =.045, p = .989) in the complete dataset, and .000 (SE = .047, p = .996) when excluding the five effects that were exactly zero. A histogram of within-variable level-slope correlations is depicted in the left panel of Figure 1 . It is important to note that the level-slope correlation is dependent on how time is coded in the growth curve model ( Biesanz et al., 2004 ; Rovine & Molenaar, 1998 ). As the tendency was for time to be coded such that time = 0 corresponded to either the baseline measurement wave or the the earlier end of the age range of the sample, it may be most appropriate to interpret these level-slope correlations as representing the relation between early ability levels and subsequent change. To test whether this association was dependent on the age at which the growth curve level was centered (as indicated in the method section, for age-based modelling, this is the age at which the growth curve slope is equal to 0; for time-based modelling, this is the average age of the participants at time 0), we fit a two-level meta-regression model with age-at-level as the independent variable and the level-slope correlation as the dependent variable, weighted by the downweights, and allowing for a random regression intercept. Results of this analysis, superimposed on a scatterplot are presented in the right panel of Figure 1 . Results indicated a significant positive association between age at level and the level-slope correlation (b=.006, SE=.003; p=.031). However, the 95% confidence interval for this age effect only excluded 0 prior to approximately age 50 years, where data were sparse. Moreover, the association was not significant when restricting analyses to effect sizes for which age at level is greater than 50 (87 observations, p = .243), or when the meta-regression is additionally weighted by the slope communality weights (p=.050).

Left: Histogram of the within-variable level-slope correlations, weighted by the inverse number of effect sizes contributed by the associated dataset. Weights are scaled to sum to the total number of effect sizes (98). The dashed vertical line represents the weighted meta-analytic estimate (−.042) for the level-slope correlation. To facilitate comparisons with results pertaining to slope communalities, the solid vertical line depicts the weighted meta-analytic estimate (.001) for the level-slope correlations using the slope communality weights. Right: Level-slope correlation plotted as a function of age at level. The overlaid regression line represents the model-implied trend and its 95% confidence interval from a meta-regression model that is weighted by the inverse number of effect sizes contributed by the associated dataset. The positive association between age at level and the level-slope correlation is significant at p=.031, but is not significant when restricting analyses to effect sizes for which age at level is greater than 50 (87 observations, p = .243).

Distributions of Communality Precision Weights

As expected from previous treatments of power to detect correlated change ( Hertzog et al., 2006 ; Rast & Hofer, 2014 ), slope communality precision values were substantially lower than level communality precision values. To index the relative precision of the estimates, we calculated ratios of the precisions of the slope communality estimates to the level communality estimates for each variable. The distribution of these ratios is depicted as a histogram in Figure 2 . It can be seen that this distribution was right skewed, with the majority of ratios falling within the 0 to .20 range. No ratio achieved a value of 1.0 or higher. The median ratio was .072, indicating that the slope communalities tend to be approximately 7% as precise as the level communalities.

Histogram of the ratio of the slope communality precision to the level communality precision. The vertical dashed line depicts the median value (.072. It can be seen that the slope communalities tend to be estimated with substantially less precision than the level communalities.

Distributions of Level and Slope Communalities

The distributions of level and slope communalities are depicted as histograms in Figure 3 . Level communality estimates ranged from .086 to 1.0. In an unconditional two-level meta-regression model, weighted by the respective precision of the individual estimates and by the inverse number of effect sizes contributed by the associated dataset, the mean level communality was .558 (SE = .029, p < .0005). This indicates that 56% of the variance in static individual differences is shared across abilities. In this two-level model that decomposed effect size variation into within-sample and between-sample components, the within-sample standard deviation of level communality estimates was .123 (SE = .026, p < .0005; variance = .123 2 =.015) and the between-sample standard deviation of level communality estimates was .047 (SE = .012, p < .0005, variance = .047 2 = .002).

Histograms of level communalities and slope communalities. Histograms are weighted by the respective precision of the individual estimates and by the inverse number of effect sizes contributed by the associated dataset. In each panel, weights are scaled to sum to the total number of effect sizes (89). The dashed vertical line represents the weighted meta-analytic estimate of the mean communality for the levels (.558) and slopes (.600), respectively. To facilitate comparisons across level and slope communalities, the solid vertical line depicts the weighted meta-analytic estimate for the level communalities using the slope communality weights (.585).

Slope communality estimates ranged from .004 to 1.0. In an unconditional two-level meta-regression model, weighted by the respective precision of the individual estimates and by the inverse number of effect sizes contributed by the associated dataset, the mean slope communality was .600 (SE = .029, p < .0005). This indicates that, on average, 60% of individual differences in aging-related cognitive change is shared across abilities. In this two-level model that decomposed effect size variation into within-sample and between-sample components, the within-sample standard deviation of slope communality estimates was .209 (SE = .021, p < .0005; variance = .209 2 =.044) and the between-sample standard deviation of slope communality estimates was .076 (SE = .028, p = .006, variance = .076 2 = .006). This indicates that the majority of variation in effect size estimates occur within samples, potentially as a function of characteristics of the individual cognitive abilities or cognitive ability variables. Cross-sample variation, alternatively, could reflect characteristics of the participants (e.g. age) and study design (e.g. time lag, or growth curve vs. latent difference score modeling). 4

The meta-analytic estimates for the mean slope and level communalities were very similar (.558 for level communalities and .600 for slope communalities). We were interested in whether slight difference in estimates stemmed from differences in the distributions of individual estimates or from differences in the relative contributions of these estimates to the meta-analytic mean resulting from employing differing sets of precision weights for the levels and the slopes. In other words, it is possible that unobserved heterogeneity in effect sizes is correlated with aspects of the study design that are differently correlated with the two sets of precision weights, such that weighting slope communalities and level communalities results in estimates that are representative of different theoretical populations of studies. We therefore ran a sensitivity analysis to determine whether differences in the meta-analytic effect size estimates for levels and slope communalities converged or diverged when using the same set of precision weights. We reran the unconditional meta-regression model for the level communalities using the precision weights for the slopes. In this model, the estimate for the mean level communality was .585 (SE = .024, p < .0005), even closer to the mean slope communality estimate of .600. This can be taken as further evidence that the mean level and slope communalities are extremely similar.

Probing for Publication Bias

One important consideration in meta-analysis is whether the corpus of effect sizes included in the meta-analytic dataset constitute an unbiased representation of the true distribution of effects within the population at large. We would expect the meta-analytic dataset to be biased and unrepresentative if, for example, there is a tendency for authors to be more likely to submit articles, or journal editors to accept articles, reporting results in which effect size estimates are large, or p values are small. This phenomenon is known as publication bias. We would also expect the meta-analytic dataset to be biased and unrepresentative in cases in which authors run multiple models, analyze multiple variables, or make multiple alternative data cleaning decisions, but only report results of a subset producing larger effect sizes or smaller p values. This phenomenon is known as p-hacking .

In order to probe for evidence of publication bias, p-hacking, or other types of systematic biases, we produced plots in which the precision of the individual effect size estimates is plotted against the estimates themselves. These plots are known as funnel plots because, when publication bias is low and true population effects are fairly homogeneous, these plots represent an inverted funnel, in which effect sizes are more tightly distributed when estimated more precisely and more widely distributed when estimated less precisely. Under unbiased conditions, the funnel should be symmetrical, with an approximately equal number of estimates above and below the meta-analytic mean. The apex of the funnel, which contains the effect sizes estimated with the highest levels of precision, should be centered close to the meta-analytic mean. The typical pattern thought to be indicative of possible publication bias is one in which lower precision estimates closer to 0 are conspicuously missing, such that the bottom area of the funnel is asymmetrical.

Funnel plots for the level and slope communality estimates are depicted in Figure 4 . It can be seen that both plots are symmetrical and that the most precise estimates are centered within the distributions, close to the respective meta-analytic means. To formally test funnel asymmetry (cf. Stanley & Doucouliagos, 2014 ) we regressed precision against communality estimates, with and without weighting by precision. For level communality, the p values for the weighted and unweighted regressions were .298 and .759, respectively. For slope communality, the p values for the weighted and unweighted regressions were .486 and .271, respectively. Thus, there was no evidence that effect size estimates were systematically associated with the precisions at which they were estimated, as might occur under conditions of publication bias or p-hacking.

Funnel plots of level communalities and slope communalities. Effect size estimates are on the x axis and precision of the estimates are on the y axis. In each panel, precision values were scaled such that they sum to the total number of effect sizes (89). It can be seen that both plots are approximately symmetrical. To formally test funnel asymmetry we regressed effect size estimates on precision, with and without weighting by precision. For level communality, the p values for the weighted and unweighted regressions were .298 and .759, respectively. For slope communality, the p values for the weighted and unweighted regressions were .486 and .271, respectively. Thus, there was no evidence that effect size estimates were systematically associated with the precisions at which they were estimated, as might occur under conditions of publication bias or p hacking.

Congruence of Level and Slope Structures

We were next interested in the extent to which the common dimension of change corresponded to the common dimension of levels. Alternatively put, we were interested in whether the extent to which the slope communality for a given variable was predicted by the level communality for that variable. To accomplish this, we fit a two-level meta-regression model with level communalities as the independent variable and slope communalities as the dependent variable, weighted by the dependent variable precision weights and downweights, and allowing for a random regression intercept, a random regression slope, and an intercept-slope covariance. The unstandardized regression coefficient (fixed effect) was .620 (SE = .129, p <.0005). To give a further sense of this correspondence, the weighted correlation between the vector of level communalities and the vector of slope communalities was r=.488. When the vectors being correlated contained loadings (i.e. the square root of communalities), rather than communalities, the weighted correlation increased to .507. A scatterplot of the association between level communalities and slope communalities is provided in the top panel of Figure 5 .

Scatterplots of the association between level communality estimates and slope communality estimates with (top) and without (bottom) centering estimates within dataset. The area of each point corresponds to the precision of the communality estimate, with larger points representing more precise communalities. Precision weights are scaled to sum to the total number of effect sizes (89).

As an alternative approach to indexing the correspondence between the change and level factors, we computed Tucker’s congruence coefficients ( Lorenzo-Seva & ten Berge, 2006 ), which are on the same scale as a correlation coefficient (−1 to +1). Whereas the correlation coefficient, which indexes the correspondence between relative ordering of factor loadings across solutions, the congruence coefficient additionally takes into account the absolute magnitudes of factor loadings. The unweighted congruence coefficient representing the congruence of level and slope structures was .968. When weighted using both the slope precision estimates and downweights, the congruence coefficient increased to .982. These very large values reflect the fact that, in addition to displaying similar relative orderings, the level and slope factor loadings were very similar in their overall magnitudes. This was also reflected in the earlier analysis indicating that the meta-analytic estimates for the mean slope and level communalities were very similar.

It was possible that the similarities of level and slope structures derived from heterogeneity in sample-level characteristics (e.g. quality of measurement, selection of variables, participant composition) that affect communality estimates, and did not actually reflect the extent to which level and slope communalities corresponded for individual variables within a given study. To test this possibility we reran the two-level meta-regression model with level communalities as the independent variable and slope communalities as the dependent variable, first centering level communality estimates within sample. The unstandardized regression coefficient (fixed effect) from this analysis was .708, (SE = .167, p<.0005), even larger than those from the earlier analysis of uncentered data, indicating that level and slope communalities indeed tended to correspond for individual variables within each study. Moreover, the weighted correlation between the vector of level communalities and the vector of slope communalities, both centered within sample, was r=.486. When the vectors being correlated contained loadings (i.e. the square root of communalities), centered within sample, the weighted correlation was r=.462. A scatterplot of the association between centered level communalities and centered slope communalities is provided in the bottom panel of Figure 5 . Note that note that because centering removes information about the absolute magnitude of the factor loading, Tucker’s congruence coefficients cannot be calculated from the centered data.

Domain-Specific Communality Estimates

Next, we were interested in obtaining conditional mean estimates for level and slope communalities in each of the several cognitive ability domains that were measured in the individual studies. To obtain these estimates we fit separate meta-regressions to the level and slope communalities associated with the 98 outcomes in the dataset with effects-coded predictors representing six of the cognitive ability domains (Episodic Memory, Working Memory, Spatial Ability, Reasoning, Verbal Knowledge, and Prospective Memory), with Processing Speed omitted as the base group (cf. Cohen, Cohen, West, & Aiken, 2003 ). The coefficients on each of the predictors were then combined with the regression intercepts using the delta method ( Ver Hoef, 2012 ) to produce the conditional mean estimates for level and slope communalities in each cognitive ability domain. Meta-regressions were weighted by the respective precision of the dependent variables (the individual communality estimates) and by the inverse number of effect sizes contributed by the associated dataset. To test whether communality estimates differed across domains, we assessed the decrement in fit associated with constraining all six regression parameters to zero. In other words, for both level communalities and slope communalities, we compared the fit of a model in which each of the seven cognitive ability domains was allowed to have its own mean communality to one in which the mean communality was constrained to be invariant across cognitive ability domains.

Results are reported in Table 2 . Mean level communalities ranged from .491 for episodic memory to .704 for reasoning. Mean slope communality estimates ranged from .320 for prospective memory slope to .684 for spatial ability slope. Constraining all level communality estimates to be invariant across abilities resulted in a significant loss of model fit, χ 2 (6) = 32.454, p < .0005. Constraining all slope communality estimates to be invariant across abilities also resulted in a significant loss of model fit, χ 2 (6) = 15.914, p = .014. These results indicate that communality estimates differed across cognitive ability domains. Individual post-hoc contrasts (uncorrected for false discovery) between the ability-specific level communalities and the simple grand mean indicated that episodic memory and working memory had significantly lower level-communality estimates than the grand mean estimate across domains, and reasoning had a significantly higher level-communality estimate than the grand mean estimate. Individual post-hoc contrasts (also uncorrected for false discovery) between the ability-specific slope communalities and the grand mean indicated that processing speed and reasoning had significantly higher slope-communality estimates than the grand mean estimate. Note, however, that because of variability in the number of effect sizes associated with the individual domains, significance levels do not correspond closely to effect sizes.

Domain-specific effect size estimates.

Note: Models were fit as effects-coded two level meta-regressions with a random intercept for sample. The coefficients on each of the predictors were then combined with the mean of the regression intercept using the delta method ( Ver Hoef, 2012 ) to produce the conditional mean estimates for each cognitive ability domain. Meta-regressions were weighted by the respective precision of the individual estimates and by the inverse number of effect sizes contributed by the associated dataset. Models were fit separately for Level Communalities, Slope Communalities, and Slope Means. Standard errors (SE) and p values are not reported for Prospective Memory communality, because there was only one data point for Prospective Memory in the meta-analytic dataset. Standard errors (SE) and p values are not reported for Working Memory mean slope, because there was only one mean slope estimate for Working Memory in the meta-analytic dataset. There were no mean slope estimates for Prospective Memory in the meta-analytic-dataset. Constraining all level communality estimates to be invariant across abilities resulted in a significant loss of model fit, χ 2 (6) = 32.454, p < .0005. Constraining all slope communality estimates to be invariant across abilities also resulted in a significant loss of model fit, χ 2 (6) = 15.914, p = .014. Constraining all slope mean estimates to be invariant across abilities resulted in a significant loss of model fit, χ 2 (5) = 27.256, p = 0.0001. These results indicate that communality estimates and slope means differed across cognitive ability domains.

Figure 6 is a path diagram representing the key results from the domain-specific analyses. This path diagram depicts a single common factor of levels and a single common factor of slopes, with levels and slopes of the seven individual cognitive ability domains loading on the respective factors. Superimposed on the paths from the common factors to the individual domains are standardized loadings, which are computed as the square root of the domain-specific communality estimates.

Path diagram representing meta-analytic estimates for standardized factor loadings of levels of individual cognitive abilities on a general factor of levels (left) and standardized factors loadings of longitudinal slopes of individual cognitive abilities on a general factor of changes (right). Variances were omitted from the diagram. Standardized factor loadings were calculated by taking the square root of the respective communalities. Reason = Reasoning. Verb Know = Verbal Knowledge. Prosp Memory = Prospective Memory.

Domain-Specific Estimates of Mean Change

Next, we were interested in obtaining conditional mean estimates for the mean rates of longitudinal change in each of the ability domains. To obtain these estimates we fit separate meta-regressions to the level and slope communalities associated with the 75 available outcomes in the dataset with effects-coded predictors representing five of the cognitive ability domains (Episodic Memory, Working Memory, Spatial Ability, Reasoning, Verbal Knowledge), with Processing Speed omitted as the base group (cf. Cohen, Cohen, West, & Aiken, 2003 ), and additional parameters derived from the primary parameters using the delta method ( Ver Hoef, 2012 ).. Note that no mean change estimates were available for Prospective Memory. Meta-regressions were weighted by the inverse sampling variance of the individual estimates and by the inverse number of effect sizes contributed by the associated dataset to the complete meta-analytic dataset.

Results are reported in the bottom portion of Table 2 . Mean change was −.045 standard deviations per year on average across abilities. Processing speed displayed particularly steep declines, with a mean estimate of −.068 standard deviations per year. In contrast, verbal knowledge displayed particularly shallow declines, with a mean estimate of −.019 standard deviations per year. The estimate for working memory change was also shallow, but because this estimate was only derived from one effect size it is unlikely to be very reliable. Constraining all slope mean estimates to be invariant across abilities resulted in a significant loss of model fit, χ 2 (5) = 27.256, p = 0.0001. Individual contrasts indicated that processing speed, spatial ability, and reasoning displayed significantly more decline (more negative) than the grand mean estimate across domains, and verbal knowledge displayed significantly less (less negative) decline than the grand mean estimate.

Age and Other Moderators of Communality Effect Sizes

We went on to test a number of additional moderators of effect size estimates for both slope communality and level communality. We were particularly interested in the static and dynamic versions of the age dedifferentiation hypothesis, which respectively predict increasing level communalities and increasing slope communalities with age. An important consideration for testing these hypotheses concerns how the basis coefficients for the growth curve slopes were parameterized. Centering relative to a constant (e.g. subtracting 65 years) changes the interpretation of growth curve levels but does not change the interpretation of growth curve slopes ( Biesanz et al., 2004 ). Thus, age-related differences in the covariance structure of levels are dependent on the age at which the basis coefficients are centered, whereas age-related differences in the covariance structure of the slopes are dependent on the age composition of the sample, but do not depend on the age at which the basis coefficients are centered. The most appropriate test of the static dedifferentiation hypothesis therefore involves testing age at level as the moderator, whereas the most appropriate test of the dynamic dedifferentiation hypothesis involves testing the actual age composition of the sample as the moderator. As an index of the age composition of the sample, we rely on mean age at baseline.

Age moderation was estimated with two-level meta-regression models with communalities as the dependent variable, weighted by the dependent variable precision weights and downweights, and allowing for a random regression intercept. As mean age at baseline is a between-sample characteristic, we did not estimate random regression slopes. Results are reported in the upper portion of Table 3 . We did not find evidence consistent with the static age dedifferentiation hypothesis. Both age at level and mean age at baseline were unrelated to level communalities (b = .001, SE = .002, p=.587; b= .000, SE = .002, p=.911, respectively). However, we did find considerable evidence for the dynamic age dedifferentiation hypothesis. Mean age at baseline was positively related to slope communalities (b = .005, SE = .002, p = .001). As expected, the association between age at level and slope communalities was weaker and non-significant (b = .003, SE = .002, p=.060).

To further visualize the moderation of slope communalities by age, we produced a scatterplot of the relation between mean age at baseline and slope communalities, and the meta-regression implied linear relation between these two variables. This plot can be found in the left panel of Figure 7 . According to the two-level meta-regression model, mean slope communalities increased from approximately 45% at age 35 years to approximately 70% at age 85 years. As can be seen, however, there were very few datapoints associated with mean baseline ages lower than 50 years. To ensure that the association between mean baseline age and slope communalities was not simply driven by high leverage exerted by these datapoints, we reran the meta-regression model only including effect sizes associated with mean baseline ages greater than 50 years. As reported in Table 3 , results were nearly identical (b = .004, SE = .002, p=.013) to those obtained from analyses of the entire meta-analytic sample (b = .005, SE = .002, p = .001).

Slope communality plotted as a function of mean age at baseline for full meta-analytic dataset (left) and for dementia-controlled sensitivity analyses (right). The area of each point corresponds to the precision of the slope communality estimate, with larger points representing more precise communalities. The overlaid regression lines represent the meta-regression model-implied linear trends and their 95% confidence intervals. For the full dataset, the positive association between slope communality and mean age at baseline remained (p = 0.013) when restricting analysis to estimates derived from mean ages at baseline that were greater than 50 years. For the dementia-controlled sensitivity analyses, t he positive association between slope communality and mean age at baseline also remained (p <.0005) when restricting analysis to estimates derived from mean ages at baseline that were greater than 50 years.

Other moderators tested included the time lag of the longitudinal study, the number of waves, whether the statistical analysis was a latent difference score model or a latent growth curve model, whether a broad or narrow cognitive ability was measured, and whether the change was modeled as occurring as a function of age or time. Parameter estimates, standard errors, and p values are reported in Table 3 . It can be seen that none of these moderators was significantly related to level communality estimates. Two moderators were significantly related to slope communalities. First, slope communalities derived latent difference score models tended to be lower than those obtained from growth curve models. Second, slope communalities for broad abilities tended to be higher than those for specific measures.

Finally, we tested whether the mean rate of change in a variable was related to its level or slope communality estimate. One might expect that individual differences in changes in variables that exhibit greater mean change are more strongly shared with individual differences in changes with other variables. This was not the case. Mean rate of change in a variable was unrelated to either its slope or level communality. In other words, variables that exhibited steeper mean rates of longitudinal aging-related decline were not more likely to share individual differences in longitudinal change more strongly with other variables.

Simultaneous Analysis of Multiple Moderators

Next, we sought to determine whether each of the associations identified above persisted in a simultaneous model. Thus, we fit a two level meta-regression model in which level communalities, mean age at baseline, whether a latent difference score (vs. growth curve) model was used, whether the outcome was a broad ability (vs. a specific measure) and effects-coded indicators of cognitive ability domains were predictors of slope communalities. Models were weighted by the respective precision of the dependent variable and by the inverse number of effect sizes contributed by the associated dataset. To maintain consistency with the above-described univariate analyses, we allowed for a random intercept, a random slope for the regression effect of level communalities on slope communalities, and a covariance between the random intercept and the random slope.

Parameter estimates from the simultaneous analysis are reported in Table 4 . Level communality and mean age at baseline remained significant moderators of slope communalities. Whether slope communalities were derived latent difference score vs. growth cure models and whether slope communalities were for broad vs. narrow abilities were no longer significant predictors.

Parameter estimates from simultaneous analysis predicting slope communalities.

Note: Two-level meta-regressions were weighted by the respective precision of the individual communality estimates and by the inverse number of effect sizes contributed by the associated dataset. We allowed for a random intercept, a random slope for the regression effect of level communalities on slope communalities, and a covariance between the random intercept and the random slope. Cognitive ability domains were effects coded, with processing speed omitted as the base group. All parameters for cognitive ability domains therefore represent the deviation of the mean communality estimate for the respective ability domain from the simple grand mean across domains. A parameter representing the deviation of the processing speed mean from the simple grand mean across all ability domains was derived from the other parameters using the delta method ( Ver Hoef, 2012 ). Standard errors (SE) and p values are not reported for Prospective Memory, because there was only one data point for prospective memory in the meta-analytic dataset. LDS = Latent Difference Score Model. LGM = Latent Growth Curve Model.

Dementia-Controlled Sensitivity Analyses

The more restrictive dataset only containing effect sizes from studies in which dementia status was carefully controlled contained 49 effect sizes representing shared variance in change in 49 outcomes from 9 unique samples. As the more restrictive dataset did not contain bivariate approaches (all were multivariate), the number of total shared variance estimates is equal to the number of total variables. All estimates came from growth curve models. Across the 49 estimates of shared variance, the average age at baseline wave ranged from 35.42 years to 84.92 years, with a median of 64.90, a mean of 67.01 years and a standard deviation of 12.92 years. Of the 49 outcomes analyzed, 12 indexed Processing Speed, 17 indexed Episodic Memory, 8 indexed Spatial Ability, 6 indexed Reasoning, 6 indexed Verbal Knowledge, and 1 indexed Prospective Memory. Twenty four outcomes were classified as broad ability composites or factors and 25 were classified as specific measures.

Level communality estimates ranged from .20 to 1.0. In an unconditional two-level meta-regression model, weighted by the level precision weights and downweights, the mean level communality was .516 (SE = .017, p < .0005), and .573 (SE=.037, p < .0005) when weighted by the slope precision weights and downweights. Slope communality estimates ranged from .084 to 1.0. In an unconditional two-level meta-regression model, weighted by the slope precision weights and downweights, the mean slope communality was .651 (SE = .037, p < .0005). As the estimates for mean level and slope communalities from this more restrictive sensitivity analysis are very similar to those from the full meta-analytic dataset, it can be inferred that the key finding that approximately half or more of the variance in aging-related cognitive changes are shared across domains is not a simple artifact of confounds associated with dementia status.

We tested whether the earlier reported association between mean age at baseline and slope communality persisted in this more restrictive dataset that only contained effect sizes from studies which dementia was carefully controlled. In a two-level meta-regression model, weighted by the slope communality precision weights and downweights, and allowing for a random regression intercept, mean age at baseline was positively related to slope communalities (b=.007, SE=.001, p<.0005; results from the full sample reported earlier were: b = .005, SE = .002, p = .001). Moreover, when we reran the meta-regression model only including effect sizes associated with mean baseline ages greater than 50 years results were nearly identical (b=.008, SE=.002, p<.0005). The right panel of Figure 7 provides a scatterplot of the relation between mean age at baseline and slope communalities, and the linear association implied by the meta-regression. The fact that this dynamic dedifferentiation pattern of age-related increases in shared variance in change was present in this more restrictive dataset indicates that the pattern is not a simple epiphenomenon of the increased prevalence of dementia at later ages.

Common factor theories of human cognitive aging have been popular for some time. Cross-sectional approaches to testing such theories, while capable of capturing information about overlapping mean age trends, are not able to directly gauge the extent to which interindividual differences in different cognitive abilities change in tandem. For more than fifteen years, researchers have used longitudinal approaches to estimate covariation among individual differences in rates of aging-related change in different abilities over time. The goal of the current meta-analysis was to systematically compile and meta-analyze results of these longitudinal studies in order to provide an estimate of the overall magnitude of shared variance in aging-related cognitive changes and test for moderators of the magnitude of shared variance in aging-related cognitive changes.

A primary finding of this meta-analysis is that individual differences in longitudinal changes in different cognitive abilities changes are moderately-to-strongly correlated with one another. A model in which individual differences in longitudinal cognitive changes are specified to load on a common change factor indicates that an average of 60% of the variance in aging-related cognitive changes is explained by the common factor. This relatively high estimate indicates that individuals who decline precipitously in, for example, processing speed relative to their peers, are also likely to be declining precipitously in, for example, reasoning and episodic memory relative to their peers. Moreover, even though verbal knowledge exhibited relatively shallow mean rates of longitudinal change, individual differences in verbal knowledge change loaded together with individual differences in changes in other cognitive abilities. This indicates that individuals who show greater decline in a cognitive ability showing strong mean decline are less likely to show positive change or stability in verbal knowledge, for which mean decline is quite shallow.

Remarkably, the magnitude of variance in cognitive ability levels that was explained by a general intelligence factor was 56%, extremely similar to the proportion of shared variance in rates of change . Moreover, there was a moderate-to-strong correspondence between a variable’s loading on the general intelligence factor and the extent to which changes in that variable loaded on a general cognitive change factor. When indexed with a correlation coefficient, which quantifies the relative ordering of communality estimates across indicators, the correspondence between level and slope communalities was moderate (r=.49). When indexed with a congruence coefficient, which takes into account the absolute magnitudes of communality estimates in addition to their relative orderings, this correspondence was strong (congruence coefficient = .98) The positive correspondence between level and slope communalities was evident even within datasets (i.e. even after centering estimates at study-specific communality means), suggesting that it is not an artifact of unobserved sources of between-study heterogeneity (e.g. the demographic composition of participants or specific aspects of the longitudinal design) that could systematically affect factor loadings. Importantly, however, differences in communality estimates across cognitive ability domain classifications were rather small, and there was not an apparent correspondence between mean level and slope communality estimates across domain classifications. Thus the positive association between level and slope communality estimates for individual study variables may not be driven by correspondence of effect sizes within domains, but instead be attributable to other features of the variables, such their construct validity, or aspects of measurement.

It is useful to compare results from the current meta-analysis to those obtained from three studies that we did not include in the meta-analysis due to the unavailability of information necessary for calculating precision weights. These three studies can therefore be treated as opportunities for out-of-meta-analytic-sample cross-validation. First, Christensen et al. (2010) and Hofer et al. (2002) estimated correlations between longitudinal growth curve slopes for cognitive measures in the Canberra Longitudinal. Christensen et al. (2010) report correlations of .42 for memory slope -reaction time slope, .71 for memory slope-processing speed slope, and .70 for eaction time slope-processing speed slope. Hofer et al. (2002) similarly report correlations of .67 for memory slope-verbal slope, .65 for memory slope-speed slope, and .46 for verbal slope-speed slope. These estimates are all very similar to the mean estimate of 60% shared variance in change from the current meta-analysis. Second, Anstey et al. (2003) reported a correlation between growth curve slopes for memory and processing speed of .62 in an unadjusted model, and .50 in a model that excluded individuals with possible cognitive impairment and adjusted for a host of covariates. Again, these estimates are very similar to those from the current meta-analysis. Third, the analysis of longitudinal data from the Religious Orders Study by Wilson et al. (2002) was particularly high quality. Wilson et al. (2002) used growth curve (random coefficient) modeling to produce a correlation matrix of individual differences in longitudinal slopes for seven different cognitive variables, including measures of working memory, visual spatial ability, perceptual speed, fluency, episodic memory, and verbal knowledge. Wilson et al (2002) found that a single principal component accounted for 61.6% of the variance in individual differences in cognitive changes, an estimate strikingly close to the estimate of 60% of shared variance in cognitive change obtained in the current meta-analysis. Importantly, Wilson et al.’s (2002) analysis found that this proportion was nearly identical (61.8%) after accounting for practice effects. A handful of other studies (e.g. Tucker-Drob, 2011a ; Ferrer, Salthouse, McArdle, & Stewart, 2005 ) have also reported that shared variance among aging-related changes persists after controlling for practice effects. However, because most studies did not include sufficient information regarding the role of practice effects, we were not well positioned to formally test their role in the current meta-analysis.

Because the general factor of individual differences in cognitive abilities are moderately stable beginning in middle childhood ( Deary, 2014 ; Humphreys & Davey, 1988 ; Tucker-Drob & Briley, 2014 ), static individual differences in adult cognitive abilities may substantially reflect processes that have unfolded over child development. Thus, the finding of similarly strong common factors of levels and slopes indirectly suggests that cognitive decline may operate along a similar general dimension as does cognitive development. As Juan-Espinosa et al. (2002) have suggested, the structure of lifespan changes in cognitive abilities may be invariant in much the same way that the structure of changes in human anatomy are invariant: Just as age-related growth and shrinkage of the human bones is organized by the anatomical structure of the human skeleton, individual differences in human cognitive abilities may have an inherent structure along which growth and decline naturally occur (see also Baltes et al., 2006; Reinert, 1970 ; Schaie, 1962 ; Tetens, 1777 ; Werner, 1948 ). Consistent with this proposal, Rhemtulla and Tucker-Drob (2011) reported evidence for a general factor of longitudinal changes across cognitive, psychomotor, and pre-academic domains in over 8,000 children followed between ages 3 and 7 years. Moreover, Tucker-Drob (2009) reported consistent evidence for a general intelligence factor across the age range from 4 to 101 years. Gignac (2014) reported similar results across the age range from 2.5 to 90 years. Cheung, Harden, and Tucker-Drob (2015) found consistent evidence for a general intelligence factor across the range from 0 to 6 years. Moreover, Cheung et al. (2015) found that the general intelligence factor, but not the domain-specific factors, became increasingly heritable with age, suggesting that a “generalist” genetic architecture ( Kovas & Plomin, 2006 ) may undergird substantial portions of individual differences in child cognitive development.

The present findings add an important qualification to two-component theories of adult intellectual development (Kühn & Lindenberger, 2016; Lindenberger, 2001; Tetens, 1777 ), such as the Cattell-Horn theory of fluid and crystallized intelligence (Gf/Gc theory; Cattell, 1971; Horn, 1989) or the mechanics versus pragmatics theory of cognition (Baltes, 1987). These theories build on the observation that cognitive abilities diverge in their associations with age, presumably reflecting differences in the relative importance of biological and cultural influences. In this meta-analysis, we replicated the well-known pattern of flat or shallow mean declines in verbal knowledge and steep mean declines in fluid/mechanic abilities, such as processing speed, episodic memory, and reasoning. Despite these pronounced differences in patterns of mean decline, all seven cognitive abilities, including verbal knowledge, showed strong and rather uniform loadings on a common factor of change. Indeed, we did not find any indication that the mean change in an outcome was related to the extent to which individual differences in change in that outcome were shared with other outcomes. What this means is that individuals who decline less in abilities such as perceptual speed or reasoning are likely to improve more on verbal knowledge relative to others. In other words, in spite of relatively stark differences between mechanic and pragmatic abilities in their patterns of mean aging-related declines, individual differences in longitudinal changes in mechanic and pragmatic abilities were moderately coupled. Methodologically, this finding adds weight to the assertion that cross-sectional methods, which are dominated by the contribution of ability differences in mean age trends, do not adequately reflect the implications of models involving covariance of change ( Hofer & Sliwinski, 2001 ; Kalveram 1965 ; Lindenberger et al., 2011 ; Lindenberger & Pötter, 1998 ).

The finding that older mean baseline age was associated with slope communalities is consistent with what has been termed the dynamic dedifferentiation hypothesis. Motivated by a general constraint theory of neurodegeneration and cognitive decline, de Frias et al. (2007) , predicted this precise pattern of increasing shared variance in change with advancing adult age. In the meta-analytic dataset, the dyanmic dedifferentiation pattern was appreciable. According to the linear moderation model that we fit, the mean expected slope communality at age 35 years is 42%, increasing to 72% by age 85 years. This result is consistent with the hypothesis that “an ensemble of common sources increasingly dominates development of intellectual abilities” ( de Frias et al., 2007 , p. 382).

In contrast, we did not find evidence supporting the static dedifferentiation hypothesis , which predicts that a global sources of change should give rise increasing shared variance among ability levels with advancing adult age (cf. Tucker-Drob, 2009 ; Hofer & Sliwinski, 2001 ). In other words, neither mean age at baseline nor age at level was associated with level communalities. Increasing correlations among static individual differences with age are predicted to arise when the shared variance in change is larger than the shared variance in levels. Here we find that these proportions are nearly identical. This may explain why it appears that the covariance structure of individual differences is stationary, or homeostatic, over much of adult age. However, particularly in light of the positive evidence for dynamic dedifferentiation, it is possible that had more samples with large proportions of older individuals been included, a static dedifferentiation pattern would have emerged.

One necessary limitation of the meta-analytic dataset is that different studies employed different modalities of measuring the constructs and of modeling change over time. Some studies employed growth curve models whereas other studies employed latent difference score models. Those employing growth curve models differed from one another in their coding of time (e.g. whether change as assumed to occur as a function of age at measurement or time since baseline measurement), and in whether they included nonlinear (e.g. quadratic) components of change in addition to linear components. We tested for differences in effect sizes across age- vs. time-based approaches as potential moderators. However, it would have been particularly informative had the original studies analyzed data using a standardized set of modeling strategies. Similarly, studies differed in both the specific assessments used and in the cognitive abilities measured. To address this, we restricted the meta-analytic dataset to only those effects that represented associations across (and not within) cognitive ability domains. According to Spearman’s (1927) theorem of “indifference of the indicator,” as long as a sufficiently diverse array of cognitive measures is used ( Little, Lindenberger, & Nesselroade, 1999 ), the same latent factor may be triangulated on across different sets of measures. This theorem has been validated empirically ( Johnson, Bouchard, Krueger, McGue, and Gottesman, 2004 ).

It is important to reiterate our statement from earlier in this article that the finding that a single common factor accounts for upwards of half of the variance in individual differences in age-related changes in different cognitive abilities indicates that a large proportion variation in cognitive aging can be organized by a single cognitive dimensions, but does not imply that a single social, genetic, or neurobiological cause of cognitive aging is likely. Ghisletta et al. (2012) wrote

“While we presented strong evidence that the dimensionality of cognitive aging is low, we cannot, based on the present behavioral evidence, draw strong conclusions about the number and nature of the underlying driving factors” (p. 267).

Tucker-Drob, Reynolds, et al. (2014) similarly wrote:

“our finding that a global dimension can account for large proportions of variation in aging-related cognitive changes in older adulthood indicates that late-life cognitive aging is manifest in a largely global pattern of change across multiple variables, but does not indicate that a single cause is responsible for global changes. It is very possible, if not likely, that many thousands of genetic and environmental causes of cognitive aging exist. What the current findings indicate is that… these many causes tend to operate at very broad levels to affect many forms of cognition” (p. 164).

Indeed, in order for the cross-domain coupling among aging-related cognitive changes documented here to represent a meaningful property of the cognitive aging process, rather than a simple epiphenomenon of a less interesting mechanism, we would expect the finding to persist after controlling for key socioeconomic and medical variables that are known to be associated with cognitive abilities. One important potential confound that we considered was dementia status. Because dementia is associated with impairment across multiple cognitive abilities, it was conceivable that correlated changes across cognitive abilities was induced by mean differences in declines between demented and nondemented groups ( Harrington et al., 2018 ; although see Boyle et al., 2013 and Sibbett et al., 2018 ), but not a more general characteristic of covariation among cognitive declines within the respective groups. Our sensitivity analyses indicated that this was not the case. Even when we performed the meta-analysis on a highly restricted dataset only containing studies that excluded data from person-waves at which dementia was present, controlled for dementia status as a time-varying covariate, or provided an estimate of a low rate of dementia in the sample, the key finding of cross-domain coupling among aging-related cognitive changes persisted. In fact, inspection of results of each of these individual studies indicates that the overall pattern is present in each study. Some individual studies included a wider range of control variables beyond dementia, and continued to document evidence for a general factor of aging-related cognitive change. For instance, Tucker-Drob (2011b) reported substantial coupling among aging-related changes in reasoning, processing speed, and episodic memory even after controlling for age, sex, years of educational attainment, MMSE score, and baseline performance levels. Tucker-Drob, Briley, et al. (2014) similarly reported that strong evidence for a general factor of longitudinal cognitive change persisted even after controlling for a host of carefully measured demographic, physical health, and medical variables, including as forced expiratory volume, walk time, grip strength, smoking status, cardiovascular disease status, hypertension status, diabetes diagnosis, along with early life IQ, educational attainment, sex, age, and time-lag. Lindenberger and Ghisletta (2009) reported that after controlling for age, time to death, and dementia risk, a general factor of change went from accounting for 60% of the variance to 65% of the variance in cognitive changes. Overall then, the evidence is very consistent with the conclusion that the factor structure of cognitive aging is more than an epiphenomenon of a small and obvious set of simple confounding variables. Rather, a common factor of cognitive change may be a more fundamental description of the cognitive aging process.

Even accepting the conclusion that coupled aging-related cognitive changes represent a meaningful property of the cognitive aging process, the question remains as to whether the common factor of aging identified in the current meta-analysis represents a coherent entity that is directly affected by biological and contextual etiological factors for cognitive aging and affects changes in individual cognitive domains. An equally logical possibility is that the common factor of change represents an emergent property of dynamical systems processes that occur more directly between etiological factors and ability domains. In the Introduction to this article, we refrained from taking a position on this issue. Complex systems approaches, such as graph-theoretic network models, for representing interrelations among individual differences in aging-related changes may be able to faithfully, or perhaps even more accurately, present the patterns of change interrelations that were captured by the factor analytic approach taken here (cf. van der Maas et al., 2006 ). When summarizing general patterns in complex networks, global metrics may be used ( Borsboom et al., 2011 ). In this sense, common factors and global network metrics capture general patterns of covariance in the data, while eliding nuance. We believe that both approaches are useful, albeit imperfect, insofar as they convey the most salient and robust patterns present in the data.

In summary, we found that over half of the variance in cognitive changes is shared across cognitive abilities. Specifically, the meta-analytic estimate of average change communality was 60%, which is very similar to the estimate of 56% for shared variance in levels. Moreover, we found that shared variance in changes increased with age, from approximately 40% at age 35 years to approximately 70% at age 85 years. These patterns persisted at full strength in a sensitivity analysis based on studies that carefully controlled for dementia. These results together provide strong evidence for a general factor of cognitive aging that strengthens with advancing adult age.

Public Significance Statement (APA Requirement)

A longstanding question in cognitive aging has been “Does it all go together when it goes?” This meta-analysis indicates that aging-related declines are interrelated across different domains of thinking. For instance, adults who decline steeply in their memory relative to other adults as they get older are also likely to decline relatively steeply in reasoning and processing speed relative to others over the same period of time. These key insights into how individual differences in longitudinal cognitive declines are structured suggest that theories and interventions for cognitive aging will need to consider mechanisms that cut across several different domains of cognitive function in addition to mechanisms that are specific to each individual cognitive domain.

Supplementary Material

Online supplement, acknowledgements:.

This research was supported by National Institutes of Health (NIH) grant R01AG054628. The Population Research Center at the University of Texas is supported by NIH grant R24HD042849.

Appendix A: Multivariate Models of Longitudinal Changes

Here we provide an overview of the statistical basis of the studies meta-analyzed. These can generally be classified as multivariate growth curves models and multivariate latent difference score models. As described below, both statistical models take similar forms.

Multivariate Growth Curve Models.

A multivariate growth curve model for a set of cognitive outcomes measured repeatedly over time can be written as:

where Y [ t ] w,n is the score Y of person n on variable w at time t; i w,n is the level for person n on variable w ; s w,n is the longitudinal slope for person n on variable w , and e [ t ] w,n is a disturbance for person n on variable w at time t . The term A [ t ] w,n is a set of growth curve basis coefficients that define the shape of the longitudinal changes over time. In the common case of linear growth curve modelling, these basis coefficients can be set to the amount of time (e.g. in years) that has passed between baseline measurement (e.g. the first wave of assessment) and each subsequent assessment for person n on variable w, or they can be set to the age of individual n at each assessment on variable w (e.g. in years, often centered relative to a meaningful early age e.g. 21 years for early adulthood or 65 years for the beginning of old age). For time-based modelling, it is common for the basis coefficients A [ t ] to be specified using the average or target time between assessment waves rather than the person-specific time-lags. The basis coefficients need not be specified to be linear over time or age. Longitudinal changes may be better represented as occurring nonlinearly, in which case the basis coefficients A [ t ] may be specified as nonlinear functions of time or age. Additional growth curve slopes, e.g. slopes representing quadratic components of change over time or retest effects may also be specified using separate sets of basis coefficients. In the current meta-analysis, we were constrained by the original modelling decisions of the authors of the primary studies (e.g. whether to specify basis coefficients as functions of age or time, or whether to include additional growth curve slopes beyond those representing linear change). 5

Multivariate Latent Difference Score Curve Models.

A multivariate latent difference score model, in which a several set of cognitive outcomes are each measured at both baseline and a single follow-up wave, can be written in a similar form to the multivariate growth model above as:

where Y [ t ] w,z,n is the score Y of person n on indicator z of latent variable w at time t (0 for baseline and 1 for follow-up wave; or to scale the difference score in annual units, 0 for baseline and the average or target number of years since baseline for follow-up wave), and λ w,z is a time-invariant factor loading relating the latent variable w to indicator z. As in the multivariate growth model, i w,n is the level for person n on (now latent) variable w and s w,n is the longitudinal change over time for person n on variable w. Whereas error in the growth curve model is separated from true change by virtue of being modeled as disturbance from a systematic function of change over time, error in the latent difference score model is separated from true scores psychometrically by specifying indicators to be functions of common factor w and time-specific indicator-specific errors, e [ t ] w,z,n , that represent a combination of indicator-specific true score variability and error of measurement.

Level and Change Covariance Structure.

Regardless of whether longitudinal changes are modeled using a multivariate growth curve approach or a multivariate latent difference score approach, the person-specific levels and slopes for each variable can be allowed to freely covary with one another, as follows:

This covariance matrix can be written more compactly as a joint covariance matrix of levels and slopes, as follows:

Key components of this covariance matrix are: a submatrix Σ i that includes level variances on its diagonal and level-level covariances off its diagonal, a submatrix Σ s that includes slope variances on its diagonal and slope-slope covariances off its diagonal, and a submatrix Σ is that includes within-variable level-slope covariances on its diagonal and cross-variable level-slope covariances off its diagonal. When this covariance matrix is freely estimated, it is sometimes referred to as a “parrallel process” model.

Higher Order Factors of Curves.

Rather than allowing the person-specific levels and slopes for each variable to freely covary, as in Equations A2a and A2b above, the interrelations among levels and among slopes can be approximated by common factors. The factor portions of such a “factors of curves” model can be written as:

where τ i w and τ s w are the mean level and slope for variable w ; λ i w is the loading of the person-specific level of variable w on the common factor of the levels, F i,n ; λ s w is the loading of the person-specific slope of variable w on the common factor of the slopes, F s,n ; u i w,n is a person-specific unique factor for the level of variable w; and u s w,n is a person-specific unique factor for the slope of variable w. Typically, within-variable covariances between level and slope unique factors are freely estimated; all remaining covariances among uniqueness are fixed to 0; and the level and slope common factors are assumed to have means of 0 and are allowed to freely covary with one another.

The approximation of level-and slope covariances by the higher order factor of curves model is given in matrix notation as:

where Λ is a 2w × m matrix of loadings for the levels and slope of each varaiable w on m common factors (typically two factors: one common factor of levels and one common factor of slopes), Ψ is an m × m covariance matrix of the common factors, and Θ is a 2w × 2w covariance matrix of level and slope unique factors (typically with unique factor variances on the diagonals, within-variable level-slope covariances freely estimated, and between-variable level-slope covariances fixed to zero).

Communality is the term used in factor analysis for the proportion of variance in a variable that is explained by a common factor. In the context of a factor of curves model, we are specifically interested in the proportions of variance in the growth curve levels and slopes that are explained by the common factors of levels and slopes respectively. Communality can be computed as

where λ is the common loading of the variable-specific level or slope on the common factor of levels or slopes, σ F 2 is the variance of that common factor, and σ u 2 is the variance of the unique factor of the variable-specific level or slope. For cases in which the factor loading has already been standardized (such that the standardized variance of the factor is 1 and the total variance in the outcome is 1), the communality is simply calculated as the square of that standardized factor loading. Level and slope communalities are the primary outcomes of the current meta-analysis.

Appendix B: Computation of Precision Weights

The raw meta-analytic precision weights, w i were computed as:

where N i is the complete sample size (at the first wave), r i,x and r i,y are the reliabilities of the i -th pair of growth curves (or difference scores, respectively) analyzed. The reliabilities were each down-weighted by the observed missing data patterns using the theorem in Appendix B of von Oertzen and Brandmaier (2013) to properly account for attrition. By definition, the weights are always positive. Raw weights were converted to scaled weights, such that they sum to the total number of effect sizes included in the meta-regression model.

As described in the Method section (under Analytic Approach: Multilevel Meta-Regression Models), precision weights were implemented in combination with downweights that adjusted for the number of effect sizes contributed per sample. Precision weights were specified at the level of the individual effect sizes (i.e. as within -cluster weights) and downweights were specified at the level of the contributing samples (i.e. as between- cluster weights). For multilevel meta-regression models, between-cluster weights were scaled such that the products of the within-cluster and between-cluster weights summed to the total number of effect sizes in the meta-analytic dataset.

1 Both growth curve models and latent difference score models are advantageous for limiting bias that is associated with random measurement error, they are less effective in limiting bias that results from systematic measurement confounds, such as the influences of variation in manual dexterity on performance on several different tasks. In many circumstances, statistical modeling approaches have limited effectiveness in controlling for confounds that result from study design and task selection.

2 For example, if a study provided loadings for processing speed slope, memory slope, and reasoning slope on a common factor of growth curve slopes, the weight for the squared loading for processing speed would be derived by inputting into the algorithm the level variance, slope variance, and time-specific variance for processing speed as information for the x variable. For the y variable, the average level variance for memory and reasoning, average slope variance for memory and reasoning, and average time-specific variance for memory and reasoning would be input as information for the y variable. The time-lags and sample sizes are generally constant across variables, such that no specific accomodations need to be made for inputting these to pieces of information.

3 In order to maintain consistency with results of other analyses, we used the downweights constructed for the complete dataset, even though there were some missing estimates for mean rate of change.

4 For two-wave designs, latent difference score models can identically be expressed as growth curve models. The effect of LDS vs. LGM modelling can therefore be equivalently interpreted as the effect of two vs. more than two waves of data collection.

5 There was a small number papers included in the current meta-analysis that specified growth curves that included nonlinear slopes in addition to linear slopes. For instance, Ghisletta et al. (2012) estimated linear and quadratic growth curve slopes, but because the quadratic slopes were not significant, they did not estimate their covariances across abilities. Additionally, both Ghisletta et al. (2012) and Tucker-Drob, 2011) estimated additional nonlinear slopes representing practice effects, which were not a direct focus of the current meta-analysis. Many studies simply estimated linear growth curve models. Thus, we exclusively coded the relevant data from the linear components of longitudinal changes.

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Tuesday, April 23, 2024

Peer-supported problem-solving may help older rural adults with depression.

Older adults who completed 12 weeks of self-guided problem-solving therapy (PST) supported by trained peer volunteers had significant and lasting improvements in their depression scores, according to a report in Psychiatric Services in Advance . PST is a psychotherapy that teaches people to generate realistic solutions to life problems that contribute to depression.

“PST with a clinician should be considered a preferred treatment option when resources are available,” wrote Brooke Hollister, Ph.D., at the University of California, San Francisco, and colleagues. However, self-guided-PST supported by trained peer counselors “may be an appropriate and acceptable alternative to [clinician-managed]-PST (or other evidence-based interventions) for older adults who live in rural areas and experience barriers to access because of stigma, poor transportation options, or a lack of available clinicians and services.”

A total of 105 rural Californians aged 60 or older who had moderate to severe unipolar depression but did not have psychotic depression, a high suicide risk, or other major psychiatric disorders participated in the study. Eighty-five participants received clinician-managed PST with specially trained master’s level therapists or social workers, while the remaining 20 embarked on self-guided PST supported by peers who had also received special training. Both groups completed the PST sessions in their homes.

Depression levels were assessed with the Hamilton Depression Rating Scale [HAM-D]. Improvements in depression were clinically significant in both groups after 12 weeks, though clinician-managed PST did lead to higher response, remission, and retention rates, the researchers reported. Still, 45% of the adults in the peer-supported, self-guided group showed a treatment response of at least 50% reduction in HAM-D scores by week 12.

At 24 weeks, the groups equalized as the rates of depression reported by the self-guided group continued to drop, while the clinician-managed group’s depression scores rose slightly. This finding might imply a longer-lasting impact of self-guided treatment, given its focus on empowering patients to address their own problems, the researchers wrote. However, because the completion rate was lower for this group, Hollister and colleagues recommended further research to explore why.

“Effective use of [trained peer counselors] to support evidence-based treatments has the potential to maintain the independence of rural older adults by improving their access to mental health services and using existing infrastructure and minimal community resources,” they concluded.

For related information, see the Psychiatric Services article “ Opportunities and Challenges to Build Behavioral Health Crisis Capacity in Rural America ,” also published today.

(Image: Getty Images/iStock/KatarzynaBialasiewicz)

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Intellectual Development Through Adulthood and the Effects of Age on the Functions of Memory

Last updated 23 Sept 2022

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In this study note we highlight some key features of intellectual development through adulthood.

Early Adulthood

During early adulthood, individuals continue to develop logical thinking . This is now applied (alongside skills and knowledge) into the workplace, where they are tasked to problem solve and make decisions about more complex situations.

Middle Adulthood

As people move into middle adulthood, their ability to retrieve information may be more difficult. There is some dispute as to whether our memory is starting to decline (where did I put my phone) or whether our brain starts to focus on other information.

How Age Affects the Functions of Memory in Later Adulthood

• Memory loss that occurs during later adulthood can result in difficulty recalling and learning new information.
• In most cases this is not clinical memory loss, but what scientists refer to as lapses in memory function.
• Daily activities such as completing word and number puzzles help to keep the brain active and healthy.
• Severe issues with recall and remembering may indicate cognitive decline and types of dementia that would require further testing by specialists.
• Scientists have discovered that throughout adulthood new brain cells are produced within the hippocampus. This is the areas of the brain that is involved in learning, memory and emotions.
• Intellectual development
• Later adulthood
• Early adulthood
• Middle adulthood

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Physical development in early childhood (3-8 years), physical changes in early adulthood (19-45 years), perimenopause in early adulthood, physical changes in middle adulthood, physical changes in middle adulthood (46-65 years): menopause, physical changes in later adulthood (65+ years), intellectual development, chomsky: language acquisition in infancy and early childhood, our subjects.

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Everything Baby Showers

15 Childhood Games That Unexpectedly Prepared Us for Adulthood

Posted: April 16, 2024 | Last updated: April 16, 2024

Childhood games aren’t just about fun and entertainment. They often teach valuable life lessons. From learning to cooperate with others to developing problem-solving skills, many childhood games offer valuable experiences that we carry into our grown-up lives. Let’s explore 15 childhood games that unexpectedly prepared us for adulthood.

Hide and Seek

Hide and Seek, a childhood favorite, offers more than just entertainment; it provides valuable lessons for adulthood. The game instills strategic thinking, as players must find clever hiding spots and stay out of sight. This teaches us the importance of planning and adapting to unforeseen challenges. Additionally, staying hidden requires patience and perseverance, essential traits when facing obstacles in adult life. 

Tag, a timeless childhood game, demands agility, quick thinking, and the ability to react swiftly to changing circumstances. As children, we learn to persevere and think on our feet, skills that prove invaluable in the fast-paced environment of adulthood. Tag teaches us the importance of resilience and adaptability, which help us navigate life’s challenges.

Simon Says, a classic childhood game, offers more than just entertainment. The game enhances our ability to follow instructions attentively and think quickly on our feet. These skills are crucial in professional settings where listening and responsiveness are highly valued. Additionally, Simon Says encourages discipline and focus, traits that are essential for success in adult life. 

Board Games (Monopoly, Scrabble, etc.)

Board games such as Monopoly and Scrabble teach strategic thinking, negotiation skills, and decision-making abilities, all essential for adult life. Monopoly, for example, teaches us about financial management and investment strategies, while Scrabble enhances our vocabulary and critical thinking skills. 

Puzzle Games

Puzzle games, whether jigsaw puzzles or Sudoku, offer more than a mental challenge. These games enhance our problem-solving abilities, patience, and persistence, which are essential for tackling complex challenges in adult life. Solving a puzzle requires focus, attention to detail, and the willingness to try different approaches, skills that prove invaluable in various professional and personal situations. 

Building Blocks (LEGO)

Building blocks like LEGO encourage creativity, spatial awareness, and the ability to envision and construct something from scratch. Through building with LEGO, children learn problem-solving skills as they experiment with different designs and solutions. Additionally, building blocks foster teamwork and collaboration, as children often work together to bring their ideas to life. 

Role-Playing Games (Pretend Play)

Role-playing games, or pretend play, through imagination, let children explore different roles and perspectives. They help foster empathy, communication skills, and the ability to work collaboratively with others. Pretend play encourages creativity and innovation, as children create scenarios and solve problems using their imagination. 

Sports (Soccer, Basketball, etc.)

Participating in sports like soccer and basketball provides more than physical exercise. Team sports teach us about leadership, cooperation, resilience, and the importance of discipline and dedication in achieving goals. Through sports, children learn to work together towards a common objective, overcoming challenges and celebrating victories as a team.

Climbing Trees

Climbing trees may seem like a simple childhood pastime, but as children navigate branches and heights, they develop physical strength, coordination, and risk-taking abilities. Climbing trees also fosters problem-solving skills as children carefully assess their surroundings and plan their ascent. These skills translate into adulthood, where confidence, assertiveness, and the willingness to take calculated risks are essential for personal and professional growth. 

Through dress-up, children explore creativity, self-expression, and confidence as they experiment with different outfits and personas. Dress-up games encourage imagination and role-play, fostering empathy and understanding of different perspectives. Children develop the foundation for effective communication and presentation skills that serve them well in their future endeavors by engaging in dress-up play.

Card Games (Uno, Poker, etc.)

Card games like Uno and Poker teach strategic thinking, probability assessment, and risk management. Card games also encourage social interaction and communication skills as players negotiate rules and strategies with one another. 

Memory Games (Memory, Concentration)

Memory games such as Memory and Concentration improve cognitive abilities, attention to detail, and information retention as players try to match pairs of cards or remember the location of hidden objects. Memory games also encourage focus and concentration, as players must stay attentive to the game board and recall information quickly.

Hopscotch, a classic outdoor game, teaches balance, coordination, and agility as players hop on one foot while navigating a series of squares. Hopscotch also encourages focus and concentration, as players must remember the sequence of squares and avoid stepping on lines. Adulthood requires skills like multitasking and managing competing priorities. This game teaches us to require balance, coordination, and the ability to focus on the task.

Telephone (Chinese Whispers)

Telephone, also known as Chinese Whispers, teaches the importance of clear communication and active listening, as players pass a message from one person to another, often resulting in miscommunication or distortion of the original message. Telephone encourages participants to pay attention to details and clarify information, skills essential for effective communication in adulthood. 

Building Forts

Building forts, whether with blankets and pillows indoors or sticks and branches outdoors, fosters creativity, resourcefulness, and teamwork as children collaborate to construct a shelter or hideout. Building forts also encourages adaptability, as children adjust their plans based on the materials available and the environment around them. 

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Everyday problem solving in adulthood and old age

Affiliation.

• 1 Department of Human Development and Family Studies, Cornell University, Ithaca, New York 14853.
• PMID: 3268204
• DOI: 10.1037//0882-7974.2.2.144

We examined everyday problem solving in adulthood and compared it with traditional measures of cognitive abilities. In the first phase of the research, we describe the construction of an inventory to assess problem solving in situations that adults might encounter in everyday life and examine raters' judgments of effective responses to the problems. In the second phase, adults (N = 126) between the ages of 20 and 78 were administered the inventory and tests of verbal and abstract problem-solving abilities. Results indicated modest but significant positive correlations between performance on the inventory and traditional ability tests. The examination of age differences revealed that performance on the Everyday Problem-Solving Inventory and verbal ability test increased with age, whereas performance on a traditional problem-solving test declined after middle age. In addition, education was unrelated to everyday problem solving, highly related to verbal ability, and moderately related to traditional problem solving. Results are discussed in relation to pluralistic conceptions of intelligence and theories of adult intellectual development.

• Adaptation, Psychological
• Aging / psychology*
• Concept Formation
• Cross-Sectional Studies
• Intelligence
• Middle Aged
• Problem Solving*