insight problem solving psychology definition

Insight Learning (Definition+ 4 Stages + Examples)

Have you ever been so focused on a problem that it took stepping away for you to figure it out? You can’t find the solution when you’re looking at all of the moving parts, but once you get distracted with something else - “A-ha!” you have it.

When a problem cannot be solved by applying an obvious step-by-step solving sequence, Insight learning occurs when the mind rearranges the elements of the problem and finds connections that were not obvious in the initial presentation of the problem. People experience this as a sudden A-ha moment.

Humans aren’t the only species that have these “A-ha” moments. Work with other species helped psychologists understand the definition and stages of Insight Learning. This video is going to break down those stages and how you can help to move these “a-ha” moments along.

What Is Insight Learning?

Insight learning is a process that leads to a sudden realization regarding a problem. Often, the learner has tried to understand the problem, but steps away before the change in perception occurs. Insight learning is often compared to trial-and-error learning, but it’s slightly different.

Rather than just trying different random solutions, insight learning requires more comprehension. Learners aim to understand the relationships between the pieces of the puzzle. They use patterns, organization, and past knowledge to solve the problem at hand.

Is Insight Learning Only Observed In Humans?

Humans aren’t the only species that learn with insight. Not all species use this process - just the ones that are closest to us intellectually. Insight learning was first discovered not by observing humans, but by observing chimps.

In the early 1900s, Wolfgang Köhler observed chimpanzees as they solved problems. Köhler’s most famous subject was a chimp named Sultan. The psychologist gave Sultan two sticks of different sizes and placed a banana outside of Sultan’s cage. He watched as Sultan looked at the sticks and tried to reach for the banana with no success. Eventually, Sultan gave up and got distracted. But it was during this time that Köhler noticed Sultan having an “epiphany.” The chimp went back to the sticks, placed one inside of the other, and used this to bring the banana to him.

Since Köhler’s original observations took place, psychologists looked deeper into the insight process and when you are more likely to experience that “a-ha” moment. There isn’t an exact science to insight learning, but certain theories suggest that some places are better for epiphanies than others.

Four Stages of Insight Learning

But how does insight learning happen? Multiple models have been developed, but the four-stage model is the most popular. The four stages of insight learning are preparation, incubation, insight, and verification.

Preparation

The process begins as you try to solve the problem. You have the materials and information in front of you and begin to make connections. Although you see the relationships between the materials, things just haven’t “clicked” yet. This is the stage where you start to get frustrated.

During the incubation period, you “give up” for a short period of time. Although you’ve abandoned the project, your brain is still making connections on an unconscious level.

When the right connections have been made in your mind, the “a-ha” moment occurs. Eureka! You have an epiphany!

Verification

Now, you just have to make sure that your epiphany is right. You test out your solution and hopefully, it works! This is a great moment in your learning journey. The connections you make solving this problem are likely to help you in the future.

Examples of Insight Learning

Insight learning refers to the sudden realization or understanding of a solution to a problem without the need for trial-and-error attempts. It's like a "light bulb" moment when things suddenly make sense. Here are some examples of insight learning:

The Matchstick Problem : Realizing you can light a match and use it to illuminate a dark room instead of fumbling around in the dark.
Sudoku Puzzles : Suddenly seeing a pattern or number placement that you hadn't noticed before, allowing you to complete the puzzle.
The Two Rope Problem : In an experiment, a person is given two ropes hanging from the ceiling and is asked to tie them together. The solution involves swinging one rope like a pendulum and grabbing it with the other.
Opening Jars : After struggling to open a jar, you remember you can tap its lid lightly or use a rubber grip to make it easier.
Tangram Puzzles : Suddenly realizing how to arrange the geometric pieces to complete the picture without any gaps.
Escape Rooms : Having an "aha" moment about a clue that helps you solve a puzzle and move to the next challenge.
The Nine Dot Problem : Connecting all nine dots using only four straight lines without lifting the pen.
Cooking : Realizing you can soften butter quickly by grating it or placing it between two sheets of parchment paper and rolling it.
Math Problems : Suddenly understanding a complex math concept or solution method after pondering it for a while.
Guitar Tuning : Realizing you can use the fifth fret of one string to tune the next string.
Traffic Routes : Discovering a faster or more efficient route to your destination without using a GPS.
Packing Suitcases : Figuring out how to fit everything by rolling clothes or rearranging items in a specific order.
The Crow and the Pitcher : A famous Aesop's fable where a thirsty crow drops pebbles into a pitcher to raise the water level and drink.
Computer Shortcuts : Discovering a keyboard shortcut that makes a task you frequently do much quicker.
Gardening : Realizing you can use eggshells or coffee grounds as a natural fertilizer.
Physics Problems : After struggling with a concept, suddenly understanding the relationship between two variables in an equation.
Art : Discovering a new technique or perspective that transforms your artwork.
Sports : Realizing a different way to grip a tennis racket or baseball bat that improves your game.
Language Learning : Suddenly understanding the grammar or pronunciation rule that was previously confusing.
DIY Projects : Figuring out a way to repurpose old items in your home, like using an old ladder as a bookshelf.

Where Is the Best Place to Have an Epiphany?

But what if you want to have an epiphany? You’re stuck on a problem and you can’t take it anymore. You want to abandon it, but you’re not sure what you should do for this epiphany to take place. Although an “a-ha” moment isn’t guaranteed, studies suggest that the following activities or places can help you solve a tough problem.

The Three B’s of Creativity

Creativity and divergent thinking are key to solving problems. And some places encourage creativity more than others. Researchers believe that you can kickstart divergent thinking with the three B’s: bed, bath, and the bus.

Sleep

“Bed” might be your best bet out of the three. Studies show that if you get a full night’s sleep, you will be twice as likely to solve a problem than if you stay up all night. This could be due to the REM sleep that you get throughout the night. During REM sleep , your brain is hard at work processing the day’s information and securing connections. Who knows - maybe you’ll dream up the answer to your problems tonight!

Meditation

The word for “insight” in the Pali language is vipassana. If you have ever been interested in meditation , you might have seen this word before. You can do a vipassana meditation at home, or you can go to a 10-day retreat. These retreats are often silent and are set up to cultivate mind-body awareness.

You certainly don’t have to sign up for a 10-day silent retreat to solve a problem that is bugging you. (Although, you may have a series of breakthroughs!) Try meditating for 20 minutes at a time. Studies show that this can increase the likelihood of solving a problem.

Laugh!

How do you feel when you have an epiphany? Good, right? The next time you’re trying to solve a problem, check in with your emotions. You are more likely to experience insight when you’re in a positive mood. Positivity opens your mind and gives your mind more freedom to explore. That exploration may just lead you to your solution.

Be patient when you’re trying to solve problems. Take breaks when you need to and make sure that you are taking care of yourself. This approach will help you solve problems faster and more efficiently!

Insight Vs. Other Types Of Learning.

Learning by insight is not learning by trial and error, nor by observation and imitation. Learning by insight is a learning theory accepted by the Gestalt school of psychology, which disagrees with the behaviorist school, which claims that all learning occurs through conditioning from the external environment.

Gestalt is a German word that approximately translates as ‘an organized whole that has properties and elements in addition to the sum of its parts .’ By viewing a problem as a ‘gestalt’ , the learner does not simply react to whatever she observes at the moment. She also imagines elements that could be present but are not and uses her imagination to combine parts of the problem that are presently not so combined in fact.

Insight Vs. Trial And Error Learning

Imagine yourself in a maze-running competition. You and your rivals each have 10 goes. The first one to run the maze successfully wins $500. You may adopt a trial-and-error strategy, making random turning decisions and remembering whether those particular turns were successful or not for your next try. If you have a good memory and with a bit of luck, you will get to the exit and win the prize.

Completing the maze through trial and error requires no insight. If you had to run a different maze, you would have no advantage over running previous mazes with different designs. You have now learned to run this particular maze as predicted by behaviorist psychologists. External factors condition your maze running behavior. The cash prize motivates you to run the maze in the first place. All maze dead ends act as punishments , which you remember not to repeat. All correct turns act as rewards , which you remember to repeat.

If you viewed the maze running competition as a gestalt, you might notice that it doesn't explicitly state in the competition rules that you must run along the designated paths to reach the exit.

Suppose you further noticed that the maze walls were made from cardboard. In that case, you may combine those 2 observations in your imagination and realize that you could just punch big holes in the walls or tear them down completely, to see around corners and directly run to the now visible exit.

Insight Vs. Learning Through Observation, Imitation, And Repetition

Observation, imitation, and repetition are at the heart of training. The violin teacher shows you how to hold your bow correctly; you practice your scales countless times before learning to play a sonata from Beethoven flawlessly. Mastering a sport or a musical instrument rarely comes from a flash of insight but a lot of repetition and error correction from your teacher.

Herbert Lawford, the Scottish tennis player, and 1887 Wimbledon champion, is credited for being the first player to play a topspin. Who could have taught it to him? Who could he have imitated? One can only speculate since no player at that time was being coached on how to hit topspin.

He could have only learned to play a topspin by having a novel insight. One possibility is that he played one by accident during training, by mistakenly hitting the ball at a flatter angle than normal. He could then have observed that his opponent was disorientated by the flatter and quicker bounce of the ball and realized the benefit of his ‘mistake’ .

Behaviorist theories of learning can probably explain how most successful and good tennis players are produced, but you need a Gestalt insight learning theory to explain Herbert Lawford.

Another interesting famous anecdote illustrating insight learning concerned Carl Friedrich Gauss when he was a 7-year-old pupil at school. His mathematics teacher seems to have adopted strict behaviorism in his teaching since the original story implies that he beat students with a switch.

One day the teacher set classwork requiring the students to add up all the numbers from 1 to 100. He expected his pupils to perform this calculation in how they were trained. He expected it to be a laborious and time-consuming task, giving him a long break. In just a few moments, young Gauss handed in the correct answer after having to make at most 2 calculations, which are easy to do in your head. How did he do it? Gauss saw the arithmetic sequence as a gestalt instead of adding all the numbers one at a time: 1+2+3+4…. +99+100 as he expected.

He realized that by breaking this sequence in half at 50, then snaking the last number (100) under the first number (1), and then adding the 2 halves of the arithmetic sequence like so:

1 + 2 + 3 + 4 + 5 + …………. + 48 + 49 + 50

100 + 99 + 98 + 97 + 96 + …………... + 53 + 52 + 51

101 + 101 + 101 + 101 + 101 + ……………. + 101 + 101 + 101

Arranged in this way, each number column adds up to 101, so all Gauss needed to do was calculate 50 x 101 = 5050.

Can Major Scientific Breakthroughs be made through observation and experiment alone?

Science is unapologetically an evidence-based inquiry. Observations, repeatable experiments, and hard, measurable data must support theories and explanations.

Since countless things can be observed and comparisons made, they cannot be done randomly for observations and experiments to advance knowledge. They must be guided by a good question and a testable hypothesis. Before performing actual experiments and observations, scientists often first perform thought experiments . They think of ideal situations by imagining ways things could be or imaging away things that are.

Atoms were talked about long before electron microscopes could observe them. How could atoms be seriously discussed in ancient Greece long before the discoveries of modern chemistry? Pre-Socratic philosophers were puzzled by a purely philosophical problem, which they termed the problem of the one and many .

People long observed that the world was made of many different things that didn't remain static but continuously changed into other various things. For example, a seed different from a tree changed into a tree over time. Small infants change into adults yet remain the same person. Boiling water became steam, and frozen water became ice.

Observing all of this in the world, philosophers didn’t simply take it for granted and aimed to profit from it practically through stimulus-response and trial and error learning. They were puzzled by how the world fit together as a whole.

To make sense of all this observable changing multiplicity, one needed to imagine an unobservable sameness behind it all. Yet, there is no obvious or immediate punishment or reward. Therefore, there seems to be no satisfying behaviorist reason behind philosophical speculations.

Thinkers such as Empedocles and Aristotle made associations between general properties in the world wetness, dryness, temperature, and phases of matter as follows:

Earth : dry, cold
Fire: dry, hot
Water: wet, cold
Air: hot or wet, depending on whether moisture or heat prevails in the atmosphere.

These 4 primitive elements transformed and combined give rise to the diversity we see in the world. However, this view was still too sensually based to provide the world with sought-for coherence and unity. How could a multiplicity of truly basic stuff interact? Doesn't such an interaction presuppose something more fundamental in common?

The ratio of these 4 elements was thought to affect the properties of things. Stone contained more earth, while a rabbit had more water and fire, thus making it soft and giving it life. Although this theory correctly predicted that seemingly basic things like stones were complex compounds, it had some serious flaws.

For example, if you break a stone in half many times, the pieces never resemble fire, air, water, or earth.

To account for how different things could be the same on one level and different on another level, Leucippus and his student Democritus reasoned that all things are the same in that they were made from some common primitive indivisible stuff but different due to the different ways or patterns in which this indivisible stuff or atoms could be arranged.

For atoms to be able to rearrange and recombine into different patterns led thinkers to the insight that if the atom idea was true, then logically, there had to be free spaces between the atoms for them to shift into. They had to imagine a vacuum, another phenomenon not directly observable since every nook and cranny in the world seems to be filled with some liquid, solid, or gas.

This ancient notion of vacuum proved to be more than just a made-up story since it led to modern practical applications in the form of vacuum cleaners and food vacuum packing.

This insight that atoms and void exist makes no sense from a behaviorist learning standpoint. It cannot be explained in terms of stimulus-response or environmental conditioning and made no practical difference in the lives of ancient Greeks.

For philosophers to feel compelled to hold onto notions, which at the time weren’t directly useful, it suggests that they must have felt some need to understand the universe as an intelligible ‘gestalt’ One may even argue that the word Cosmos, from the Greek word Kosmos, which roughly translates to ‘harmonious arrangement’ is at least a partial synonym.

The Historical Development Of The Theory of Insight Learning

Wolfgang Kohler , the German gestalt psychologist, is credited for formulating the theory of insight learning, one of the first cognitive learning theories. He came up with the theory while first conducting experiments in 1913 on 7 chimpanzees on the island of Tenerife to observe how they learned to solve problems.

In one experiment, he dangled a banana from the top of a high cage. Boxes and poles were left in the cage with the chimpanzees. At first, the chimps used trial and error to get at the banana. They tried to jump up to the banana without success. After many failed attempts, Kohler noticed that they paused to think for a while.

After some time, they behaved more methodically by stacking the boxes on top of each other, making a raised platform from which they could swipe at the banana using the available poles. Kohler believed that chimps, like humans, were capable of experiencing flashes of insight, just like humans.

In another experiment, he placed a peanut down a long narrow tube attached to the cage's outer side. The chimpanzee tried scooping the peanut out with his hand and fingers, but to no avail, since the tube was too long and narrow. After sitting down to think, the chimp filled its mouth with water from a nearby water container in the cage and spat it into the tube.

The peanut floated up the tube within the chimp's reach. What is essential is that the chimp realized it could use water as a tool in a flash of insight, something it had never done before or never shown how to do . Kohler's conclusions contrasted with those of American psychologist Edward Thorndike , who, years back, conducted learning experiments on cats, dogs, and monkeys.

Through his experiments and research, Thorndike concluded that although there was a vast difference in learning speed and potential between monkey dogs and cats, he concluded that all animals, unlike humans, are not capable of genuine reasoned thought. According to him, Animals can only learn through stimulus-response conditioning, trial and error, and solve problems accidentally.

Kohler’s 4 Stage Model Of Insight Learning

From his observations of how chimpanzees solve complex problems, he concluded that the learning process went through the following 4 stages:

Preparation: Learners encounter the problem and begin to survey all relevant information and materials. They process stimuli and begin to make connections.
Incubation: Learners get frustrated and may even seem to observers as giving up. However, their brains carry on processing information unconsciously.
Insight: The learner finally achieves a breakthrough, otherwise called an epiphany or ‘Aha’ moment. This insight comes in a flash and is often a radical reorganization of the problem. It is a discontinuous leap in understanding rather than continuous with reasoning undertaken in the preparation phase.
Verification: The learner now formally tests the new insight and sees if it works in multiple different situations. Mathematical insights are formally proved.

The 2 nd and 3 rd stages of insight learning are well described in anecdotes of famous scientific breakthroughs. In 1861, August Kekulé was contemplating the structure of the Benzene molecule. He knew it was a chain of 6 carbon atoms attached to 6 hydrogen atoms. Still, He got stuck (incubation phase) on working out how they could fit together to remain chemically stable.

He turned away from his desk and, facing the fireplace, fell asleep. He dreamt of a snake eating its tail and then spinning around. He woke up and realized (insight phase) that these carbon-hydrogen chains can close onto themselves to form hexagonal rings. He then worked out the consequences of his new insight on Benzene rings. (Verification phase)

Suitably prepared minds can experience insights while observing ordinary day-to-day events. Many people must have seen apples fall from trees and thought nothing of it. When Newton saw an apple fall, he connected its fall to the action of the moon. If an unseen force pulls the apple from the tree top, couldn't the same force extends to the moon? This same force must be keeping the moon tethered in orbit around the earth, keeping it from whizzing off into space. Of course, this seems counterintuitive because if the moon is like the apple, should it not be crashing down to earth?

Newton's prepared mind understood the moon to be continuously falling to earth around the horizon's curve. Earth's gravitational pull on the moon balanced its horizontal velocity tangential to its orbit. If the apple were shot fast enough over the horizon from a cannon, it too, like the moon, would stay in orbit.

So, although before Newton, everyone was aware of gravity in a stimulus-response kind of way and even made practical use of it to weigh things, no one understood its universal implications.

Applying Insight Learning To The Classroom

The preparation-incubation-insight- verification cycle could be implemented by teachers in the classroom. Gestalt theory predicts that students learn best when they engage with the material; they are mentally prepared for age, and maturity, having had experiences enabling them to relate to the material and having background knowledge that allows them to contextualize the material. When first presenting content they want to teach the students, teachers must make sure students are suitably prepared to receive the material, to successfully go through the preparation stage of learning.

Teachers should present the material holistically and contextually. For example, when teaching about the human heart, they should also teach where it is in the human body and its functional importance and relationship to other organs and parts of the body. Teachers could also connect other fields, such as comparing hearts to mechanical pumps.

Once the teacher has imparted sufficient background information to students, they should set a problem for their students to solve independently or in groups. The problem should require the students to apply what they have learned in a new way and make novel connections not explicitly made by the teacher during the lesson.

However, they must already know and be familiar with all the material they need to solve the problem. Students must be allowed to fumble their way to a solution and make many mistakes , as this is vital for the incubation phase. The teacher should resist the temptation to spoon-feed them. Instead, teachers should use the Socratic method to coax the students into arriving at solutions and answers themselves.

Allowing the students to go through a sufficiently challenging incubation phase engages all their higher cognitive functions, such as logical and abstract reasoning, visualization, and imagination. It also habituates them to a bit of frustration to build the mental toughness to stay focused.

It also forces their brains to work hard in processing combining information to sufficiently own the insights they achieve, making it more likely that they will retain the knowledge they gained and be able to apply it across different contexts.

Once students have written down their insights and solutions, the teacher should guide them through the verification phase. The teacher and students need to check and test the validity of the answers. Solutions should be checked for errors and inconsistencies and checked against the norms and standards of the field.

However, one should remember that mass education is aimed at students of average capacity and that not all students are always equally capable of learning through insight. Also, students need to be prepared to gain the ability and potential to have fruitful insights.

Learning purely from stimulus-response conditioning is insufficient for progress and major breakthroughs to be made in the sciences. For breakthroughs to be made, humans need to be increasingly capable of higher and higher levels of abstract thinking.

However, we are not all equally capable of having epiphanies on the cutting edge of scientific research. Most education aims to elevate average reasoning, knowledge, and skill acquisition. For insight, learning must build on rather than replace behaviorist teaching practices.

The Psychology of Long Distance Relationships
Beck’s Depression Inventory (BDI Test)
Operant Conditioning (Examples + Research)
Variable Interval Reinforcement Schedule (Examples)
Concrete Operational Stage (3rd Cognitive Development)

Reference this article:

About The Author

Operant Conditioning

Classical Conditioning

Observational Learning

Latent Learning

Experiential Learning

The Little Albert Study

Bobo Doll Experiment

Spacing Effect

Von Restorff Effect

PracticalPie.com is a participant in the Amazon Associates Program. As an Amazon Associate we earn from qualifying purchases.

Youtube Facebook Instagram X/Twitter

Psychology Resources

Developmental

Personality

Relationships

Psychologists

Serial Killers

Psychology Tests

Personality Quiz

Memory Test

Depression test

Type A/B Personality Test

Insight Learning Theory: Definition, Stages, and Examples

Categories Learning

Insight learning theory is all about those “lightbulb moments” we experience when we suddenly understand something. Instead of slowly figuring things out through trial and error, insight theory says we can suddenly see the solution to a problem in our minds.

This theory is super important because it helps us understand how our brains work when we learn and solve problems. It can help teachers find better ways to teach and improve our problem-solving skills and creativity. It’s not just useful in school—insight theory also greatly impacts science, technology, and business.

The four stages of insight learning theory

Table of Contents

What Is Insight Learning?

Insight learning is like having a lightbulb moment in your brain. It’s when you suddenly understand something without needing to go through a step-by-step process. Instead of slowly figuring things out by trial and error, insight learning happens in a flash. One moment, you’re stuck, and the next, you have the solution.

This type of learning is all about those “aha” experiences that feel like magic. The key principles of insight learning involve recognizing patterns, making connections, and restructuring our thoughts. It’s as if our brains suddenly rearrange the pieces of a puzzle, revealing the big picture. So, next time you have a brilliant idea pop into your head out of nowhere, you might just be experiencing insight learning in action!

Three Components of Insight Learning Theory

Insight learning, a concept rooted in psychology, comprises three distinct properties that characterize its unique nature:

1. Sudden Realization

Unlike gradual problem-solving methods, insight learning involves sudden and profound understanding. Individuals may be stuck on a problem for a while, but then, seemingly out of nowhere, the solution becomes clear. This sudden “aha” moment marks the culmination of mental processes that have been working behind the scenes to reorganize information and generate a new perspective .

2. Restructuring of Problem-Solving Strategies

Insight learning often involves a restructuring of mental representations or problem-solving strategies . Instead of simply trying different approaches until stumbling upon the correct one, individuals experience a shift in how they perceive and approach the problem. This restructuring allows for a more efficient and direct path to the solution once insight occurs.

3. Aha Moments

A hallmark of insight learning is the experience of “aha” moments. These moments are characterized by a sudden sense of clarity and understanding, often accompanied by a feeling of satisfaction or excitement. It’s as if a mental lightbulb turns on, illuminating the solution to a previously perplexing problem.

These moments of insight can be deeply rewarding and serve as powerful motivators for further learning and problem-solving endeavors.

Four Stages of Insight Learning Theory

Insight learning unfolds in a series of distinct stages, each contributing to the journey from problem recognition to the sudden realization of a solution. These stages are as follows:

1. Problem Recognition

The first stage of insight learning involves recognizing and defining the problem at hand. This may entail identifying obstacles, discrepancies, or gaps in understanding that need to be addressed. Problem recognition sets the stage for the subsequent stages of insight learning by framing the problem and guiding the individual’s cognitive processes toward finding a solution.

2. Incubation

After recognizing the problem, individuals often enter a period of incubation where the mind continues to work on the problem unconsciously. During this stage, the brain engages in background processing, making connections, and reorganizing information without the individual’s conscious awareness.

While it may seem like a period of inactivity on the surface, incubation is a crucial phase where ideas gestate, and creative solutions take shape beneath the surface of conscious thought.

3. Illumination

The illumination stage marks the sudden emergence of insight or understanding. It is characterized by a moment of clarity and realization, where the solution to the problem becomes apparent in a flash of insight.

This “aha” moment often feels spontaneous and surprising, as if the solution has been waiting just below the surface of conscious awareness to be revealed. Illumination is the culmination of the cognitive processes initiated during problem recognition and incubation, resulting in a breakthrough in understanding.

4. Verification

Following the illumination stage, individuals verify the validity and feasibility of their insights by testing the proposed solution. This may involve applying the solution in practice, checking it against existing knowledge or expertise, or seeking feedback from others.

Verification serves to confirm the efficacy of the newfound understanding and ensure its practical applicability in solving the problem at hand. It also provides an opportunity to refine and iterate on the solution based on real-world feedback and experience.

Famous Examples of Insight Learning

Examples of insight learning can be observed in various contexts, ranging from everyday problem-solving to scientific discoveries and creative breakthroughs. Some well-known examples of how insight learning theory works include the following:

Archimedes’ Principle

According to legend, the ancient Greek mathematician Archimedes experienced a moment of insight while taking a bath. He noticed that the water level rose as he immersed his body, leading him to realize that the volume of water displaced was equal to the volume of the submerged object. This insight led to the formulation of Archimedes’ principle, a fundamental concept in fluid mechanics.

Köhler’s Chimpanzee Experiments

In Wolfgang Köhler’s experiments with chimpanzees on Tenerife in the 1920s, the primates demonstrated insight learning in solving novel problems. One famous example involved a chimpanzee named Sultan, who used sticks to reach bananas placed outside his cage. After unsuccessful attempts at using a single stick, Sultan suddenly combined two sticks to create a longer tool, demonstrating insight into the problem and the ability to use tools creatively.

Eureka Moments in Science

Many scientific discoveries are the result of insight learning. For instance, the famed naturalist Charles Darwin had many eureka moments where he gained sudden insights that led to the formation of his influential theories.

Everyday Examples of Insight Learning Theory

You can probably think of some good examples of the role that insight learning theory plays in your everyday life. A few common real-life examples include:

Finding a lost item : You might spend a lot of time searching for a lost item, like your keys or phone, but suddenly remember exactly where you left them when you’re doing something completely unrelated. This sudden recollection is an example of insight learning.
Untangling knots : When trying to untangle a particularly tricky knot, you might struggle with it for a while without making progress. Then, suddenly, you realize a new approach or see a pattern that helps you quickly unravel the knot.
Cooking improvisation : If you’re cooking and run out of a particular ingredient, you might suddenly come up with a creative substitution or alteration to the recipe that works surprisingly well. This moment of improvisation demonstrates insight learning in action.
Solving riddles or brain teasers : You might initially be stumped when trying to solve a riddle or a brain teaser. However, after some time pondering the problem, you suddenly grasp the solution in a moment of insight.
Learning a new skill : Learning to ride a bike or play a musical instrument often involves moments of insight. You might struggle with a certain technique or concept but then suddenly “get it” and experience a significant improvement in your performance.
Navigating a maze : While navigating through a maze, you might encounter dead ends and wrong turns. However, after some exploration, you suddenly realize the correct path to take and reach the exit efficiently.
Remembering information : When studying for a test, you might find yourself unable to recall a particular piece of information. Then, when you least expect it, the answer suddenly comes to you in a moment of insight.

These everyday examples illustrate how insight learning is a common and natural part of problem-solving and learning in our daily lives.

Exploring the Uses of Insight Learning

Insight learning isn’t an interesting explanation for how we suddenly come up with a solution to a problem—it also has many practical applications. Here are just a few ways that people can use insight learning in real life:

Problem-Solving

Insight learning helps us solve all sorts of problems, from finding lost items to untangling knots. When we’re stuck, our brains might suddenly come up with a genius idea or a new approach that saves the day. It’s like having a mental superhero swoop in to rescue us when we least expect it!

Ever had a brilliant idea pop into your head out of nowhere? That’s insight learning at work! Whether you’re writing a story, composing music, or designing something new, insight can spark creativity and help you come up with fresh, innovative ideas.

Learning New Skills

Learning isn’t always about memorizing facts or following step-by-step instructions. Sometimes, it’s about having those “aha” moments that make everything click into place. Insight learning can help us grasp tricky concepts, master difficult skills, and become better learners overall.

Insight learning isn’t just for individuals—it’s also crucial for innovation and progress in society. Scientists, inventors, and entrepreneurs rely on insight to make groundbreaking discoveries and develop new technologies that improve our lives. Who knows? The next big invention could start with someone having a brilliant idea in the shower!

Overcoming Challenges

Life is full of challenges, but insight learning can help us tackle them with confidence. Whether it’s navigating a maze, solving a puzzle, or facing a tough decision, insight can provide the clarity and creativity we need to overcome obstacles and achieve our goals.

The next time you’re feeling stuck or uninspired, remember: the solution might be just one “aha” moment away!

Alternatives to Insight Learning Theory

While insight learning theory emphasizes sudden understanding and restructuring of problem-solving strategies, several alternative theories offer different perspectives on how learning and problem-solving occur. Here are some of the key alternative theories:

Behaviorism

Behaviorism is a theory that focuses on observable, overt behaviors and the external factors that influence them. According to behaviorists like B.F. Skinner, learning is a result of conditioning, where behaviors are reinforced or punished based on their consequences.

In contrast to insight learning theory, behaviorism suggests that learning occurs gradually through repeated associations between stimuli and responses rather than sudden insights or realizations.

Cognitive Learning Theory

Cognitive learning theory, influenced by psychologists such as Jean Piaget and Lev Vygotsky , emphasizes the role of mental processes in learning. This theory suggests that individuals actively construct knowledge and understanding through processes like perception, memory, and problem-solving.

Cognitive learning theory acknowledges the importance of insight and problem-solving strategies but places greater emphasis on cognitive structures and processes underlying learning.

Gestalt Psychology

Gestalt psychology, which influenced insight learning theory, proposes that learning and problem-solving involve the organization of perceptions into meaningful wholes or “gestalts.”

Gestalt psychologists like Max Wertheimer emphasized the role of insight and restructuring in problem-solving, but their theories also consider other factors, such as perceptual organization, pattern recognition, and the influence of context.

Information Processing Theory

Information processing theory views the mind as a computer-like system that processes information through various stages, including input, processing, storage, and output. This theory emphasizes the role of attention, memory, and problem-solving strategies in learning and problem-solving.

While insight learning theory focuses on sudden insights and restructuring, information processing theory considers how individuals encode, manipulate, and retrieve information to solve problems.

What Is Insight? Definition, Psychology, And Practical Examples

The Merriam-Webster dictionary defines insight as “the act or result of apprehending the inner nature of things or of seeing intuitively.” Psychology sees insight not as a means of acquiring insightful knowledge but rather as the act of becoming aware of insightful solutions. It can be helpful to understand both definitions of insight to know how to use it to improve your mental health.

What’s the difference between insight and knowledge?

Some subjects may be taught directly, while you can learn others from observation and repetition. You might notice that some knowledge seems to appear out of thin air. Before understanding insight psychology, taking a detour to understanding knowledge can be beneficial.

What is knowledge?

Knowledge is an awareness or familiarity with objects, events, ideas, or actions learned from experience, being taught, or instinct from birth. Articulation of this concept can be found in the movie Memento (2000) when the main character, who experiences short-term memory loss, explains that, despite not being able to remember what he had completed a few moments ago, he could understand inherent knowledge. For example, he knew the sound of knocking on wood and the feeling of lifting a glass of water. He says this type of knowledge is different because it is a form of memory.

How does insight relate to knowledge?

Wolfgang Kohler and his work with the Gestalt theory led him to some interesting findings in the early 1900s. He experimented with his chimp Sultan. In his experiment, Sultan was hungry. A banana was held out of reach. The only tools Sultan could use to reach the banana were two bamboo sticks of differing lengths, neither long enough to reach the banana.

Eventually, Sultan figured out that he could fit them together by playing with the sticks to form one long rod that would reach the banana. Unlike trial and error, Sultan used reason for this solution. He had given up actively trying different ways to get the banana when he discovered the sticks could be combined. The answer came to him in what is commonly referred to as an “Aha!” moment.

The key to this insight psychology is idleness or a reduced ability to see the finish line. Like Sultan, the subject or client may give up on finding a solution. As desperation approaches, they may use creativity and insight by combining their current knowledge of events with new knowledge.

How psychologists interpret insight

Among psychologists, there are varying interpretations of how knowledge and reasoning combine to present the consciousness with a viable solution to a given task. Below are a few theories.

The nothing special view

In the “nothing special” theory, insight occurs as a natural process of the brain continually taking in information and working to make the best use of it. A solution may arrive when presented with a task or issue due to how a person processes information. In this theory, no special or esoteric significance is given to intuition.

The neo-Gestaltist view

As with Kohler and Sultan, the Gestaltist view states that insight solution problem solving is not simple. Instead, they believe it has a special quality, placing it cognitively higher than routine problem-solving.

The three process view

The three-process view posits that there are three individual types of insight, including the following:

Selective-Encoding Insight: Distinguishing relevant from irrelevant information
Selective-Comparison Insight: Renewed perception of the relationship between old information and new information
Selective-Combination Insight: Using encoded information and applying it in a novel way.

The four stages of behavioral processes

Insight is marked by four stages of behavioral processes, including impasse, fixation, incubation, and the eureka moment.

Impasse: An impasse occurs when one gives up or reaches an area they struggle to solve.
Fixation: Fixation may be a particular solution attempted that is ineffective but attempted more than once, often with an obsessive focus.
Incubation: Incubation is a gap in solution attempts allowing the mind to clear itself of irrelevant information pertaining to the solution.
Eureka: Eureka involves the appearance of a solution in the individual’s mind that suddenly becomes clear.

What does psychological research say about insight?

Insight may affect how you live your life, tackle obstacles, and practice mental health and well-being. Below are a few studies on insight.

Graham Wallas and the nine dot puzzle

When dealing with abstract concepts, reframing them into concrete examples may be helpful. For example, Graham Wallas used the nine-dot puzzle in 1926 to show how individuals can arrive at solutions by insight. The goal was to connect all nine dots with a pencil without lifting the pencil off the paper and using the fewest possible lines. At first glance, it may seem impossible to complete the task due to a narrow perception.

Because the dots appear to be in a rectangle shape, your brain may assume the solution must be derived by drawing a rectangle. Once the insight that the rectangle does not exist or limit the puzzle, the solution to “go outside the lines” may be more prominent. You may be able to solve the puzzle using triangles or a zig-zag pattern.

Responses to the nine dot puzzle and banana problem

When you apply the insight psychology definition to mental health, it is not a banana or a puzzle on a piece of paper but rather an insight into the psyche. Many symptoms of mental health conditions are challenging to treat because of a lack of insight.

Not being aware that a symptom is a symptom of a mental health condition can be detrimental to finding the correct treatment. For example, those who experience substance use disorders may struggle to see that their substance use is a problem, rationalizing it by saying they can stop when they want to. Believing they do not have a problem can be a lack of insight. In these cases, having a guiding voice like a therapist can be beneficial.

If you are struggling with substance use, contact the SAMHSA National Helpline at (800) 662-4357 to receive support and resources. Support is available 24/7.

What are a few examples of insight?

Anyone can use insight, which doesn’t necessarily relate to psychology or treating mental illness. Problem-solving comes in all different shapes and sizes. In relationships, conflict can be an area where individuals use insight. Whether in familial or romantic relationships, you may find yourself at an impasse stage, feeling you’ve exhausted all options. Conflict at an impasse can be stressful for all parties and make the relationship seem hopeless. Below are a couple of examples that showcase insight.

Relationship example

If two spouses experience a pattern of constantly arguing, with communication breaking down, there can be a tendency to want to give up on the marriage. Taking time to step back from the situation, let emotions settle, and allow reason to prevail can provide insight. Introspection psychology , an act of examining or observing thoughts, emotions, and perceptions, allows individuals to gain insight. Knowledge of oneself and time to breathe can offer a different perspective for the “Aha!” moment to occur. A relationship is often complex and unique. Applying these concepts when appropriate may help you avoid conflict and stress.

Therapy example

Insight can also be helpful in a therapeutic session. For example, clients with social anxiety can shift their paradigm from fear of social situations to learning to manage their symptoms from within. Someone who pushes people away but craves intimacy can benefit from the insight that their actions may stem from a fear of abandonment. Many people may experience “Aha!” moments of eureka in therapy.

What to expect from insight psychology therapy

While it can be empowering to become aware of the above processes and apply them in your personal life, it can be overwhelming to wade through the ideas in your mind alone. In therapy, you can discuss these concerns with your therapist while maintaining an open, trusting relationship. If this is not your experience or you find in-person therapy inaccessible due to finances, location, or accessibility, you might try online therapy through a platform like BetterHelp.

Some methods of therapy have been aligned to elicit insight. For example, researchers have developed metacognitive insight and reflection therapy (MERIT) to help individuals recover from psychosis. Following a three-month trial, a 2020 study found significantly improved metacognition and other benefits from administering MERIT.

These insights were particularly pronounced among those who did not understand or believe that they had a problem, a common effect of psychosis. MERIT is increasingly available to psychologists around the US, as well as those who practice online. A recent survey revealed that nearly a third of respondents would not seek in-person counseling but would do so if online therapy were available. Online therapy continues to gain popularity, with four out of ten Americans using it since 2021.

Therapy is insight: Learn more about insight and insight therapy

Please find us inside these links:

If you need a crisis hotline or want more insight into therapy, please see below:

RAINN (Rape, Abuse, and Incest National Network) - 1-800-656-4673
The National Suicide Prevention Lifeline - 1-800-273-8255
National Domestic Violence Hotline - 1-800-799-7233
NAMI Helpline (National Alliance on Mental Illness) - 1-800-950-6264

For more insight on mental health, please see:

SAMHSA (Substance Abuse and Mental Health Services Administration) SAMHSA Facebook , SAMHSA Twitter , SAMHSA LinkedIn
Mental Health America, MHA Twitter , MHA Facebook , MHA Instagram , MHA Pinterest , MHA LinkedIn
WebMD, WebMD Facebook , WebMD Twitter , WebMD Pinterest , WebMD LinkedIn
NIMH (National Institute of Mental Health), NIMH Facebook , NIMH Twitter, NIMH YouTube , NIMH LinkedIn
APA (American Psychiatric Association), APA Twitter , APA Facebook , APA LinkedIn , APA Instagram

Get help and insight now:

Emergency: 911
National Domestic Violence Hotline: 1- 800-799-7233
National Suicide Prevention Lifeline: 1-800-273-TALK (8255)
National Hopeline Network: 1-800-SUICIDE (784-2433)
Crisis Text Line: Text “DESERVE” TO 741-741
Lifeline Crisis Chat (Online live messaging): https://suicidepreventionlifeline.org/chat/
What Is Theory Of Mind? Psychology And Knowledge Of Self And Others Medically reviewed by April Justice , LICSW
Memory Consolidation: Definition And Examples In Psychology Medically reviewed by Laura Angers Maddox , NCC, LPC
Psychologists
Relationships and Relations

HYPOTHESIS AND THEORY article

Intuition and insight: two processes that build on each other or fundamentally differ.

$\r\nThea Zander*$

1 Department of Psychology, University of Basel, Basel, Switzerland
2 Parmenides Foundation, Munich, Germany
3 Department Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
4 Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany

Intuition and insight are intriguing phenomena of non-analytical mental functioning: whereas intuition denotes ideas that have been reached by sensing the solution without any explicit representation of it, insight has been understood as the sudden and unexpected apprehension of the solution by recombining the single elements of a problem. By face validity, the two processes appear similar; according to a lay perspective, it is assumed that intuition precedes insight. Yet, predominant scientific conceptualizations of intuition and insight consider the two processes to differ with regard to their (dis-)continuous unfolding. That is, intuition has been understood as an experience-based and gradual process, whereas insight is regarded as a genuinely discontinuous phenomenon. Unfortunately, both processes have been investigated differently and without much reference to each other. In this contribution, we therefore set out to fill this lacuna by examining the conceptualizations of the assumed underlying cognitive processes of both phenomena, and by also referring to the research traditions and paradigms of the respective field. Based on early work put forward by Bowers et al. (1990 , 1995 ), we referred to semantic coherence tasks consisting of convergent word triads (i.e., the solution has the same meaning to all three clue words) and/or divergent word triads (i.e., the solution means something different with respect to each clue word) as an excellent kind of paradigm that may be used in the future to disentangle intuition and insight experimentally. By scrutinizing the underlying mechanisms of intuition and insight, with this theoretical contribution, we hope to launch lacking but needed experimental studies and to initiate scientific cooperation between the research fields of intuition and insight that are currently still separated from each other.

Introduction

There are situations, in which decision makers arrive at an idea or a decision not by analytically inferring the solution but by either sensing the correct solution without being able to give reasons for it, or by realizing the solution all of a sudden without being able to report on the solution process. Roughly, the former phenomenon has been called intuition, the latter insight. Both have fascinated the public as well as the scientific audience.

Here are two historical cases that illustrate the two phenomena ( Gladwell, 2005 ; Mclean, as cited in Klein and Jarosz, 2011 ): The first is known as the Getty kouros and happened to the J. Paul Getty Museum in Los Angeles at the end of the 20th century. The museum was offered to add an over-life-sized statue in form of a kouros – allegedly from Ancient Greece, and thus several millions worth – to its art collection. Before the contract could be concluded, several experts set out to assure the authenticity of the statue and its origin thereby using a substantial number of high-tech methods for their analyses. After a year of thorough inspection, the experts reached the conclusion that the statue was authentic. At the same time, the former curator of the Metropolitan Museum of Art in New York, by chance, cast a glance at the artwork and spontaneously raised doubts regarding its authenticity. Thereupon, other men of renown who were asked for their spontaneous assessment of the kouros, also reported that they felt that something was wrong with it – without being able to tell the reason for this impression (cf. Gladwell, 2005 ). Interestingly, up to now, it could not be entirely cleared whether the statue stems from Ancient Greece or whether it is a modern forgery. Yet, the curator – instantaneously “feeling” that something was wrong and acting upon this impression although not being able to name a specific reason – is a paramount example of what it means to have an intuition being strong enough to act accordingly.

For an example of a sudden insight into the solution of a complex problem, consider Wagner Dodge, a smokejumper who survived the Mann Gulch Fire in August 1949 (Mclean, as cited in Klein and Jarosz, 2011 ). On a very hot day, a fire broke out in Mann Gulch, a canyon near Helena in Montana. Sixteen smokejumpers were flown close to the fire in order to extinguish it. After they had parachuted out of the aircraft, they realized that the fire was much worse than expected: They faced an uncontrollable blaze. The biggest problem was that they were in the danger of being entrapped by the fire. They could not escape and thus their lives were immediately threatened. For a moment they were desperately helpless and bustled around without a plan. They faced an impasse : well-known routines would not bring them forward and they might be caught in a mental set , that is, the tendency to try to solve a problem based on previous successful solution attempts to similar kinds of problems that are inefficient or cannot be transferred to the problem at hand (see Luchins and Luchins, 1959 , as well as Öllinger et al., 2008 ). After a while, all at once, Wagner Dodge had the sudden idea to ignite an “escape fire” ahead of the group (i.e., he had a sudden aha-experience ). Although he had never heard of such a possibility, he abruptly realized that when he could quickly stub an area of vegetation, the blaze would have no basis to continue when arriving at the cinder. He put his idea into action, ignited an additional fire and stepped into the middle of the newly burnt area. This way, he could save his life; the other smokejumpers who did not trust him lost their lives in the fire. Today, escape fires belong to the standard practice of fire services in the wild (Mclean, as cited in Klein and Jarosz, 2011 ).

Based on these examples, both phenomena – intuition and insight – may be conceived of as non-analytical thought processes that result in certain behavior that is not based on an exclusively deliberate and stepwise search for a solution. Non-analytical thought means a thought process in which no deliberate deduction takes place: individuals are not engaged in the consecutive testing of the obvious and/or typical routes to solution that define deliberate analysis. Instead, intuitions are characterized by the decision maker feeling out the solution without an available, tangible explanation for it; insights are characterized by the fact that the solution suddenly and unexpectedly pops into the mind of the decision maker or problem solver being instantaneously self-evident. Despite these apparent similarities of the two phenomena, intuition and insight have been conceptualized rather differently in the scientific literature up to now with regard to the underlying cognitive mechanisms as well as to the experimental designs routinely being used to gain empirical evidence. The aim of our contribution is therefore to scrutinize the similarities and differences of the cognitive mechanisms underlying intuition and insight by drawing on and extending early ideas by Bowers et al. (1990 , 1995 ). The gripping question is whether intuition and insight are two qualitatively distinct phenomena, appearing similar only by face validity, or whether they are indeed similar/related and may only unfold on different levels of processing. To address this question, we draw on the latest contributions in the field and include recent research findings that have not been available in Bowers et al. (1990 , 1995 ) time.

First, we will give an overview of predominant definitions of intuition and insight from a cognitive-psychological perspective. Second, we will elaborate on the underlying cognitive processes of both phenomena, thereby aiming to pin down similarities and differences. Both, similarities and differences will be addressed against the background of the research history of intuition and insight as well as in light of predominant, experimental paradigms that have been used to investigate the two phenomena. The paper ends by outlining open questions and highlighting future directions in scientific research that may progress our understanding of the underlying cognitive processes of intuition and insight (as well as on their relatedness).

Defining Intuition and Insight

Theoretical characterization of intuition.

Although most people “intuitively” know what an intuition is, the scientific community is split over its definition as well as its conceptualization. Despite disagreement about any definition, common ground is that intuition is an experienced-based process resulting in a spontaneous tendency toward a hunch or a hypothesis ( Bowers et al., 1990 ; Volz and Zander, 2014 ). Taking all major definitions into consideration, it is possible to distil certain characteristics that prominent definitions of intuition have in common ( Glöckner and Witteman, 2010 ; Volz and Zander, 2014 ).

Firstly, there is the aspect of non-conscious processing , which means that intuition occurs with very little awareness about the underlying cognitive processes so that people are mostly not able to report on these. Yet, intuitive processes can partly or completely be made conscious at some point in the entire judgmental process (e.g., Gigerenzer, 2008 ). In this regard, intuitive processing is not directly conscious or non-conscious, but can be viewed as reflecting cognitive processing on the fringe of human consciousness ( Mangan, 1993 , 2001 , 2015 ; Norman, 2002 , 2016 ; Price, 2002 ; Norman et al., 2006 , 2010 ). Secondly, there is the aspect of automaticity or uncontrollability . Intuitive processing appears in the form of spontaneous and instantaneous ideas or hunches that cannot be intentionally controlled in the way that they cannot be neither intentionally evoked nor ignored (e.g., Topolinski and Strack, 2008 ). The unintentional nature of intuition implies that intuition comes along without attentional effort and thus intuitive processing has been described as fast and effortless (e.g., Hogarth, 2001 ). Thirdly, there is the aspect of experientiality . Intuitive processing is based on tacit knowledge that has been acquired without attention during a person’s life and is thus fueled by it (e.g., Bowers et al., 1990 ). In combination these aspects result in the subjective experience of “knowing without knowing why” as Claxton (1998 , p. 217) put it. Lastly, there is the aspect of the initiation of action . The non-conscious, experience-based, and unintentional process finally results in a strong tendency toward a hunch, which serves as a go-signal that is strong enough to initiate action. As a result, people act in accordance with their intuitive impression or feeling (e.g., Gigerenzer, 2008 ). For a more detailed overview of the different aspects, consult Glöckner and Witteman (2010) or Volz and Zander (2014) .

In line with these aspects, Gigerenzer (2008) has focused, inter alia, on the experiential basis of intuition and states that intuition may hardly be possible without pre-existing knowledge and experiences. To revert to the example of the Getty kouros, the interplay of the given (visible) information was dissonant for someone who had seen lots of antique statues before; a beginner to the field may have arrived at a completely different judgment. By intuitively apprehending the situation, the curator relied on specific long-term-memory content that had been primarily acquired by studying, analyzing, and reflecting about a great number of statues resulting in associative and unattended learning. Volz and Zander (2014) refer to this kind of memory content as tacitly (in)formed cue-criterion relationships . On this view, different environmental cues can have different predictive power with respect to the criterion at hand; the situational validity of the cues will moderate whether the cue is used outright. In the above example, the curator judged the grade of authenticity of the kouros (criterion) from the subjective impression that the statue’s outer appearance had on him (cue). By doing this, the curator could not only rely on the given information (i.e., the visible kouros), but had to non-consciously activate further relevant knowledge from memory, that is to activate associatively learned cue-criterion relationships. Thus, the mental representation constructed during intuitive processing goes beyond the existing, perceivable information. Consequently, the curator’s feeling of unease when having a look at the statue resulted from an incomplete cue-criterion relationship that was taken as diagnostic for the assessment of the statue’s authenticity.

In addition to the aspect of experientiality and the unconscious read-out of implicitly learned cue-criterion relationships, Gigerenzer (2008) describes intuition as felt knowledge that aids decision making not only in cases, in which the decision maker already has a huge amount of prior experiences with a particular situation, but also when time and cognitive capacity is limited. According to the author, shadowy situations – either caused by a blurry sensory input that is only hardly detectable, or by the temporary non-availability of necessary information about the individual decisional components, which does not allow for foreseeing all consequences of a decision – foster intuitive processing. Intuition then manifests itself in the use of certain heuristics that may form highly successful, cognitive shortcuts ( Gigerenzer, 2008 ; Gigerenzer and Gaissmaier, 2011 ).

Insight and Aha-Experience

In contrast to the above elaborations on intuition, the term insight has been used to refer to the sudden and unexpected understanding of a previously incomprehensible problem or concept. In this sense, Jung-Beeman et al. (2004 , p. 506) explicate the nature of insight as “the recognition of new connections across existing knowledge.” Sometimes the solution to a difficult problem may suddenly pop out in the mind and the decision maker or problem solver may immediately recognize the complex nexuses, as formerly illustrated in the episode of the smokejumper Wagner Dodge. Problems seem to be processed and solved by re-grouping or re-combining (i.e., re-structuring) existing information in a new way so that self-imposed constraints can elegantly be relaxed ( Duncker, 1935 ; Wertheimer, 1959 ; Ohlsson, 1992 ). Wagner Dodge had prior knowledge: For instance, he knew how fires most commonly can be extinguished and that fires need vegetation or some other foundation to burn on. Furthermore, he knew about terrestrial conditions, and most important, he knew that smoke and fire could kill him. The solution to the problem occurred when he non-consciously combined all pieces of knowledge with each other in a new way so as to circumvent the fire death.

Such insightful solutions are associated with a privileged storage in long-term memory. Likewise as single trial learning. Recent studies observed a memory advantage for items that were solved by insight compared with non-insight solutions ( Danek et al., 2013 ) as well as compared with items that were not self-generated ( Kizilirmak et al., 2015 ). So, it is very likely, that Wagner Dodge never forgot how to ignite escape fires in the wild.

Yet, it has to be emphasized that an exact definition of the term insight has proven to be difficult, not least because the term insight has been used in many different ways in problem-solving research. Another hindrance is that it is very difficult to empirically operationalize the psychological construct of insight ( Knoblich and Öllinger, 2006 ), which is a similar problem as in research on intuition. Hitherto, researchers disagree whether there are certain necessary and/or sufficient conditions to determine whether an insight has occurred. For example, due to the absence of objective physiological markers indicating the occurrence of an insight, mainly reports in form of the subjective aha-experience have been used ex post to determine whether an insight has occurred during the solution process of a certain problem (e.g., Gick and Lockhardt, 1995 ; Bowden et al., 2005 ; Danek et al., 2013 ). Danek et al. (2013 , p. 2) state that the aha-experience is “the clearest defining characteristic of insight problem solving.” Topolinski and Reber (2010) define the aha-experience as the sudden and unexpected understanding of the solution, which comes with ease and is accompanied by positive affect as well as confidence in the truth of the solution. Given scientific endeavors to (objectively) pin down whether an insight had occurred, it can be summarized that insight and aha-experience have been equated. However, to date, there is disagreement whether (a) every insight is accompanied by an aha-experience, and (b) aha-experiences can only accompany insights and do never occur for presented solutions (i.e., solutions that are not generated by the individual herself; cf. Klein and Jarosz, 2011 ; Kizilirmak et al., 2015 ).

In order to help clarifying the conceptual muddle on insight, Knoblich and Öllinger (2006) proposed a classification of insight on three dimensions: first, on a phenomenological dimension, insight is opposed to a systematic and stepwise solution approach. Instead, it can be described as the sudden, unintended, and unexpected appearance of a solution idea, which is accompanied by a strong emotional component – the subjective and involuntary aha-experience. Second, on a task dimension, the literature on insight distinguishes between predefined insight problems and non-insight problems, with insight problems requiring sudden solution ideas and non-insight problems requiring a rather incremental solution approach. In case such an insight problem is solved, it is inferred that it is very likely that an insight has taken place. For example, the nine-dot problem ( Maier, 1930 ), the eight-coin problem ( Ormerod et al., 2002 ), and the candle problem ( Duncker, 1935 ) belong to such classical insight problems. However, a disadvantage of this distinction is that there are no unique criteria for an insight problem, and most of these problem could be solved with or without having an insight ( Öllinger et al., 2014 ); the most proposed criteria refer back to the subjective experience of aha, which has led to a circular definition of insight and insight problems. To circumvent this disadvantage, Bowden et al. (2005) have suggested using a class of problems that can be solved either with insight or without insight. Last, on a process dimension, recent research is concerned with the underlying cognitive mechanisms of insight and how these are different from non-insight problem solving. The predominant assumption here is that the non-conscious cognitive process of a mental set shift enables a changed representation of the problem’s elements ( Ohlsson, 1992 , 2011 ), which in turn leads to a sudden insight into the solution. For instance, in the nine-dot problem, the sudden realization that moves beyond the virtual nine-dot square are possible may lead to the relaxation of the perceptually driven boundary constraints and thus to a representational change of the problem space, which in the following enable insightful solutions (for a detailed explanation of the three dimensions consult Knoblich and Öllinger, 2006 ) 1 .

Different Research Traditions of Intuition and Insight

After having defined both cognitive phenomena, intuition and insight, it becomes obvious that both share a similarity in terms of persisting conceptual difficulties. Moreover, with regard to the subjective phenomenology they reveal a distinct picture: While intuition means to non-consciously understand environmental patterns and to act according with this first impression without being able to justify it ( Bowers et al., 1990 ), insight problem solving deals with situations in which a solution pops into a person’s mind out of the blue ( Durso et al., 1994 ). Yet, both processes can be viewed as non-analytical solution or thought processes, where no incremental search takes place. In the following, we will critically elaborate on the cognitive processes assumed to underlie intuition and insight. Starting point will be a few words on the research history of both, which allow to understand why both fields of research have developed independently over time.

The Single- vs. Dual-System View on Intuition

Intuition research has been deeply integrated in research on judgment and decision making that investigates how humans decide between alternatives and judge situations ( Plessner et al., 2008 ). Yet this took some time, in which intuition had been neglected due to its elusiveness ( Betsch, 2008 ). Now researchers agree that “intuition need not to be “magical” – it can be defined and explained scientifically” ( Sadler-Smith, 2008 , p. 1). It has to be emphasized, though, that, historically, the concept of intuition has fallen between (at least) two stools: The fast-and-frugal-heuristic approach – which sees the concept in a positive light as it serves as the basis for heuristics and thus is a valid strategy successfully be used when time and cognitive capacity is limited in a fuzzy real world ( Gigerenzer et al., 1999 ) –, and the heuristics-and-biases approach – which conceives of heuristics based on intuition as a source of erroneous and biased thinking that demonstrates human cognitive fallibility ( Kahneman and Tversky, 1974 ). Both approaches have localized the concept of intuition completely differently within human thought processes and assign qualitatively different functions to it. Today, due to their continuing, fundamentally contradictory assumptions concerning human cognition, the fast-and-frugal-heuristic approach and the heuristics-and-biases approach pit themselves against each other. Conceptually, the key difference may be that Kahneman and Tversky (1974) and Kahneman (2011) advocate a dual-system view on human thinking (intuition vs. deliberation), whereas Kruglanski and Gigerenzer (2011) and Mega et al. (2015) favor a single system view of unified processes in thinking and reasoning. Additionally, it has to be emphasized that, since interest in intuition has mainly originated from the area of judgment and decision making, implications for intuition with respect to problem solving processes (and insight) are rather hard to derive from this kind of research. This may have complicated experimentally clarifying the relationship between intuition and insight.

Intuition As Experienced-Based Perception of Coherence and As an Antecedent of Insight

To anticipate elaboration taking place later in this contribution, we mention a third approach in intuition research, which has developed independently from any dual- or single perspective and has its roots in the creativity and problem-solving literature ( Mednick, 1962 ; Bowers et al., 1995 ; Dorfman et al., 1996 ). Intuition is here conceived as the experience-based perception or recognition of environmental meaning/coherence in terms of a sensitization toward the detection of hidden patterns whose structure cannot be immediately verbalized. For example, in the different versions of the semantic coherence task originally developed by Bowers et al. (1990) , participants are asked to judge the semantic coherence of word triads and to name a forth word that may be the semantic link between the words, if it exists. Research found out that in these tasks participants are able to correctly categorize word triads as semantic coherent or incoherent – intriguingly even when they are not able to name the forth word, which is a paramount example of intuitive processing (e.g., Bowers et al., 1990 ; Bolte and Goschke, 2005 ). They rather feel the semantic link between the three words, but are not (yet) able to report on the reasons in terms of a solution concept that describes the semantic associations between the triad’s constituents. The concept of fringe consciousness ( Mangan, 1993 , 2001 , 2015 ) may be helpful to further understand intuition as the preliminary perception of environmental coherence. Price and Norman (2008) , referring to the concept of fringe consciousness, have explained that the stream of consciousness does not only include a nucleus of consciously available information , but also a non-conscious fringe that contains cognitive signals of temporarily unavailable, non-conscious information processing that is constantly going on in the background (as it accompanies cognition). These signals are continuously going on as cognitive byproducts of cognitive processes . Yet, they are only consciously experienced when attention is drawn to them ( Reber et al., 2004 ). Regarding the semantic coherence task, the product of this non-conscious processing on the fringe (i.e., the subjectively experienced intuition) is consciously perceivable, but its antecedents, direct content, and underlying processing mechanisms are outside of awareness (see also Topolinski and Strack, 2009a ).

On this view, intuitive responses have been understood as “intuitive antecedents of insight” ( Bowers et al., 1995 , p. 27). As far as we know, this has been the first (and only) conception that up to now has addressed a potential link between intuition and insight. Their early work allows deriving assumptions concerning the interaction of intuition and insight in more detail. Moreover, this conceptualization produced valuable empirical paradigms (e.g., semantic and visual coherence judgment tasks) that are particularly suited to investigate insight and its intuitive precursors. Therefore, we will elaborate on this conception later in this contribution when aiming to clarify the conceptual relationship between intuition and insight 2 .

The Special-Process vs. Nothing-Special View on Insight

In contrast, research on insightful thinking has its roots in Gestalt psychology, which investigated the integration and ordering mechanisms of human perception and problem solving (e.g., Köhler, 1921 ; Duncker, 1945 ; Metzger, 1953 ). Similar to intuition research, the research on insight problem solving is also located between two different views: The special-process view – which posits that insight problem solving involves a unique cognitive process that is qualitatively different from the processes non-insight problem solving utilizes – and the business-as-usual or nothing-special view – which assumes that mainly the same cognitive processes are involved in insight and non-insight problem solving ( Seifert et al., 1995 ). Despite these two views, scientists have been highly fascinated by the topic since its early description by the Gestalt psychologists. This great interest culminated in the seminal book “The nature of insight,” which mainly deals with the Gestalt psychologist’s view on insight problem solving ( Sternberg and Davidson, 1995 ).

Interim Summary I

In sum, both concepts, due to their elusiveness, had to fight for recognition as an established field of research. Nevertheless, regrettably, research on intuition and research on insight has developed mostly independently from each other. However, this is in sharp contrast to a lay perspective on the two phenomena, which would rather endorse the perspective that intuition and insight are inherently intertwined with intuition being an antecedent of insight (in terms of a slight previous impression on the fringe of consciousness). Yet, the two branches of research evolved from different research traditions using different scientific paradigms and, unfortunately, have referred to one another only marginally (i.e., for instance by Bowers et al., 1990 ). Therefore, we think it is now time to scrutinize the relationship between the two phenomena in greater depth. Based on Bowers et al. (1990 , 1995 ) work, we will do this by elaborating on the cognitive similarities and differences of the two phenomena and by offering preliminary process ideas on their relationship.

Differences in the Cognitive Processes Assumed to Underlie Intuition and Insight

The continuity model of intuition: intuition as a gradual process.

In the majority of conceptualizations, intuitive processing has been described within a continuity model locating intuition on one end of the continuum and insight on the other. A prominent example is the two-stage model put forward by Bowers et al. (1990) . The authors determine intuition as the preliminary perception of coherence in the environment triggered by tacit knowledge that has been acquired unintentionally during a person’s life (i.e., the cue-criterion relationships that we addressed earlier in this contribution, see also Volz and Zander, 2014 ). While tacit, or implicit, knowledge is seen as the foundation on which intuitions are based (e.g., Lieberman, 2000 ), in our view, intuition must not be regarded solely as a phenomenon of or even be equated with implicit memory processing. As Volz and Zander (2014) clarify, there are several important differences between intuition and implicit memory concerning both the format in which information is stored in memory and the kind of signal that accompanies the respective cognitive process. The fact that implicit knowledge is seen only as one component of processing is similar to the field of implicit cognition in general. Here, implicit knowledge is assumed to be supplemented and/or completed by antecedent hunches of correct solution, the subjectively experienced nearness to the solution ( Reber et al., 2007 ).

Based on Polanyi’s (1966) concept of tacit knowledge, Bowers (1984 , p. 256) defined intuition as “sensitivity and responsiveness to information that is not consciously represented, but which nevertheless guides inquiry toward productive and sometimes profound insights.” According to the author, the cognitive processing from an intuitive hunch toward an explicit insight is gradual and proceeds in two stages. In the first stage, the guiding or intuitive stage , environmental cues trigger the activation of tacit knowledge associatively connected in semantic memory, which results in an implicit perception of coherence that (yet) cannot be explained verbally. This process is characterized by the automatic spread of activation proposed by Collins and Loftus (1975) . In the second stage of intuition, the integrative or insight stage , information becomes consciously available, which is enabled via a gradual accumulation of the previously activated concepts. The previous, implicit activation becomes now explicitly represented, which may thus be also interpreted as a form of insight processing. Hence, in Bowers et al. (1990 , 1995 ) conception, intuition precedes insight in the way that explicit representations are anticipated by the sensitization of environmental pattern or structure. Yet, besides the idea of a gradual, successive accumulation of activated concepts in associative memory, unfortunately, it has remained unclear which cognitive and/or physiological conditions foster the transition from sensed intuition to justified insight.

Bowers et al. (1990) approach is not only theoretically important it also carries paradigmatic weight. In order to empirically test their model’s assumptions, the authors developed several novel paradigms (verbal as well as perceptual ones), which today, after slight revisions, belong to the standard paradigms of intuition research (e.g., Bolte and Goschke, 2005 ; Volz and von Cramon, 2006 ; Topolinski and Strack, 2009b ; Hicks et al., 2010 ; Remmers et al., 2014 ; Zander et al., 2015 ). One of them is the semantic coherence task mentioned above, consisting of word triads that can be either semantically coherent (e.g., SALT, DEEP, and FOAM) or incoherent (DREAM; BALL; BOOK). Semantic coherence is determined via a fourth word each word of the word triad’s constituents associatively hints at (e.g., SEA for the coherent triad). Participants are instructed to perform a semantic coherence judgment , that is, to indicate via button press whether a given triad is coherent or incoherent. Researchers found that people showed an above-chance discrimination between coherent and incoherent triads even when they are not able to name the forth word (e.g., Bowers et al., 1990 ; Bolte and Goschke, 2005 ). In other words, people were intuitively sensitized to the detection of coherence prior to its explicit recognition (i.e., before having an explicit insight into the underlying semantic structure). Using a similar task, which consists of up to 15 semantically target-related clue words (i.e., the Accumulated Clues Task), it could be observed that participants continuously approached the explicit representation of environmental patterns/meaning ( Bowers et al., 1990 ; Reber et al., 2007 ), which could be recently also demonstrated on a neuronal level when using the semantic coherence task ( Zander et al., 2015 ). These results are perfectly in line with Bowers et al. (1990) definition of intuition and the corresponding gradual two-stage model. As another important aspect concerning the link between intuition and insight, Bowers et al. (1990) suggested the concept of semantic convergence to differentiate between triads that are rather easily solved by non-consciously reading out the common association (i.e., convergent triads) and triads that require a reorganization of semantic associations (i.e., divergent triads; see also the section Bridging the gap between the underlying processes of insight and intuition , second part).

To put it in a nutshell, according to the continuity model, – as Bowers et al. (1990) defined and tested it by means of verbal and visual coherence tasks – intuition and insight (in terms of an explicit representation that can be verbalized) are inherently intertwined: intuition and insight build upon each other and the one can hardly occur without the other. That is, intuitive processing is the non-conscious precursor of insight and thus, intuition and insight build on each other evolving on different processing stages. Accordingly, intuition and insight are not considered qualitatively distinct or mutually exclusive. Instead a crosstalk between the two is possible and even required to some extent. Importantly, Bowers et al. (1995) noted, that a thought process that appears to be sudden on a phenomenological level (like an aha-experience) nevertheless could have continuous underlying processes that have led to the particular subjective experience. Thus, they do not exclude the existence of subjective aha-experiences accompanying the successful solution generation in their verbal tasks.

Along these lines, when investigating insights from a naturalistic perspective (i.e., in a field setting and not in controlled laboratory settings), Klein and Jarosz (2011) found out that a substantial number of insights occurred gradually and in an (non-conscious) evidence-accumulating fashion. Following the naturalistic-decision-making approach ( Zsambok and Klein, 1997 ), the authors aimed at investigating the natural occurrence of insights by analyzing a collection of reported insight incidents (comprising a radical shift in understanding) having occurred in the different domains of everyday life of different occupation (e.g., invention, firefighting, management, and the like). The authors found out that (a) impasses did not occur in each insight case, (b) not every incident of an insight was accompanied by an aha-experience, and (c) an intuitive feeling of how near the solution might be occurred in many cases before the actual solution was reached. These results indicate that insights in a naturalistic setting may differ from insights synthetically induced by the class of pre-defined insight problems (e.g., eight-coin-problem, Ormerod et al., 2002 ) according to the degree with which the solution is derived gradually. Thus, in the naturalistic setting, a continuous solution approach (as advocated in intuition research) may be adoptable.

The Discontinuity Model of Insight: Insight As the Result of a Mental Restructuring Process

Contrary to the idea of a gradual solution approach, there is the discontinuity model of problem solving: insight is strongly linked to cognitive processes that restructure mental problem representations in order to allow the generation of a solution to a complex problem. A prominent example of a discontinuity model is the representational change theory put forward by Ohlsson (1992 , 2011 ) that combines the Gestalt psychological approach (characterized by a person being unable to report conscious solution strategies, cf. Duncker, 1945 ) and the information-processing view on problem solving (characterized by a conscious search through alternatives in a problem space, which is a controllable and reportable process, cf. Newell and Simon, 1972 ). According to the representational change theory, and in sharp contrast to the two-stage model developed by Bowers et al. (1990) , prior knowledge and experiences are postulated to hamper (instead of promote) the generation of solutions since they easily turn into constraints ( Knoblich et al., 1999 ). Based on this, Ohlsson (1992) introduced the idea that an impasse, that is a “blind lane” where one is caught in wrong solution attempts finding no expedient or problem solving attempts ceases, is the precondition for a representational change that results in an insight. According to the author, a restructuring process is required, during which self-imposed constraints of the problem representation change and the problem solver obtains a “fresh look” at the problem. Problem solvers may then be able to rearrange either the individual components or the general assumptions how to solve the problem. A putative mechanism assumed to drive such restructuring processes is the relaxation of self-imposed constraints . The representational change theory became very influential; there are several studies that have tested and could corroborate its assumptions (e.g., Knoblich et al., 2001 ; Kershaw and Ohlsson, 2004 ; Öllinger et al., 2006 , 2013 ).

In an eye movement study, for example, participants were asked to transform an incorrect arithmetic statement, which is made up of Roman numbers made of matchsticks, into a correct one moving only one single matchstick. Interestingly, it could be observed that before the correct solution of difficult problems was generated, suddenly, solvers attended such problem elements of the equation (e.g., the operators) longer that they had hardly noticed before. This was taken as evidence that successful solvers overcame self-imposed constraints ( Knoblich et al., 2001 ). Research on the underlying cognition of the representational change theory could also help in understanding the subjective aha-experience as a subjective marker of insight: a recent study conducted by Danek et al. (2016) provides first evidence that the self-reported rates of aha-experiences depend on the degree of constraint relaxation that is necessary to solve the given problem. The authors found that the more constraints had to be relaxed, the less aha-experiences were reported, which was interpreted such that the execution of several necessary solution steps (that are needed to gain a representational change) minimizes or even eliminates the experience of suddenness as a key attribute of subjective aha-experiences.

Interim Summary II

To summarize, according to a discontinuity model, the cognitive processes of intuition and insight seem to be qualitatively distinct. No crosstalk between them is possible. Moreover, the first (intuitive) look on a problem resulting in a mental impasse biases the subsequent solution. To be more precise, the intuitive apprehension of a problem necessarily leads to an impasse and restructuring processes are needed so as to overcome the bias and to solve the problem. This can be demonstrated, for example, via the utilization of magic tricks in order to probe insight problem solving. To explicate, Danek et al. (2013) recently introduced a novel paradigm consisting of magic tricks to investigate the cognitive underpinnings of insight problem solving. When viewing these magic tricks, the intuitive viewing pattern, which the magician intentionally utilizes, will very likely prohibit the understanding of the trick, that is, to first impede the solution to the problem. The solution is only within reach when the intuitive apprehension of the magic-trick situation, that is the first and rapidly formed impression, can be overcome. Classical insight problems as for example the famous candle problem ( Duncker, 1935 ) utilize the same rationale.

Bridging the Gap between the Underlying Processes of Insight and Intuition

Dual-system models of thinking and reasoning.

This discontinuity approach resembles the experimental procedure in typical judgment and decision-making studies conducted within the heuristics-and-biases framework ( Kahneman, 2011 ). This framework draws on a class of psychological models that are very well known in social and cognitive psychology and are called dual-system or dual-process models (e.g., Evans and Frankish, 2009 ; Kahneman, 2011 ). These models assume two different modes of thinking, which Stanovich and West (2000) called System 1 (described as e.g., non-conscious, fast, associative, holistic, automatic, and emotional) and System 2 (described as e.g., conscious, slow, analytic, serial, controlled, and affect-free). In other words, according to dual-system models, judgments may be formed via two qualitatively distinct processes or systems – an intuitive one (System 1) or a deliberate one (System 2). The intuitive strategy, thereby, is thought to require some sort of a feeling that “tells” a person which option is the optimal one. Thus, affective feelings are here seen as a crucial component that is inherent to the entire decision process. In contrast, when thoroughly deliberating on the pros and cons of multiple options, the solution to the decision process is considered to come to mind by way of logic and exhaustively sensible considerations of probable consequences. Thus, System 2 processing is here thought to not need or even to not involve any affective contribution.

Despite the large number of contributions that support the dual-systems view both theoretically and empirically, such theories have nevertheless recently come under strong fire ( Keren and Schul, 2009 ; Kruglanski and Gigerenzer, 2011 ). The main point of criticism put forward by Keren and Schul (2009 , p. 534) is that “the different dual-system theories lack conceptual clarity, that they are based upon methodological methods that are questionable, and that they rely on insufficient (and often inadequate) empirical evidence.” Kruglanski and Gigerenzer (2011) provide a unified approach and explain that both, intuition and deliberation, rely on the same functional principles (i.e., they are based on if – then rules), which is dependent on environmental conditions. As a reply to such criticism, Evans and Stanovich (2013) recently riposted that it is overstated since such criticism refers to dual-system models as a class of purely the same theoretical assumptions. They clarify that there are indeed different assumptions and terminologies subsumed under the dual-system framework, which needs to be considered. Nevertheless, there is also neuronal evidence against the assumptions of the dual-system approach ( Mega et al., 2015 ). The authors did a functional-magnetic-resonance-imaging study and asked participants to judge either intuitively or deliberately the authenticity of emotional facial expressions. Interestingly, the authors found that intuition and deliberation recruit the same neuronal networks – a finding well in line with Kruglanski and Gigerenzer’s (2011) proposal. It can be summarized that the dual-system framework is being much debated at the moment (see also volume 8 of Perspectives on Psychological Science , 2013) and therefore, it is very likely that there will be a revised conception in the foreseeable future.

Dual-System Models and the Discontinuity Model of Insight: Intuition As the First and Biased Problem Representation

After having shortly named the key assumptions of the dual-system framework as well as potential critical points, we will continue by elaborating on why we think the experimental approach of the insight problem solving literature (e.g., Danek et al., 2013 ) is similar to the one pursued by the heuristics-and-biases framework ( Kahneman, 2011 ). A typical task used by researchers of the heuristics-and-biases approach is the bat and the ball problem . Participants are told that a bat and ball together cost $ 1.10 in total and that the bat costs $ 1 more than the ball. Then they are asked to state how much the ball costs. A vast number of experiments showed that the first “intuitive answer,” following Kahneman’s terminology, is 10 cent, but after a while of conscious deliberation (i.e., analytical thought) participants find out that the correct answer is 5 cent ( Kahneman, 2011 ). Here is employed the same principle as in the magic-trick paradigm: the first and rapidly formed judgment, which is intentionally induced by the task material, is incorrect and hampers the generation of the correct solution (here 5 cent). In terms of the representational change theory an over-constraint problem representation is activated, where a simple goal representation is set up: total sum minus bat results immediately in the cost of the ball. Overcoming these assumptions seems difficult and requires a more sophisticated goal representation that combines two sets of information: (1) bat - ball = 1 AND (2) bat + ball = 1.10 => 1 in (2) ball + ball + 1 = 1.10 => ball = 0.05).

Together, experiments from both scientific fields show that by exploiting peoples’ intuitive apprehension of a problem, the solution is precluded from the beginning. To overcome the impasse or bias, it is suggested that the problem solver may engage in restructuring the problem space or in analytic strategies so as to eventually being able to solve the problem and to arrive at the objectively correct answer. Thus, there might be a reasonable mapping of the discontinuity model to the common dual-system model: first, the intuitive system starts (whether by default first or in parallel to System 2), and will lead to an over-constrained or biased problem representation that subsequently may lead to an impasse or conflict. Essential for reaching a solution is, (i) that the problem solver or decision maker realizes that the fast initial apprehension of the problem precludes its solution and (ii) engages in a representational change to overcome the initial problem representation ( Öllinger et al., 2014 ). Since, by definition, System 2 processing is slower than System 1 processing it can smooth out the first and hasty attempts made by System 1. In the diction of dual system theorists, the analytic mind is called up when encountering an impasse or conflict and will attempt to deliberately solve the problem by applying certain rational strategies. Importantly, Systems 1 and 2, or intuition and insight, are here considered to be qualitatively different – “hare and tortoise.”

Equally important, System 1 is considered subordinate to System 2 and its hasty responses needs to be tamed (cf. Kahneman, 2011 , p. 185). Kahneman (2011 , p. 44) states: “One of the main functions of System 2 is to monitor and control thought and actions “suggested” by System 1, allowing some to be expressed directly in behavior and suppressing or modifying others.” Given such an understanding of intuition and insight, the discontinuity model may suffer from the very same conceptual problem as a dual-system account of reasoning: that is, how and by which factors is a conflict or impasse detected? “Who” eventually launches restructuring processes that are needed to overcome the error? How does restructuring of the first problem representation take place? This may be viewed as a variation of the “homunculus problem.”

Hence, within the discontinuity conception of insight, intuition is not regarded as helpful or diagnostic for the generation of a pending insight. In line with this idea, Metcalfe and Wiebe (1987) investigated feeling of warmth accompanying insight and incremental problem solving using classical insight problems and algebraic problems. They used feeling-of-warmth ratings as the assessment of how close participants intuitively felt to the solution, which was taken to indicate the subjective nearness to the solution . Interestingly, they found out that these subjective feelings of warmth differed for insight and non-insight solutions insofar that they could predict performance only on incremental algebra problems. For insight problems such intuitive feelings were lacking. Given this result, one may conclude that intuition differs from insight concerning the (introspective) access to non-conscious processing: whereas decision makers intuit the solution to a problem, people solving the problem by insight show to lack such hunches. Thus, additionally to the continuity/discontinuity distinction, insightful solutions as in contrast to intuitive ones seem to be discrete phenomena in terms of availability to awareness. However, it could be also possible that the conscious assessment of how close/far the solution is, just easier for non-insight tasks. Since non-insight tasks are well-defined insofar that there are clear starts, solution paths, and goals, which enables exact planning of the necessary steps and its order (as for example in algebraic problems). Conversely, classical insight problems may be technically well-defined (in that there is also a clear start and goal, see e.g., the famous nine-dot problem), but since the problem’s different components are unhelpfully represented in the problem solvers mental set, it is difficult or rather impossible to estimate how far/close the solution is.

Interim Summary III

As an interim summary, it may be concluded that intuition research advocates a continuity model, in which intuition and insight build upon each other in a gradual and cumulative fashion: people are non-consciously sensitized toward pattern or meaning in the environment and act accordingly (e.g., Bowers et al., 1990 ). In contrast, insight research focuses on a discontinuity model, in which the initial representation of the problem (i.e., early intuition) biases later solution attempts and has to be overcome in order to reach a solution. Here, no intuitive precursors of insight in terms of a subjectively felt nearness toward the solution are assumed. This latter model resembles famous, yet recently heavily criticized, dual-system models in judgment and decision-making research insofar as in both approaches the participants first intuitive apprehension of a problem biases its later solution.

Semantic Coherence Tasks Used in Intuition and Insight Research: Word Triads and Remote Associates

Interestingly, in the semantic domain, intuition research following Bowers et al. (1990) approach and contemporary insight research do have used similar stimuli yet with different task rationales, which could be used as an excellent starting point for necessary, and up to now lacking, common investigations. As described earlier in this contribution, in the tradition of Bowers et al. (1990 , 1995 ), typical coherence judgment tasks include semantically coherent and incoherent word triads – a task that dates back to the work of Mednick (1962) . Here, response patterns of both triad types (i.e., coherent vs. incoherent) are compared to each other. In recent research on insight problem solving, Bowden et al. (2005) presented a novel framework and a new class of problems in order to probe insight problem solving. The authors equate subjectively reported aha-experiences with insight. The authors have used word triads based on Mednick’s (1962) task to investigate the neuronal underpinnings of insight. They presented a large number of problems that can be solved either by insight or by non-insight (i.e., Aha! vs. Non-Aha!) and do not require a lot of time to be solved ( Kounios and Beeman, 2014 ). As a result they found that Aha! solutions revealed distinguish neural patterns than Non-Aha!-solutions. Unlike intuition research, they (1) only applied word triads that are principally solvable (i.e., no incoherent triads), and (2) word triads that consist of compound remote associate.

Bowers et al. (1990) , distinguished two types of triads and termed them convergent and divergent triads , respectively. For convergent triads the common associate means the same with respect to each clue word, whereas for divergent triads the common associate is more remote and changes its meaning with respect to each clue word. An example for a coherent convergent triad is SALT DEEP FOAM– SEA; and an example for a divergent triad is AGE MILE SAND– STONE. Unlike convergent triads, divergent triads are built in a way one need to detect the multiple meanings of the solution word to associate it with the meanings of the three clue words. As divergent triads may require a restructuring of the different meanings of the clues with respect to the solution, these kinds of triads could be nicely seen as an insight condition.

According to Bowden and Jung-Beeman (2007) , divergent triads are not as complex as classical insight problems, but they can nevertheless be used as a kind of insight problems. Like typical insight tasks (1) they misdirect retrieval processes (i.e., the first word of a divergent triad biases later thought toward a specific, yet wrong direction), (2) the strategy that has led to the correct solution cannot be reported by the problem solver, and (3) aha-experiences can occur.

For such divergent triads, Cranford and Moss (2012) , using a verbal protocol method, found out that there are two different types of insight problems, for which only one type shows the typical traditional characteristics of an insight. It has to be emphasized that, unlike Bowden et al. (2005) , the authors consider all three components, subjective aha-experience, impasse, and restructuring, as necessary for an insight to occur. They could show that some problems, consisting of divergent triads, could be solved via immediate insight , whereas others were solved by non-immediate or delayed insight . Interestingly, only the latter type of insights showed the supposed phases of insight. Fedor et al. (2015) detailed on this question and found that the classical insight sequence (i.e., constrained search, impasse, insight, extended search, and solution) is a rather rare event. They found that participants showed much more often fairly different insight sequences (i.e., a flexible order of the different problem-solving stages), which has to be further specified in the future. We consider this line of research ( Cranford and Moss, 2012 ; Kounios and Beeman, 2014 ; Fedor et al., 2015 ) as promising and important for future endeavors, which may initiate the common investigations of intuition and insight.

Conclusion, Open Research Questions, and Future Directions

To conclude, we set out to disentangle the underlying mechanisms of intuition and insight so as to clarify their relationship. At first sight, intuition and insight seem to be very differently conceptualized: while the intuition literature favors a continuity model, insight has been described within in a discontinuity model. In a continuity model, early (semantic) readout processes are taken as diagnostic for the non-conscious detection of environmental patterns and/or meaning (in terms of an antecedent of later explicit mental representation or insight). Intuition is described as aiding decision making and problem solving when time and cognitive capacity is limited and necessary information is temporarily unavailable. Contrary to this, in a discontinuity model early intuitive responses misdirect the generation of a correct solution or are experimentally utilized to bias solution attempts. In this case, intuitions lead people astray. Instead of employing intuition, mental restructuring processes (i.e., qualitative changes in the non-conscious search processes) are needed to overcome biased intuitive impressions or apprehensions so as to eventually solve the problem. In that respect, a discontinuity model resembles dual-process accounts in judgment and decision making.

Except early work by Bowers et al. (1990 , 1995 ) and Dorfman et al. (1996) , there have not been much empirical investigations so far aiming at exploring similarities and differences in the underlying neurocognitive mechanisms of intuition and insight. A major drawback here may be that there are no tasks that easily enable a direct empirical comparison between the two concepts. Nevertheless, we consider it very important to test intuitive and insight solution processes by means of exactly the same task and within the same participants. Such a task needs to be created. With this theoretical contribution, we therefore aim to initiate common investigations of both fields of research to detect neurocognitive similarities and differences between intuitive processing and insight problem solving. A good starting point for common empirical investigations may be the use of different types of triads [as for example divergent and convergent triads, as formerly suggested by Bowers et al. (1990) ] in order to induce gradual and discontinuous solution attempts. We also consider it important to investigate not only the cognitive processes that may underlie intuition and insight, but also the neuronal processes involved. Future studies may shed light on the specific (and maybe distinct) neuronal correlates, which will then also allow drawing conclusions about the theoretical conceptualization of the two phenomena. Interesting research questions would be (as non-exhaustive list): (1) Are the neuronal correlates different for the two types of triads (convergent versus divergent triads)? (2) Do aha-experiences also occur for convergent triads? (3) Do feelings-of-warmth ratings occur for both types of triads or only for convergent triads? (4) Do verbal protocols differ for the two types of triads? (5) How can the assumed recursive coherence building process be neuronally mapped? The further investigation of the underlying cognitive and neuronal processes of restructuring may also deeply progress our understanding of the topic. Here, Öllinger et al. (2006 , 2013 ) reached influential results that may be carried forward in future research. Equally important, following Kounios and Beeman (2014) in using current neuroimaging techniques may promote the detection of objective physiological markers of insight (in form of a specific neuronal or electrophysiological activation pattern accompanying the experience of impasses and aha’s as well as correlating mental restructuring processes). Kounios and Beeman (2014) as well as Sandkühler and Bhattacharya (2008) already gained promising results in this respect, thus their research may be a good starting point for the future. To sum up, intuition and insight are intriguing (non-analytical) mental phenomena that need to be further investigated in the future.

Author Contributions

TZ developed the theoretical conception; wrote the article. MÖ developed the theoretical conception; revised the manuscript. KV developed the theoretical conception; revised the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was funded by the Werner Reichardt Centre for Integrative Neuroscience (CIN) at the University of Tübingen (an Excellence Cluster within the framework of the Excellence Initiative (EXG 307) funded by the Deutsche Forschungsgemeinschaft (DFG).

^ There is the idea that a period, in which a person after encountering an impasse is not being consciously engaged in finding the solution anymore and puts the problem aside (i.e., the incubation period ) fosters sudden insights of the solution (e.g., Gilhooly et al., 2012 ). Ritter and Dijksterhuis (2014) explain that unconscious thought processes continue to find the problem’s solution by re-organizing memory content eventually resulting in gist-based representations. This occurs in the absence of a person’s conscious attempts. It has to be emphasized, however, that empirical studies revealed different results as to whether incubation periods are beneficial for problem solving. The specific conditions under which positive incubation effects take place have to be further investigated ( Sio and Ormerod, 2009 ).
^ For the sake of completeness, it has to be emphasized that metacognitive processes may play a role as well in intuitive processing. To strengthen the scope of our argumentation, we decided not to detail on this notion. Please see Mealor and Dienes (2013) ; Storm and Hickman (2015) , or Thompson et al. (2011) . A particular emphasize may be laid on the concept of experience-based metacognitive feelings (e.g., Koriat and Levy-Sadot, 1999 ).

Betsch, T. (2008). “The nature of intuition and its neglect in research on judgment and decision making,” in Intuition in Judgment and Decision Making , eds H. Plessner, C. Betsch, and T. Betsch (New York, NY: Lawrence Erlbaum Associates), 3–22.

Google Scholar

Bolte, A., and Goschke, T. (2005). On the speed of intuition: intuitive judgments of semantic coherence under different response deadlines. Mem. Cognit. 33, 1248–1255. doi: 10.3758/BF03193226

CrossRef Full Text | Google Scholar

Bowden, E. M., and Jung-Beeman, M. (2007). Methods for investigating the neural components of insight. Methods 42, 87–99. doi: 10.1016/j.ymeth.2006.11.007

Bowden, E. M., Jung-Beeman, M., Fleck, J., and Kounios, J. (2005). New approaches to demystifying insight. Trends Cogn. Sci. 9, 322–328. doi: 10.1016/j.tics.2005.05.012

Bowers, K. S. (1984). “On being unconsciously influenced and informed,” in The Unconscious Reconsidered , eds K. S. Bowers and D. Meichenbaum (New York, NY: John Wiley & Sons), 227–272.

Bowers, K. S., Farvolden, P., and Mermigis, L. (1995). “Intuitive antecedents of insight,” in The Creative Cognition Approach , eds S. M. Smith, T. B. Ward, and R. A. Finke (Cambridge, MA: The MIT Press), 27–51.

Bowers, K. S., Regehr, G., Balthazard, C., and Parker, K. (1990). Intuition in the context of discovery. Cognit. Psychol. 22, 72–110. doi: 10.1016/0010-0285(90)90004-N

Claxton, G. (1998). Investigating human intuition: knowing without knowing why. Psychologist 11, 217–220. doi: 10.3758/s13415-014-0286-7

Collins, A. M., and Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychol. Rev. 82, 407–428. doi: 10.1037/0033-295X.82.6.407

Cranford, E. A., and Moss, J. (2012). Is insight always the same? A verbal protocol analysis of insight in compound remote associate problems. J. Probl. Solving 4, 128–153.

Danek, A. H., Fraps, T., von Müller, A., Grothe, B., and Öllinger, M. (2013). Aha! experiences leave a mark: facilitated recall of insight solutions. Psychol. Res. 77, 659–669. doi: 10.1007/s00426-012-0454-8

Danek, A. H., Wiley, J., and Öllinger, M. (2016). Solving classical insight problems without Aha! experience: 9 dot, 8 coin, and matchstick arithmetic problems. J. Probl. Solving 9, 47–57. doi: 10.7771/1932-6246.1183

Dorfman, J., Shames, V. A., and Kihlstrom, J. F. (1996). “Intuition, incubation and insight: implicit cognition in problem solving,” in Implicit Cognition , ed. G. Underwood (Oxford: Oxford University Press), 257–296.

Duncker, K. (1935). Zur Psychologie des Produktiven Denkens [On the psychology of productive Thinking]. Berlin: Springer.

Duncker, K. (1945). On problem solving. Psychol. Monogr. 58:270. doi: 10.1037/h0093599

Durso, F. T. F., Rea, C. C. B., and Dayton, T. (1994). Graph-theoretic confirmation of restructuring during insight. Psychol. Sci. 5, 94–97. doi: 10.1111/j.1467-9280.1994.tb00637.x

Evans, J., and Frankish, K. (eds) (2009). In Two Minds: Dual Processes and Beyond . Oxford: Oxford University Press.

Evans, J., and Stanovich, K. S. (2013). Dual-process theories of higher cognition: advancing the debate. Perspect. Psychol. Sci. 8, 223–241. doi: 10.1177/1745691612460685

Fedor, A., Szatmary, E., and Öllinger, M. (2015). Problem solving stages in the five square problem. Front. Psychol. 6:1050. doi: 10.3389/fpsyg.2015.01050

Gick, M. L., and Lockhardt, R. S. (1995). “Cognitive and affective components of insight,” in The Nature of Insight , eds R. J. Sternberg and J. E. Davidson (Cambridge, MA: The MIT Press), 197–228.

Gigerenzer, G. (2008). Gut Feelings: The Intelligence of the Unconscious . New York, NY: Viking.

Gigerenzer, G., and Gaissmaier, W. (2011). Heuristic decision making. Annu. Rev. Psychol. 62, 451–482. doi: 10.1146/annurev-psych-120709-145346

Gigerenzer, G., Todd, P. M., and The Abc Research Group. (eds) (1999). Simple Heuristics that Make us Smart. New York, NY: Oxford University Press.

Gilhooly, K. J., Georgiou, G. J., Garrison, J., Reston, J. D., and Sirota, M. (2012). Don’t wait to incubate: immediate versus delayed incubation in divergent thinking. Mem. Cognit. 40, 966–975. doi: 10.3758/s13421-012-0199-z

Gladwell, M. (2005). Blink. The Power of Thinking Without Thinking. London: Penguin Books.

Glöckner, A., and Witteman, C. (2010). “Foundations for tracing intuition: models, findings, categorizations,” in Foundations for Tracing Intuition: Challenges and Methods , eds A. Glöckner, and C. Witteman (East Sussex: Psychology Press), 1–23.

Hicks, J. A., Burton, C. M., Cicero, D. C., Trent, J., and King, L. A. (2010). Positive affect, intuition, and feelings of meaning. J. Pers. Soc. Psychol. 89, 967–979. doi: 10.1037/a0019377

Hogarth, R. M. (2001). Educating Intuition. Chicago, IL: University of Chicago Press.

Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., et al. (2004). Neural activity when people solve verbal problems with insight. PLoS Biol. 2:E97. doi: 10.1371/journal.pbio.0020097

Kahneman, D. (2011). Thinking, Fast and Slow . London: Penguin Books.

Kahneman, D., and Tversky, A. (1974). Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131. doi: 10.1126/science.185.4157.1124

Keren, G., and Schul, Y. (2009). Two is not always better than one: a critical evaluation of two-system theories. Perspect. Psychol. Sci. 4, 533–550. doi: 10.1111/j.1745-6924.2009.01164.x

Kershaw, T. C., and Ohlsson, S. (2004). Multiple causes of difficulty in insight: the case of thenine-dot problem. J. Exp. Psychol. Learn. Mem. Cogn. 30, 3–13. doi: 10.1037/0278-7393.30.1.3

Kizilirmak, J., Galvao Gomes da Silva, J., Imamoglu, F., and Richardsohn-Klavehn, R. (2015). Generation and the subjective feeling of “aha!” are independently related to learning from insight. Psychol. Res. doi: 10.1007/s00426-015-0697-2 [Epub ahead of print].

Klein, G., and Jarosz, A. (2011). A naturalistic study of insight. J. Cogn. Eng. Decis. Mak. 5, 335–351. doi: 10.1177/1555343411427013

Knoblich, G., Ohlsson, S., Haider, H., and Rhenius, D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 25, 1534–1555. doi: 10.1037/0278-7393.25.6.1534

Knoblich, G., Ohlsson, S., and Raney, G. E. (2001). An eye movement study of insight problem solving. Mem. Cognit. 29, 1000–1009. doi: 10.3758/BF03195762

Knoblich, G., and Öllinger, M. (2006). “Einsicht und umstrukturierung beim problemlösen [insight and restructering in problem solving],” in Denken und Problemlösen. Enzyklopädie der Psychologie [Thinking and Problem Solving. Encyclopedia of Psychology] , ed. J. Funke (Göttingen: Hogrefe), 3–86.

Köhler, W. (1921). Intelligenzprüfungen am Menschenaffen [Investigating intelligence in great apes] . Berlin: Springer.

Koriat, A., and Levy-Sadot, R. (1999). “Processes underlying metacognitive judgments: information-based and experience-based monitoring of one’s own knowledge,” in Dual-Process Theories in Social Psychology , eds S. Chaiken and Y. Trope (New York, NY: Guilford Press), 483–502.

Kounios, J., and Beeman, M. (2014). The cognitive neuroscience of insight. Annu. Rev. Psychol. 65, 71–93. doi: 10.1146/annurev-psych-010213-115154

Kruglanski, A. W., and Gigerenzer, G. (2011). Intuitive and deliberate judgments are based on common principles. Psychol. Rev. 118, 97–109. doi: 10.1037/a0020762

Lieberman, M. D. (2000). Intuition: a social cognitive neuroscience approach. Psychol. Bull. 126, 109–137. doi: 10.1037/0033-2909.126.1.109

Luchins, A. S., and Luchins, E. H. (1959). Rigidity of Behavior: A Variational Approach to the Effect of Einstellung . Eugene, OR: University of Oregon Books.

Maier, N. R. F. (1930). Reasoning in humans. I. On direction. J. Comp. Psychol. 10, 115–143. doi: 10.1037/h0073232

Mangan, B. (1993). Taking phenomenology seriously: the “fringe” and its implications for cognitive research. Consci. Cogn. 2, 89–108. doi: 10.1006/ccog.1993.1008

Mangan, B. (2001). Sensation’s ghost. The non-sensory “fringe” of consciousness. Psyche 7.

Mangan, B. (2015). The uncanny valley as fringe experience. Interact. Stud. 16, 193–199. doi: 10.1075/is.16.2.05man

Mealor, A. D., and Dienes, Z. (2013). The speed of metacognition: taking time to get to know one’s structural knowledge. Conscious. Cogn. 22, 123–136. doi: 10.1016/j.concog.2012.11.009

Mednick, S. A. (1962). The associative basis of the creative process. Psychol. Rev. 69, 220–232. doi: 10.1037/h0048850

Mega, L. F., Gigerenzer, G., and Volz, K. G. (2015). Do intuitive and deliberate judgments rely on two distinct neural systems? A case study in face processing. Front. Hum. Neurosci. 9:456. doi: 10.3389/fnhum.2015.00465

Metcalfe, J., and Wiebe, D. (1987). Intuition in insight and noninsight problem solving. Mem. Cognit. 15, 238–246. doi: 10.3758/BF03197722

Metzger, W. (1953). Gesetze des Sehens [Rules of vision]. Frankfurt: Kramer.

Newell, A., and Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.

Norman, E. (2002). Subcategories of “fringe consciousness” and their related nonconscious contexts. Psyche 8, 1–15.

Norman, E. (2016). Metacognition and minfulness: the role of fringe consciousness. Mindfulness doi: 10.1007/s12671-016-0494-z

Norman, E., Price, M. C., and Duff, S. C. (2006). Fringe consciousness in sequence learning: the influence of individual differences. Consci. Cogn. 15, 723–760. doi: 10.1016/j.concog.2005.06.003

Norman, E., Price, M. C., and Duff, S. C. (2010). “Fringe consciousness: a useful framework for clarifying the nature of experience-based feelings,” in Trends and Prospects in Metacognition Research , eds A. Efklides and P. Misailidi (New York, NY: Springer), 63–89.

Ohlsson, S. (1992). “Information-processing explanations of insight and related phenomena,” in Advances in the Psychology of Thinking , eds M. Keane and K. Gilhooly (London: Harvester-Wheatsheaf), 1–44.

Ohlsson, S. (2011). Deep Learning. How the Mind Overrides Experience. New York, NY: Cambridge University Press.

Öllinger, M., Jones, G., Faber, A. H., and Knoblich, G. (2013). Cognitive mechanisms of insight: the role of heuristics and the representational change in solving the eight-coin problem. J. Exp. Psychol. Learn. Mem. Cogn. 39, 931–939. doi: 10.1037/a0029194

Öllinger, M., Jones, G., and Knoblich, G. (2006). Heuristics and representational change in two-move matchstick arithmetic task. Adv. Cogn. Psychol. 2, 239–253. doi: 10.2478/v10053-008-0059-3

CrossRef Full Text

Öllinger, M., Jones, G., and Knoblich, G. (2008). Investigating the effect of mental set on insight problem solving. Exp. Psychol. 55, 270–282.

Öllinger, M., Jones, G., and Knoblich, G. (2014). The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem. Psychol. Res. 78, 266–275. doi: 10.1007/s00426-013-0494-8

Ormerod, T. C., MacGregor, J. N., and Chronicle, E. P. (2002). Dynamics and constraints in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 28, 791–799. doi: 10.1037/0278-7393.28.4.791

Plessner, H., Betsch, C., and Betsch, T. (eds). (2008). Intuition in Judgment and Decision Making. New York, NY: Lawrence Erlbaum Associates.

Polanyi, M. (1966). Implizites Wissen [The tacit dimension]. Frankfurt: Suhrkamp.

Price, M. C. (2002). Measuring the fringe of experience. Psyche 8, 1–24.

Price, M. C., and Norman, E. (2008). Intuitive feelings on the fringe of consciousness: are they conscious and does it matter? Judgm. Decis. Mak. 3, 28–41.

Reber, R., Ruch-Monachon, M.-A., and Perrig, W. J. (2007). Decomposing intuitive components in a conceptual problem solving task. Conscious. Cogn. 16, 294–309. doi: 10.1016/j.concog.2006.05.004

Reber, R., Schwarz, N., and Winkielmann, P. (2004). Processing fluency and aesthetic pleasure: is beauty in the perceiver’s processing experience? Pers. Soc. Psychol. Rev. 8, 364–382. doi: 10.1207/s15327957pspr0804_3

Remmers, C., Topolinski, T., Dietrich, D. E., and Michalak, J. (2014). Impaired intuition in pateints with major depressive disorder. Br. J. Clin. Psychol. 54, 200–213. doi: 10.1111/bjc.12069

Ritter, S., and Dijksterhuis, A. (2014). Creativity – the unconscious foundations of the incubation period. Front. Hum. Neurosci. 8:215. doi: 10.3389/fnhum.2014.00215

Sadler-Smith, E. (2008). Inside Intuition. Abingdon: Routledge.

Sandkühler, S., and Bhattacharya, J. (2008). Deconstructing insight: EEG correlates of insightful problem solving. PLoS ONE 3:1459. doi: 10.1371/journal.pone.0001459

Seifert, C. M., Meyer, D. E., Davidson, N., Patalano, A. L., and Yaniv, I. (1995). “Demystification of cognitive insight: opportunistic assimilation and the prepared-mind perspective,” in The Nature of Insight , eds R. J. Sternberg and J. E. Davidson (Cambridge, MA: The MIT Press), 65–124.

Sio, U. N., and Ormerod, T. C. (2009). Does incubation enhance problem solving? A meta- analytic review. Psychol. Bull. 135, 94–120. doi: 10.1037/a0014212

Stanovich, K. E., and West, R. F. (2000). Individual differences in reasoning: implications for the rationality debate. Behav. Brain Sci. 23, 645–665. doi: 10.1017/S0140525X00003435

Sternberg, R. J., and Davidson, J. E. (eds). (1995). The Nature of Insight. Cambridge, MA: The MIT Press.

Storm, B. C., and Hickman, M. L. (2015). Mental fixation and metacognitive predictions of insight in creative problem solving. Q. J. Exp. Psychol. 68, 802–813. doi: 10.1080/17470218.2014.966730

Thompson, V. A., Prowse Turner, J. A., and Pennycook, G. (2011). Intuition, reason, and metacognition. Cognit. Psychol. 63, 107–140. doi: 10.1016/j.cogpsych.2011.06.001

Topolinski, S., and Reber, R. (2010). Gaining insight into the “aha” experience. Curr. Dir. Psychol. Sci. 19, 402–405. doi: 10.3389/fpsyg.2014.01408

Topolinski, S., and Strack, F. (2008). Where there’s a will – there’s no intuition. The unintentional basis of semantic coherence judgments. J. Mem. Lang. 58, 1032–1048. doi: 10.1016/j.jml.2008.01.002

Topolinski, S., and Strack, F. (2009a). Scanning the “fringe” of consciousness: what is felt and what is not felt in intuitions about semantic coherence. Consci. Cogn. 18, 608–618. doi: 10.1016/j.concog.2008.06.002

Topolinski, S., and Strack, F. (2009b). The analysis of intuition: processing fluency and affect in judgments of semantic coherence. Cogn. Emot. 23, 1465–1503. doi: 10.1080/02699930802420745

Volz, K. G., and von Cramon, D. Y. (2006). What neuroscience can tell about intuitive processes in the context of perceptual discovery. J. Cogn. Neurosci. 18, 1–11. doi: 10.1162/jocn.2006.18.12.2077

Volz, K. G., and Zander, T. (2014). Primed for intuition? Neurosci. Decis. Mak. 1, 26–34. doi: 10.2478/ndm-2014-0001

Wertheimer, M. (1959). Productive Thinking. New York, NY: Harper.

Zander, T., Horr, N. K., Bolte, A., and Volz, K. G. (2015). Intuition as a gradual process: investigating intuition-based and priming-based decisions with fMRI. Brain Behav. 6:e00420. doi: 10.1002/brb3.420

Zsambok, C. E., and Klein, G. (eds). (1997). Naturalistic Decision Making. New York, NY: Routlege.

Keywords : intuitive decision making, insight problem solving, continuity, discontinuity, non-analytical solution processes

Citation: Zander T, Öllinger M and Volz KG (2016) Intuition and Insight: Two Processes That Build on Each Other or Fundamentally Differ? Front. Psychol. 7:1395. doi: 10.3389/fpsyg.2016.01395

Received: 02 May 2016; Accepted: 31 August 2016; Published: 13 September 2016.

Reviewed by:

Copyright © 2016 Zander, Öllinger and Volz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Thea Zander, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Psychological Research on Insight Problem Solving

Cite this chapter.

insight problem solving psychology definition

Michael öllinger 3 &
Günther Knoblich 4

1239 Accesses

18 Citations

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Available as PDF
Read on any device
Instant download
Own it forever
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info
Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Unable to display preview. Download preview PDF.

Ash, M.G. (1998): Gestalt Psychology in German Culture, 1890-1967: Holism and the Quest for Objectivity . Cambridge University Press, Cambridge.

Google Scholar

Bowden, E.M. and Jung-Beeman, M. (2003): Aha! – Insight experience correlates with solution activation in the right hemisphere. Psychonomic Bulletin and Review 10 , 730–737.

Bowden, E.M., Jung-Beeman, M., Fleck, J. and Kounios, J. (2005): New approaches to demystifying insight. Trends in Cognitive Sciences 9 , 322–328.

Article Google Scholar

Chronicle, E.P., MacGregor, J.N. and Ormerod, T.C. (2004): What makes an insight problem ? The roles of heuristics, goal conception, and solution recoding in knowledge-lean problems. Journal of Experimental Psychology: Learning, Memory, and Cognition 30 , 14–27.

Dominowski, R.L. and Dallob, P. (1995): Insight and problem solving. In: R.J. Sternberg and J.E. Davidson (eds.), The Nature of Insight . MIT Press, Cambridge (MA), pp. 33–62.

Duncker, K. (1935): Zur Psychologie des produktiven Denkens . Springer, Berlin.

Duncker, K. (1945): On problem solving. In: J.F. Dashiell (ed.), Psychological Monographs 58 (5). The American Psychological Association, Washington (DC).

Grant, E.R. and Spivey, M.J. (2003): Eye movements and problem solving: Guiding attention guides thought. Psychological Science , 14 , 462–466.

Greeno, J. (1974): Hobbits and Orcs – Acquisition of a sequential concept. Cognitive Psychology 6 , 270–292.

Gruber, H.E. (1995): Insight and affect in the history of science. In: R.J. Sternberg and J.E. Davidson (eds.), The Nature of Insight . MIT Press, Cambridge (MA), pp. 397–431.

Jones, G. (2003): Testing two cognitive theories of insight. Journal of Experimental Psychology: Learning, Memory, and Cognition 29 , 1017–1027.

Jung-Beeman, M., Bowden, E.M., Haberman, J., Frymiare, J.L., Arambel-Liu, S., Greenblatt, R., Reber, P. and Kounios, J. (2004): Neural activity when people solve verbal problems with insight. PLoS Biology 2 , 500–510.

Kaplan, C.A. and Simon, H.A. (1990): In search of insight. Cognitive Psychology 22 , 374–419.

Kershaw, T.C. and Ohlsson, S. (2004): Multiple causes of difficulty in insight: The case of the nine-dot problem. Journal of Experimental Psychology: Learning, Memory, and Cognition 30 , 3–13.

Knoblich, G., Ohlsson, S., Haider, H. and Rhenius, D. (1999): Constraint relaxation and chunk decomposition in insight problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition 25 , 1534–1555.

Knoblich, G., Ohlsson, S. and Raney, G. (1999): Resolving impasses in problem solving. In: M. Hahn and S.C. Stoness (eds.), Proceedings of the 21st Meeting of the Cognitive Science Society . Erlbaum, Mahwah (NJ), pp. 276–281.

Knoblich, G., Ohlsson, S. and Raney, G.E. (2001): An eye movement study of insight problem solving. Memory and Cognition , 29 , 1000–1009.

Knoblich, G. and öllinger, M. (2006): The eureka moment. Scientific American Mind , issue #10, 38–43.

Knoblich, G., öllinger, M. and Spivey, M.J. (2005): Tracking the eyes to obtain insight into insight problem solving. In: G.D.M. Underwood (ed.), Cognitve Processes in Eye Guidance . Oxford University Press, Oxford, pp. 355–375.

Koffka, K. (1935): Principles of Gestalt Psychology . Harcourt, Brace and World, New York.

Köhler, W. (1921): Intelligenzprüfungen an Menschenaffen. Springer, Berlin.

Lavric, A., Forstmeier, S. and Rippon, G. (2000): Differences in working memory involvement in analytical and creative tasks: An ERP study. Neuroreport 11 , 1613–1618.

Lavric, A., Rippon, G. and Forstmeier, S. (1998): ERP studies of working memory in reasoning tasks. International Journal of Psychophysiology 30 , 95–271, see p. 145.

Lindsay, P.H. and Norman, D.A. (1981): Einführung in die Psychologie. Informationsaufnahme und -verarbeitung beim Menschen. Springer, Berlin.

Lovett, M.C. and Anderson, J.R. (1996): History of success and current context in problem solving: Combined influences on operator selection. Cognitive Psychology 31 , 168–217.

Luchins, A.S. (1942): Mechanization in problem solving – the effect of Einstellung. Psychological Monographs 54 (6), 1–95.

Luchins, A.S. and Luchins, E.H. (1959): Rigidity of Behavior: A Variational Approach to the Effect of Einstellung . University of Oregon Books, Eugene (OR).

Luo, J. and Niki, K. (2003): Function of hippocampus in “insight” of problem solving. Hippocampus 13 , 316–323.

MacGregor, J.N., Ormerod, T.C. and Chronicle, E.P. (2001): Information processing and insight: A process model of performance on the nine-dot and related problems. Journal of Experimental Psychology: Learning, Memory, and Cognition 27 , 176–201.

Mai, X.-Q., Luo, J., Wu, J.-H. and Luo, Y.-J. (2004). “Aha!” effects in a guessing riddle task: An event-related potential study. Human Brain Mapping 22 , 261–271.

Maier, N.R.F. (1931): Reasoning in humans. II. The solution of a problem and its appearance in consciousness. Journal of Comparative Psychology 12 , 181–194.

McClelland, J.L., McNaughton, B.L. and O’Reilly, R.C. (1995): Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review 102 , 419–457.

Metcalfe, J. (1986): Feeling of knowing in memory and problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition 12 , 288–294.

Metcalfe, J. and Wiebe, D. (1987): Intuition in insight and noninsight problem solving. Memory and Cognition 15 , 238–246.

Metzger, W. (1986): Gestalt-Psychologie. Ausgewählte Werke aus den Jahren 1950 bis 1982 . Waldemar Kramer, Frankfurt.

Newell, A. and Simon, H.A. (1972): Human Problem Solving . Prentice Hall, Englewood Cliffs (NJ).

Ohlsson, S. (1992): Information-processing explanations of insight and related phenomena. In: M. Keane and K. Gilhooly (eds.), Advances in the Psychology of Thinking . Harvester-Wheatsheaf, London, pp. 1-44.

öllinger, M., Jones, G. and Knoblich, G. (2006): Heuristics and representational change in two-move matchstick arithmetic tasks. Advances in Cognitive Psychology 2 , 239–253.

öllinger, M., Jones, G. and Knoblich, G. (2008): Investigating the effect of mental set on insight problem solving. Experimental Psychology 55 , 270–282.

Ormerod, T.C., MacGregor, J.N. and Chronicle, E P. (2002): Dynamics and constraints in insight problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition 28 , 791–799.

Reverberi, C., Toraldo, A., D’Agostini, S. and Skrap, M. (2005): Better without (lateral) frontal cortex ? Insight problems solved by frontal patients. Brain 128 , 2882–2890.

Scheerer, M. (1963): Problem-solving. Scientific American , 208 (4), 118–128.

Schooler, J.W., Ohlsson, S. and Brooks, K. (1993): Thoughts beyond words: When language overshadows insight. Journal of Experimental Psychology: General 122 , 166–183.

Thomas, J.C. (1974): An analysis of behavior in the hobbits-orcs problem. Cognitive Psychology 6 , 257-269.

Wagner, U., Gais, S., Haider, H., Verleger, R. and Born, J. (2004): Sleep inspires insight. Nature 427 , 352–355.

Wegner, D.M. (2002): The Illusion of Conscious Will . MIT Press, Cambridge (MA).

Weisberg, R.W. and Alba, J.W. (1982): Problem solving is not like perception: More on Gestalt theory. Journal of Experimental Psychology: General 111 , 326–330.

Weisberg, R.W. (1992): Metacognition and insight during problem solving: Comment on Metcalfe. Journal of Experimental Psychology: Learning, Memory, and Cognition 18 , 426–431.

Weisberg, R W. (1995): Prolegomena to theories of insight in problem solving: A taxonomy of problems. In: R.J. Sternberg and J.E. Davidson (eds.), The Nature of Insight . MIT Press, Cambridge (MA), pp. 157–196.

Wertheimer, M. (1912): Experimentelle Studien über das Sehen von Bewegung. Zeitschrift für Psychologie 61 , 161–265.

Wertheimer, M. (1923): Untersuchungen zur Lehre von der Gestalt. II, Psychologi-sche Forschung 4 , 301–350.

Wertheimer, M. (1925): Drei Abhandlungen zur Gestalttheorie . Verlag der Philosophischen Akademie, Erlangen.

Wertheimer, M. (1959): Productive Thinking . Harper, New York.

Wertheimer, Mich. (1985): A Gestalt perspective on computer simulations of cognitive processes. Computers in Human Behavior 1 , 19–33.

Wickelgren, W.A. (1974). How to Solve Problems: Elements of a Theory of Problems and Problem Solving . Freeman, San Francisco.

Download references

Author information

Authors and affiliations.

Parmenides Center for the Study of Thinking, 80333 München, Germany

Michael öllinger

Psychology Department, Rutgers University, Newark NJ 07102, USA

Günther Knoblich

You can also search for this author in PubMed Google Scholar

Editor information

Editors and affiliations.

Institut für Grenzgebiete der Psychologie und Psychohygiene e. V. Abt. Theorie und Datenanalyse Wilhelmstr. 3a, 79098 Freiburg, Germany

Harald Atmanspacher ( Dr ) ( Dr )

Kusenstr. 21, 8700 Kuesnacht, Switzerland

Hans Primas ( Prof.Dr ) ( Prof.Dr )

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

öllinger, M., Knoblich, G. (2009). Psychological Research on Insight Problem Solving. In: Atmanspacher, H., Primas, H. (eds) Recasting Reality. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85198-1_14

Download citation

DOI : https://doi.org/10.1007/978-3-540-85198-1_14

Publisher Name : Springer, Berlin, Heidelberg

Print ISBN : 978-3-540-85197-4

Online ISBN : 978-3-540-85198-1

eBook Packages : Humanities, Social Sciences and Law Philosophy and Religion (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Front Psychol

Intuition and Insight: Two Processes That Build on Each Other or Fundamentally Differ?

Thea zander.

1 Department of Psychology, University of Basel, Basel, Switzerland

Michael Öllinger

2 Parmenides Foundation, Munich, Germany

3 Department Psychology, Ludwig-Maximilians-Universität München, Munich, Germany

Kirsten G. Volz

4 Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany

Introduction

Defining Intuition and Insight

Theoretical characterization of intuition.

Insight and Aha-Experience

Different Research Traditions of Intuition and Insight

The Single- vs. Dual-System View on Intuition

Intuition As Experienced-Based Perception of Coherence and As an Antecedent of Insight

The Special-Process vs. Nothing-Special View on Insight

Interim Summary I

Differences in the Cognitive Processes Assumed to Underlie Intuition and Insight

The continuity model of intuition: intuition as a gradual process.

The Discontinuity Model of Insight: Insight As the Result of a Mental Restructuring Process

Interim Summary II

Bridging the Gap between the Underlying Processes of Insight and Intuition

Dual-system models of thinking and reasoning.

Dual-System Models and the Discontinuity Model of Insight: Intuition As the First and Biased Problem Representation

Interim Summary III

Semantic Coherence Tasks Used in Intuition and Insight Research: Word Triads and Remote Associates

Conclusion, Open Research Questions, and Future Directions

Author Contributions

TZ developed the theoretical conception; wrote the article. MÖ developed the theoretical conception; revised the manuscript. KV developed the theoretical conception; revised the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

1 There is the idea that a period, in which a person after encountering an impasse is not being consciously engaged in finding the solution anymore and puts the problem aside (i.e., the incubation period ) fosters sudden insights of the solution (e.g., Gilhooly et al., 2012 ). Ritter and Dijksterhuis (2014) explain that unconscious thought processes continue to find the problem’s solution by re-organizing memory content eventually resulting in gist-based representations. This occurs in the absence of a person’s conscious attempts. It has to be emphasized, however, that empirical studies revealed different results as to whether incubation periods are beneficial for problem solving. The specific conditions under which positive incubation effects take place have to be further investigated ( Sio and Ormerod, 2009 ).

2 For the sake of completeness, it has to be emphasized that metacognitive processes may play a role as well in intuitive processing. To strengthen the scope of our argumentation, we decided not to detail on this notion. Please see Mealor and Dienes (2013) ; Storm and Hickman (2015) , or Thompson et al. (2011) . A particular emphasize may be laid on the concept of experience-based metacognitive feelings (e.g., Koriat and Levy-Sadot, 1999 ).

Betsch T. (2008). “The nature of intuition and its neglect in research on judgment and decision making,” in Intuition in Judgment and Decision Making , eds Plessner H., Betsch C., Betsch T. (New York, NY: Lawrence Erlbaum Associates; ), 3–22. [ Google Scholar ]
Bolte A., Goschke T. (2005). On the speed of intuition: intuitive judgments of semantic coherence under different response deadlines. Mem. Cognit. 33 1248–1255. 10.3758/BF03193226 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bowden E. M., Jung-Beeman M. (2007). Methods for investigating the neural components of insight. Methods 42 87–99. 10.1016/j.ymeth.2006.11.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bowden E. M., Jung-Beeman M., Fleck J., Kounios J. (2005). New approaches to demystifying insight. Trends Cogn. Sci. 9 322–328. 10.1016/j.tics.2005.05.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bowers K. S. (1984). “On being unconsciously influenced and informed,” in The Unconscious Reconsidered , eds Bowers K. S., Meichenbaum D. (New York, NY: John Wiley & Sons; ), 227–272. [ Google Scholar ]
Bowers K. S., Farvolden P., Mermigis L. (1995). “Intuitive antecedents of insight,” in The Creative Cognition Approach , eds Smith S. M., Ward T. B., Finke R. A. (Cambridge, MA: The MIT Press; ), 27–51. [ Google Scholar ]
Bowers K. S., Regehr G., Balthazard C., Parker K. (1990). Intuition in the context of discovery. Cognit. Psychol. 22 72–110. 10.1016/0010-0285(90)90004-N [ CrossRef ] [ Google Scholar ]
Claxton G. (1998). Investigating human intuition: knowing without knowing why. Psychologist 11 217–220. 10.3758/s13415-014-0286-7 [ CrossRef ] [ Google Scholar ]
Collins A. M., Loftus E. F. (1975). A spreading-activation theory of semantic processing. Psychol. Rev. 82 407–428. 10.1037/0033-295X.82.6.407 [ CrossRef ] [ Google Scholar ]
Cranford E. A., Moss J. (2012). Is insight always the same? A verbal protocol analysis of insight in compound remote associate problems. J. Probl. Solving 4 128–153. [ Google Scholar ]
Danek A. H., Fraps T., von Müller A., Grothe B., Öllinger M. (2013). Aha! experiences leave a mark: facilitated recall of insight solutions. Psychol. Res. 77 659–669. 10.1007/s00426-012-0454-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Danek A. H., Wiley J., Öllinger M. (2016). Solving classical insight problems without Aha! experience: 9 dot, 8 coin, and matchstick arithmetic problems. J. Probl. Solving 9 47–57. 10.7771/1932-6246.1183 [ CrossRef ] [ Google Scholar ]
Dorfman J., Shames V. A., Kihlstrom J. F. (1996). “Intuition, incubation and insight: implicit cognition in problem solving,” in Implicit Cognition , ed. Underwood G. (Oxford: Oxford University Press; ), 257–296. [ Google Scholar ]
Duncker K. (1935). Zur Psychologie des Produktiven Denkens [On the psychology of productive Thinking]. Berlin: Springer. [ Google Scholar ]
Duncker K. (1945). On problem solving. Psychol. Monogr. 58 : 270 10.1037/h0093599 [ CrossRef ] [ Google Scholar ]
Durso F. T. F., Rea C. C. B., Dayton T. (1994). Graph-theoretic confirmation of restructuring during insight. Psychol. Sci. 5 94–97. 10.1111/j.1467-9280.1994.tb00637.x [ CrossRef ] [ Google Scholar ]
Evans J., Frankish K. (eds) (2009). In Two Minds: Dual Processes and Beyond . Oxford: Oxford University Press. [ Google Scholar ]
Evans J., Stanovich K. S. (2013). Dual-process theories of higher cognition: advancing the debate. Perspect. Psychol. Sci. 8 223–241. 10.1177/1745691612460685 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fedor A., Szatmary E., Öllinger M. (2015). Problem solving stages in the five square problem. Front. Psychol. 6 : 1050 10.3389/fpsyg.2015.01050 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Gick M. L., Lockhardt R. S. (1995). “Cognitive and affective components of insight,” in The Nature of Insight , eds Sternberg R. J., Davidson J. E. (Cambridge, MA: The MIT Press; ), 197–228. [ Google Scholar ]
Gigerenzer G. (2008). Gut Feelings: The Intelligence of the Unconscious . New York, NY: Viking. [ Google Scholar ]
Gigerenzer G., Gaissmaier W. (2011). Heuristic decision making. Annu. Rev. Psychol. 62 451–482. 10.1146/annurev-psych-120709-145346 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Gigerenzer G., Todd P. M., The Abc Research Group (eds) (1999). Simple Heuristics that Make us Smart. New York, NY: Oxford University Press. [ Google Scholar ]
Gilhooly K. J., Georgiou G. J., Garrison J., Reston J. D., Sirota M. (2012). Don’t wait to incubate: immediate versus delayed incubation in divergent thinking. Mem. Cognit. 40 966–975. 10.3758/s13421-012-0199-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
Gladwell M. (2005). Blink. The Power of Thinking Without Thinking. London: Penguin Books. [ Google Scholar ]
Glöckner A., Witteman C. (2010). “Foundations for tracing intuition: models, findings, categorizations,” in Foundations for Tracing Intuition: Challenges and Methods , eds Glöckner A., Witteman C. (East Sussex: Psychology Press; ), 1–23. [ Google Scholar ]
Hicks J. A., Burton C. M., Cicero D. C., Trent J., King L. A. (2010). Positive affect, intuition, and feelings of meaning. J. Pers. Soc. Psychol. 89 967–979. 10.1037/a0019377 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Hogarth R. M. (2001). Educating Intuition. Chicago, IL: University of Chicago Press. [ Google Scholar ]
Jung-Beeman M., Bowden E. M., Haberman J., Frymiare J. L., Arambel-Liu S., Greenblatt R., et al. (2004). Neural activity when people solve verbal problems with insight. PLoS Biol. 2 : E97 10.1371/journal.pbio.0020097 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kahneman D. (2011). Thinking, Fast and Slow . London: Penguin Books. [ Google Scholar ]
Kahneman D., Tversky A. (1974). Judgment under uncertainty: heuristics and biases. Science 185 1124–1131. 10.1126/science.185.4157.1124 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Keren G., Schul Y. (2009). Two is not always better than one: a critical evaluation of two-system theories. Perspect. Psychol. Sci. 4 533–550. 10.1111/j.1745-6924.2009.01164.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kershaw T. C., Ohlsson S. (2004). Multiple causes of difficulty in insight: the case of thenine-dot problem. J. Exp. Psychol. Learn. Mem. Cogn. 30 3–13. 10.1037/0278-7393.30.1.3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kizilirmak J., Galvao Gomes da Silva J., Imamoglu F., Richardsohn-Klavehn R. (2015). Generation and the subjective feeling of “aha!” are independently related to learning from insight. Psychol. Res. 10.1007/s00426-015-0697-2 [Epub ahead of print]. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Klein G., Jarosz A. (2011). A naturalistic study of insight. J. Cogn. Eng. Decis. Mak. 5 335–351. 10.1177/1555343411427013 [ CrossRef ] [ Google Scholar ]
Knoblich G., Ohlsson S., Haider H., Rhenius D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 25 1534–1555. 10.1037/0278-7393.25.6.1534 [ CrossRef ] [ Google Scholar ]
Knoblich G., Ohlsson S., Raney G. E. (2001). An eye movement study of insight problem solving. Mem. Cognit. 29 1000–1009. 10.3758/BF03195762 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Knoblich G., Öllinger M. (2006). “Einsicht und umstrukturierung beim problemlösen [insight and restructering in problem solving],” in Denken und Problemlösen. Enzyklopädie der Psychologie [Thinking and Problem Solving. Encyclopedia of Psychology] , ed. Funke J. (Göttingen: Hogrefe; ), 3–86. [ Google Scholar ]
Köhler W. (1921). Intelligenzprüfungen am Menschenaffen [Investigating intelligence in great apes] . Berlin: Springer. [ Google Scholar ]
Koriat A., Levy-Sadot R. (1999). “Processes underlying metacognitive judgments: information-based and experience-based monitoring of one’s own knowledge,” in Dual-Process Theories in Social Psychology , eds Chaiken S., Trope Y. (New York, NY: Guilford Press; ), 483–502. [ Google Scholar ]
Kounios J., Beeman M. (2014). The cognitive neuroscience of insight. Annu. Rev. Psychol. 65 71–93. 10.1146/annurev-psych-010213-115154 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kruglanski A. W., Gigerenzer G. (2011). Intuitive and deliberate judgments are based on common principles. Psychol. Rev. 118 97–109. 10.1037/a0020762 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lieberman M. D. (2000). Intuition: a social cognitive neuroscience approach. Psychol. Bull. 126 109–137. 10.1037/0033-2909.126.1.109 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Luchins A. S., Luchins E. H. (1959). Rigidity of Behavior: A Variational Approach to the Effect of Einstellung . Eugene, OR: University of Oregon Books. [ Google Scholar ]
Maier N. R. F. (1930). Reasoning in humans. I. On direction. J. Comp. Psychol. 10 115–143. 10.1037/h0073232 [ CrossRef ] [ Google Scholar ]
Mangan B. (1993). Taking phenomenology seriously: the “fringe” and its implications for cognitive research. Consci. Cogn. 2 89–108. 10.1006/ccog.1993.1008 [ CrossRef ] [ Google Scholar ]
Mangan B. (2001). Sensation’s ghost. The non-sensory “fringe” of consciousness. Psyche 7 . [ Google Scholar ]
Mangan B. (2015). The uncanny valley as fringe experience. Interact. Stud. 16 193–199. 10.1075/is.16.2.05man [ CrossRef ] [ Google Scholar ]
Mealor A. D., Dienes Z. (2013). The speed of metacognition: taking time to get to know one’s structural knowledge. Conscious. Cogn. 22 123–136. 10.1016/j.concog.2012.11.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mednick S. A. (1962). The associative basis of the creative process. Psychol. Rev. 69 220–232. 10.1037/h0048850 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mega L. F., Gigerenzer G., Volz K. G. (2015). Do intuitive and deliberate judgments rely on two distinct neural systems? A case study in face processing. Front. Hum. Neurosci. 9 : 456 10.3389/fnhum.2015.00465 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Metcalfe J., Wiebe D. (1987). Intuition in insight and noninsight problem solving. Mem. Cognit. 15 238–246. 10.3758/BF03197722 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Metzger W. (1953). Gesetze des Sehens [Rules of vision]. Frankfurt: Kramer. [ Google Scholar ]
Newell A., Simon H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall. [ Google Scholar ]
Norman E. (2002). Subcategories of “fringe consciousness” and their related nonconscious contexts. Psyche 8 1–15. [ Google Scholar ]
Norman E. (2016). Metacognition and minfulness: the role of fringe consciousness. Mindfulness 10.1007/s12671-016-0494-z [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Norman E., Price M. C., Duff S. C. (2006). Fringe consciousness in sequence learning: the influence of individual differences. Consci. Cogn. 15 723–760. 10.1016/j.concog.2005.06.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Norman E., Price M. C., Duff S. C. (2010). “Fringe consciousness: a useful framework for clarifying the nature of experience-based feelings,” in Trends and Prospects in Metacognition Research , eds Efklides A., Misailidi P. (New York, NY: Springer; ), 63–89. [ Google Scholar ]
Ohlsson S. (1992). “Information-processing explanations of insight and related phenomena,” in Advances in the Psychology of Thinking , eds Keane M., Gilhooly K. (London: Harvester-Wheatsheaf; ), 1–44. [ Google Scholar ]
Ohlsson S. (2011). Deep Learning. How the Mind Overrides Experience. New York, NY: Cambridge University Press. [ Google Scholar ]
Öllinger M., Jones G., Faber A. H., Knoblich G. (2013). Cognitive mechanisms of insight: the role of heuristics and the representational change in solving the eight-coin problem. J. Exp. Psychol. Learn. Mem. Cogn. 39 931–939. 10.1037/a0029194 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Öllinger M., Jones G., Knoblich G. (2006). Heuristics and representational change in two-move matchstick arithmetic task. Adv. Cogn. Psychol. 2 239–253. 10.2478/v10053-008-0059-3 [ CrossRef ] [ Google Scholar ]
Öllinger M., Jones G., Knoblich G. (2008). Investigating the effect of mental set on insight problem solving. Exp. Psychol. 55 270–282. [ PubMed ] [ Google Scholar ]
Öllinger M., Jones G., Knoblich G. (2014). The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem. Psychol. Res. 78 266–275. 10.1007/s00426-013-0494-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ormerod T. C., MacGregor J. N., Chronicle E. P. (2002). Dynamics and constraints in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 28 791–799. 10.1037/0278-7393.28.4.791 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Plessner H., Betsch C., Betsch T. (eds) (2008). Intuition in Judgment and Decision Making. New York, NY: Lawrence Erlbaum Associates. [ Google Scholar ]
Polanyi M. (1966). Implizites Wissen [The tacit dimension]. Frankfurt: Suhrkamp. [ Google Scholar ]
Price M. C. (2002). Measuring the fringe of experience. Psyche 8 1–24. [ Google Scholar ]
Price M. C., Norman E. (2008). Intuitive feelings on the fringe of consciousness: are they conscious and does it matter? Judgm. Decis. Mak. 3 28–41. [ Google Scholar ]
Reber R., Ruch-Monachon M.-A., Perrig W. J. (2007). Decomposing intuitive components in a conceptual problem solving task. Conscious. Cogn. 16 294–309. 10.1016/j.concog.2006.05.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Reber R., Schwarz N., Winkielmann P. (2004). Processing fluency and aesthetic pleasure: is beauty in the perceiver’s processing experience? Pers. Soc. Psychol. Rev. 8 364–382. 10.1207/s15327957pspr0804_3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Remmers C., Topolinski T., Dietrich D. E., Michalak J. (2014). Impaired intuition in pateints with major depressive disorder. Br. J. Clin. Psychol. 54 200–213. 10.1111/bjc.12069 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ritter S., Dijksterhuis A. (2014). Creativity – the unconscious foundations of the incubation period. Front. Hum. Neurosci. 8 : 215 10.3389/fnhum.2014.00215 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sadler-Smith E. (2008). Inside Intuition. Abingdon: Routledge. [ Google Scholar ]
Sandkühler S., Bhattacharya J. (2008). Deconstructing insight: EEG correlates of insightful problem solving. PLoS ONE 3 : 1459 10.1371/journal.pone.0001459 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Seifert C. M., Meyer D. E., Davidson N., Patalano A. L., Yaniv I. (1995). “Demystification of cognitive insight: opportunistic assimilation and the prepared-mind perspective,” in The Nature of Insight , eds Sternberg R. J., Davidson J. E. (Cambridge, MA: The MIT Press; ), 65–124. [ Google Scholar ]
Sio U. N., Ormerod T. C. (2009). Does incubation enhance problem solving? A meta- analytic review. Psychol. Bull. 135 94–120. 10.1037/a0014212 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Stanovich K. E., West R. F. (2000). Individual differences in reasoning: implications for the rationality debate. Behav. Brain Sci. 23 645–665. 10.1017/S0140525X00003435 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sternberg R. J., Davidson J. E. (eds) (1995). The Nature of Insight. Cambridge, MA: The MIT Press. [ Google Scholar ]
Storm B. C., Hickman M. L. (2015). Mental fixation and metacognitive predictions of insight in creative problem solving. Q. J. Exp. Psychol. 68 802–813. 10.1080/17470218.2014.966730 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Thompson V. A., Prowse Turner J. A., Pennycook G. (2011). Intuition, reason, and metacognition. Cognit. Psychol. 63 107–140. 10.1016/j.cogpsych.2011.06.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Topolinski S., Reber R. (2010). Gaining insight into the “aha” experience. Curr. Dir. Psychol. Sci. 19 402–405. 10.3389/fpsyg.2014.01408 [ CrossRef ] [ Google Scholar ]
Topolinski S., Strack F. (2008). Where there’s a will – there’s no intuition. The unintentional basis of semantic coherence judgments. J. Mem. Lang. 58 1032–1048. 10.1016/j.jml.2008.01.002 [ CrossRef ] [ Google Scholar ]
Topolinski S., Strack F. (2009a). Scanning the “fringe” of consciousness: what is felt and what is not felt in intuitions about semantic coherence. Consci. Cogn. 18 608–618. 10.1016/j.concog.2008.06.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Topolinski S., Strack F. (2009b). The analysis of intuition: processing fluency and affect in judgments of semantic coherence. Cogn. Emot. 23 1465–1503. 10.1080/02699930802420745 [ CrossRef ] [ Google Scholar ]
Volz K. G., von Cramon D. Y. (2006). What neuroscience can tell about intuitive processes in the context of perceptual discovery. J. Cogn. Neurosci. 18 1–11. 10.1162/jocn.2006.18.12.2077 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Volz K. G., Zander T. (2014). Primed for intuition? Neurosci. Decis. Mak. 1 26–34. 10.2478/ndm-2014-0001 [ CrossRef ] [ Google Scholar ]
Wertheimer M. (1959). Productive Thinking. New York, NY: Harper. [ Google Scholar ]
Zander T., Horr N. K., Bolte A., Volz K. G. (2015). Intuition as a gradual process: investigating intuition-based and priming-based decisions with fMRI. Brain Behav. 6 : e00420 10.1002/brb3.420 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Zsambok C. E., Klein G. (eds) (1997). Naturalistic Decision Making. New York, NY: Routlege. [ Google Scholar ]

7.3 Problem-Solving

Learning objectives.

By the end of this section, you will be able to:

Describe problem solving strategies
Define algorithm and heuristic
Explain some common roadblocks to effective problem solving

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.

PROBLEM-SOLVING STRATEGIES

When people are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

When one is faced with too much information
When the time to make a decision is limited
When the decision to be made is unimportant
When there is access to very little information to use in making the decision
When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.

Additional Problem Solving Strategies :

Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
Analogy – is using a solution that solves a similar problem.
Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
Divide and conquer – breaking down large complex problems into smaller more manageable problems.
Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
Reduction – adapting the problem to be as similar problems where a solution exists.
Research – using existing knowledge or solutions to similar problems to solve the problem.
Root cause analysis – trying to identify the cause of the problem.

The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.

One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :

Missionary-Cannibal Problem

Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.

Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.

The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:

1. Only one disk can be moved at a time.
2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
3. No disc may be placed on top of a smaller disk.

Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.

Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.

The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.

As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage (1990) suggesting that while collecting data for what would later be his book The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.

While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).

While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.

Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground. Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).

Solving puzzles.

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (see figure) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

Here is another popular type of puzzle (figure below) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

Take a look at the “Puzzling Scales” logic puzzle below (figure below). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

A puzzle involving a scale is shown. At the top of the figure it reads: “Sam Loyds Puzzling Scales.” The first row of the puzzle shows a balanced scale with 3 blocks and a top on the left and 12 marbles on the right. Below this row it reads: “Since the scales now balance.” The next row of the puzzle shows a balanced scale with just the top on the left, and 1 block and 8 marbles on the right. Below this row it reads: “And balance when arranged this way.” The third row shows an unbalanced scale with the top on the left side, which is much lower than the right side. The right side is empty. Below this row it reads: “Then how many marbles will it require to balance with that top?”

What steps did you take to solve this puzzle? You can read the solution at the end of this section.

Pitfalls to problem solving.

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.

Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.

The first puzzle is a Sudoku grid of 16 squares (4 rows of 4 squares) is shown. Half of the numbers were supplied to start the puzzle and are colored blue, and half have been filled in as the puzzle’s solution and are colored red. The numbers in each row of the grid, left to right, are as follows. Row 1: blue 3, red 1, red 4, blue 2. Row 2: red 2, blue 4, blue 1, red 3. Row 3: red 1, blue 3, blue 2, red 4. Row 4: blue 4, red 2, red 3, blue 1.The second puzzle consists of 9 dots arranged in 3 rows of 3 inside of a square. The solution, four straight lines made without lifting the pencil, is shown in a red line with arrows indicating the direction of movement. In order to solve the puzzle, the lines must extend beyond the borders of the box. The four connecting lines are drawn as follows. Line 1 begins at the top left dot, proceeds through the middle and right dots of the top row, and extends to the right beyond the border of the square. Line 2 extends from the end of line 1, through the right dot of the horizontally centered row, through the middle dot of the bottom row, and beyond the square’s border ending in the space beneath the left dot of the bottom row. Line 3 extends from the end of line 2 upwards through the left dots of the bottom, middle, and top rows. Line 4 extends from the end of line 3 through the middle dot in the middle row and ends at the right dot of the bottom row.

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. A specific formula for solving a problem is called ________.

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

2. Solving the Tower of Hanoi problem tends to utilize a ________ strategy of problem solving.

a. divide and conquer

b. means-end analysis

d. experiment

3. A mental shortcut in the form of a general problem-solving framework is called ________.

4. Which type of bias involves becoming fixated on a single trait of a problem?

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

5. Which type of bias involves relying on a false stereotype to make a decision?

6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.

a. functional fixedness

c. working memory

Critical Thinking Questions:

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question:

1. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

algorithm: problem-solving strategy characterized by a specific set of instructions

anchoring bias: faulty heuristic in which you fixate on a single aspect of a problem to find a solution

availability heuristic: faulty heuristic in which you make a decision based on information readily available to you

confirmation bias: faulty heuristic in which you focus on information that confirms your beliefs

functional fixedness: inability to see an object as useful for any other use other than the one for which it was intended

heuristic: mental shortcut that saves time when solving a problem

hindsight bias: belief that the event just experienced was predictable, even though it really wasn’t

mental set: continually using an old solution to a problem without results

problem-solving strategy: method for solving problems

representative bias: faulty heuristic in which you stereotype someone or something without a valid basis for your judgment

trial and error: problem-solving strategy in which multiple solutions are attempted until the correct one is found

working backwards: heuristic in which you begin to solve a problem by focusing on the end result

Share This Book

Increase Font Size

Search Menu
Sign in through your institution
Browse content in Arts and Humanities
Browse content in Archaeology
Anglo-Saxon and Medieval Archaeology
Archaeological Methodology and Techniques
Archaeology by Region
Archaeology of Religion
Archaeology of Trade and Exchange
Biblical Archaeology
Contemporary and Public Archaeology
Environmental Archaeology
Historical Archaeology
History and Theory of Archaeology
Industrial Archaeology
Landscape Archaeology
Mortuary Archaeology
Prehistoric Archaeology
Underwater Archaeology
Zooarchaeology
Browse content in Architecture
Architectural Structure and Design
History of Architecture
Residential and Domestic Buildings
Theory of Architecture
Browse content in Art
Art Subjects and Themes
History of Art
Industrial and Commercial Art
Theory of Art
Biographical Studies
Byzantine Studies
Browse content in Classical Studies
Classical Literature
Classical Reception
Classical History
Classical Philosophy
Classical Mythology
Classical Art and Architecture
Classical Oratory and Rhetoric
Greek and Roman Papyrology
Greek and Roman Archaeology
Greek and Roman Epigraphy
Greek and Roman Law
Late Antiquity
Religion in the Ancient World
Digital Humanities
Browse content in History
Colonialism and Imperialism
Diplomatic History
Environmental History
Genealogy, Heraldry, Names, and Honours
Genocide and Ethnic Cleansing
Historical Geography
History by Period
History of Emotions
History of Agriculture
History of Education
History of Gender and Sexuality
Industrial History
Intellectual History
International History
Labour History
Legal and Constitutional History
Local and Family History
Maritime History
Military History
National Liberation and Post-Colonialism
Oral History
Political History
Public History
Regional and National History
Revolutions and Rebellions
Slavery and Abolition of Slavery
Social and Cultural History
Theory, Methods, and Historiography
Urban History
World History
Browse content in Language Teaching and Learning
Language Learning (Specific Skills)
Language Teaching Theory and Methods
Browse content in Linguistics
Applied Linguistics
Cognitive Linguistics
Computational Linguistics
Forensic Linguistics
Grammar, Syntax and Morphology
Historical and Diachronic Linguistics
History of English
Language Evolution
Language Reference
Language Variation
Language Families
Language Acquisition
Lexicography
Linguistic Anthropology
Linguistic Theories
Linguistic Typology
Phonetics and Phonology
Psycholinguistics
Sociolinguistics
Translation and Interpretation
Writing Systems
Browse content in Literature
Bibliography
Children's Literature Studies
Literary Studies (Romanticism)
Literary Studies (American)
Literary Studies (Modernism)
Literary Studies (Asian)
Literary Studies (European)
Literary Studies (Eco-criticism)
Literary Studies - World
Literary Studies (1500 to 1800)
Literary Studies (19th Century)
Literary Studies (20th Century onwards)
Literary Studies (African American Literature)
Literary Studies (British and Irish)
Literary Studies (Early and Medieval)
Literary Studies (Fiction, Novelists, and Prose Writers)
Literary Studies (Gender Studies)
Literary Studies (Graphic Novels)
Literary Studies (History of the Book)
Literary Studies (Plays and Playwrights)
Literary Studies (Poetry and Poets)
Literary Studies (Postcolonial Literature)
Literary Studies (Queer Studies)
Literary Studies (Science Fiction)
Literary Studies (Travel Literature)
Literary Studies (War Literature)
Literary Studies (Women's Writing)
Literary Theory and Cultural Studies
Mythology and Folklore
Shakespeare Studies and Criticism
Browse content in Media Studies
Browse content in Music
Applied Music
Dance and Music
Ethics in Music
Ethnomusicology
Gender and Sexuality in Music
Medicine and Music
Music Cultures
Music and Media
Music and Culture
Music and Religion
Music Education and Pedagogy
Music Theory and Analysis
Musical Scores, Lyrics, and Libretti
Musical Structures, Styles, and Techniques
Musicology and Music History
Performance Practice and Studies
Race and Ethnicity in Music
Sound Studies
Browse content in Performing Arts
Browse content in Philosophy
Aesthetics and Philosophy of Art
Epistemology
Feminist Philosophy
History of Western Philosophy
Metaphysics
Moral Philosophy
Non-Western Philosophy
Philosophy of Language
Philosophy of Mind
Philosophy of Perception
Philosophy of Action
Philosophy of Law
Philosophy of Religion
Philosophy of Science
Philosophy of Mathematics and Logic
Practical Ethics
Social and Political Philosophy
Browse content in Religion
Biblical Studies
Christianity
East Asian Religions
History of Religion
Judaism and Jewish Studies
Qumran Studies
Religion and Education
Religion and Health
Religion and Politics
Religion and Science
Religion and Law
Religion and Art, Literature, and Music
Religious Studies
Browse content in Society and Culture
Cookery, Food, and Drink
Cultural Studies
Customs and Traditions
Ethical Issues and Debates
Hobbies, Games, Arts and Crafts
Natural world, Country Life, and Pets
Popular Beliefs and Controversial Knowledge
Sports and Outdoor Recreation
Technology and Society
Travel and Holiday
Visual Culture
Browse content in Law
Arbitration
Browse content in Company and Commercial Law
Commercial Law
Company Law
Browse content in Comparative Law
Systems of Law
Competition Law
Browse content in Constitutional and Administrative Law
Government Powers
Judicial Review
Local Government Law
Military and Defence Law
Parliamentary and Legislative Practice
Construction Law
Contract Law
Browse content in Criminal Law
Criminal Procedure
Criminal Evidence Law
Sentencing and Punishment
Employment and Labour Law
Environment and Energy Law
Browse content in Financial Law
Banking Law
Insolvency Law
History of Law
Human Rights and Immigration
Intellectual Property Law
Browse content in International Law
Private International Law and Conflict of Laws
Public International Law
IT and Communications Law
Jurisprudence and Philosophy of Law
Law and Society
Law and Politics
Browse content in Legal System and Practice
Courts and Procedure
Legal Skills and Practice
Primary Sources of Law
Regulation of Legal Profession
Medical and Healthcare Law
Browse content in Policing
Criminal Investigation and Detection
Police and Security Services
Police Procedure and Law
Police Regional Planning
Browse content in Property Law
Personal Property Law
Study and Revision
Terrorism and National Security Law
Browse content in Trusts Law
Wills and Probate or Succession
Browse content in Medicine and Health
Browse content in Allied Health Professions
Arts Therapies
Clinical Science
Dietetics and Nutrition
Occupational Therapy
Operating Department Practice
Physiotherapy
Radiography
Speech and Language Therapy
Browse content in Anaesthetics
General Anaesthesia
Neuroanaesthesia
Clinical Neuroscience
Browse content in Clinical Medicine
Acute Medicine
Cardiovascular Medicine
Clinical Genetics
Clinical Pharmacology and Therapeutics
Dermatology
Endocrinology and Diabetes
Gastroenterology
Genito-urinary Medicine
Geriatric Medicine
Infectious Diseases
Medical Toxicology
Medical Oncology
Pain Medicine
Palliative Medicine
Rehabilitation Medicine
Respiratory Medicine and Pulmonology
Rheumatology
Sleep Medicine
Sports and Exercise Medicine
Community Medical Services
Critical Care
Emergency Medicine
Forensic Medicine
Haematology
History of Medicine
Browse content in Medical Skills
Clinical Skills
Communication Skills
Nursing Skills
Surgical Skills
Medical Ethics
Browse content in Medical Dentistry
Oral and Maxillofacial Surgery
Paediatric Dentistry
Restorative Dentistry and Orthodontics
Surgical Dentistry
Medical Statistics and Methodology
Browse content in Neurology
Clinical Neurophysiology
Neuropathology
Nursing Studies
Browse content in Obstetrics and Gynaecology
Gynaecology
Occupational Medicine
Ophthalmology
Otolaryngology (ENT)
Browse content in Paediatrics
Neonatology
Browse content in Pathology
Chemical Pathology
Clinical Cytogenetics and Molecular Genetics
Histopathology
Medical Microbiology and Virology
Patient Education and Information
Browse content in Pharmacology
Psychopharmacology
Browse content in Popular Health
Caring for Others
Complementary and Alternative Medicine
Self-help and Personal Development
Browse content in Preclinical Medicine
Cell Biology
Molecular Biology and Genetics
Reproduction, Growth and Development
Primary Care
Professional Development in Medicine
Browse content in Psychiatry
Addiction Medicine
Child and Adolescent Psychiatry
Forensic Psychiatry
Learning Disabilities
Old Age Psychiatry
Psychotherapy
Browse content in Public Health and Epidemiology
Epidemiology
Public Health
Browse content in Radiology
Clinical Radiology
Interventional Radiology
Nuclear Medicine
Radiation Oncology
Reproductive Medicine
Browse content in Surgery
Cardiothoracic Surgery
Gastro-intestinal and Colorectal Surgery
General Surgery
Neurosurgery
Paediatric Surgery
Peri-operative Care
Plastic and Reconstructive Surgery
Surgical Oncology
Transplant Surgery
Trauma and Orthopaedic Surgery
Vascular Surgery
Browse content in Science and Mathematics
Browse content in Biological Sciences
Aquatic Biology
Biochemistry
Bioinformatics and Computational Biology
Developmental Biology
Ecology and Conservation
Evolutionary Biology
Genetics and Genomics
Microbiology
Molecular and Cell Biology
Natural History
Plant Sciences and Forestry
Research Methods in Life Sciences
Structural Biology
Systems Biology
Zoology and Animal Sciences
Browse content in Chemistry
Analytical Chemistry
Computational Chemistry
Crystallography
Environmental Chemistry
Industrial Chemistry
Inorganic Chemistry
Materials Chemistry
Medicinal Chemistry
Mineralogy and Gems
Organic Chemistry
Physical Chemistry
Polymer Chemistry
Study and Communication Skills in Chemistry
Theoretical Chemistry
Browse content in Computer Science
Artificial Intelligence
Computer Architecture and Logic Design
Game Studies
Human-Computer Interaction
Mathematical Theory of Computation
Programming Languages
Software Engineering
Systems Analysis and Design
Virtual Reality
Browse content in Computing
Business Applications
Computer Games
Computer Security
Computer Networking and Communications
Digital Lifestyle
Graphical and Digital Media Applications
Operating Systems
Browse content in Earth Sciences and Geography
Atmospheric Sciences
Environmental Geography
Geology and the Lithosphere
Maps and Map-making
Meteorology and Climatology
Oceanography and Hydrology
Palaeontology
Physical Geography and Topography
Regional Geography
Soil Science
Urban Geography
Browse content in Engineering and Technology
Agriculture and Farming
Biological Engineering
Civil Engineering, Surveying, and Building
Electronics and Communications Engineering
Energy Technology
Engineering (General)
Environmental Science, Engineering, and Technology
History of Engineering and Technology
Mechanical Engineering and Materials
Technology of Industrial Chemistry
Transport Technology and Trades
Browse content in Environmental Science
Applied Ecology (Environmental Science)
Conservation of the Environment (Environmental Science)
Environmental Sustainability
Environmentalist Thought and Ideology (Environmental Science)
Management of Land and Natural Resources (Environmental Science)
Natural Disasters (Environmental Science)
Nuclear Issues (Environmental Science)
Pollution and Threats to the Environment (Environmental Science)
Social Impact of Environmental Issues (Environmental Science)
History of Science and Technology
Browse content in Materials Science
Ceramics and Glasses
Composite Materials
Metals, Alloying, and Corrosion
Nanotechnology
Browse content in Mathematics
Applied Mathematics
Biomathematics and Statistics
History of Mathematics
Mathematical Education
Mathematical Finance
Mathematical Analysis
Numerical and Computational Mathematics
Probability and Statistics
Pure Mathematics
Browse content in Neuroscience
Cognition and Behavioural Neuroscience
Development of the Nervous System
Disorders of the Nervous System
History of Neuroscience
Invertebrate Neurobiology
Molecular and Cellular Systems
Neuroendocrinology and Autonomic Nervous System
Neuroscientific Techniques
Sensory and Motor Systems
Browse content in Physics
Astronomy and Astrophysics
Atomic, Molecular, and Optical Physics
Biological and Medical Physics
Classical Mechanics
Computational Physics
Condensed Matter Physics
Electromagnetism, Optics, and Acoustics
History of Physics
Mathematical and Statistical Physics
Measurement Science
Nuclear Physics
Particles and Fields
Plasma Physics
Quantum Physics
Relativity and Gravitation
Semiconductor and Mesoscopic Physics
Browse content in Psychology
Affective Sciences
Clinical Psychology
Cognitive Psychology
Cognitive Neuroscience
Criminal and Forensic Psychology
Developmental Psychology
Educational Psychology
Evolutionary Psychology
Health Psychology
History and Systems in Psychology
Music Psychology
Neuropsychology
Organizational Psychology
Psychological Assessment and Testing
Psychology of Human-Technology Interaction
Psychology Professional Development and Training
Research Methods in Psychology
Social Psychology
Browse content in Social Sciences
Browse content in Anthropology
Anthropology of Religion
Human Evolution
Medical Anthropology
Physical Anthropology
Regional Anthropology
Social and Cultural Anthropology
Theory and Practice of Anthropology
Browse content in Business and Management
Business Ethics
Business History
Business Strategy
Business and Technology
Business and Government
Business and the Environment
Comparative Management
Corporate Governance
Corporate Social Responsibility
Entrepreneurship
Health Management
Human Resource Management
Industrial and Employment Relations
Industry Studies
Information and Communication Technologies
International Business
Knowledge Management
Management and Management Techniques
Operations Management
Organizational Theory and Behaviour
Pensions and Pension Management
Public and Nonprofit Management
Strategic Management
Supply Chain Management
Browse content in Criminology and Criminal Justice
Criminal Justice
Criminology
Forms of Crime
International and Comparative Criminology
Youth Violence and Juvenile Justice
Development Studies
Browse content in Economics
Agricultural, Environmental, and Natural Resource Economics
Asian Economics
Behavioural Finance
Behavioural Economics and Neuroeconomics
Econometrics and Mathematical Economics
Economic History
Economic Methodology
Economic Systems
Economic Development and Growth
Financial Markets
Financial Institutions and Services
General Economics and Teaching
Health, Education, and Welfare
History of Economic Thought
International Economics
Labour and Demographic Economics
Law and Economics
Macroeconomics and Monetary Economics
Microeconomics
Public Economics
Urban, Rural, and Regional Economics
Welfare Economics
Browse content in Education
Adult Education and Continuous Learning
Care and Counselling of Students
Early Childhood and Elementary Education
Educational Equipment and Technology
Educational Strategies and Policy
Higher and Further Education
Organization and Management of Education
Philosophy and Theory of Education
Schools Studies
Secondary Education
Teaching of a Specific Subject
Teaching of Specific Groups and Special Educational Needs
Teaching Skills and Techniques
Browse content in Environment
Applied Ecology (Social Science)
Climate Change
Conservation of the Environment (Social Science)
Environmentalist Thought and Ideology (Social Science)
Natural Disasters (Environment)
Social Impact of Environmental Issues (Social Science)
Browse content in Human Geography
Cultural Geography
Economic Geography
Political Geography
Browse content in Interdisciplinary Studies
Communication Studies
Museums, Libraries, and Information Sciences
Browse content in Politics
African Politics
Asian Politics
Chinese Politics
Comparative Politics
Conflict Politics
Elections and Electoral Studies
Environmental Politics
Ethnic Politics
European Union
Foreign Policy
Gender and Politics
Human Rights and Politics
Indian Politics
International Relations
International Organization (Politics)
International Political Economy
Irish Politics
Latin American Politics
Middle Eastern Politics
Political Behaviour
Political Economy
Political Institutions
Political Theory
Political Methodology
Political Communication
Political Philosophy
Political Sociology
Politics and Law
Politics of Development
Public Policy
Public Administration
Quantitative Political Methodology
Regional Political Studies
Russian Politics
Security Studies
State and Local Government
UK Politics
US Politics
Browse content in Regional and Area Studies
African Studies
Asian Studies
East Asian Studies
Japanese Studies
Latin American Studies
Middle Eastern Studies
Native American Studies
Scottish Studies
Browse content in Research and Information
Research Methods
Browse content in Social Work
Addictions and Substance Misuse
Adoption and Fostering
Care of the Elderly
Child and Adolescent Social Work
Couple and Family Social Work
Direct Practice and Clinical Social Work
Emergency Services
Human Behaviour and the Social Environment
International and Global Issues in Social Work
Mental and Behavioural Health
Social Justice and Human Rights
Social Policy and Advocacy
Social Work and Crime and Justice
Social Work Macro Practice
Social Work Practice Settings
Social Work Research and Evidence-based Practice
Welfare and Benefit Systems
Browse content in Sociology
Childhood Studies
Community Development
Comparative and Historical Sociology
Economic Sociology
Gender and Sexuality
Gerontology and Ageing
Health, Illness, and Medicine
Marriage and the Family
Migration Studies
Occupations, Professions, and Work
Organizations
Population and Demography
Race and Ethnicity
Social Theory
Social Movements and Social Change
Social Research and Statistics
Social Stratification, Inequality, and Mobility
Sociology of Religion
Sociology of Education
Sport and Leisure
Urban and Rural Studies
Browse content in Warfare and Defence
Defence Strategy, Planning, and Research
Land Forces and Warfare
Military Administration
Military Life and Institutions
Naval Forces and Warfare
Other Warfare and Defence Issues
Peace Studies and Conflict Resolution
Weapons and Equipment

The Oxford Handbook of Cognitive Psychology

< Previous chapter
Next chapter >

48 Problem Solving

Department of Psychological and Brain Sciences, University of California, Santa Barbara

Published: 03 June 2013
Cite Icon Cite
Permissions Icon Permissions

Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined. The major cognitive processes in problem solving are representing, planning, executing, and monitoring. The major kinds of knowledge required for problem solving are facts, concepts, procedures, strategies, and beliefs. Classic theoretical approaches to the study of problem solving are associationism, Gestalt, and information processing. Current issues and suggested future issues include decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific thinking, everyday thinking, and the cognitive neuroscience of problem solving. Common themes concern the domain specificity of problem solving and a focus on problem solving in authentic contexts.

The study of problem solving begins with defining problem solving, problem, and problem types. This introduction to problem solving is rounded out with an examination of cognitive processes in problem solving, the role of knowledge in problem solving, and historical approaches to the study of problem solving.

Definition of Problem Solving

Problem solving refers to cognitive processing directed at achieving a goal for which the problem solver does not initially know a solution method. This definition consists of four major elements (Mayer, 1992 ; Mayer & Wittrock, 2006 ):

Cognitive —Problem solving occurs within the problem solver’s cognitive system and can only be inferred indirectly from the problem solver’s behavior (including biological changes, introspections, and actions during problem solving). Process —Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of a new mental representation. Directed —Problem solving is aimed at achieving a goal. Personal —Problem solving depends on the existing knowledge of the problem solver so that what is a problem for one problem solver may not be a problem for someone who already knows a solution method.

The definition is broad enough to include a wide array of cognitive activities such as deciding which apartment to rent, figuring out how to use a cell phone interface, playing a game of chess, making a medical diagnosis, finding the answer to an arithmetic word problem, or writing a chapter for a handbook. Problem solving is pervasive in human life and is crucial for human survival. Although this chapter focuses on problem solving in humans, problem solving also occurs in nonhuman animals and in intelligent machines.

How is problem solving related to other forms of high-level cognition processing, such as thinking and reasoning? Thinking refers to cognitive processing in individuals but includes both directed thinking (which corresponds to the definition of problem solving) and undirected thinking such as daydreaming (which does not correspond to the definition of problem solving). Thus, problem solving is a type of thinking (i.e., directed thinking).

Reasoning refers to problem solving within specific classes of problems, such as deductive reasoning or inductive reasoning. In deductive reasoning, the reasoner is given premises and must derive a conclusion by applying the rules of logic. For example, given that “A is greater than B” and “B is greater than C,” a reasoner can conclude that “A is greater than C.” In inductive reasoning, the reasoner is given (or has experienced) a collection of examples or instances and must infer a rule. For example, given that X, C, and V are in the “yes” group and x, c, and v are in the “no” group, the reasoning may conclude that B is in “yes” group because it is in uppercase format. Thus, reasoning is a type of problem solving.

Definition of Problem

A problem occurs when someone has a goal but does not know to achieve it. This definition is consistent with how the Gestalt psychologist Karl Duncker ( 1945 , p. 1) defined a problem in his classic monograph, On Problem Solving : “A problem arises when a living creature has a goal but does not know how this goal is to be reached.” However, today researchers recognize that the definition should be extended to include problem solving by intelligent machines. This definition can be clarified using an information processing approach by noting that a problem occurs when a situation is in the given state, the problem solver wants the situation to be in the goal state, and there is no obvious way to move from the given state to the goal state (Newell & Simon, 1972 ). Accordingly, the three main elements in describing a problem are the given state (i.e., the current state of the situation), the goal state (i.e., the desired state of the situation), and the set of allowable operators (i.e., the actions the problem solver is allowed to take). The definition of “problem” is broad enough to include the situation confronting a physician who wishes to make a diagnosis on the basis of preliminary tests and a patient examination, as well as a beginning physics student trying to solve a complex physics problem.

Types of Problems

It is customary in the problem-solving literature to make a distinction between routine and nonroutine problems. Routine problems are problems that are so familiar to the problem solver that the problem solver knows a solution method. For example, for most adults, “What is 365 divided by 12?” is a routine problem because they already know the procedure for long division. Nonroutine problems are so unfamiliar to the problem solver that the problem solver does not know a solution method. For example, figuring out the best way to set up a funding campaign for a nonprofit charity is a nonroutine problem for most volunteers. Technically, routine problems do not meet the definition of problem because the problem solver has a goal but knows how to achieve it. Much research on problem solving has focused on routine problems, although most interesting problems in life are nonroutine.

Another customary distinction is between well-defined and ill-defined problems. Well-defined problems have a clearly specified given state, goal state, and legal operators. Examples include arithmetic computation problems or games such as checkers or tic-tac-toe. Ill-defined problems have a poorly specified given state, goal state, or legal operators, or a combination of poorly defined features. Examples include solving the problem of global warming or finding a life partner. Although, ill-defined problems are more challenging, much research in problem solving has focused on well-defined problems.

Cognitive Processes in Problem Solving

The process of problem solving can be broken down into two main phases: problem representation , in which the problem solver builds a mental representation of the problem situation, and problem solution , in which the problem solver works to produce a solution. The major subprocess in problem representation is representing , which involves building a situation model —that is, a mental representation of the situation described in the problem. The major subprocesses in problem solution are planning , which involves devising a plan for how to solve the problem; executing , which involves carrying out the plan; and monitoring , which involves evaluating and adjusting one’s problem solving.

For example, given an arithmetic word problem such as “Alice has three marbles. Sarah has two more marbles than Alice. How many marbles does Sarah have?” the process of representing involves building a situation model in which Alice has a set of marbles, there is set of marbles for the difference between the two girls, and Sarah has a set of marbles that consists of Alice’s marbles and the difference set. In the planning process, the problem solver sets a goal of adding 3 and 2. In the executing process, the problem solver carries out the computation, yielding an answer of 5. In the monitoring process, the problem solver looks over what was done and concludes that 5 is a reasonable answer. In most complex problem-solving episodes, the four cognitive processes may not occur in linear order, but rather may interact with one another. Although some research focuses mainly on the execution process, problem solvers may tend to have more difficulty with the processes of representing, planning, and monitoring.

Knowledge for Problem Solving

An important theme in problem-solving research is that problem-solving proficiency on any task depends on the learner’s knowledge (Anderson et al., 2001 ; Mayer, 1992 ). Five kinds of knowledge are as follows:

Facts —factual knowledge about the characteristics of elements in the world, such as “Sacramento is the capital of California” Concepts —conceptual knowledge, including categories, schemas, or models, such as knowing the difference between plants and animals or knowing how a battery works Procedures —procedural knowledge of step-by-step processes, such as how to carry out long-division computations Strategies —strategic knowledge of general methods such as breaking a problem into parts or thinking of a related problem Beliefs —attitudinal knowledge about how one’s cognitive processing works such as thinking, “I’m good at this”

Although some research focuses mainly on the role of facts and procedures in problem solving, complex problem solving also depends on the problem solver’s concepts, strategies, and beliefs (Mayer, 1992 ).

Historical Approaches to Problem Solving

Psychological research on problem solving began in the early 1900s, as an outgrowth of mental philosophy (Humphrey, 1963 ; Mandler & Mandler, 1964 ). Throughout the 20th century four theoretical approaches developed: early conceptions, associationism, Gestalt psychology, and information processing.

Early Conceptions

The start of psychology as a science can be set at 1879—the year Wilhelm Wundt opened the first world’s psychology laboratory in Leipzig, Germany, and sought to train the world’s first cohort of experimental psychologists. Instead of relying solely on philosophical speculations about how the human mind works, Wundt sought to apply the methods of experimental science to issues addressed in mental philosophy. His theoretical approach became structuralism —the analysis of consciousness into its basic elements.

Wundt’s main contribution to the study of problem solving, however, was to call for its banishment. According to Wundt, complex cognitive processing was too complicated to be studied by experimental methods, so “nothing can be discovered in such experiments” (Wundt, 1911/1973 ). Despite his admonishments, however, a group of his former students began studying thinking mainly in Wurzburg, Germany. Using the method of introspection, subjects were asked to describe their thought process as they solved word association problems, such as finding the superordinate of “newspaper” (e.g., an answer is “publication”). Although the Wurzburg group—as they came to be called—did not produce a new theoretical approach, they found empirical evidence that challenged some of the key assumptions of mental philosophy. For example, Aristotle had proclaimed that all thinking involves mental imagery, but the Wurzburg group was able to find empirical evidence for imageless thought .

Associationism

The first major theoretical approach to take hold in the scientific study of problem solving was associationism —the idea that the cognitive representations in the mind consist of ideas and links between them and that cognitive processing in the mind involves following a chain of associations from one idea to the next (Mandler & Mandler, 1964 ; Mayer, 1992 ). For example, in a classic study, E. L. Thorndike ( 1911 ) placed a hungry cat in what he called a puzzle box—a wooden crate in which pulling a loop of string that hung from overhead would open a trap door to allow the cat to escape to a bowl of food outside the crate. Thorndike placed the cat in the puzzle box once a day for several weeks. On the first day, the cat engaged in many extraneous behaviors such as pouncing against the wall, pushing its paws through the slats, and meowing, but on successive days the number of extraneous behaviors tended to decrease. Overall, the time required to get out of the puzzle box decreased over the course of the experiment, indicating the cat was learning how to escape.

Thorndike’s explanation for how the cat learned to solve the puzzle box problem is based on an associationist view: The cat begins with a habit family hierarchy —a set of potential responses (e.g., pouncing, thrusting, meowing, etc.) all associated with the same stimulus (i.e., being hungry and confined) and ordered in terms of strength of association. When placed in the puzzle box, the cat executes its strongest response (e.g., perhaps pouncing against the wall), but when it fails, the strength of the association is weakened, and so on for each unsuccessful action. Eventually, the cat gets down to what was initially a weak response—waving its paw in the air—but when that response leads to accidentally pulling the string and getting out, it is strengthened. Over the course of many trials, the ineffective responses become weak and the successful response becomes strong. Thorndike refers to this process as the law of effect : Responses that lead to dissatisfaction become less associated with the situation and responses that lead to satisfaction become more associated with the situation. According to Thorndike’s associationist view, solving a problem is simply a matter of trial and error and accidental success. A major challenge to assocationist theory concerns the nature of transfer—that is, where does a problem solver find a creative solution that has never been performed before? Associationist conceptions of cognition can be seen in current research, including neural networks, connectionist models, and parallel distributed processing models (Rogers & McClelland, 2004 ).

Gestalt Psychology

The Gestalt approach to problem solving developed in the 1930s and 1940s as a counterbalance to the associationist approach. According to the Gestalt approach, cognitive representations consist of coherent structures (rather than individual associations) and the cognitive process of problem solving involves building a coherent structure (rather than strengthening and weakening of associations). For example, in a classic study, Kohler ( 1925 ) placed a hungry ape in a play yard that contained several empty shipping crates and a banana attached overhead but out of reach. Based on observing the ape in this situation, Kohler noted that the ape did not randomly try responses until one worked—as suggested by Thorndike’s associationist view. Instead, the ape stood under the banana, looked up at it, looked at the crates, and then in a flash of insight stacked the crates under the bananas as a ladder, and walked up the steps in order to reach the banana.

According to Kohler, the ape experienced a sudden visual reorganization in which the elements in the situation fit together in a way to solve the problem; that is, the crates could become a ladder that reduces the distance to the banana. Kohler referred to the underlying mechanism as insight —literally seeing into the structure of the situation. A major challenge of Gestalt theory is its lack of precision; for example, naming a process (i.e., insight) is not the same as explaining how it works. Gestalt conceptions can be seen in modern research on mental models and schemas (Gentner & Stevens, 1983 ).

Information Processing

The information processing approach to problem solving developed in the 1960s and 1970s and was based on the influence of the computer metaphor—the idea that humans are processors of information (Mayer, 2009 ). According to the information processing approach, problem solving involves a series of mental computations—each of which consists of applying a process to a mental representation (such as comparing two elements to determine whether they differ).

In their classic book, Human Problem Solving , Newell and Simon ( 1972 ) proposed that problem solving involved a problem space and search heuristics . A problem space is a mental representation of the initial state of the problem, the goal state of the problem, and all possible intervening states (based on applying allowable operators). Search heuristics are strategies for moving through the problem space from the given to the goal state. Newell and Simon focused on means-ends analysis , in which the problem solver continually sets goals and finds moves to accomplish goals.

Newell and Simon used computer simulation as a research method to test their conception of human problem solving. First, they asked human problem solvers to think aloud as they solved various problems such as logic problems, chess, and cryptarithmetic problems. Then, based on an information processing analysis, Newell and Simon created computer programs that solved these problems. In comparing the solution behavior of humans and computers, they found high similarity, suggesting that the computer programs were solving problems using the same thought processes as humans.

An important advantage of the information processing approach is that problem solving can be described with great clarity—as a computer program. An important limitation of the information processing approach is that it is most useful for describing problem solving for well-defined problems rather than ill-defined problems. The information processing conception of cognition lives on as a keystone of today’s cognitive science (Mayer, 2009 ).

Classic Issues in Problem Solving

Three classic issues in research on problem solving concern the nature of transfer (suggested by the associationist approach), the nature of insight (suggested by the Gestalt approach), and the role of problem-solving heuristics (suggested by the information processing approach).

Transfer refers to the effects of prior learning on new learning (or new problem solving). Positive transfer occurs when learning A helps someone learn B. Negative transfer occurs when learning A hinders someone from learning B. Neutral transfer occurs when learning A has no effect on learning B. Positive transfer is a central goal of education, but research shows that people often do not transfer what they learned to solving problems in new contexts (Mayer, 1992 ; Singley & Anderson, 1989 ).

Three conceptions of the mechanisms underlying transfer are specific transfer , general transfer , and specific transfer of general principles . Specific transfer refers to the idea that learning A will help someone learn B only if A and B have specific elements in common. For example, learning Spanish may help someone learn Latin because some of the vocabulary words are similar and the verb conjugation rules are similar. General transfer refers to the idea that learning A can help someone learn B even they have nothing specifically in common but A helps improve the learner’s mind in general. For example, learning Latin may help people learn “proper habits of mind” so they are better able to learn completely unrelated subjects as well. Specific transfer of general principles is the idea that learning A will help someone learn B if the same general principle or solution method is required for both even if the specific elements are different.

In a classic study, Thorndike and Woodworth ( 1901 ) found that students who learned Latin did not subsequently learn bookkeeping any better than students who had not learned Latin. They interpreted this finding as evidence for specific transfer—learning A did not transfer to learning B because A and B did not have specific elements in common. Modern research on problem-solving transfer continues to show that people often do not demonstrate general transfer (Mayer, 1992 ). However, it is possible to teach people a general strategy for solving a problem, so that when they see a new problem in a different context they are able to apply the strategy to the new problem (Judd, 1908 ; Mayer, 2008 )—so there is also research support for the idea of specific transfer of general principles.

Insight refers to a change in a problem solver’s mind from not knowing how to solve a problem to knowing how to solve it (Mayer, 1995 ; Metcalfe & Wiebe, 1987 ). In short, where does the idea for a creative solution come from? A central goal of problem-solving research is to determine the mechanisms underlying insight.

The search for insight has led to five major (but not mutually exclusive) explanatory mechanisms—insight as completing a schema, insight as suddenly reorganizing visual information, insight as reformulation of a problem, insight as removing mental blocks, and insight as finding a problem analog (Mayer, 1995 ). Completing a schema is exemplified in a study by Selz (Fridja & de Groot, 1982 ), in which people were asked to think aloud as they solved word association problems such as “What is the superordinate for newspaper?” To solve the problem, people sometimes thought of a coordinate, such as “magazine,” and then searched for a superordinate category that subsumed both terms, such as “publication.” According to Selz, finding a solution involved building a schema that consisted of a superordinate and two subordinate categories.

Reorganizing visual information is reflected in Kohler’s ( 1925 ) study described in a previous section in which a hungry ape figured out how to stack boxes as a ladder to reach a banana hanging above. According to Kohler, the ape looked around the yard and found the solution in a flash of insight by mentally seeing how the parts could be rearranged to accomplish the goal.

Reformulating a problem is reflected in a classic study by Duncker ( 1945 ) in which people are asked to think aloud as they solve the tumor problem—how can you destroy a tumor in a patient without destroying surrounding healthy tissue by using rays that at sufficient intensity will destroy any tissue in their path? In analyzing the thinking-aloud protocols—that is, transcripts of what the problem solvers said—Duncker concluded that people reformulated the goal in various ways (e.g., avoid contact with healthy tissue, immunize healthy tissue, have ray be weak in healthy tissue) until they hit upon a productive formulation that led to the solution (i.e., concentrating many weak rays on the tumor).

Removing mental blocks is reflected in classic studies by Duncker ( 1945 ) in which solving a problem involved thinking of a novel use for an object, and by Luchins ( 1942 ) in which solving a problem involved not using a procedure that had worked well on previous problems. Finding a problem analog is reflected in classic research by Wertheimer ( 1959 ) in which learning to find the area of a parallelogram is supported by the insight that one could cut off the triangle on one side and place it on the other side to form a rectangle—so a parallelogram is really a rectangle in disguise. The search for insight along each of these five lines continues in current problem-solving research.

Heuristics are problem-solving strategies, that is, general approaches to how to solve problems. Newell and Simon ( 1972 ) suggested three general problem-solving heuristics for moving from a given state to a goal state: random trial and error , hill climbing , and means-ends analysis . Random trial and error involves randomly selecting a legal move and applying it to create a new problem state, and repeating that process until the goal state is reached. Random trial and error may work for simple problems but is not efficient for complex ones. Hill climbing involves selecting the legal move that moves the problem solver closer to the goal state. Hill climbing will not work for problems in which the problem solver must take a move that temporarily moves away from the goal as is required in many problems.

Means-ends analysis involves creating goals and seeking moves that can accomplish the goal. If a goal cannot be directly accomplished, a subgoal is created to remove one or more obstacles. Newell and Simon ( 1972 ) successfully used means-ends analysis as the search heuristic in a computer program aimed at general problem solving, that is, solving a diverse collection of problems. However, people may also use specific heuristics that are designed to work for specific problem-solving situations (Gigerenzer, Todd, & ABC Research Group, 1999 ; Kahneman & Tversky, 1984 ).

Current and Future Issues in Problem Solving

Eight current issues in problem solving involve decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific problem solving, everyday thinking, and the cognitive neuroscience of problem solving.

Decision Making

Decision making refers to the cognitive processing involved in choosing between two or more alternatives (Baron, 2000 ; Markman & Medin, 2002 ). For example, a decision-making task may involve choosing between getting $240 for sure or having a 25% change of getting $1000. According to economic theories such as expected value theory, people should chose the second option, which is worth $250 (i.e., .25 x $1000) rather than the first option, which is worth $240 (1.00 x $240), but psychological research shows that most people prefer the first option (Kahneman & Tversky, 1984 ).

Research on decision making has generated three classes of theories (Markman & Medin, 2002 ): descriptive theories, such as prospect theory (Kahneman & Tversky), which are based on the ideas that people prefer to overweight the cost of a loss and tend to overestimate small probabilities; heuristic theories, which are based on the idea that people use a collection of short-cut strategies such as the availability heuristic (Gigerenzer et al., 1999 ; Kahneman & Tversky, 2000 ); and constructive theories, such as mental accounting (Kahneman & Tversky, 2000 ), in which people build a narrative to justify their choices to themselves. Future research is needed to examine decision making in more realistic settings.

Intelligence and Creativity

Although researchers do not have complete consensus on the definition of intelligence (Sternberg, 1990 ), it is reasonable to view intelligence as the ability to learn or adapt to new situations. Fluid intelligence refers to the potential to solve problems without any relevant knowledge, whereas crystallized intelligence refers to the potential to solve problems based on relevant prior knowledge (Sternberg & Gregorenko, 2003 ). As people gain more experience in a field, their problem-solving performance depends more on crystallized intelligence (i.e., domain knowledge) than on fluid intelligence (i.e., general ability) (Sternberg & Gregorenko, 2003 ). The ability to monitor and manage one’s cognitive processing during problem solving—which can be called metacognition —is an important aspect of intelligence (Sternberg, 1990 ). Research is needed to pinpoint the knowledge that is needed to support intelligent performance on problem-solving tasks.

Creativity refers to the ability to generate ideas that are original (i.e., other people do not think of the same idea) and functional (i.e., the idea works; Sternberg, 1999 ). Creativity is often measured using tests of divergent thinking —that is, generating as many solutions as possible for a problem (Guilford, 1967 ). For example, the uses test asks people to list as many uses as they can think of for a brick. Creativity is different from intelligence, and it is at the heart of creative problem solving—generating a novel solution to a problem that the problem solver has never seen before. An important research question concerns whether creative problem solving depends on specific knowledge or creativity ability in general.

Teaching of Thinking Skills

How can people learn to be better problem solvers? Mayer ( 2008 ) proposes four questions concerning teaching of thinking skills:

What to teach —Successful programs attempt to teach small component skills (such as how to generate and evaluate hypotheses) rather than improve the mind as a single monolithic skill (Covington, Crutchfield, Davies, & Olton, 1974 ). How to teach —Successful programs focus on modeling the process of problem solving rather than solely reinforcing the product of problem solving (Bloom & Broder, 1950 ). Where to teach —Successful programs teach problem-solving skills within the specific context they will be used rather than within a general course on how to solve problems (Nickerson, 1999 ). When to teach —Successful programs teaching higher order skills early rather than waiting until lower order skills are completely mastered (Tharp & Gallimore, 1988 ).

Overall, research on teaching of thinking skills points to the domain specificity of problem solving; that is, successful problem solving depends on the problem solver having domain knowledge that is relevant to the problem-solving task.

Expert Problem Solving

Research on expertise is concerned with differences between how experts and novices solve problems (Ericsson, Feltovich, & Hoffman, 2006 ). Expertise can be defined in terms of time (e.g., 10 years of concentrated experience in a field), performance (e.g., earning a perfect score on an assessment), or recognition (e.g., receiving a Nobel Prize or becoming Grand Master in chess). For example, in classic research conducted in the 1940s, de Groot ( 1965 ) found that chess experts did not have better general memory than chess novices, but they did have better domain-specific memory for the arrangement of chess pieces on the board. Chase and Simon ( 1973 ) replicated this result in a better controlled experiment. An explanation is that experts have developed schemas that allow them to chunk collections of pieces into a single configuration.

In another landmark study, Larkin et al. ( 1980 ) compared how experts (e.g., physics professors) and novices (e.g., first-year physics students) solved textbook physics problems about motion. Experts tended to work forward from the given information to the goal, whereas novices tended to work backward from the goal to the givens using a means-ends analysis strategy. Experts tended to store their knowledge in an integrated way, whereas novices tended to store their knowledge in isolated fragments. In another study, Chi, Feltovich, and Glaser ( 1981 ) found that experts tended to focus on the underlying physics concepts (such as conservation of energy), whereas novices tended to focus on the surface features of the problem (such as inclined planes or springs). Overall, research on expertise is useful in pinpointing what experts know that is different from what novices know. An important theme is that experts rely on domain-specific knowledge rather than solely general cognitive ability.

Analogical Reasoning

Analogical reasoning occurs when people solve one problem by using their knowledge about another problem (Holyoak, 2005 ). For example, suppose a problem solver learns how to solve a problem in one context using one solution method and then is given a problem in another context that requires the same solution method. In this case, the problem solver must recognize that the new problem has structural similarity to the old problem (i.e., it may be solved by the same method), even though they do not have surface similarity (i.e., the cover stories are different). Three steps in analogical reasoning are recognizing —seeing that a new problem is similar to a previously solved problem; abstracting —finding the general method used to solve the old problem; and mapping —using that general method to solve the new problem.

Research on analogical reasoning shows that people often do not recognize that a new problem can be solved by the same method as a previously solved problem (Holyoak, 2005 ). However, research also shows that successful analogical transfer to a new problem is more likely when the problem solver has experience with two old problems that have the same underlying structural features (i.e., they are solved by the same principle) but different surface features (i.e., they have different cover stories) (Holyoak, 2005 ). This finding is consistent with the idea of specific transfer of general principles as described in the section on “Transfer.”

Mathematical and Scientific Problem Solving

Research on mathematical problem solving suggests that five kinds of knowledge are needed to solve arithmetic word problems (Mayer, 2008 ):

Factual knowledge —knowledge about the characteristics of problem elements, such as knowing that there are 100 cents in a dollar Schematic knowledge —knowledge of problem types, such as being able to recognize time-rate-distance problems Strategic knowledge —knowledge of general methods, such as how to break a problem into parts Procedural knowledge —knowledge of processes, such as how to carry our arithmetic operations Attitudinal knowledge —beliefs about one’s mathematical problem-solving ability, such as thinking, “I am good at this”

People generally possess adequate procedural knowledge but may have difficulty in solving mathematics problems because they lack factual, schematic, strategic, or attitudinal knowledge (Mayer, 2008 ). Research is needed to pinpoint the role of domain knowledge in mathematical problem solving.

Research on scientific problem solving shows that people harbor misconceptions, such as believing that a force is needed to keep an object in motion (McCloskey, 1983 ). Learning to solve science problems involves conceptual change, in which the problem solver comes to recognize that previous conceptions are wrong (Mayer, 2008 ). Students can be taught to engage in scientific reasoning such as hypothesis testing through direct instruction in how to control for variables (Chen & Klahr, 1999 ). A central theme of research on scientific problem solving concerns the role of domain knowledge.

Everyday Thinking

Everyday thinking refers to problem solving in the context of one’s life outside of school. For example, children who are street vendors tend to use different procedures for solving arithmetic problems when they are working on the streets than when they are in school (Nunes, Schlieman, & Carraher, 1993 ). This line of research highlights the role of situated cognition —the idea that thinking always is shaped by the physical and social context in which it occurs (Robbins & Aydede, 2009 ). Research is needed to determine how people solve problems in authentic contexts.

Cognitive Neuroscience of Problem Solving

The cognitive neuroscience of problem solving is concerned with the brain activity that occurs during problem solving. For example, using fMRI brain imaging methodology, Goel ( 2005 ) found that people used the language areas of the brain to solve logical reasoning problems presented in sentences (e.g., “All dogs are pets…”) and used the spatial areas of the brain to solve logical reasoning problems presented in abstract letters (e.g., “All D are P…”). Cognitive neuroscience holds the potential to make unique contributions to the study of problem solving.

Problem solving has always been a topic at the fringe of cognitive psychology—too complicated to study intensively but too important to completely ignore. Problem solving—especially in realistic environments—is messy in comparison to studying elementary processes in cognition. The field remains fragmented in the sense that topics such as decision making, reasoning, intelligence, expertise, mathematical problem solving, everyday thinking, and the like are considered to be separate topics, each with its own separate literature. Yet some recurring themes are the role of domain-specific knowledge in problem solving and the advantages of studying problem solving in authentic contexts.

Future Directions

Some important issues for future research include the three classic issues examined in this chapter—the nature of problem-solving transfer (i.e., How are people able to use what they know about previous problem solving to help them in new problem solving?), the nature of insight (e.g., What is the mechanism by which a creative solution is constructed?), and heuristics (e.g., What are some teachable strategies for problem solving?). In addition, future research in problem solving should continue to pinpoint the role of domain-specific knowledge in problem solving, the nature of cognitive ability in problem solving, how to help people develop proficiency in solving problems, and how to provide aids for problem solving.

Anderson L. W. , Krathwohl D. R. , Airasian P. W. , Cruikshank K. A. , Mayer R. E. , Pintrich P. R. , Raths, J., & Wittrock M. C. ( 2001 ). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York : Longman.

Baron J. ( 2000 ). Thinking and deciding (3rd ed.). New York : Cambridge University Press.

Google Scholar

Google Preview

Bloom B. S. , & Broder B. J. ( 1950 ). Problem-solving processes of college students: An exploratory investigation. Chicago : University of Chicago Press.

Chase W. G. , & Simon H. A. ( 1973 ). Perception in chess. Cognitive Psychology, 4, 55–81.

Chen Z. , & Klahr D. ( 1999 ). All other things being equal: Acquisition and transfer of the control of variable strategy . Child Development, 70, 1098–1120.

Chi M. T. H. , Feltovich P. J. , & Glaser R. ( 1981 ). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

Covington M. V. , Crutchfield R. S. , Davies L. B. , & Olton R. M. ( 1974 ). The productive thinking program. Columbus, OH : Merrill.

de Groot A. D. ( 1965 ). Thought and choice in chess. The Hague, The Netherlands : Mouton.

Duncker K. ( 1945 ). On problem solving. Psychological Monographs, 58 (3) (Whole No. 270).

Ericsson K. A. , Feltovich P. J. , & Hoffman R. R. (Eds.). ( 2006 ). The Cambridge handbook of expertise and expert performance. New York : Cambridge University Press.

Fridja N. H. , & de Groot A. D. ( 1982 ). Otto Selz: His contribution to psychology. The Hague, The Netherlands : Mouton.

Gentner D. , & Stevens A. L. (Eds.). ( 1983 ). Mental models. Hillsdale, NJ : Erlbaum.

Gigerenzer G. , Todd P. M. , & ABC Research Group (Eds.). ( 1999 ). Simple heuristics that make us smart. Oxford, England : Oxford University Press.

Goel V. ( 2005 ). Cognitive neuroscience of deductive reasoning. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 475–492). New York : Cambridge University Press.

Guilford J. P. ( 1967 ). The nature of human intelligence. New York : McGraw-Hill.

Holyoak K. J. ( 2005 ). Analogy. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 117–142). New York : Cambridge University Press.

Humphrey G. ( 1963 ). Thinking: An introduction to experimental psychology. New York : Wiley.

Judd C. H. ( 1908 ). The relation of special training and general intelligence. Educational Review, 36, 28–42.

Kahneman D. , & Tversky A. ( 1984 ). Choices, values, and frames. American Psychologist, 39, 341–350.

Kahneman D. , & Tversky A. (Eds.). ( 2000 ). Choices, values, and frames. New York : Cambridge University Press.

Kohler W. ( 1925 ). The mentality of apes. New York : Liveright.

Larkin J. H. , McDermott J. , Simon D. P. , & Simon H. A. ( 1980 ). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.

Luchins A. ( 1942 ). Mechanization in problem solving. Psychological Monographs, 54 (6) (Whole No. 248).

Mandler J. M. , & Mandler G. ( 1964 ). Thinking from associationism to Gestalt. New York : Wiley.

Markman A. B. , & Medin D. L. ( 2002 ). Decision making. In D. Medin (Ed.), Stevens’ handbook of experimental psychology, Vol. 2. Memory and cognitive processes (2nd ed., pp. 413–466). New York : Wiley.

Mayer R. E. ( 1992 ). Thinking, problem solving, cognition (2nd ed). New York : Freeman.

Mayer R. E. ( 1995 ). The search for insight: Grappling with Gestalt psychology’s unanswered questions. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 3–32). Cambridge, MA : MIT Press.

Mayer R. E. ( 2008 ). Learning and instruction. Upper Saddle River, NJ : Merrill Prentice Hall.

Mayer R. E. ( 2009 ). Information processing. In T. L. Good (Ed.), 21st century education: A reference handbook (pp. 168–174). Thousand Oaks, CA : Sage.

Mayer R. E. , & Wittrock M. C. ( 2006 ). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–304). Mahwah, NJ : Erlbaum.

McCloskey M. ( 1983 ). Intuitive physics. Scientific American, 248 (4), 122–130.

Metcalfe J. , & Wiebe D. ( 1987 ). Intuition in insight and non-insight problem solving. Memory and Cognition, 15, 238–246.

Newell A. , & Simon H. A. ( 1972 ). Human problem solving. Englewood Cliffs, NJ : Prentice-Hall.

Nickerson R. S. ( 1999 ). Enhancing creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 392–430). New York : Cambridge University Press.

Nunes T. , Schliemann A. D. , & Carraher D. W , ( 1993 ). Street mathematics and school mathematics. Cambridge, England : Cambridge University Press.

Robbins P. , & Aydede M. (Eds.). ( 2009 ). The Cambridge handbook of situated cognition. New York : Cambridge University Press.

Rogers T. T. , & McClelland J. L. ( 2004 ). Semantic cognition: A parallel distributed processing approach. Cambridge, MA : MIT Press.

Singley M. K. , & Anderson J. R. ( 1989 ). The transfer of cognitive skill. Cambridge, MA : Harvard University Press.

Sternberg R. J. ( 1990 ). Metaphors of mind: Conceptions of the nature of intelligence. New York : Cambridge University Press.

Sternberg R. J. ( 1999 ). Handbook of creativity. New York : Cambridge University Press.

Sternberg R. J. , & Gregorenko E. L. (Eds.). ( 2003 ). The psychology of abilities, competencies, and expertise. New York : Cambridge University Press.

Tharp R. G. , & Gallimore R. ( 1988 ). Rousing minds to life: Teaching, learning, and schooling in social context. New York : Cambridge University Press.

Thorndike E. L. ( 1911 ). Animal intelligence. New York: Hafner.

Thorndike E. L. , & Woodworth R. S. ( 1901 ). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247–261.

Wertheimer M. ( 1959 ). Productive thinking. New York : Harper and Collins.

Wundt W. ( 1973 ). An introduction to experimental psychology. New York : Arno Press. (Original work published in 1911).

This Feature Is Available To Subscribers Only

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Bipolar Disorder
Therapy Center
When To See a Therapist
Types of Therapy
Best Online Therapy
Best Couples Therapy
Best Family Therapy
Managing Stress
Sleep and Dreaming
Understanding Emotions
Self-Improvement
Healthy Relationships
Student Resources
Personality Types
Guided Meditations
Verywell Mind Insights
2024 Verywell Mind 25
Mental Health in the Classroom
Editorial Process
Meet Our Review Board
Crisis Support

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.

JGI / Jamie Grill / Getty Images

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.

Sarathy V. Real world problem-solving . Front Hum Neurosci . 2018;12:261. doi:10.3389/fnhum.2018.00261

Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality . Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition . Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

National Alliance on Mental Illness. Warning signs and symptoms .

Mayer RE. Thinking, problem solving, cognition, 2nd ed .

Schooler JW, Ohlsson S, Brooks K. Thoughts beyond words: When language overshadows insight. J Experiment Psychol: General . 1993;122:166-183. doi:10.1037/0096-3445.2.166

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

IMAGES

Insight Learning: 10 Examples, Definition, Case Studies (2024)
Problem Solving Strategies & Examples —Viquepedia
How psychology does define problem solving?
| Three levels of description of insight problem solving stages for a
Insight Problem Solving (1) (1)
PPT

VIDEO

Problem Solving
Insight Meaning
Think:Moment: Attention Seeking
TIPS
TIPS
TIPS

COMMENTS

Insight Learning (Definition+ 4 Stages
Insight: The learner finally achieves a breakthrough, otherwise called an epiphany or 'Aha' moment. This insight comes in a flash and is often a radical reorganization of the problem. It is a discontinuous leap in understanding rather than continuous with reasoning undertaken in the preparation phase.
Insight Learning Theory: Definition, Stages, and Examples
2. Restructuring of Problem-Solving Strategies. Insight learning often involves a restructuring of mental representations or problem-solving strategies. Instead of simply trying different approaches until stumbling upon the correct one, individuals experience a shift in how they perceive and approach the problem.
Insight
When a chapter on insight appears within a volume that includes a separate chapter on problem solving (see Bassok & Novick, Chapter 21), it is implicitly assumed that insight problem solving is fundamentally different from other types of problem solving.However, the possibility that insightful problem-solving processes share the same mechanisms as analytic processes must be considered.
Insight Psychology
When you apply the insight psychology definition to mental health, it is not a banana or a puzzle on a piece of paper but rather an insight into the psyche. ... Problem-solving comes in all different shapes and sizes. In relationships, conflict can be an area where individuals use insight. Whether in familial or romantic relationships, you may ...
Frontiers
Other Gestalt psychologists adapted Köhler's problem solving methodology to study insight in humans. Duncker (1945), for example, designed situations in which everyday objects had to be used in unusual ways to solve a task (e.g., the candle problem, see Figure 1; Duncker, 1945).Notably, if he asked the subjects to use these objects in their usual way before the test, the success rate was ...
Insight
Abstract. In the early 1900s, the Gestalt psychologists introduced insight as a component process in perception and problem solving. Since the inception of the scientific study of insight, researchers have examined the phenomenological, behavioral, and neural components of insight, and how insight and other forms of cognition (e.g., analysis ...
Insight Is Not in the Problem: Investigating Insight in Problem Solving
The feeling of insight in problem solving is typically associated with the sudden realization of a solution that appears obviously correct (Kounios et al., 2006).Salvi et al. found that a solution accompanied with sudden insight is more likely to be correct than a problem solved through conscious and incremental steps.However, Metcalfe indicated that participants would often present an ...
PDF Psychological Research on Insight Problem Solving
Psychological Research on Insight Problem Solving. Psychological Research on Insight Problem Solving. Michael Ollinger¨1and Gun¨ ther Knoblich2. 1Parmenides Center for the Study of Thinking, 80333 Munc¨ hen, Germany, [email protected]. 2Rutgers University, Psychology Department, Newark, NJ 07102, USA, knoblich ...
Restructuring insight: An integrative review of insight in problem
In textbooks of psychology and cognition, insight is typically given short shrift as it is discussed as a specific subprocess of problem-solving (Eysenck and Keane, ... Insight in problem-solving2.1. Definition of insight. There exists a long tradition in psychological research dating back to Gestalt-psychology to study insight in problem ...
Insights about Insightful Problem Solving (Chapter 5)
In addition, getting on track in one's problem solving, after being off course, is frequently accompanied by a feeling of suddenly knowing what to do. Insight has long been associated with creative thoughts and products. For example, Graham Wallas (1926) proposed four stages involved in the creative process. Type.
Frontiers
Similar to intuition research, the research on insight problem solving is also located between two different views: The special-process view - which posits that insight problem solving involves a unique cognitive process that is qualitatively different from the processes non-insight problem solving utilizes - and the business-as-usual or ...
Current Understanding of the "Insight" Phenomenon Across Disciplines
Despite countless anecdotes and the historical significance of insight as a problem solving mechanism, its nature has long remained elusive. The conscious experience of insight is notoriously difficult to trace in non-verbal animals. Although studying insight has presented a significant challenge even to neurobiology and psychology, human ...
Insight
Insight refers to a family of phenomena related to problem-solving and understanding. In humans these typically involve the sudden realization of how to solve a problem that previously resisted resolution and, in animals, the sudden execution of a solution where previously there was failure, with discontinuity between the inappropriate and the correct behavior.
Psychological Research on Insight Problem Solving
Weisberg, R.W. (1992): Metacognition and insight during problem solving: Comment on Metcalfe. Journal of Experimental Psychology: Learning, Memory, and Cognition 18, 426-431. Article Google Scholar Weisberg, R W. (1995): Prolegomena to theories of insight in problem solving: A taxonomy of problems.
Insight Problem Solving: A Critical Examination of the Possibility of
The Journal of Problem Solving • 58 W. H. Batchelder and G. E. Alexander of this paper is to explain the reasons why it has been so difficult to achieve a scientific understanding of the cognitive processes involved in insight problem solving. There have been many scientific books and papers on insight problem solving, start -
Insight
Insight occurs in human learning when people recognize relationships (or make novel associations between objects or actions) that can help them solve new problems. Much of the scientific knowledge concerning insight derives from work on animal behaviour that was conducted by 20th-century German Gestalt psychologist Wolfgang Köhler.
Problem-Solving Strategies: Definition and 5 Techniques to Try
In insight problem-solving, the cognitive processes that help you solve a problem happen outside your conscious awareness. 4. Working backward. Working backward is a problem-solving approach often ...
Intuition and Insight: Two Processes That Build on Each Other or
Similar to intuition research, the research on insight problem solving is also located between two different views: The special-process view - which posits that insight problem solving involves a unique cognitive process that is qualitatively different from the processes non-insight problem solving utilizes - and the business-as-usual or ...
APA Dictionary of Psychology
n. the clear and often sudden discernment of a solution to a problem by means that are not obvious and may never become so, even after one has tried hard to work out how one has arrived at the solution. There are many different theories of how insights are formed and of the kinds of insights that exist. For example, in the 1990s, U.S ...
7.3 Problem-Solving
Additional Problem Solving Strategies:. Abstraction - refers to solving the problem within a model of the situation before applying it to reality.; Analogy - is using a solution that solves a similar problem.; Brainstorming - refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal ...
Problem Solving
Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined.
Problem-Solving Strategies and Obstacles
Problem-solving is a vital skill for coping with various challenges in life. This webpage explains the different strategies and obstacles that can affect how you solve problems, and offers tips on how to improve your problem-solving skills. Learn how to identify, analyze, and overcome problems with Verywell Mind.
Full article: Creative thinking and insight problem-solving in Keats
2. Writings on creativity. The issue of creativity as an insight problem experience has attracted increasing scholarly interest in the last two decades, from many different disciplines and fields of study: psychology, cognitive psychology, sociology, economy, and education (Sawyer, Citation 2012, p. 463).The domain of research on this aspect of creativity, together with its theoretical and ...

Insight Learning (Definition+ 4 Stages + Examples)

What Is Insight Learning?

Is Insight Learning Only Observed In Humans?

Four Stages of Insight Learning

Preparation

Verification

Examples of Insight Learning

Where Is the Best Place to Have an Epiphany?

The Three B’s of Creativity

Sleep

Meditation

Laugh!

Insight Vs. Other Types Of Learning.

Insight Vs. Trial And Error Learning

Insight Vs. Learning Through Observation, Imitation, And Repetition

Can Major Scientific Breakthroughs be made through observation and experiment alone?

The Historical Development Of The Theory of Insight Learning

Kohler’s 4 Stage Model Of Insight Learning

Applying Insight Learning To The Classroom

Related posts:

About The Author

Psychology Resources

Psychology Tests

Insight Learning Theory: Definition, Stages, and Examples

What Is Insight Learning?

Three Components of Insight Learning Theory

1. Sudden Realization

2. Restructuring of Problem-Solving Strategies

3. Aha Moments

Four Stages of Insight Learning Theory

1. Problem Recognition

2. Incubation

3. Illumination

4. Verification

Famous Examples of Insight Learning

Archimedes’ Principle

Köhler’s Chimpanzee Experiments

Eureka Moments in Science

Everyday Examples of Insight Learning Theory

Exploring the Uses of Insight Learning

Problem-Solving

Learning New Skills

Overcoming Challenges

Alternatives to Insight Learning Theory

Behaviorism

Cognitive Learning Theory

Gestalt Psychology

Information Processing Theory

What Is Insight? Definition, Psychology, And Practical Examples

What’s the difference between insight and knowledge?

What is knowledge?

How does insight relate to knowledge?

How psychologists interpret insight

The nothing special view

The neo-Gestaltist view

The three process view

The four stages of behavioral processes

What does psychological research say about insight?

Graham Wallas and the nine dot puzzle

Responses to the nine dot puzzle and banana problem

What are a few examples of insight?

Relationship example

Therapy example

What to expect from insight psychology therapy

Therapy is insight: Learn more about insight and insight therapy

If you need a crisis hotline or want more insight into therapy, please see below:

For more insight on mental health, please see:

Get help and insight now:

HYPOTHESIS AND THEORY article

Introduction

Defining Intuition and Insight

Insight and Aha-Experience

Different Research Traditions of Intuition and Insight

The Single- vs. Dual-System View on Intuition

Intuition As Experienced-Based Perception of Coherence and As an Antecedent of Insight

The Special-Process vs. Nothing-Special View on Insight

Interim Summary I

Differences in the Cognitive Processes Assumed to Underlie Intuition and Insight

The Discontinuity Model of Insight: Insight As the Result of a Mental Restructuring Process

Interim Summary II