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35 Scientific Thinking and Reasoning

Kevin N. Dunbar, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

David Klahr, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA

• Published: 21 November 2012
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Scientific thinking refers to both thinking about the content of science and the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. Here we cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research. Future research will focus on the collaborative aspects of scientific thinking, on effective methods for teaching science, and on the neural underpinnings of the scientific mind.

There is no unitary activity called “scientific discovery”; there are activities of designing experiments, gathering data, inventing and developing observational instruments, formulating and modifying theories, deducing consequences from theories, making predictions from theories, testing theories, inducing regularities and invariants from data, discovering theoretical constructs, and others. — Simon, Langley, & Bradshaw, 1981 , p. 2

What Is Scientific Thinking and Reasoning?

There are two kinds of thinking we call “scientific.” The first, and most obvious, is thinking about the content of science. People are engaged in scientific thinking when they are reasoning about such entities and processes as force, mass, energy, equilibrium, magnetism, atoms, photosynthesis, radiation, geology, or astrophysics (and, of course, cognitive psychology!). The second kind of scientific thinking includes the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. However, these reasoning processes are not unique to scientific thinking: They are the very same processes involved in everyday thinking. As Einstein put it:

The scientific way of forming concepts differs from that which we use in our daily life, not basically, but merely in the more precise definition of concepts and conclusions; more painstaking and systematic choice of experimental material, and greater logical economy. (The Common Language of Science, 1941, reprinted in Einstein, 1950 , p. 98)

Nearly 40 years after Einstein's remarkably insightful statement, Francis Crick offered a similar perspective: that great discoveries in science result not from extraordinary mental processes, but rather from rather common ones. The greatness of the discovery lies in the thing discovered.

I think what needs to be emphasized about the discovery of the double helix is that the path to it was, scientifically speaking, fairly commonplace. What was important was not the way it was discovered , but the object discovered—the structure of DNA itself. (Crick, 1988 , p. 67; emphasis added)

Under this view, scientific thinking involves the same general-purpose cognitive processes—such as induction, deduction, analogy, problem solving, and causal reasoning—that humans apply in nonscientific domains. These processes are covered in several different chapters of this handbook: Rips, Smith, & Medin, Chapter 11 on induction; Evans, Chapter 8 on deduction; Holyoak, Chapter 13 on analogy; Bassok & Novick, Chapter 21 on problem solving; and Cheng & Buehner, Chapter 12 on causality. One might question the claim that the highly specialized procedures associated with doing science in the “real world” can be understood by investigating the thinking processes used in laboratory studies of the sort described in this volume. However, when the focus is on major scientific breakthroughs, rather than on the more routine, incremental progress in a field, the psychology of problem solving provides a rich source of ideas about how such discoveries might occur. As Simon and his colleagues put it:

It is understandable, if ironic, that ‘normal’ science fits … the description of expert problem solving, while ‘revolutionary’ science fits the description of problem solving by novices. It is understandable because scientific activity, particularly at the revolutionary end of the continuum, is concerned with the discovery of new truths, not with the application of truths that are already well-known … it is basically a journey into unmapped terrain. Consequently, it is mainly characterized, as is novice problem solving, by trial-and-error search. The search may be highly selective—but it reaches its goal only after many halts, turnings, and back-trackings. (Simon, Langley, & Bradshaw, 1981 , p. 5)

The research literature on scientific thinking can be roughly categorized according to the two types of scientific thinking listed in the opening paragraph of this chapter: (1) One category focuses on thinking that directly involves scientific content . Such research ranges from studies of young children reasoning about the sun-moon-earth system (Vosniadou & Brewer, 1992 ) to college students reasoning about chemical equilibrium (Davenport, Yaron, Klahr, & Koedinger, 2008 ), to research that investigates collaborative problem solving by world-class researchers in real-world molecular biology labs (Dunbar, 1995 ). (2) The other category focuses on “general” cognitive processes, but it tends to do so by analyzing people's problem-solving behavior when they are presented with relatively complex situations that involve the integration and coordination of several different types of processes, and that are designed to capture some essential features of “real-world” science in the psychology laboratory (Bruner, Goodnow, & Austin, 1956 ; Klahr & Dunbar, 1988 ; Mynatt, Doherty, & Tweney, 1977 ).

There are a number of overlapping research traditions that have been used to investigate scientific thinking. We will cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research.

A Brief History of Research on Scientific Thinking

Science is often considered one of the hallmarks of the human species, along with art and literature. Illuminating the thought processes used in science thus reveal key aspects of the human mind. The thought processes underlying scientific thinking have fascinated both scientists and nonscientists because the products of science have transformed our world and because the process of discovery is shrouded in mystery. Scientists talk of the chance discovery, the flash of insight, the years of perspiration, and the voyage of discovery. These images of science have helped make the mental processes underlying the discovery process intriguing to cognitive scientists as they attempt to uncover what really goes on inside the scientific mind and how scientists really think. Furthermore, the possibilities that scientists can be taught to think better by avoiding mistakes that have been clearly identified in research on scientific thinking, and that their scientific process could be partially automated, makes scientific thinking a topic of enduring interest.

The cognitive processes underlying scientific discovery and day-to-day scientific thinking have been a topic of intense scrutiny and speculation for almost 400 years (e.g., Bacon, 1620 ; Galilei 1638 ; Klahr 2000 ; Tweney, Doherty, & Mynatt, 1981 ). Understanding the nature of scientific thinking has been a central issue not only for our understanding of science but also for our understating of what it is to be human. Bacon's Novumm Organum in 1620 sketched out some of the key features of the ways that experiments are designed and data interpreted. Over the ensuing 400 years philosophers and scientists vigorously debated about the appropriate methods that scientists should use (see Giere, 1993 ). These debates over the appropriate methods for science typically resulted in the espousal of a particular type of reasoning method, such as induction or deduction. It was not until the Gestalt psychologists began working on the nature of human problem solving, during the 1940s, that experimental psychologists began to investigate the cognitive processes underlying scientific thinking and reasoning.

The Gestalt psychologist Max Wertheimer pioneered the investigation of scientific thinking (of the first type described earlier: thinking about scientific content ) in his landmark book Productive Thinking (Wertheimer, 1945 ). Wertheimer spent a considerable amount of time corresponding with Albert Einstein, attempting to discover how Einstein generated the concept of relativity. Wertheimer argued that Einstein had to overcome the structure of Newtonian physics at each step in his theorizing, and the ways that Einstein actually achieved this restructuring were articulated in terms of Gestalt theories. (For a recent and different account of how Einstein made his discovery, see Galison, 2003 .) We will see later how this process of overcoming alternative theories is an obstacle that both scientists and nonscientists need to deal with when evaluating and theorizing about the world.

One of the first investigations of scientific thinking of the second type (i.e., collections of general-purpose processes operating on complex, abstract, components of scientific thought) was carried out by Jerome Bruner and his colleagues at Harvard (Bruner et al., 1956 ). They argued that a key activity engaged in by scientists is to determine whether a particular instance is a member of a category. For example, a scientist might want to discover which substances undergo fission when bombarded by neutrons and which substances do not. Here, scientists have to discover the attributes that make a substance undergo fission. Bruner et al. saw scientific thinking as the testing of hypotheses and the collecting of data with the end goal of determining whether something is a member of a category. They invented a paradigm where people were required to formulate hypotheses and collect data that test their hypotheses. In one type of experiment, the participants were shown a card such as one with two borders and three green triangles. The participants were asked to determine the concept that this card represented by choosing other cards and getting feedback from the experimenter as to whether the chosen card was an example of the concept. In this case the participant may have thought that the concept was green and chosen a card with two green squares and one border. If the underlying concept was green, then the experimenter would say that the card was an example of the concept. In terms of scientific thinking, choosing a new card is akin to conducting an experiment, and the feedback from the experimenter is similar to knowing whether a hypothesis is confirmed or disconfirmed. Using this approach, Bruner et al. identified a number of strategies that people use to formulate and test hypotheses. They found that a key factor determining which hypothesis-testing strategy that people use is the amount of memory capacity that the strategy takes up (see also Morrison & Knowlton, Chapter 6 ; Medin et al., Chapter 11 ). Another key factor that they discovered was that it was much more difficult for people to discover negative concepts (e.g., not blue) than positive concepts (e.g., blue). Although Bruner et al.'s research is most commonly viewed as work on concepts, they saw their work as uncovering a key component of scientific thinking.

A second early line of research on scientific thinking was developed by Peter Wason and his colleagues (Wason, 1968 ). Like Bruner et al., Wason saw a key component of scientific thinking as being the testing of hypotheses. Whereas Bruner et al. focused on the different types of strategies that people use to formulate hypotheses, Wason focused on whether people adopt a strategy of trying to confirm or disconfirm their hypotheses. Using Popper's ( 1959 ) theory that scientists should try and falsify rather than confirm their hypotheses, Wason devised a deceptively simple task in which participants were given three numbers, such as 2-4-6, and were asked to discover the rule underlying the three numbers. Participants were asked to generate other triads of numbers and the experimenter would tell the participant whether the triad was consistent or inconsistent with the rule. They were told that when they were sure they knew what the rule was they should state it. Most participants began the experiment by thinking that the rule was even numbers increasing by 2. They then attempted to confirm their hypothesis by generating a triad like 8-10-12, then 14-16-18. These triads are consistent with the rule and the participants were told yes, that the triads were indeed consistent with the rule. However, when they proposed the rule—even numbers increasing by 2—they were told that the rule was incorrect. The correct rule was numbers of increasing magnitude! From this research, Wason concluded that people try to confirm their hypotheses, whereas normatively speaking, they should try to disconfirm their hypotheses. One implication of this research is that confirmation bias is not just restricted to scientists but is a general human tendency.

It was not until the 1970s that a general account of scientific reasoning was proposed. Herbert Simon, often in collaboration with Allan Newell, proposed that scientific thinking is a form of problem solving. He proposed that problem solving is a search in a problem space. Newell and Simon's theory of problem solving is discussed in many places in this handbook, usually in the context of specific problems (see especially Bassok & Novick, Chapter 21 ). Herbert Simon, however, devoted considerable time to understanding many different scientific discoveries and scientific reasoning processes. The common thread in his research was that scientific thinking and discovery is not a mysterious magical process but a process of problem solving in which clear heuristics are used. Simon's goal was to articulate the heuristics that scientists use in their research at a fine-grained level. By constructing computer programs that simulated the process of several major scientific discoveries, Simon and colleagues were able to articulate the specific computations that scientists could have used in making those discoveries (Langley, Simon, Bradshaw, & Zytkow, 1987 ; see section on “Computational Approaches to Scientific Thinking”). Particularly influential was Simon and Lea's ( 1974 ) work demonstrating that concept formation and induction consist of a search in two problem spaces: a space of instances and a space of rules. This idea has influenced problem-solving accounts of scientific thinking that will be discussed in the next section.

Overall, the work of Bruner, Wason, and Simon laid the foundations for contemporary research on scientific thinking. Early research on scientific thinking is summarized in Tweney, Doherty and Mynatt's 1981 book On Scientific Thinking , where they sketched out many of the themes that have dominated research on scientific thinking over the past few decades. Other more recent books such as Cognitive Models of Science (Giere, 1993 ), Exploring Science (Klahr, 2000 ), Cognitive Basis of Science (Carruthers, Stich, & Siegal, 2002 ), and New Directions in Scientific and Technical Thinking (Gorman, Kincannon, Gooding, & Tweney, 2004 ) provide detailed analyses of different aspects of scientific discovery. Another important collection is Vosnadiau's handbook on conceptual change research (Vosniadou, 2008 ). In this chapter, we discuss the main approaches that have been used to investigate scientific thinking.

How does one go about investigating the many different aspects of scientific thinking? One common approach to the study of the scientific mind has been to investigate several key aspects of scientific thinking using abstract tasks designed to mimic some essential characteristics of “real-world” science. There have been numerous methodologies that have been used to analyze the genesis of scientific concepts, theories, hypotheses, and experiments. Researchers have used experiments, verbal protocols, computer programs, and analyzed particular scientific discoveries. A more recent development has been to increase the ecological validity of such research by investigating scientists as they reason “live” (in vivo studies of scientific thinking) in their own laboratories (Dunbar, 1995 , 2002 ). From a “Thinking and Reasoning” standpoint the major aspects of scientific thinking that have been most actively investigated are problem solving, analogical reasoning, hypothesis testing, conceptual change, collaborative reasoning, inductive reasoning, and deductive reasoning.

Scientific Thinking as Problem Solving

One of the primary goals of accounts of scientific thinking has been to provide an overarching framework to understand the scientific mind. One framework that has had a great influence in cognitive science is that scientific thinking and scientific discovery can be conceived as a form of problem solving. As noted in the opening section of this chapter, Simon ( 1977 ; Simon, Langley, & Bradshaw, 1981 ) argued that both scientific thinking in general and problem solving in particular could be thought of as a search in a problem space. A problem space consists of all the possible states of a problem and all the operations that a problem solver can use to get from one state to the next. According to this view, by characterizing the types of representations and procedures that people use to get from one state to another it is possible to understand scientific thinking. Thus, scientific thinking can be characterized as a search in various problem spaces (Simon, 1977 ). Simon investigated a number of scientific discoveries by bringing participants into the laboratory, providing the participants with the data that a scientist had access to, and getting the participants to reason about the data and rediscover a scientific concept. He then analyzed the verbal protocols that participants generated and mapped out the types of problem spaces that the participants search in (e.g., Qin & Simon, 1990 ). Kulkarni and Simon ( 1988 ) used a more historical approach to uncover the problem-solving heuristics that Krebs used in his discovery of the urea cycle. Kulkarni and Simon analyzed Krebs's diaries and proposed a set of problem-solving heuristics that he used in his research. They then built a computer program incorporating the heuristics and biological knowledge that Krebs had before he made his discoveries. Of particular importance are the search heuristics that the program uses, which include experimental proposal heuristics and data interpretation heuristics. A key heuristic was an unusualness heuristic that focused on unusual findings, which guided search through a space of theories and a space of experiments.

Klahr and Dunbar ( 1988 ) extended the search in a problem space approach and proposed that scientific thinking can be thought of as a search through two related spaces: an hypothesis space and an experiment space. Each problem space that a scientist uses will have its own types of representations and operators used to change the representations. Search in the hypothesis space constrains search in the experiment space. Klahr and Dunbar found that some participants move from the hypothesis space to the experiment space, whereas others move from the experiment space to the hypothesis space. These different types of searches lead to the proposal of different types of hypotheses and experiments. More recent work has extended the dual-space approach to include alternative problem-solving spaces, including those for data, instrumentation, and domain-specific knowledge (Klahr & Simon, 1999 ; Schunn & Klahr, 1995 , 1996 ).

Scientific Thinking as Hypothesis Testing

Many researchers have regarded testing specific hypotheses predicted by theories as one of the key attributes of scientific thinking. Hypothesis testing is the process of evaluating a proposition by collecting evidence regarding its truth. Experimental cognitive research on scientific thinking that specifically examines this issue has tended to fall into two broad classes of investigations. The first class is concerned with the types of reasoning that lead scientists astray, thus blocking scientific ingenuity. A large amount of research has been conducted on the potentially faulty reasoning strategies that both participants in experiments and scientists use, such as considering only one favored hypothesis at a time and how this prevents the scientists from making discoveries. The second class is concerned with uncovering the mental processes underlying the generation of new scientific hypotheses and concepts. This research has tended to focus on the use of analogy and imagery in science, as well as the use of specific types of problem-solving heuristics.

Turning first to investigations of what diminishes scientific creativity, philosophers, historians, and experimental psychologists have devoted a considerable amount of research to “confirmation bias.” This occurs when scientists only consider one hypothesis (typically the favored hypothesis) and ignore other alternative hypotheses or potentially relevant hypotheses. This important phenomenon can distort the design of experiments, formulation of theories, and interpretation of data. Beginning with the work of Wason ( 1968 ) and as discussed earlier, researchers have repeatedly shown that when participants are asked to design an experiment to test a hypothesis they will predominantly design experiments that they think will yield results consistent with the hypothesis. Using the 2-4-6 task mentioned earlier, Klayman and Ha ( 1987 ) showed that in situations where one's hypothesis is likely to be confirmed, seeking confirmation is a normatively incorrect strategy, whereas when the probability of confirming one's hypothesis is low, then attempting to confirm one's hypothesis can be an appropriate strategy. Historical analyses by Tweney ( 1989 ), concerning the way that Faraday made his discoveries, and experiments investigating people testing hypotheses, have revealed that people use a confirm early, disconfirm late strategy: When people initially generate or are given hypotheses, they try and gather evidence that is consistent with the hypothesis. Once enough evidence has been gathered, then people attempt to find the boundaries of their hypothesis and often try to disconfirm their hypotheses.

In an interesting variant on the confirmation bias paradigm, Gorman ( 1989 ) showed that when participants are told that there is the possibility of error in the data that they receive, participants assume that any data that are inconsistent with their favored hypothesis are due to error. Thus, the possibility of error “insulates” hypotheses against disconfirmation. This intriguing hypothesis has not been confirmed by other researchers (Penner & Klahr, 1996 ), but it is an intriguing hypothesis that warrants further investigation.

Confirmation bias is very difficult to overcome. Even when participants are asked to consider alternate hypotheses, they will often fail to conduct experiments that could potentially disconfirm their hypothesis. Tweney and his colleagues provide an excellent overview of this phenomenon in their classic monograph On Scientific Thinking (1981). The precise reasons for this type of block are still widely debated. Researchers such as Michael Doherty have argued that working memory limitations make it difficult for people to consider more than one hypothesis. Consistent with this view, Dunbar and Sussman ( 1995 ) have shown that when participants are asked to hold irrelevant items in working memory while testing hypotheses, the participants will be unable to switch hypotheses in the face of inconsistent evidence. While working memory limitations are involved in the phenomenon of confirmation bias, even groups of scientists can also display confirmation bias. For example, the controversy over cold fusion is an example of confirmation bias. Here, large groups of scientists had other hypotheses available to explain their data yet maintained their hypotheses in the face of other more standard alternative hypotheses. Mitroff ( 1974 ) provides some interesting examples of NASA scientists demonstrating confirmation bias, which highlight the roles of commitment and motivation in this process. See also MacPherson and Stanovich ( 2007 ) for specific strategies that can be used to overcome confirmation bias.

Causal Thinking in Science

Much of scientific thinking and scientific theory building pertains to the development of causal models between variables of interest. For example, do vaccines cause illnesses? Do carbon dioxide emissions cause global warming? Does water on a planet indicate that there is life on the planet? Scientists and nonscientists alike are constantly bombarded with statements regarding the causal relationship between such variables. How does one evaluate the status of such claims? What kinds of data are informative? How do scientists and nonscientists deal with data that are inconsistent with their theory?

A central issue in the causal reasoning literature, one that is directly relevant to scientific thinking, is the extent to which scientists and nonscientists alike are governed by the search for causal mechanisms (i.e., how a variable works) versus the search for statistical data (i.e., how often variables co-occur). This dichotomy can be boiled down to the search for qualitative versus quantitative information about the paradigm the scientist is investigating. Researchers from a number of cognitive psychology laboratories have found that people prefer to gather more information about an underlying mechanism than covariation between a cause and an effect (e.g., Ahn, Kalish, Medin, & Gelman, 1995 ). That is, the predominant strategy that students in simulations of scientific thinking use is to gather as much information as possible about how the objects under investigation work, rather than collecting large amounts of quantitative data to determine whether the observations hold across multiple samples. These findings suggest that a central component of scientific thinking may be to formulate explicit mechanistic causal models of scientific events.

One type of situation in which causal reasoning has been observed extensively is when scientists obtain unexpected findings. Both historical and naturalistic research has revealed that reasoning causally about unexpected findings plays a central role in science. Indeed, scientists themselves frequently state that a finding was due to chance or was unexpected. Given that claims of unexpected findings are such a frequent component of scientists' autobiographies and interviews in the media, Dunbar ( 1995 , 1997 , 1999 ; Dunbar & Fugelsang, 2005 ; Fugelsang, Stein, Green, & Dunbar, 2004 ) decided to investigate the ways that scientists deal with unexpected findings. In 1991–1992 Dunbar spent 1 year in three molecular biology laboratories and one immunology laboratory at a prestigious U.S. university. He used the weekly laboratory meeting as a source of data on scientific discovery and scientific reasoning. (He termed this type of study “in vivo” cognition.) When he looked at the types of findings that the scientists made, he found that over 50% of the findings were unexpected and that these scientists had evolved a number of effective strategies for dealing with such findings. One clear strategy was to reason causally about the findings: Scientists attempted to build causal models of their unexpected findings. This causal model building results in the extensive use of collaborative reasoning, analogical reasoning, and problem-solving heuristics (Dunbar, 1997 , 2001 ).

Many of the key unexpected findings that scientists reasoned about in the in vivo studies of scientific thinking were inconsistent with the scientists' preexisting causal models. A laboratory equivalent of the biology labs involved creating a situation in which students obtained unexpected findings that were inconsistent with their preexisting theories. Dunbar and Fugelsang ( 2005 ) examined this issue by creating a scientific causal thinking simulation where experimental outcomes were either expected or unexpected. Dunbar ( 1995 ) has called the study of people reasoning in a cognitive laboratory “in vitro” cognition. These investigators found that students spent considerably more time reasoning about unexpected findings than expected findings. In addition, when assessing the overall degree to which their hypothesis was supported or refuted, participants spent the majority of their time considering unexpected findings. An analysis of participants' verbal protocols indicates that much of this extra time was spent formulating causal models for the unexpected findings. Similarly, scientists spend more time considering unexpected than expected findings, and this time is devoted to building causal models (Dunbar & Fugelsang, 2004 ).

Scientists know that unexpected findings occur often, and they have developed many strategies to take advantage of their unexpected findings. One of the most important places that they anticipate the unexpected is in designing experiments (Baker & Dunbar, 2000 ). They build different causal models of their experiments incorporating many conditions and controls. These multiple conditions and controls allow unknown mechanisms to manifest themselves. Thus, rather than being the victims of the unexpected, they create opportunities for unexpected events to occur, and once these events do occur, they have causal models that allow them to determine exactly where in the causal chain their unexpected finding arose. The results of these in vivo and in vitro studies all point to a more complex and nuanced account of how scientists and nonscientists alike test and evaluate hypotheses about theories.

The Roles of Inductive, Abductive, and Deductive Thinking in Science

One of the most basic characteristics of science is that scientists assume that the universe that we live in follows predictable rules. Scientists reason using a variety of different strategies to make new scientific discoveries. Three frequently used types of reasoning strategies that scientists use are inductive, abductive, and deductive reasoning. In the case of inductive reasoning, a scientist may observe a series of events and try to discover a rule that governs the event. Once a rule is discovered, scientists can extrapolate from the rule to formulate theories of observed and yet-to-be-observed phenomena. One example is the discovery using inductive reasoning that a certain type of bacterium is a cause of many ulcers (Thagard, 1999 ). In a fascinating series of articles, Thagard documented the reasoning processes that Marshall and Warren went through in proposing this novel hypothesis. One key reasoning process was the use of induction by generalization. Marshall and Warren noted that almost all patients with gastric entritis had a spiral bacterium in their stomachs, and he formed the generalization that this bacterium is the cause of stomach ulcers. There are numerous other examples of induction by generalization in science, such as Tycho De Brea's induction about the motion of planets from his observations, Dalton's use of induction in chemistry, and the discovery of prions as the source of mad cow disease. Many theories of induction have used scientific discovery and reasoning as examples of this important reasoning process.

Another common type of inductive reasoning is to map a feature of one member of a category to another member of a category. This is called categorical induction. This type of induction is a way of projecting a known property of one item onto another item that is from the same category. Thus, knowing that the Rous Sarcoma virus is a retrovirus that uses RNA rather than DNA, a biologist might assume that another virus that is thought to be a retrovirus also uses RNA rather than DNA. While research on this type of induction typically has not been discussed in accounts of scientific thinking, this type of induction is common in science. For an influential contribution to this literature, see Smith, Shafir, and Osherson ( 1993 ), and for reviews of this literature see Heit ( 2000 ) and Medin et al. (Chapter 11 ).

While less commonly mentioned than inductive reasoning, abductive reasoning is an important form of reasoning that scientists use when they are seeking to propose explanations for events such as unexpected findings (see Lombrozo, Chapter 14 ; Magnani, et al., 2010 ). In Figure 35.1 , taken from King ( 2011 ), the differences between inductive, abductive, and deductive thinking are highlighted. In the case of abduction, the reasoner attempts to generate explanations of the form “if situation X had occurred, could it have produced the current evidence I am attempting to interpret?” (For an interesting of analysis of abductive reasoning see the brief paper by Klahr & Masnick, 2001 ). Of course, as in classical induction, such reasoning may produce a plausible account that is still not the correct one. However, abduction does involve the generation of new knowledge, and is thus also related to research on creativity.

The different processes underlying inductive, abductive, and deductive reasoning in science. (Figure reproduced from King 2011 ).)

Turning now to deductive thinking, many thinking processes that scientists adhere to follow traditional rules of deductive logic. These processes correspond to those conditions in which a hypothesis may lead to, or is deducible to, a conclusion. Though they are not always phrased in syllogistic form, deductive arguments can be phrased as “syllogisms,” or as brief, mathematical statements in which the premises lead to the conclusion. Deductive reasoning is an extremely important aspect of scientific thinking because it underlies a large component of how scientists conduct their research. By looking at many scientific discoveries, we can often see that deductive reasoning is at work. Deductive reasoning statements all contain information or rules that state an assumption about how the world works, as well as a conclusion that would necessarily follow from the rule. Numerous discoveries in physics such as the discovery of dark matter by Vera Rubin are based on deductions. In the dark matter case, Rubin measured galactic rotation curves and based on the differences between the predicted and observed angular motions of galaxies she deduced that the structure of the universe was uneven. This led her to propose that dark matter existed. In contemporary physics the CERN Large Hadron Collider is being used to search for the Higgs Boson. The Higgs Boson is a deductive prediction from contemporary physics. If the Higgs Boson is not found, it may lead to a radical revision of the nature of physics and a new understanding of mass (Hecht, 2011 ).

The Roles of Analogy in Scientific Thinking

One of the most widely mentioned reasoning processes used in science is analogy. Scientists use analogies to form a bridge between what they already know and what they are trying to explain, understand, or discover. In fact, many scientists have claimed that the making of certain analogies was instrumental in their making a scientific discovery, and almost all scientific autobiographies and biographies feature one particular analogy that is discussed in depth. Coupled with the fact that there has been an enormous research program on analogical thinking and reasoning (see Holyoak, Chapter 13 ), we now have a number of models and theories of analogical reasoning that suggest how analogy can play a role in scientific discovery (see Gentner, Holyoak, & Kokinov, 2001 ). By analyzing several major discoveries in the history of science, Thagard and Croft ( 1999 ), Nersessian ( 1999 , 2008 ), and Gentner and Jeziorski ( 1993 ) have all shown that analogical reasoning is a key aspect of scientific discovery.

Traditional accounts of analogy distinguish between two components of analogical reasoning: the target and the source (Holyoak, Chapter 13 ; Gentner 2010 ). The target is the concept or problem that a scientist is attempting to explain or solve. The source is another piece of knowledge that the scientist uses to understand the target or to explain the target to others. What the scientist does when he or she makes an analogy is to map features of the source onto features of the target. By mapping the features of the source onto the target, new features of the target may be discovered, or the features of the target may be rearranged so that a new concept is invented and a scientific discovery is made. For example, a common analogy that is used with computers is to describe a harmful piece of software as a computer virus. Once a piece of software is called a virus, people can map features of biological viruses, such as that it is small, spreads easily, self-replicates using a host, and causes damage. People not only map individual features of the source onto the target but also the systems of relations. For example, if a computer virus is similar to a biological virus, then an immune system can be created on computers that can protect computers from future variants of a virus. One of the reasons that scientific analogy is so powerful is that it can generate new knowledge, such as the creation of a computational immune system having many of the features of a real biological immune system. This analogy also leads to predictions that there will be newer computer viruses that are the computational equivalent of retroviruses, lacking DNA, or standard instructions, that will elude the computational immune system.

The process of making an analogy involves a number of key steps: retrieval of a source from memory, aligning the features of the source with those of the target, mapping features of the source onto those of the target, and possibly making new inferences about the target. Scientific discoveries are made when the source highlights a hitherto unknown feature of the target or restructures the target into a new set of relations. Interestingly, research on analogy has shown that participants do not easily use remote analogies (see Gentner et al., 1997 ; Holyoak & Thagard 1995 ). Participants in experiments tend to focus on the sharing of a superficial feature between the source and the target, rather than the relations among features. In his in vivo studies of science, Dunbar ( 1995 , 2001 , 2002 ) investigated the ways that scientists use analogies while they are conducting their research and found that scientists use both relational and superficial features when they make analogies. Whether they use superficial or relational features depends on their goals. If their goal is to fix a problem in an experiment, their analogies are based upon superficial features. However, if their goal is to formulate hypotheses, they focus on analogies based upon sets of relations. One important difference between scientists and participants in experiments is that the scientists have deep relational knowledge of the processes that they are investigating and can hence use this relational knowledge to make analogies (see Holyoak, Chapter 13 for a thorough review of analogical reasoning).

Are scientific analogies always useful? Sometimes analogies can lead scientists and students astray. For example, Evelyn Fox-Keller ( 1985 ) shows how an analogy between the pulsing of a lighthouse and the activity of the slime mold dictyostelium led researchers astray for a number of years. Likewise, the analogy between the solar system (the source) and the structure of the atom (the target) has been shown to be potentially misleading to students taking more advanced courses in physics or chemistry. The solar system analogy has a number of misalignments to the structure of the atom, such as electrons being repelled from each other rather than attracted; moreover, electrons do not have individual orbits like planets but have orbit clouds of electron density. Furthermore, students have serious misconceptions about the nature of the solar system, which can compound their misunderstanding of the nature of the atom (Fischler & Lichtfeld, 1992 ). While analogy is a powerful tool in science, like all forms of induction, incorrect conclusions can be reached.

Conceptual Change in Science

Scientific knowledge continually accumulates as scientists gather evidence about the natural world. Over extended time, this knowledge accumulation leads to major revisions, extensions, and new organizational forms for expressing what is known about nature. Indeed, these changes are so substantial that philosophers of science speak of “revolutions” in a variety of scientific domains (Kuhn, 1962 ). The psychological literature that explores the idea of revolutionary conceptual change can be roughly divided into (a) investigations of how scientists actually make discoveries and integrate those discoveries into existing scientific contexts, and (b) investigations of nonscientists ranging from infants, to children, to students in science classes. In this section we summarize the adult studies of conceptual change, and in the next section we look at its developmental aspects.

Scientific concepts, like all concepts, can be characterized as containing a variety of “knowledge elements”: representations of words, thoughts, actions, objects, and processes. At certain points in the history of science, the accumulated evidence has demanded major shifts in the way these collections of knowledge elements are organized. This “radical conceptual change” process (see Keil, 1999 ; Nersessian 1998 , 2002 ; Thagard, 1992 ; Vosniadou 1998, for reviews) requires the formation of a new conceptual system that organizes knowledge in new ways, adds new knowledge, and results in a very different conceptual structure. For more recent research on conceptual change, The International Handbook of Research on Conceptual Change (Vosniadou, 2008 ) provides a detailed compendium of theories and controversies within the field.

While conceptual change in science is usually characterized by large-scale changes in concepts that occur over extensive periods of time, it has been possible to observe conceptual change using in vivo methodologies. Dunbar ( 1995 ) reported a major conceptual shift that occurred in immunologists, where they obtained a series of unexpected findings that forced the scientists to propose a new concept in immunology that in turn forced the change in other concepts. The drive behind this conceptual change was the discovery of a series of different unexpected findings or anomalies that required the scientists to both revise and reorganize their conceptual knowledge. Interestingly, this conceptual change was achieved by a group of scientists reasoning collaboratively, rather than by a scientist working alone. Different scientists tend to work on different aspects of concepts, and also different concepts, that when put together lead to a rapid change in entire conceptual structures.

Overall, accounts of conceptual change in individuals indicate that it is indeed similar to that of conceptual change in entire scientific fields. Individuals need to be confronted with anomalies that their preexisting theories cannot explain before entire conceptual structures are overthrown. However, replacement conceptual structures have to be generated before the old conceptual structure can be discarded. Sometimes, people do not overthrow their original conceptual theories and through their lives maintain their original views of many fundamental scientific concepts. Whether people actively possess naive theories, or whether they appear to have a naive theory because of the demand characteristics of the testing context, is a lively source of debate within the science education community (see Gupta, Hammer, & Redish, 2010 ).

Scientific Thinking in Children

Well before their first birthday, children appear to know several fundamental facts about the physical world. For example, studies with infants show that they behave as if they understand that solid objects endure over time (e.g., they don't just disappear and reappear, they cannot move through each other, and they move as a result of collisions with other solid objects or the force of gravity (Baillargeon, 2004 ; Carey 1985 ; Cohen & Cashon, 2006 ; Duschl, Schweingruber, & Shouse, 2007 ; Gelman & Baillargeon, 1983 ; Gelman & Kalish, 2006 ; Mandler, 2004 ; Metz 1995 ; Munakata, Casey, & Diamond, 2004 ). And even 6-month-olds are able to predict the future location of a moving object that they are attempting to grasp (Von Hofsten, 1980 ; Von Hofsten, Feng, & Spelke, 2000 ). In addition, they appear to be able to make nontrivial inferences about causes and their effects (Gopnik et al., 2004 ).

The similarities between children's thinking and scientists' thinking have an inherent allure and an internal contradiction. The allure resides in the enthusiastic wonder and openness with which both children and scientists approach the world around them. The paradox comes from the fact that different investigators of children's thinking have reached diametrically opposing conclusions about just how “scientific” children's thinking really is. Some claim support for the “child as a scientist” position (Brewer & Samarapungavan, 1991 ; Gelman & Wellman, 1991 ; Gopnik, Meltzoff, & Kuhl, 1999 ; Karmiloff-Smith 1988 ; Sodian, Zaitchik, & Carey, 1991 ; Samarapungavan 1992 ), while others offer serious challenges to the view (Fay & Klahr, 1996 ; Kern, Mirels, & Hinshaw, 1983 ; Kuhn, Amsel, & O'Laughlin, 1988 ; Schauble & Glaser, 1990 ; Siegler & Liebert, 1975 .) Such fundamentally incommensurate conclusions suggest that this very field—children's scientific thinking—is ripe for a conceptual revolution!

A recent comprehensive review (Duschl, Schweingruber, & Shouse, 2007 ) of what children bring to their science classes offers the following concise summary of the extensive developmental and educational research literature on children's scientific thinking:

Children entering school already have substantial knowledge of the natural world, much of which is implicit.

What children are capable of at a particular age is the result of a complex interplay among maturation, experience, and instruction. What is developmentally appropriate is not a simple function of age or grade, but rather is largely contingent on children's prior opportunities to learn.

Students' knowledge and experience play a critical role in their science learning, influencing four aspects of science understanding, including (a) knowing, using, and interpreting scientific explanations of the natural world; (b) generating and evaluating scientific evidence and explanations, (c) understanding how scientific knowledge is developed in the scientific community, and (d) participating in scientific practices and discourse.

Students learn science by actively engaging in the practices of science.

In the previous section of this article we discussed conceptual change with respect to scientific fields and undergraduate science students. However, the idea that children undergo radical conceptual change in which old “theories” need to be overthrown and reorganized has been a central topic in understanding changes in scientific thinking in both children and across the life span. This radical conceptual change is thought to be necessary for acquiring many new concepts in physics and is regarded as the major source of difficulty for students. The factors that are at the root of this conceptual shift view have been difficult to determine, although there have been a number of studies in cognitive development (Carey, 1985 ; Chi 1992 ; Chi & Roscoe, 2002 ), in the history of science (Thagard, 1992 ), and in physics education (Clement, 1982 ; Mestre 1991 ) that give detailed accounts of the changes in knowledge representation that occur while people switch from one way of representing scientific knowledge to another.

One area where students show great difficulty in understanding scientific concepts is physics. Analyses of students' changing conceptions, using interviews, verbal protocols, and behavioral outcome measures, indicate that large-scale changes in students' concepts occur in physics education (see McDermott & Redish, 1999 , for a review of this literature). Following Kuhn ( 1962 ), many researchers, but not all, have noted that students' changing conceptions resemble the sequences of conceptual changes in physics that have occurred in the history of science. These notions of radical paradigm shifts and ensuing incompatibility with past knowledge-states have called attention to interesting parallels between the development of particular scientific concepts in children and in the history of physics. Investigations of nonphysicists' understanding of motion indicate that students have extensive misunderstandings of motion. Some researchers have interpreted these findings as an indication that many people hold erroneous beliefs about motion similar to a medieval “impetus” theory (McCloskey, Caramazza, & Green, 1980 ). Furthermore, students appear to maintain “impetus” notions even after one or two courses in physics. In fact, some authors have noted that students who have taken one or two courses in physics can perform worse on physics problems than naive students (Mestre, 1991 ). Thus, it is only after extensive learning that we see a conceptual shift from impetus theories of motion to Newtonian scientific theories.

How one's conceptual representation shifts from “naive” to Newtonian is a matter of contention, as some have argued that the shift involves a radical conceptual change, whereas others have argued that the conceptual change is not really complete. For example, Kozhevnikov and Hegarty ( 2001 ) argue that much of the naive impetus notions of motion are maintained at the expense of Newtonian principles even with extensive training in physics. However, they argue that such impetus principles are maintained at an implicit level. Thus, although students can give the correct Newtonian answer to problems, their reaction times to respond indicate that they are also using impetus theories when they respond. An alternative view of conceptual change focuses on whether there are real conceptual changes at all. Gupta, Hammer and Redish ( 2010 ) and Disessa ( 2004 ) have conducted detailed investigations of changes in physics students' accounts of phenomena covered in elementary physics courses. They have found that rather than students possessing a naive theory that is replaced by the standard theory, many introductory physics students have no stable physical theory but rather construct their explanations from elementary pieces of knowledge of the physical world.

Computational Approaches to Scientific Thinking

Computational approaches have provided a more complete account of the scientific mind. Computational models provide specific detailed accounts of the cognitive processes underlying scientific thinking. Early computational work consisted of taking a scientific discovery and building computational models of the reasoning processes involved in the discovery. Langley, Simon, Bradshaw, and Zytkow ( 1987 ) built a series of programs that simulated discoveries such as those of Copernicus, Bacon, and Stahl. These programs had various inductive reasoning algorithms built into them, and when given the data that the scientists used, they were able to propose the same rules. Computational models make it possible to propose detailed models of the cognitive subcomponents of scientific thinking that specify exactly how scientific theories are generated, tested, and amended (see Darden, 1997 , and Shrager & Langley, 1990 , for accounts of this branch of research). More recently, the incorporation of scientific knowledge into computer programs has resulted in a shift in emphasis from using programs to simulate discoveries to building programs that are used to help scientists make discoveries. A number of these computer programs have made novel discoveries. For example, Valdes-Perez ( 1994 ) has built systems for discoveries in chemistry, and Fajtlowicz has done this in mathematics (Erdos, Fajtlowicz, & Staton, 1991 ).

These advances in the fields of computer discovery have led to new fields, conferences, journals, and even departments that specialize in the development of programs devised to search large databases in the hope of making new scientific discoveries (Langley, 2000 , 2002 ). This process is commonly known as “data mining.” This approach has only proved viable relatively recently, due to advances in computer technology. Biswal et al. ( 2010 ), Mitchell ( 2009 ), and Yang ( 2009 ) provide recent reviews of data mining in different scientific fields. Data mining is at the core of drug discovery, our understanding of the human genome, and our understanding of the universe for a number of reasons. First, vast databases concerning drug actions, biological processes, the genome, the proteome, and the universe itself now exist. Second, the development of high throughput data-mining algorithms makes it possible to search for new drug targets, novel biological mechanisms, and new astronomical phenomena in relatively short periods of time. Research programs that took decades, such as the development of penicillin, can now be done in days (Yang, 2009 ).

Another recent shift in the use of computers in scientific discovery has been to have both computers and people make discoveries together, rather than expecting that computers make an entire scientific discovery. Now instead of using computers to mimic the entire scientific discovery process as used by humans, computers can use powerful algorithms that search for patterns on large databases and provide the patterns to humans who can then use the output of these computers to make discoveries, ranging from the human genome to the structure of the universe. However, there are some robots such as ADAM, developed by King ( 2011 ), that can actually perform the entire scientific process, from the generation of hypotheses, to the conduct of experiments and the interpretation of results, with little human intervention. The ongoing development of scientific robots by some scientists (King et al., 2009 ) thus continues the tradition started by Herbert Simon in the 1960s. However, many of the controversies as to whether the robot is a “real scientist” or not continue to the present (Evans & Rzhetsky, 2010 , Gianfelici, 2010 ; Haufe, Elliott, Burian, & O' Malley, 2010 ; O'Malley 2011 ).

Scientific Thinking and Science Education

Accounts of the nature of science and research on scientific thinking have had profound effects on science education along many levels, particularly in recent years. Science education from the 1900s until the 1970s was primarily concerned with teaching students both the content of science (such as Newton's laws of motion) or the methods that scientists need to use in their research (such as using experimental and control groups). Beginning in the 1980s, a number of reports (e.g., American Association for the Advancement of Science, 1993; National Commission on Excellence in Education, 1983; Rutherford & Ahlgren, 1991 ) stressed the need for teaching scientific thinking skills rather than just methods and content. The addition of scientific thinking skills to the science curriculum from kindergarten through adulthood was a major shift in focus. Many of the particular scientific thinking skills that have been emphasized are skills covered in previous sections of this chapter, such as teaching deductive and inductive thinking strategies. However, rather than focusing on one particular skill, such as induction, researchers in education have focused on how the different components of scientific thinking are put together in science. Furthermore, science educators have focused upon situations where science is conducted collaboratively, rather than being the product of one person thinking alone. These changes in science education parallel changes in methodologies used to investigate science, such as analyzing the ways that scientists think and reason in their laboratories.

By looking at science as a complex multilayered and group activity, many researchers in science education have adopted a constructivist approach. This approach sees learning as an active rather than a passive process, and it suggests that students learn through constructing their scientific knowledge. We will first describe a few examples of the constructivist approach to science education. Following that, we will address several lines of work that challenge some of the assumptions of the constructivist approach to science education.

Often the goal of constructivist science education is to produce conceptual change through guided instruction where the teacher or professor acts as a guide to discovery, rather than the keeper of all the facts. One recent and influential approach to science education is the inquiry-based learning approach. Inquiry-based learning focuses on posing a problem or a puzzling event to students and asking them to propose a hypothesis that could explain the event. Next, the student is asked to collect data that test the hypothesis, make conclusions, and then reflect upon both the original problem and the thought processes that they used to solve the problem. Often students use computers that aid in their construction of new knowledge. The computers allow students to learn many of the different components of scientific thinking. For example, Reiser and his colleagues have developed a learning environment for biology, where students are encouraged to develop hypotheses in groups, codify the hypotheses, and search databases to test these hypotheses (Reiser et al., 2001 ).

One of the myths of science is the lone scientist suddenly shouting “Eureka, I have made a discovery!” Instead, in vivo studies of scientists (e.g., Dunbar, 1995 , 2002 ), historical analyses of scientific discoveries (Nersessian, 1999 ), and studies of children learning science at museums have all pointed to collaborative scientific discovery mechanisms as being one of the driving forces of science (Atkins et al., 2009 ; Azmitia & Crowley, 2001 ). What happens during collaborative scientific thinking is that there is usually a triggering event, such as an unexpected result or situation that a student does not understand. This results in other members of the group adding new information to the person's representation of knowledge, often adding new inductions and deductions that both challenge and transform the reasoner's old representations of knowledge (Chi & Roscoe, 2002 ; Dunbar 1998 ). Social mechanisms play a key component in fostering changes in concepts that have been ignored in traditional cognitive research but are crucial for both science and science education. In science education there has been a shift to collaborative learning, particularly at the elementary level; however, in university education, the emphasis is still on the individual scientist. As many domains of science now involve collaborations across scientific disciplines, we expect the explicit teaching of heuristics for collaborative science to increase.

What is the best way to teach and learn science? Surprisingly, the answer to this question has been difficult to uncover. For example, toward the end of the last century, influenced by several thinkers who advocated a constructivist approach to learning, ranging from Piaget (Beilin, 1994 ) to Papert ( 1980 ), many schools answered this question by adopting a philosophy dubbed “discovery learning.” Although a clear operational definition of this approach has yet to be articulated, the general idea is that children are expected to learn science by reconstructing the processes of scientific discovery—in a range of areas from computer programming to chemistry to mathematics. The premise is that letting students discover principles on their own, set their own goals, and collaboratively explore the natural world produces deeper knowledge that transfers widely.

The research literature on science education is far from consistent in its use of terminology. However, our reading suggests that “discovery learning” differs from “inquiry-based learning” in that few, if any, guidelines are given to students in discovery learning contexts, whereas in inquiry learning, students are given hypotheses and specific goals to achieve (see the second paragraph of this section for a definition of inquiry-based learning). Even though thousands of schools have adopted discovery learning as an alternative to more didactic approaches to teaching and learning, the evidence showing that it is more effective than traditional, direct, teacher-controlled instructional approaches is mixed, at best (Lorch et al., 2010 ; Minner, Levy, & Century, 2010 ). In several cases where the distinctions between direct instruction and more open-ended constructivist instruction have been clearly articulated, implemented, and assessed, direct instruction has proven to be superior to the alternatives (Chen & Klahr, 1999 ; Toth, Klahr, & Chen, 2000 ). For example, in a study of third- and fourth-grade children learning about experimental design, Klahr and Nigam ( 2004 ) found that many more children learned from direct instruction than from discovery learning. Furthermore, they found that among the few children who did manage to learn from a discovery method, there was no better performance on a far transfer test of scientific reasoning than that observed for the many children who learned from direct instruction.

The idea of children learning most of their science through a process of self-directed discovery has some romantic appeal, and it may accurately describe the personal experience of a handful of world-class scientists. However, the claim has generated some contentious disagreements (Kirschner, Sweller, & Clark, 2006 ; Klahr, 2010 ; Taber 2009 ; Tobias & Duffy, 2009 ), and the jury remains out on the extent to which most children can learn science that way.

Conclusions and Future Directions

The field of scientific thinking is now a thriving area of research with strong underpinnings in cognitive psychology and cognitive science. In recent years, a new professional society has been formed that aims to facilitate this integrative and interdisciplinary approach to the psychology of science, with its own journal and regular professional meetings. 1 Clearly the relations between these different aspects of scientific thinking need to be combined in order to produce a truly comprehensive picture of the scientific mind.

While much is known about certain aspects of scientific thinking, much more remains to be discovered. In particular, there has been little contact between cognitive, neuroscience, social, personality, and motivational accounts of scientific thinking. Research in thinking and reasoning has been expanded to use the methods and theories of cognitive neuroscience (see Morrison & Knowlton, Chapter 6 ). A similar approach can be taken in exploring scientific thinking (see Dunbar et al., 2007 ). There are two main reasons for taking a neuroscience approach to scientific thinking. First, functional neuroimaging allows the researcher to look at the entire human brain, making it possible to see the many different sites that are involved in scientific thinking and gain a more complete understanding of the entire range of mechanisms involved in this type of thought. Second, these brain-imaging approaches allow researchers to address fundamental questions in research on scientific thinking, such as the extent to which ordinary thinking in nonscientific contexts and scientific thinking recruit similar versus disparate neural structures of the brain.

Dunbar ( 2009 ) has used some novel methods to explore Simon's assertion, cited at the beginning of this chapter, that scientific thinking uses the same cognitive mechanisms that all human beings possess (rather than being an entirely different type of thinking) but combines them in ways that are specific to a particular aspect of science or a specific discipline of science. For example, Fugelsang and Dunbar ( 2009 ) compared causal reasoning when two colliding circular objects were labeled balls or labeled subatomic particles. They obtained different brain activation patterns depending on whether the stimuli were labeled balls or subatomic particles. In another series of experiments, Dunbar and colleagues used functional magnetic resonance imaging (fMRI) to study patterns of activation in the brains of students who have and who have not undergone conceptual change in physics. For example, Fugelsang and Dunbar ( 2005 ) and Dunbar et al. ( 2007 ) have found differences in the activation of specific brain sites (such as the anterior cingulate) for students when they encounter evidence that is inconsistent with their current conceptual understandings. These initial cognitive neuroscience investigations have the potential to reveal the ways that knowledge is organized in the scientific brain and provide detailed accounts of the nature of the representation of scientific knowledge. Petitto and Dunbar ( 2004 ) proposed the term “educational neuroscience” for the integration of research on education, including science education, with research on neuroscience. However, see Fitzpatrick (in press) for a very different perspective on whether neuroscience approaches are relevant to education. Clearly, research on the scientific brain is just beginning. We as scientists are beginning to get a reasonable grasp of the inner workings of the subcomponents of the scientific mind (i.e., problem solving, analogy, induction). However, great advances remain to be made concerning how these processes interact so that scientific discoveries can be made. Future research will focus on both the collaborative aspects of scientific thinking and the neural underpinnings of the scientific mind.

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Scientific Thinking and Research: Essential Guide to Methodological Approaches

Scientific thinking and research are integral to the advancement of human knowledge and understanding. Scientific thinking is a type of knowledge-seeking process that encompasses various cognitive aspects such as asking questions, testing hypotheses, making observations, recognizing patterns, and making inferences 1 . This process goes beyond mere facts and figures; it involves critical thinking, problem-solving, and creativity, which contribute to scientific discovery and innovation.

Moreover, research in the sciences serves as a practical application of scientific thinking, focusing on systematically investigating the natural world to extend, correct, or confirm existing scientific knowledge. The scientific method, which forms the core of scientific research, entails designing experiments, collecting and analyzing data, and drawing conclusions based on empirical evidence. This rigorous approach increases the likelihood of generating accurate, unbiased, and reliable findings across various scientific disciplines.

Key Takeaways

• Scientific thinking involves critical thinking and problem-solving skills to advance human knowledge.
• Research in the sciences applies scientific thinking through the systematic investigation of the natural world.
• Combining scientific thinking and research leads to accurate, unbiased, and reliable findings in different disciplines.

Principles of Scientific Thinking

Observation and experiment.

Scientific thinking begins with the observation of the natural world and the phenomena occurring around us. Through observation, scientists gather data and gain insights into how things work. To better understand these phenomena, scientists often design and conduct experiments. Controlled experiments allow them to isolate variables, manipulate conditions, and analyze the effects of these manipulations on the outcomes.

Hypothesis and Testing

A central aspect of scientific thinking involves formulating hypotheses. A hypothesis is a testable statement or question based on prior knowledge, observations, and evidence. Once a hypothesis is established, scientists use various methods to test its validity. Testing hypotheses is crucial in refining scientific knowledge and can involve experimentation, conducting case studies, or analyzing existing data. This step incorporates critical thinking and evaluation of the evidence in order to ascertain if the hypothesis holds true or needs further investigation.

Induction and Deduction

Inductive and deductive reasoning are key components of scientific thinking. Induction involves drawing general conclusions from specific observations. For example, a researcher might observe patterns in the data and use these patterns to make broader predictions about a phenomenon. On the other hand, deduction involves applying general principles to specific cases. In deduction, a scientist might start with a broader theory and then test it by making predictions about what should happen in particular situations. Both forms of reasoning help scientists develop theories and models that explain the world around them and allow them to assess the validity of hypotheses.

Scientific Theories

Scientific theories are well-substantiated explanations of some aspect of the natural world. They are the result of rigorous testing, refinement, and a convergence of evidence from multiple sources. A robust scientific theory should be falsifiable, meaning it can be potentially disproven through further experimentation or analysis. The process of developing and refining theories is ongoing, and as new evidence emerges, theories may be adjusted or replaced. Thomas Kuhn’s concept of scientific revolutions highlights how scientific theories can undergo significant shifts based on new insights and perspectives.

Internalizing the principles of scientific thinking enables individuals to evaluate the evidence and uncertainty in various situations, apply critical thinking skills, and develop a deeper understanding of the world and its intricate workings.

Scientific Research

Authorship and articles.

Scientific research involves the process of generating new knowledge and understanding by following a systematic approach. When researchers work on a problem, they publish their findings in the form of articles. These articles are authored by researchers and include the details of the study, the results obtained, and the analysis of the data. Publishing research articles allows other scientists to build upon the findings and advance the field of knowledge.

Experimental Methods

Effective experimental methods are crucial in conducting scientific research. Researchers use various experimental designs and techniques to investigate hypotheses, test theories, and obtain accurate and replicable results. Some common experimental methods include controlled experiments, field experiments, and natural experiments. These experiments help to establish causal relationships between variables and provide valuable information for problem-solving and advancing scientific knowledge.

Case Studies

In some situations, researchers may choose to conduct case studies as part of their research process. Case studies involve an in-depth examination of a particular event, phenomenon, or individual. They provide detailed information on specific instances, allowing researchers to gain a deeper understanding of complex phenomena and identify patterns that may be overlooked in broader studies. While case studies cannot establish causal relationships, they offer valuable insights into real-world situations and can be a valuable complement to other research approaches.

Scientific Argumentation

Scientific argumentation is a critical aspect of the research process, as it enables researchers to communicate their findings, defend their conclusions, and engage in constructive debates. It is essential for researchers to present their arguments clearly and systematically, avoiding confusion and ambiguity. A strong scientific argument consists of a well-structured, evidence-based reasoning that logically connects the premises and conclusions. Through scientific argumentation, researchers contribute to the ongoing dialogue in their field, fostering the development and refinement of theories and ideas.

Importance of Scientific Thinking in Education

Children’s cognitive development.

Scientific thinking plays a crucial role in children’s cognitive development. By engaging in learning processes such as observation, reflection, and motivation, children enhance their critical thinking and problem-solving abilities. Through these processes, they learn to make sense of the world and understand complex concepts. It has been shown that the use of scientific thinking contributes significantly to young children’s development.

Incorporating scientific thinking into early education helps children progress into adulthood with a solid foundation in evidence-based decision-making. A strong emphasis on the scientific method in science education equips children with tools to combat the spread of misinformation and pseudoscience. As a result, an educated population can better understand the significance of scientific advancements and their applications to everyday life.

Teaching Science

The methods used in teaching science have evolved to ensure students develop a deep understanding of the subject. An approach based on constructivism fosters active learning and allows students to construct their knowledge through experiences and reflection. This method is particularly effective for middle school science education, where students can leverage their prior knowledge and engage in hands-on experiments.

Teachers play a vital role in nurturing scientific thinking and encouraging students to question, analyze, and interpret data. Providing activities and environments that promote collaboration among peers can also enhance the development of 21st-century skills like teamwork, communication, and adaptability. As science education continues to evolve and improve, the significance of critical thinking in the field becomes increasingly apparent.

Addressing the importance of scientific thinking in education , educators, and researchers must work together to implement effective strategies and curricula. By fostering students’ scientific thinking abilities, educators pave the way for a generation prepared to tackle complex problems and contribute positively to society’s progress.

Scientific Thinking in Different Disciplines

Scientific thinking is a crucial aspect of many disciplines, as it allows for systematic inquiry and the acquisition of new knowledge. This section explores how scientific thinking manifests in three specific disciplines: psychology and creativity, engineering and problem solving, and philosophy and critical thinking.

Psychology and Creativity

In the field of psychology, scientific thinking focuses on understanding the human mind and its processes. Psychologists employ a variety of methodologies to develop and test hypotheses, often through experiments or observations of human behavior. A key aspect of scientific thinking in psychology involves creativity and curiosity, as researchers must be open to new ideas and novel ways of interpreting data. Embracing creativity enables psychologists to develop innovative theories, experimental designs, and therapeutic interventions, ultimately contributing to the advancement of the field [^1^].

Engineering and Problem Solving

Engineering is another discipline where scientific thinking is essential, as it relies on systematic, evidence-based approaches to solve complex problems. Engineers use their expertise in various fields like mechanical, electrical, and civil engineering, combined with critical thinking skills, to design, build, and maintain infrastructure and machinery. Problem-solving abilities are vital in engineering, as they enable engineers to identify challenges, evaluate potential solutions, and optimize designs for performance, sustainability, and cost-effectiveness. In this discipline, scientific thinking is directly applied to real-world challenges, enhancing the functionality of our society and the built environment [^2^].

Philosophy and Critical Thinking

Scientific thinking is also influential in the discipline of philosophy, as it encourages rigorous analysis and critical evaluation of ideas. Philosophers examine concepts, principles, and theories related to a wide range of areas, such as ethics, metaphysics, and epistemology. Through their work, they aim to improve our understanding of the world and inform the development of ethical and practical guidelines for personal conduct and decision-making. Scientific thinking in philosophy often involves critical thinking and logical reasoning, allowing philosophers to assess the validity of arguments, identify underlying assumptions, and uncover inconsistencies or fallacies in philosophical debates [^3^].

In each of these disciplines, scientific thinking plays a significant role in shaping the investigative methods, analytical approaches, and intellectual development of their respective fields. By fostering curiosity, creativity, critical thinking, and problem-solving, scientific thinking drives progress and innovation across a diverse spectrum of academic and professional pursuits.

Assessing Scientific Thinking

Assessing scientific thinking involves evaluating a person’s ability to reason, question beliefs, and maintain an open-minded attitude when exploring complex concepts. It is crucial to develop methods to evaluate one’s scientific thinking skills, as they play a significant role in understanding scientific principles and contribute to an individual’s overall scientific literacy.

Several assessment methods are applied to gauge scientific thinking. One common method includes analyzing a person’s ability to make inductive and deductive reasoning when presented with scientific information. Inductive reasoning involves making general conclusions based on specific observations, while deductive reasoning entails using established principles to evaluate and predict outcomes. By analyzing an individual’s ability to make logical inferences and predictions, educators can evaluate their scientific thinking skills.

Another important aspect to assess is an individual’s beliefs. When faced with contrasting ideas and theories, it is crucial for a person to evaluate their beliefs, weigh the evidence, and adopt a theory-evidence coordination approach. This method encourages the balance between existing beliefs and newly acquired information, allowing for a comprehensive understanding of scientific concepts.

To foster open-mindedness, assessments can evaluate a person’s capacity to consider multiple perspectives and alternate explanations. Asking individuals to compare and contrast different scientific theories, recognize patterns, and make connections between unrelated concepts can help in measuring their flexibility in thinking.

Furthermore, the ability to design and conduct experiments reflects strong scientific thinking. Assessing an individual’s experimental skills, such as formulating hypotheses, selecting appropriate variables, and interpreting results, can provide valuable insight into their overall scientific thinking abilities.

In summary, assessing scientific thinking requires a multifaceted approach, considering an individual’s reasoning, beliefs, open-mindedness, and experimental capabilities. By developing effective methods to evaluate scientific thinking skills, educators can help individuals to become confident, knowledgeable, and adept at understanding complex scientific concepts.

Fundamental Concepts in Science

Force and energy.

Force and energy are fundamental concepts in the development of science. Force is a push or a pull that causes an object with mass to accelerate or change its motion, while energy is the ability to do work, such as moving an object or causing it to change its state. Exploration of these concepts has led to various predictions and conjectures, often guided by the scientific method.

Scientific curiosity has driven researchers to investigate the interaction between force and energy, leading to the discovery of numerous natural laws and phenomena. For example, Johannes Kepler’s work on planetary motion was instrumental in understanding the relationship between gravitational force and energy.

Equilibrium and Magnetism

Equilibrium is a state in which opposing forces or actions are balanced, resulting in a stable system. Magnetism, on the other hand, is a force that acts upon charged particles, causing a mutual attraction or repulsion between them. These concepts have a pervasive presence in the scientific world, as they govern a wide range of phenomena.

The study of equilibrium and magnetism has prompted scientists to make numerous observations and develop theories to help explain various anomalies. For instance, magnetism plays a crucial role in understanding the behavior of electrical currents and the way they interact with magnetic fields.

Atoms and the Universe

Atoms are the basic building blocks of matter, and their study is essential for comprehending the composition and structure of the universe. Scientific thinking has enabled researchers to delve deeper into the nature of atoms, leading to several important discoveries.

Atoms consist of a nucleus, which contains protons and neutrons, and electrons that orbit the nucleus. Understanding the internal structure and behavior of atoms has been crucial in advancing our knowledge of the universe and its origins.

The scientific method, characterized by observation, experimentation, and hypothesis testing, has been essential in the study of atoms and the universe. It has allowed researchers to make accurate predictions and refine our understanding of fundamental forces and particles.

Frequently Asked Questions

What are the main characteristics of scientific research.

Scientific research is characterized by systematic investigation, empirical evidence, objectivity, and replicability. It aims to understand and predict natural phenomena through the application of the scientific method , which involves observing, hypothesizing, experimenting, and analyzing data. The research should be transparent, and the results must be open to peer review.

Can you provide examples of scientific thinking in everyday life?

Scientific thinking can be applied to everyday situations, such as problem-solving, decision-making, and evaluating information. For example, when you notice your plants are not thriving, you may hypothesize that they need more sunlight or water. You can then design an experiment by altering the amount of light or water, and monitor the results. This process mirrors the scientific method in everyday life.

How can one develop scientific thinking skills?

Developing scientific thinking skills requires practice, curiosity, and openness to new ideas. Engaging in activities such as critical reading, observation, and data analysis is essential to cultivate these skills. You can try to:

• Ask meaningful questions that can be investigated empirically
• Analyze information critically – evaluate the credibility of sources and the validity of the arguments
• Familiarize yourself with scientific concepts to understand the underlying principles
• Design and conduct experiments, analyze the results, and draw conclusions based on the evidence
• Be open to changing your mind when presented with new evidence

What are the different types of scientific thinking?

Different types of scientific thinking include inductive reasoning, deductive reasoning, and abductive reasoning.

• Inductive reasoning involves drawing general conclusions from specific observations. For example, you might notice that birds fly and conclude that all birds can fly.
• Deductive reasoning is the process of drawing logical conclusions from previously established facts. For example, knowing that mammals have hair and that cats are mammals, you deduce that cats have hair.
• Abductive reasoning involves forming a hypothesis to explain observed data or phenomena that are not entirely understood in the existing theories.

What are the three principles of scientific thinking?

The three principles of scientific thinking are empiricism , parsimony, and comprehensiveness.

• Empiricism emphasizes the importance of observational data and the critical examination of evidence when forming knowledge and understanding.
• Parsimony is the principle of selecting the simplest explanation that accurately accounts for the observed data or phenomena.
• Comprehensiveness refers to the quality of a theory or explanation that can account for all, or most, of the available evidence and data.

What are the five steps of the scientific thinking process?

The steps of the scientific thinking process are:

• Asking a question: Identify a problem or phenomenon that can be investigated.
• Conducting background research: Study existing literature and research on the topic to better understand it and identify possible explanations.
• Developing a hypothesis: Formulate a testable statement or prediction based on your research.
• Designing and carrying out an experiment: Develop a procedure to test the hypothesis, collect data, and analyze the results.
• Drawing conclusions: Determine whether the data supports the hypothesis, and consider the implications of the findings for further research.

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Understanding the Complex Relationship between Critical Thinking and Science Reasoning among Undergraduate Thesis Writers

Jason e. dowd.

† Department of Biology, Duke University, Durham, NC 27708

Robert J. Thompson, Jr.

‡ Department of Psychology and Neuroscience, Duke University, Durham, NC 27708

Leslie A. Schiff

§ Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN 55455

Julie A. Reynolds

Associated data.

This study empirically examines the relationship between students’ critical-thinking skills and scientific reasoning as reflected in undergraduate thesis writing in biology. Writing offers a unique window into studying this relationship, and the findings raise potential implications for instruction.

Developing critical-thinking and scientific reasoning skills are core learning objectives of science education, but little empirical evidence exists regarding the interrelationships between these constructs. Writing effectively fosters students’ development of these constructs, and it offers a unique window into studying how they relate. In this study of undergraduate thesis writing in biology at two universities, we examine how scientific reasoning exhibited in writing (assessed using the Biology Thesis Assessment Protocol) relates to general and specific critical-thinking skills (assessed using the California Critical Thinking Skills Test), and we consider implications for instruction. We find that scientific reasoning in writing is strongly related to inference , while other aspects of science reasoning that emerge in writing (epistemological considerations, writing conventions, etc.) are not significantly related to critical-thinking skills. Science reasoning in writing is not merely a proxy for critical thinking. In linking features of students’ writing to their critical-thinking skills, this study 1) provides a bridge to prior work suggesting that engagement in science writing enhances critical thinking and 2) serves as a foundational step for subsequently determining whether instruction focused explicitly on developing critical-thinking skills (particularly inference ) can actually improve students’ scientific reasoning in their writing.

INTRODUCTION

Critical-thinking and scientific reasoning skills are core learning objectives of science education for all students, regardless of whether or not they intend to pursue a career in science or engineering. Consistent with the view of learning as construction of understanding and meaning ( National Research Council, 2000 ), the pedagogical practice of writing has been found to be effective not only in fostering the development of students’ conceptual and procedural knowledge ( Gerdeman et al. , 2007 ) and communication skills ( Clase et al. , 2010 ), but also scientific reasoning ( Reynolds et al. , 2012 ) and critical-thinking skills ( Quitadamo and Kurtz, 2007 ).

Critical thinking and scientific reasoning are similar but different constructs that include various types of higher-order cognitive processes, metacognitive strategies, and dispositions involved in making meaning of information. Critical thinking is generally understood as the broader construct ( Holyoak and Morrison, 2005 ), comprising an array of cognitive processes and dispostions that are drawn upon differentially in everyday life and across domains of inquiry such as the natural sciences, social sciences, and humanities. Scientific reasoning, then, may be interpreted as the subset of critical-thinking skills (cognitive and metacognitive processes and dispositions) that 1) are involved in making meaning of information in scientific domains and 2) support the epistemological commitment to scientific methodology and paradigm(s).

Although there has been an enduring focus in higher education on promoting critical thinking and reasoning as general or “transferable” skills, research evidence provides increasing support for the view that reasoning and critical thinking are also situational or domain specific ( Beyer et al. , 2013 ). Some researchers, such as Lawson (2010) , present frameworks in which science reasoning is characterized explicitly in terms of critical-thinking skills. There are, however, limited coherent frameworks and empirical evidence regarding either the general or domain-specific interrelationships of scientific reasoning, as it is most broadly defined, and critical-thinking skills.

The Vision and Change in Undergraduate Biology Education Initiative provides a framework for thinking about these constructs and their interrelationship in the context of the core competencies and disciplinary practice they describe ( American Association for the Advancement of Science, 2011 ). These learning objectives aim for undergraduates to “understand the process of science, the interdisciplinary nature of the new biology and how science is closely integrated within society; be competent in communication and collaboration; have quantitative competency and a basic ability to interpret data; and have some experience with modeling, simulation and computational and systems level approaches as well as with using large databases” ( Woodin et al. , 2010 , pp. 71–72). This framework makes clear that science reasoning and critical-thinking skills play key roles in major learning outcomes; for example, “understanding the process of science” requires students to engage in (and be metacognitive about) scientific reasoning, and having the “ability to interpret data” requires critical-thinking skills. To help students better achieve these core competencies, we must better understand the interrelationships of their composite parts. Thus, the next step is to determine which specific critical-thinking skills are drawn upon when students engage in science reasoning in general and with regard to the particular scientific domain being studied. Such a determination could be applied to improve science education for both majors and nonmajors through pedagogical approaches that foster critical-thinking skills that are most relevant to science reasoning.

Writing affords one of the most effective means for making thinking visible ( Reynolds et al. , 2012 ) and learning how to “think like” and “write like” disciplinary experts ( Meizlish et al. , 2013 ). As a result, student writing affords the opportunities to both foster and examine the interrelationship of scientific reasoning and critical-thinking skills within and across disciplinary contexts. The purpose of this study was to better understand the relationship between students’ critical-thinking skills and scientific reasoning skills as reflected in the genre of undergraduate thesis writing in biology departments at two research universities, the University of Minnesota and Duke University.

In the following subsections, we discuss in greater detail the constructs of scientific reasoning and critical thinking, as well as the assessment of scientific reasoning in students’ thesis writing. In subsequent sections, we discuss our study design, findings, and the implications for enhancing educational practices.

Critical Thinking

The advances in cognitive science in the 21st century have increased our understanding of the mental processes involved in thinking and reasoning, as well as memory, learning, and problem solving. Critical thinking is understood to include both a cognitive dimension and a disposition dimension (e.g., reflective thinking) and is defined as “purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considera­tions upon which that judgment is based” ( Facione, 1990, p. 3 ). Although various other definitions of critical thinking have been proposed, researchers have generally coalesced on this consensus: expert view ( Blattner and Frazier, 2002 ; Condon and Kelly-Riley, 2004 ; Bissell and Lemons, 2006 ; Quitadamo and Kurtz, 2007 ) and the corresponding measures of critical-­thinking skills ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ).

Both the cognitive skills and dispositional components of critical thinking have been recognized as important to science education ( Quitadamo and Kurtz, 2007 ). Empirical research demonstrates that specific pedagogical practices in science courses are effective in fostering students’ critical-thinking skills. Quitadamo and Kurtz (2007) found that students who engaged in a laboratory writing component in the context of a general education biology course significantly improved their overall critical-thinking skills (and their analytical and inference skills, in particular), whereas students engaged in a traditional quiz-based laboratory did not improve their critical-thinking skills. In related work, Quitadamo et al. (2008) found that a community-based inquiry experience, involving inquiry, writing, research, and analysis, was associated with improved critical thinking in a biology course for nonmajors, compared with traditionally taught sections. In both studies, students who exhibited stronger presemester critical-thinking skills exhibited stronger gains, suggesting that “students who have not been explicitly taught how to think critically may not reach the same potential as peers who have been taught these skills” ( Quitadamo and Kurtz, 2007 , p. 151).

Recently, Stephenson and Sadler-McKnight (2016) found that first-year general chemistry students who engaged in a science writing heuristic laboratory, which is an inquiry-based, writing-to-learn approach to instruction ( Hand and Keys, 1999 ), had significantly greater gains in total critical-thinking scores than students who received traditional laboratory instruction. Each of the four components—inquiry, writing, collaboration, and reflection—have been linked to critical thinking ( Stephenson and Sadler-McKnight, 2016 ). Like the other studies, this work highlights the value of targeting critical-thinking skills and the effectiveness of an inquiry-based, writing-to-learn approach to enhance critical thinking. Across studies, authors advocate adopting critical thinking as the course framework ( Pukkila, 2004 ) and developing explicit examples of how critical thinking relates to the scientific method ( Miri et al. , 2007 ).

In these examples, the important connection between writing and critical thinking is highlighted by the fact that each intervention involves the incorporation of writing into science, technology, engineering, and mathematics education (either alone or in combination with other pedagogical practices). However, critical-thinking skills are not always the primary learning outcome; in some contexts, scientific reasoning is the primary outcome that is assessed.

Scientific Reasoning

Scientific reasoning is a complex process that is broadly defined as “the skills involved in inquiry, experimentation, evidence evaluation, and inference that are done in the service of conceptual change or scientific understanding” ( Zimmerman, 2007 , p. 172). Scientific reasoning is understood to include both conceptual knowledge and the cognitive processes involved with generation of hypotheses (i.e., inductive processes involved in the generation of hypotheses and the deductive processes used in the testing of hypotheses), experimentation strategies, and evidence evaluation strategies. These dimensions are interrelated, in that “experimentation and inference strategies are selected based on prior conceptual knowledge of the domain” ( Zimmerman, 2000 , p. 139). Furthermore, conceptual and procedural knowledge and cognitive process dimensions can be general and domain specific (or discipline specific).

With regard to conceptual knowledge, attention has been focused on the acquisition of core methodological concepts fundamental to scientists’ causal reasoning and metacognitive distancing (or decontextualized thinking), which is the ability to reason independently of prior knowledge or beliefs ( Greenhoot et al. , 2004 ). The latter involves what Kuhn and Dean (2004) refer to as the coordination of theory and evidence, which requires that one question existing theories (i.e., prior knowledge and beliefs), seek contradictory evidence, eliminate alternative explanations, and revise one’s prior beliefs in the face of contradictory evidence. Kuhn and colleagues (2008) further elaborate that scientific thinking requires “a mature understanding of the epistemological foundations of science, recognizing scientific knowledge as constructed by humans rather than simply discovered in the world,” and “the ability to engage in skilled argumentation in the scientific domain, with an appreciation of argumentation as entailing the coordination of theory and evidence” ( Kuhn et al. , 2008 , p. 435). “This approach to scientific reasoning not only highlights the skills of generating and evaluating evidence-based inferences, but also encompasses epistemological appreciation of the functions of evidence and theory” ( Ding et al. , 2016 , p. 616). Evaluating evidence-based inferences involves epistemic cognition, which Moshman (2015) defines as the subset of metacognition that is concerned with justification, truth, and associated forms of reasoning. Epistemic cognition is both general and domain specific (or discipline specific; Moshman, 2015 ).

There is empirical support for the contributions of both prior knowledge and an understanding of the epistemological foundations of science to scientific reasoning. In a study of undergraduate science students, advanced scientific reasoning was most often accompanied by accurate prior knowledge as well as sophisticated epistemological commitments; additionally, for students who had comparable levels of prior knowledge, skillful reasoning was associated with a strong epistemological commitment to the consistency of theory with evidence ( Zeineddin and Abd-El-Khalick, 2010 ). These findings highlight the importance of the need for instructional activities that intentionally help learners develop sophisticated epistemological commitments focused on the nature of knowledge and the role of evidence in supporting knowledge claims ( Zeineddin and Abd-El-Khalick, 2010 ).

Scientific Reasoning in Students’ Thesis Writing

Pedagogical approaches that incorporate writing have also focused on enhancing scientific reasoning. Many rubrics have been developed to assess aspects of scientific reasoning in written artifacts. For example, Timmerman and colleagues (2011) , in the course of describing their own rubric for assessing scientific reasoning, highlight several examples of scientific reasoning assessment criteria ( Haaga, 1993 ; Tariq et al. , 1998 ; Topping et al. , 2000 ; Kelly and Takao, 2002 ; Halonen et al. , 2003 ; Willison and O’Regan, 2007 ).

At both the University of Minnesota and Duke University, we have focused on the genre of the undergraduate honors thesis as the rhetorical context in which to study and improve students’ scientific reasoning and writing. We view the process of writing an undergraduate honors thesis as a form of professional development in the sciences (i.e., a way of engaging students in the practices of a community of discourse). We have found that structured courses designed to scaffold the thesis-­writing process and promote metacognition can improve writing and reasoning skills in biology, chemistry, and economics ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In the context of this prior work, we have defined scientific reasoning in writing as the emergent, underlying construct measured across distinct aspects of students’ written discussion of independent research in their undergraduate theses.

The Biology Thesis Assessment Protocol (BioTAP) was developed at Duke University as a tool for systematically guiding students and faculty through a “draft–feedback–revision” writing process, modeled after professional scientific peer-review processes ( Reynolds et al. , 2009 ). BioTAP includes activities and worksheets that allow students to engage in critical peer review and provides detailed descriptions, presented as rubrics, of the questions (i.e., dimensions, shown in Table 1 ) upon which such review should focus. Nine rubric dimensions focus on communication to the broader scientific community, and four rubric dimensions focus on the accuracy and appropriateness of the research. These rubric dimensions provide criteria by which the thesis is assessed, and therefore allow BioTAP to be used as an assessment tool as well as a teaching resource ( Reynolds et al. , 2009 ). Full details are available at www.science-writing.org/biotap.html .

Theses assessment protocol dimensions

In previous work, we have used BioTAP to quantitatively assess students’ undergraduate honors theses and explore the relationship between thesis-writing courses (or specific interventions within the courses) and the strength of students’ science reasoning in writing across different science disciplines: biology ( Reynolds and Thompson, 2011 ); chemistry ( Dowd et al. , 2015b ); and economics ( Dowd et al. , 2015a ). We have focused exclusively on the nine dimensions related to reasoning and writing (questions 1–9), as the other four dimensions (questions 10–13) require topic-specific expertise and are intended to be used by the student’s thesis supervisor.

Beyond considering individual dimensions, we have investigated whether meaningful constructs underlie students’ thesis scores. We conducted exploratory factor analysis of students’ theses in biology, economics, and chemistry and found one dominant underlying factor in each discipline; we termed the factor “scientific reasoning in writing” ( Dowd et al. , 2015a , b , 2016 ). That is, each of the nine dimensions could be understood as reflecting, in different ways and to different degrees, the construct of scientific reasoning in writing. The findings indicated evidence of both general and discipline-specific components to scientific reasoning in writing that relate to epistemic beliefs and paradigms, in keeping with broader ideas about science reasoning discussed earlier. Specifically, scientific reasoning in writing is more strongly associated with formulating a compelling argument for the significance of the research in the context of current literature in biology, making meaning regarding the implications of the findings in chemistry, and providing an organizational framework for interpreting the thesis in economics. We suggested that instruction, whether occurring in writing studios or in writing courses to facilitate thesis preparation, should attend to both components.

Research Question and Study Design

The genre of thesis writing combines the pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-­McKnight, 2016 ). However, there is no empirical evidence regarding the general or domain-specific interrelationships of scientific reasoning and critical-thinking skills, particularly in the rhetorical context of the undergraduate thesis. The BioTAP studies discussed earlier indicate that the rubric-based assessment produces evidence of scientific reasoning in the undergraduate thesis, but it was not designed to foster or measure critical thinking. The current study was undertaken to address the research question: How are students’ critical-thinking skills related to scientific reasoning as reflected in the genre of undergraduate thesis writing in biology? Determining these interrelationships could guide efforts to enhance students’ scientific reasoning and writing skills through focusing instruction on specific critical-thinking skills as well as disciplinary conventions.

To address this research question, we focused on undergraduate thesis writers in biology courses at two institutions, Duke University and the University of Minnesota, and examined the extent to which students’ scientific reasoning in writing, assessed in the undergraduate thesis using BioTAP, corresponds to students’ critical-thinking skills, assessed using the California Critical Thinking Skills Test (CCTST; August, 2016 ).

Study Sample

The study sample was composed of students enrolled in courses designed to scaffold the thesis-writing process in the Department of Biology at Duke University and the College of Biological Sciences at the University of Minnesota. Both courses complement students’ individual work with research advisors. The course is required for thesis writers at the University of Minnesota and optional for writers at Duke University. Not all students are required to complete a thesis, though it is required for students to graduate with honors; at the University of Minnesota, such students are enrolled in an honors program within the college. In total, 28 students were enrolled in the course at Duke University and 44 students were enrolled in the course at the University of Minnesota. Of those students, two students did not consent to participate in the study; additionally, five students did not validly complete the CCTST (i.e., attempted fewer than 60% of items or completed the test in less than 15 minutes). Thus, our overall rate of valid participation is 90%, with 27 students from Duke University and 38 students from the University of Minnesota. We found no statistically significant differences in thesis assessment between students with valid CCTST scores and invalid CCTST scores. Therefore, we focus on the 65 students who consented to participate and for whom we have complete and valid data in most of this study. Additionally, in asking students for their consent to participate, we allowed them to choose whether to provide or decline access to academic and demographic background data. Of the 65 students who consented to participate, 52 students granted access to such data. Therefore, for additional analyses involving academic and background data, we focus on the 52 students who consented. We note that the 13 students who participated but declined to share additional data performed slightly lower on the CCTST than the 52 others (perhaps suggesting that they differ by other measures, but we cannot determine this with certainty). Among the 52 students, 60% identified as female and 10% identified as being from underrepresented ethnicities.

In both courses, students completed the CCTST online, either in class or on their own, late in the Spring 2016 semester. This is the same assessment that was used in prior studies of critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). It is “an objective measure of the core reasoning skills needed for reflective decision making concerning what to believe or what to do” ( Insight Assessment, 2016a ). In the test, students are asked to read and consider information as they answer multiple-choice questions. The questions are intended to be appropriate for all users, so there is no expectation of prior disciplinary knowledge in biology (or any other subject). Although actual test items are protected, sample items are available on the Insight Assessment website ( Insight Assessment, 2016b ). We have included one sample item in the Supplemental Material.

The CCTST is based on a consensus definition of critical thinking, measures cognitive and metacognitive skills associated with critical thinking, and has been evaluated for validity and reliability at the college level ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ). In addition to providing overall critical-thinking score, the CCTST assesses seven dimensions of critical thinking: analysis, interpretation, inference, evaluation, explanation, induction, and deduction. Scores on each dimension are calculated based on students’ performance on items related to that dimension. Analysis focuses on identifying assumptions, reasons, and claims and examining how they interact to form arguments. Interpretation, related to analysis, focuses on determining the precise meaning and significance of information. Inference focuses on drawing conclusions from reasons and evidence. Evaluation focuses on assessing the credibility of sources of information and claims they make. Explanation, related to evaluation, focuses on describing the evidence, assumptions, or rationale for beliefs and conclusions. Induction focuses on drawing inferences about what is probably true based on evidence. Deduction focuses on drawing conclusions about what must be true when the context completely determines the outcome. These are not independent dimensions; the fact that they are related supports their collective interpretation as critical thinking. Together, the CCTST dimensions provide a basis for evaluating students’ overall strength in using reasoning to form reflective judgments about what to believe or what to do ( August, 2016 ). Each of the seven dimensions and the overall CCTST score are measured on a scale of 0–100, where higher scores indicate superior performance. Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and below) skills.

Scientific Reasoning in Writing

At the end of the semester, students’ final, submitted undergraduate theses were assessed using BioTAP, which consists of nine rubric dimensions that focus on communication to the broader scientific community and four additional dimensions that focus on the exhibition of topic-specific expertise ( Reynolds et al. , 2009 ). These dimensions, framed as questions, are displayed in Table 1 .

Student theses were assessed on questions 1–9 of BioTAP using the same procedures described in previous studies ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In this study, six raters were trained in the valid, reliable use of BioTAP rubrics. Each dimension was rated on a five-point scale: 1 indicates the dimension is missing, incomplete, or below acceptable standards; 3 indicates that the dimension is adequate but not exhibiting mastery; and 5 indicates that the dimension is excellent and exhibits mastery (intermediate ratings of 2 and 4 are appropriate when different parts of the thesis make a single category challenging). After training, two raters independently assessed each thesis and then discussed their independent ratings with one another to form a consensus rating. The consensus score is not an average score, but rather an agreed-upon, discussion-based score. On a five-point scale, raters independently assessed dimensions to be within 1 point of each other 82.4% of the time before discussion and formed consensus ratings 100% of the time after discussion.

In this study, we consider both categorical (mastery/nonmastery, where a score of 5 corresponds to mastery) and numerical treatments of individual BioTAP scores to better relate the manifestation of critical thinking in BioTAP assessment to all of the prior studies. For comprehensive/cumulative measures of BioTAP, we focus on the partial sum of questions 1–5, as these questions relate to higher-order scientific reasoning (whereas questions 6–9 relate to mid- and lower-order writing mechanics [ Reynolds et al. , 2009 ]), and the factor scores (i.e., numerical representations of the extent to which each student exhibits the underlying factor), which are calculated from the factor loadings published by Dowd et al. (2016) . We do not focus on questions 6–9 individually in statistical analyses, because we do not expect critical-thinking skills to relate to mid- and lower-order writing skills.

The final, submitted thesis reflects the student’s writing, the student’s scientific reasoning, the quality of feedback provided to the student by peers and mentors, and the student’s ability to incorporate that feedback into his or her work. Therefore, our assessment is not the same as an assessment of unpolished, unrevised samples of students’ written work. While one might imagine that such an unpolished sample may be more strongly correlated with critical-thinking skills measured by the CCTST, we argue that the complete, submitted thesis, assessed using BioTAP, is ultimately a more appropriate reflection of how students exhibit science reasoning in the scientific community.

Statistical Analyses

We took several steps to analyze the collected data. First, to provide context for subsequent interpretations, we generated descriptive statistics for the CCTST scores of the participants based on the norms for undergraduate CCTST test takers. To determine the strength of relationships among CCTST dimensions (including overall score) and the BioTAP dimensions, partial-sum score (questions 1–5), and factor score, we calculated Pearson’s correlations for each pair of measures. To examine whether falling on one side of the nonmastery/mastery threshold (as opposed to a linear scale of performance) was related to critical thinking, we grouped BioTAP dimensions into categories (mastery/nonmastery) and conducted Student’s t tests to compare the means scores of the two groups on each of the seven dimensions and overall score of the CCTST. Finally, for the strongest relationship that emerged, we included additional academic and background variables as covariates in multiple linear-regression analysis to explore questions about how much observed relationships between critical-thinking skills and science reasoning in writing might be explained by variation in these other factors.

Although BioTAP scores represent discreet, ordinal bins, the five-point scale is intended to capture an underlying continuous construct (from inadequate to exhibiting mastery). It has been argued that five categories is an appropriate cutoff for treating ordinal variables as pseudo-continuous ( Rhemtulla et al. , 2012 )—and therefore using continuous-variable statistical methods (e.g., Pearson’s correlations)—as long as the underlying assumption that ordinal scores are linearly distributed is valid. Although we have no way to statistically test this assumption, we interpret adequate scores to be approximately halfway between inadequate and mastery scores, resulting in a linear scale. In part because this assumption is subject to disagreement, we also consider and interpret a categorical (mastery/nonmastery) treatment of BioTAP variables.

We corrected for multiple comparisons using the Holm-Bonferroni method ( Holm, 1979 ). At the most general level, where we consider the single, comprehensive measures for BioTAP (partial-sum and factor score) and the CCTST (overall score), there is no need to correct for multiple comparisons, because the multiple, individual dimensions are collapsed into single dimensions. When we considered individual CCTST dimensions in relation to comprehensive measures for BioTAP, we accounted for seven comparisons; similarly, when we considered individual dimensions of BioTAP in relation to overall CCTST score, we accounted for five comparisons. When all seven CCTST and five BioTAP dimensions were examined individually and without prior knowledge, we accounted for 35 comparisons; such a rigorous threshold is likely to reject weak and moderate relationships, but it is appropriate if there are no specific pre-existing hypotheses. All p values are presented in tables for complete transparency, and we carefully consider the implications of our interpretation of these data in the Discussion section.

CCTST scores for students in this sample ranged from the 39th to 99th percentile of the general population of undergraduate CCTST test takers (mean percentile = 84.3, median = 85th percentile; Table 2 ); these percentiles reflect overall scores that range from moderate to superior. Scores on individual dimensions and overall scores were sufficiently normal and far enough from the ceiling of the scale to justify subsequent statistical analyses.

Descriptive statistics of CCTST dimensions a

a Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and lower) skills.

The Pearson’s correlations between students’ cumulative scores on BioTAP (the factor score based on loadings published by Dowd et al. , 2016 , and the partial sum of scores on questions 1–5) and students’ overall scores on the CCTST are presented in Table 3 . We found that the partial-sum measure of BioTAP was significantly related to the overall measure of critical thinking ( r = 0.27, p = 0.03), while the BioTAP factor score was marginally related to overall CCTST ( r = 0.24, p = 0.05). When we looked at relationships between comprehensive BioTAP measures and scores for individual dimensions of the CCTST ( Table 3 ), we found significant positive correlations between the both BioTAP partial-sum and factor scores and CCTST inference ( r = 0.45, p < 0.001, and r = 0.41, p < 0.001, respectively). Although some other relationships have p values below 0.05 (e.g., the correlations between BioTAP partial-sum scores and CCTST induction and interpretation scores), they are not significant when we correct for multiple comparisons.

Correlations between dimensions of CCTST and dimensions of BioTAP a

a In each cell, the top number is the correlation, and the bottom, italicized number is the associated p value. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

b This is the partial sum of BioTAP scores on questions 1–5.

c This is the factor score calculated from factor loadings published by Dowd et al. (2016) .

When we expanded comparisons to include all 35 potential correlations among individual BioTAP and CCTST dimensions—and, accordingly, corrected for 35 comparisons—we did not find any additional statistically significant relationships. The Pearson’s correlations between students’ scores on each dimension of BioTAP and students’ scores on each dimension of the CCTST range from −0.11 to 0.35 ( Table 3 ); although the relationship between discussion of implications (BioTAP question 5) and inference appears to be relatively large ( r = 0.35), it is not significant ( p = 0.005; the Holm-Bonferroni cutoff is 0.00143). We found no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions (unpublished data), regardless of whether we correct for multiple comparisons.

The results of Student’s t tests comparing scores on each dimension of the CCTST of students who exhibit mastery with those of students who do not exhibit mastery on each dimension of BioTAP are presented in Table 4 . Focusing first on the overall CCTST scores, we found that the difference between those who exhibit mastery and those who do not in discussing implications of results (BioTAP question 5) is statistically significant ( t = 2.73, p = 0.008, d = 0.71). When we expanded t tests to include all 35 comparisons—and, like above, corrected for 35 comparisons—we found a significant difference in inference scores between students who exhibit mastery on question 5 and students who do not ( t = 3.41, p = 0.0012, d = 0.88), as well as a marginally significant difference in these students’ induction scores ( t = 3.26, p = 0.0018, d = 0.84; the Holm-Bonferroni cutoff is p = 0.00147). Cohen’s d effect sizes, which reveal the strength of the differences for statistically significant relationships, range from 0.71 to 0.88.

The t statistics and effect sizes of differences in ­dimensions of CCTST across dimensions of BioTAP a

a In each cell, the top number is the t statistic for each comparison, and the middle, italicized number is the associated p value. The bottom number is the effect size. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

Finally, we more closely examined the strongest relationship that we observed, which was between the CCTST dimension of inference and the BioTAP partial-sum composite score (shown in Table 3 ), using multiple regression analysis ( Table 5 ). Focusing on the 52 students for whom we have background information, we looked at the simple relationship between BioTAP and inference (model 1), a robust background model including multiple covariates that one might expect to explain some part of the variation in BioTAP (model 2), and a combined model including all variables (model 3). As model 3 shows, the covariates explain very little variation in BioTAP scores, and the relationship between inference and BioTAP persists even in the presence of all of the covariates.

Partial sum (questions 1–5) of BioTAP scores ( n = 52)

** p < 0.01.

*** p < 0.001.

The aim of this study was to examine the extent to which the various components of scientific reasoning—manifested in writing in the genre of undergraduate thesis and assessed using BioTAP—draw on general and specific critical-thinking skills (assessed using CCTST) and to consider the implications for educational practices. Although science reasoning involves critical-thinking skills, it also relates to conceptual knowledge and the epistemological foundations of science disciplines ( Kuhn et al. , 2008 ). Moreover, science reasoning in writing , captured in students’ undergraduate theses, reflects habits, conventions, and the incorporation of feedback that may alter evidence of individuals’ critical-thinking skills. Our findings, however, provide empirical evidence that cumulative measures of science reasoning in writing are nonetheless related to students’ overall critical-thinking skills ( Table 3 ). The particularly significant roles of inference skills ( Table 3 ) and the discussion of implications of results (BioTAP question 5; Table 4 ) provide a basis for more specific ideas about how these constructs relate to one another and what educational interventions may have the most success in fostering these skills.

Our results build on previous findings. The genre of thesis writing combines pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). Quitadamo and Kurtz (2007) reported that students who engaged in a laboratory writing component in a general education biology course significantly improved their inference and analysis skills, and Quitadamo and colleagues (2008) found that participation in a community-based inquiry biology course (that included a writing component) was associated with significant gains in students’ inference and evaluation skills. The shared focus on inference is noteworthy, because these prior studies actually differ from the current study; the former considered critical-­thinking skills as the primary learning outcome of writing-­focused interventions, whereas the latter focused on emergent links between two learning outcomes (science reasoning in writing and critical thinking). In other words, inference skills are impacted by writing as well as manifested in writing.

Inference focuses on drawing conclusions from argument and evidence. According to the consensus definition of critical thinking, the specific skill of inference includes several processes: querying evidence, conjecturing alternatives, and drawing conclusions. All of these activities are central to the independent research at the core of writing an undergraduate thesis. Indeed, a critical part of what we call “science reasoning in writing” might be characterized as a measure of students’ ability to infer and make meaning of information and findings. Because the cumulative BioTAP measures distill underlying similarities and, to an extent, suppress unique aspects of individual dimensions, we argue that it is appropriate to relate inference to scientific reasoning in writing . Even when we control for other potentially relevant background characteristics, the relationship is strong ( Table 5 ).

In taking the complementary view and focusing on BioTAP, when we compared students who exhibit mastery with those who do not, we found that the specific dimension of “discussing the implications of results” (question 5) differentiates students’ performance on several critical-thinking skills. To achieve mastery on this dimension, students must make connections between their results and other published studies and discuss the future directions of the research; in short, they must demonstrate an understanding of the bigger picture. The specific relationship between question 5 and inference is the strongest observed among all individual comparisons. Altogether, perhaps more than any other BioTAP dimension, this aspect of students’ writing provides a clear view of the role of students’ critical-thinking skills (particularly inference and, marginally, induction) in science reasoning.

While inference and discussion of implications emerge as particularly strongly related dimensions in this work, we note that the strongest contribution to “science reasoning in writing in biology,” as determined through exploratory factor analysis, is “argument for the significance of research” (BioTAP question 2, not question 5; Dowd et al. , 2016 ). Question 2 is not clearly related to critical-thinking skills. These findings are not contradictory, but rather suggest that the epistemological and disciplinary-specific aspects of science reasoning that emerge in writing through BioTAP are not completely aligned with aspects related to critical thinking. In other words, science reasoning in writing is not simply a proxy for those critical-thinking skills that play a role in science reasoning.

In a similar vein, the content-related, epistemological aspects of science reasoning, as well as the conventions associated with writing the undergraduate thesis (including feedback from peers and revision), may explain the lack of significant relationships between some science reasoning dimensions and some critical-thinking skills that might otherwise seem counterintuitive (e.g., BioTAP question 2, which relates to making an argument, and the critical-thinking skill of argument). It is possible that an individual’s critical-thinking skills may explain some variation in a particular BioTAP dimension, but other aspects of science reasoning and practice exert much stronger influence. Although these relationships do not emerge in our analyses, the lack of significant correlation does not mean that there is definitively no correlation. Correcting for multiple comparisons suppresses type 1 error at the expense of exacerbating type 2 error, which, combined with the limited sample size, constrains statistical power and makes weak relationships more difficult to detect. Ultimately, though, the relationships that do emerge highlight places where individuals’ distinct critical-thinking skills emerge most coherently in thesis assessment, which is why we are particularly interested in unpacking those relationships.

We recognize that, because only honors students submit theses at these institutions, this study sample is composed of a selective subset of the larger population of biology majors. Although this is an inherent limitation of focusing on thesis writing, links between our findings and results of other studies (with different populations) suggest that observed relationships may occur more broadly. The goal of improved science reasoning and critical thinking is shared among all biology majors, particularly those engaged in capstone research experiences. So while the implications of this work most directly apply to honors thesis writers, we provisionally suggest that all students could benefit from further study of them.

There are several important implications of this study for science education practices. Students’ inference skills relate to the understanding and effective application of scientific content. The fact that we find no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions suggests that such mid- to lower-order elements of BioTAP ( Reynolds et al. , 2009 ), which tend to be more structural in nature, do not focus on aspects of the finished thesis that draw strongly on critical thinking. In keeping with prior analyses ( Reynolds and Thompson, 2011 ; Dowd et al. , 2016 ), these findings further reinforce the notion that disciplinary instructors, who are most capable of teaching and assessing scientific reasoning and perhaps least interested in the more mechanical aspects of writing, may nonetheless be best suited to effectively model and assess students’ writing.

The goal of the thesis writing course at both Duke University and the University of Minnesota is not merely to improve thesis scores but to move students’ writing into the category of mastery across BioTAP dimensions. Recognizing that students with differing critical-thinking skills (particularly inference) are more or less likely to achieve mastery in the undergraduate thesis (particularly in discussing implications [question 5]) is important for developing and testing targeted pedagogical interventions to improve learning outcomes for all students.

The competencies characterized by the Vision and Change in Undergraduate Biology Education Initiative provide a general framework for recognizing that science reasoning and critical-thinking skills play key roles in major learning outcomes of science education. Our findings highlight places where science reasoning–related competencies (like “understanding the process of science”) connect to critical-thinking skills and places where critical thinking–related competencies might be manifested in scientific products (such as the ability to discuss implications in scientific writing). We encourage broader efforts to build empirical connections between competencies and pedagogical practices to further improve science education.

One specific implication of this work for science education is to focus on providing opportunities for students to develop their critical-thinking skills (particularly inference). Of course, as this correlational study is not designed to test causality, we do not claim that enhancing students’ inference skills will improve science reasoning in writing. However, as prior work shows that science writing activities influence students’ inference skills ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ), there is reason to test such a hypothesis. Nevertheless, the focus must extend beyond inference as an isolated skill; rather, it is important to relate inference to the foundations of the scientific method ( Miri et al. , 2007 ) in terms of the epistemological appreciation of the functions and coordination of evidence ( Kuhn and Dean, 2004 ; Zeineddin and Abd-El-Khalick, 2010 ; Ding et al. , 2016 ) and disciplinary paradigms of truth and justification ( Moshman, 2015 ).

Although this study is limited to the domain of biology at two institutions with a relatively small number of students, the findings represent a foundational step in the direction of achieving success with more integrated learning outcomes. Hopefully, it will spur greater interest in empirically grounding discussions of the constructs of scientific reasoning and critical-thinking skills.

This study contributes to the efforts to improve science education, for both majors and nonmajors, through an empirically driven analysis of the relationships between scientific reasoning reflected in the genre of thesis writing and critical-thinking skills. This work is rooted in the usefulness of BioTAP as a method 1) to facilitate communication and learning and 2) to assess disciplinary-specific and general dimensions of science reasoning. The findings support the important role of the critical-thinking skill of inference in scientific reasoning in writing, while also highlighting ways in which other aspects of science reasoning (epistemological considerations, writing conventions, etc.) are not significantly related to critical thinking. Future research into the impact of interventions focused on specific critical-thinking skills (i.e., inference) for improved science reasoning in writing will build on this work and its implications for science education.

Supplementary Material

Acknowledgments.

We acknowledge the contributions of Kelaine Haas and Alexander Motten to the implementation and collection of data. We also thank Mine Çetinkaya-­Rundel for her insights regarding our statistical analyses. This research was funded by National Science Foundation award DUE-1525602.

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5 Scientific Thinking

Learning Objectives

• Describe the principles of the scientific method and explain its importance in conducting and interpreting research.
• Differentiate laws from theories and explain how research hypotheses are developed and tested.
• Identify the role of the research hypothesis in psychological research.

Psychologists aren’t the only people who seek to understand human behavior and solve social problems. Philosophers, religious leaders, and politicians, among others, also strive to provide explanations for human behavior. But psychologists believe that research is the best tool for understanding human beings and their relationships with others. Rather than accepting the claim of a philosopher that people do (or do not) have free will, a psychologist would collect data to empirically test whether or not people are able to actively control their own behavior. Rather than accepting a politician’s contention that creating (or abandoning) a new center for mental health will improve the lives of individuals in the inner city, a psychologist would empirically assess the effects of receiving mental health treatment on the quality of life of the recipients. The statements made by psychologists are empirical , which means they are based on systematic collection and analysis of data .

The Scientific Method

All scientists (whether they are physicists, chemists, biologists, sociologists, or psychologists) are engaged in the basic processes of collecting data and drawing conclusions about those data. The methods used by scientists have developed over many years and provide a common framework for developing, organizing, and sharing information. The scientific method is the set of assumptions, rules, and procedures scientists use to conduct research .

In addition to requiring that science be empirical, the scientific method demands that the procedures used be objective , or free from the personal bias or emotions of the scientist . The scientific method describes how scientists collect and analyze data, how they draw conclusions from data, and how they share data with others. These rules increase objectivity by placing data under the scrutiny of other scientists and even the public at large. Because data are reported objectively, other scientists know exactly how the scientist collected and analyzed the data. This means that they do not have to rely only on the scientist’s own interpretation of the data; they may draw their own, potentially different, conclusions.

The scientific method is an iterative process. The scientific process often starts with making a hypothesis (which is also an educated guess). Then, research studies are designed to test the hypothesis. The results obtained from experiments then inform the researchers how behaviors may be predicted or explained. This is a recurring process in which the results then loop back to modify the hypothesis if necessary. With an updated hypothesis, researchers then continue to employ the scientific process to conduct experiments.

Figure 2.1 The scientific process employed by psychologists

Most new research is designed to replicate —that is, to repeat, add to, or modify—previous research findings. The scientific method therefore results in an accumulation of scientific knowledge through the reporting of research and the addition to and modifications of these reported findings by other scientists.

Laws and Theories as Organizing Principles

One goal of research is to organize information into meaningful statements that can be applied in many situations. Principles that are so general as to apply to all situations in a given domain of inquiry are known as laws . There are well-known laws in the physical sciences, such as the law of gravity and the laws of thermodynamics, and there are some universally accepted laws in psychology, such as the law of effect and Weber’s law. But because laws are very general principles and their validity has already been well established, they are themselves rarely directly subjected to scientific testing.

The next step down from laws in the hierarchy of organizing principles is theory. A theory is an integrated set of principles that explains and predicts many, but not all, observed relationships within a given domain of inquiry . One example of an important theory in psychology is the stage theory of cognitive development proposed by the Swiss psychologist Jean Piaget. The theory states that children pass through a series of cognitive stages as they grow, each of which must be mastered in succession before movement to the next cognitive stage can occur. This is an extremely useful theory in human development because it can be applied to many different content areas and can be tested in many different ways.

Good theories have four important characteristics. First, good theories are general , meaning they summarize many different outcomes. Second, they are parsimonious , meaning they provide the simplest possible account of those outcomes. The stage theory of cognitive development meets both of these requirements. It can account for developmental changes in behavior across a wide variety of domains, and yet it does so parsimoniously—by hypothesizing a simple set of cognitive stages. Third, good theories provide ideas for future research . The stage theory of cognitive development has been applied not only to learning about cognitive skills but also to the study of children’s moral (Kohlberg, 1966) and gender (Ruble & Martin, 1998) development.

Finally, good theories are falsifiable (Popper, 1959), which means the variables of interest can be adequately measured and the relationships between the variables that are predicted by the theory can be shown through research to be incorrect . The stage theory of cognitive development is falsifiable because the stages of cognitive reasoning can be measured and because if research discovers, for instance, that children learn new tasks before they have reached the cognitive stage hypothesized to be required for that task, then the theory will be shown to be incorrect.

No single theory is able to account for all behavior in all cases. Rather, theories are each limited in that they make accurate predictions in some situations or for some people but not in other situations or for other people. As a result, there is a constant exchange between theory and data: Existing theories are modified on the basis of collected data, and the newly modified theories then make new predictions that are tested by new data, and so forth. When a better theory is found, it will replace the old one. This is part of the accumulation of scientific knowledge.

The Research Hypothesis

Theories are usually framed too broadly to be tested in a single experiment. Therefore, scientists use a more precise statement of the presumed relationship among specific parts of a theory—a research hypothesis—as the basis for their research. A research hypothesis is a specific and falsifiable prediction about the relationship between or among two or more variables, where a variable is any attribute that can assume different values among different people or across different times or places. The research hypothesis states the existence of a relationship between the variables of interest and the specific direction of that relationship. For instance, the research hypothesis “Using marijuana will reduce learning” predicts that there is a relationship between a variable “using marijuana” and another variable called “learning.” Similarly, in the research hypothesis “participating in psychotherapy will reduce anxiety,” the variables that are expected to be related are “participating in psychotherapy” and “level of anxiety.”

When stated in an abstract manner, the ideas that form the basis of a research hypothesis are known as conceptual variables. Conceptual variables are abstract ideas that form the basis of research hypotheses. Sometimes the conceptual variables are rather simple—for instance, “age,” “gender,” or “weight.” In other cases, the conceptual variables represent more complex ideas, such as “anxiety,” “cognitive development,” “learning,” “self-esteem,” or “sexism.”

The first step in testing a research hypothesis involves turning the conceptual variables into measured variables, which are variables consisting of numbers that represent the conceptual variables. For instance, the conceptual variable “participating in psychotherapy” could be represented as the measured variable “number of psychotherapy hours the patient has accrued,” and the conceptual variable “using marijuana” could be assessed by having the research participants rate, on a scale from 1 to 10, how often they use marijuana or by administering a blood test that measures the presence of the chemicals in marijuana.

Psychologists use the term operational definition to refer to a precise statement of how a conceptual variable is turned into a measured variable. The relationship between conceptual and measured variables in a research hypothesis is diagrammed in Figure 2.2 “Diagram of a Research Hypothesis.” The conceptual variables are represented within circles at the top of the figure, and the measured variables are represented within squares at the bottom. The two vertical arrows, which lead from the conceptual variables to the measured variables, represent the operational definitions of the two variables. The arrows indicate the expectation that changes in the conceptual variables (psychotherapy and anxiety in this example) will cause changes in the corresponding measured variables. The measured variables are then used to draw inferences about the conceptual variables.

Table 2.1, “Examples of the Operational Definitions of Conceptual Variables That Have Been Used in Psychological Research,” lists some potential operational definitions of conceptual variables that have been used in psychological research. As you read through this list, note that in contrast to the abstract conceptual variables, the measured variables are very specific. This specificity is important for two reasons. First, more specific definitions mean that there is less danger that the collected data will be misunderstood by others. Second, specific definitions will enable future researchers to replicate the research.

Table 2.1 Examples of the Operational Definitions of Conceptual Variables That Have Been Used in Psychological Research

the set of assumptions, rules, and procedures scientists use to conduct research

free from the personal bias or emotions of the scientist

Introduction to Psychology Copyright © 2022 by LOUIS: The Louisiana Library Network is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Critical thinking refers to deliberately scrutinizing and evaluating theories, concepts, or ideas using reasoned reflection and analysis. The act of thinking critically implies moving beyond simply understanding information, but questioning its source, its production, and its presentation in order to expose potential bias or researcher subjectivity. Applying critical thinking to investigating a research problem involves the act of challenging assumptions and questioning the choices and potential motives underpinning how the author designed the study and arrived at particular conclusions or recommended courses of action.

Mintz, Steven. "How the Word "Critical" Came to Signify the Leading Edge of Cultural Analysis." Higher Ed Gamma Blog , Inside Higher Ed, February 13, 2024; Van Merriënboer, Jeroen JG and Paul A. Kirschner. Ten Steps to Complex Learning: A Systematic Approach to Four-component Instructional Design . New York: Routledge, 2017.

Thinking Critically

Applying Critical Thinking to Research and Writing

Professors like to use the term critical thinking; in fact, the idea of being critical permeates much of academe writ large. In the classroom, the idea of thinking critically is often mentioned by professors when students ask how they should approach a research and writing assignment [other approaches your professor might mention include interdisciplinarity, comparative, gendered, global, etc.]. However, critical thinking is more than just an approach to research and writing. It is an acquired skill used in becoming a complex learner capable of discerning important relationships among the elements of, as well as integrating multiple ways of understanding applied to, the research problem. Critical thinking is a lens through which you holistically interrogate a topic.

Given this, thinking critically encompasses a variety of inter-related connotations applied to college-level research and writing * :

• Integrated and Multi-Dimensional . Critical thinking is not focused on any one element of the research design, but rather, is applied holistically throughout the process of identifying the research problem, reviewing of literature, applying methods of analysis, describing the results, discussing their implications, and, if appropriate, offering recommendations for further research. The act of thinking critically is non-linear [i.e., applies to going back and changing prior thoughts when new evidence emerges]; it permeates the entire research endeavor from contemplating what to write to proofreading the final product.
• Normative . This refers to the idea that critical thinking can be used to challenge prior assumptions in ways that advocate for social justice, equity, and inclusion in ways that are transformative and have lasting impact. In this respect, critical thinking can be a method for breaking out of dominant culture norms so as to produce research outcomes that illuminate previously hidden aspects of exploitation and injustice.
• Power Dynamics . Research in the social sciences often includes examining aspects of power and influence that shape social relations, organizations, institutions, and the production of knowledge. This involves how power operates, how it can be acquired, and how power and influence can be maintained. Critical thinking can reveal how societal structures perpetuate power in ways that marginalizes and oppresses group and within historical , political, economic, and cultural contexts.
• Reflection . A key aspect of critical thinking is practicing reflexivity; the act of turning ideas and concepts back onto yourself in order to reveal and clarify your own beliefs, assumptions, and perspectives. Being critically reflexive is important because it can reveal hidden biases you may have that could unintentionally influence how you interpret and validate information. The more reflexive you are, the better able and more comfortable you are about opening yourself up to new modes of understanding.
• Rigorous Questioning . Thinking critically is guided by asking questions that lead to addressing complex concepts, principles, theories, or problems more effectively and to help distinguish what is known from from what is not known [or that may be hidden]. In this way, critical thinking involves deliberately framing inquiries not just as research questions, but as a way to focus on systematic, disciplined,  in-depth questioning concerning the research problem and your positionality as a researcher.
• Social Change . An overarching goal of critical thinking applied to research and writing is to seek to identify and challenge sources of inequality, exploitation, oppression, and marinalization that contributes to maintaining the status quo. This way of thinking can help humanize the research problem, extending the scope of interpretive analysis beyond the boundaries of traditional approaches to understanding the topic.

In writing a research paper, the act of critical thinking applies most directly to the literature review and discussion sections of your paper . In reviewing the literature, it is important to reflect upon specific aspects of a study, such as, determining if the research design effectively establishes cause and effect relationships or provides insight into explaining why certain phenomena do or do not occur, assessing whether the method of gathering data or information supports the objectives of the study, and evaluating if the assumptions used t o arrive at a specific conclusion are evidence-based and relevant to addressing the research problem. An assessment of whether a source is helpful to investigating the research problem also involves critically analyzing how the research challenges conventional approaches to investigations that perpetuate inequalities or hides the voices of others.

Critical thinking also applies to the discussion section of your paper because this is where you interpret the findings of your study and explain its significance. This involves more than summarizing findings and describing outcomes. It includes reflecting on their importance and providing reasoned explanations why the research study is important in filling a gap in the literature or expanding knowledge and understanding about the topic in ways that inform practice. Critical reflection helps you think introspectively about your own beliefs concerning the significance of the findings but in ways that avoid biased judgment and decision making.

* Mintz, Steven. "How the Word "Critical" Came to Signify the Leading Edge of Cultural Analysis." Higher Ed Gamma Blog , Inside Higher Ed, February 13, 2024; Suter, W. Newton. Introduction to Educational Research: A Critical Thinking Approach. 2nd edition. Thousand Oaks, CA: SAGE Publications, 2012

Behar-Horenstein, Linda S., and Lian Niu. “Teaching Critical Thinking Skills in Higher Education: A Review of the Literature.” Journal of College Teaching and Learning 8 (February 2011): 25-41; Bayou, Yemeserach and Tamene Kitila. "Exploring Instructors’ Beliefs about and Practices in Promoting Students’ Critical Thinking Skills in Writing Classes." GIST–Education and Learning Research Journal 26 (2023): 123-154; Butcher, Charity. "Using In-class Writing to Promote Critical Thinking and Application of Course Concepts." Journal of Political Science Education 18 (2022): 3-21; Loseke, Donileen R. Methodological Thinking: Basic Principles of Social Research Design. Thousand Oaks, CA: Sage, 2012; Hart, Claire et al. “Exploring Higher Education Students’ Critical Thinking Skills through Content Analysis.” Thinking Skills and Creativity 41 (September 2021): 100877; Sabrina, R., Emilda Sulasmi, and Mandra Saragih. "Student Critical Thinking Skills and Student Writing Ability: The Role of Teachers' Intellectual Skills and Student Learning." Cypriot Journal of Educational Sciences 17 (2022): 2493-2510.Van Merriënboer, Jeroen JG and Paul A. Kirschner. Ten Steps to Complex Learning: A Systematic Approach to Four-component Instructional Design. New York: Routledge, 2017; Yeh, Hui-Chin, Shih-hsien Yang, Jo Shan Fu, and Yen-Chen Shih. "Developing College Students’ Critical Thinking through Reflective Writing." Higher Education Research & Development 42 (2023): 244-259.

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New state of mind: rethinking how researchers understand brain activity.

(© stock.adobe.com)

Understanding the link between brain activity and behavior is among the core interests of neuroscience. Having a better grasp of this relationship will both help scientists understand how the brain works on a basic level and uncover what specifically goes awry in cases of neurological and psychological disease.

One way that researchers study this connection is through what are known as “brain states,” patterns of neural activity or connectivity that emerge during specific cognitive tasks and are common enough in all individuals that they become predictable. Another, newer, approach is the study of brain waves, rhythmic, repetitive patterns of brain cell activity caused by synchronization across cells.

In a new paper, two Yale researchers propose that these two ways of thinking about brain activity may not represent separate events but two aspects of the same occurrence. Essentially, they suggest that though brain states are traditionally thought of as a snapshot of brain activity while waves are more like a movie, they’re capturing parts of the same story.

Reconsidering these two approaches in this context, the researchers say, could help both fields benefit from the methods and knowledge of the other and advance our understanding of the brain.

Inspired by ecological, conservation, and Indigenous philosophies, Maya Foster, a third-year Ph.D. student in the Department of Biomedical Engineering, began pursuing this idea once she joined the lab of Dustin Scheinost , an associate professor in the Department of Radiology and Biomedical Imaging at Yale School of Medicine.

They are co-authors of the new paper , published April 5 in the journal Trends in Cognitive Sciences.

“ We’re arguing that rather than a brain state being one single thing, it’s a collection of things, a collection of discrete patterns that emerge in time in a predictable way,” she said.

In an interview with Yale News, Foster and Scheinost describe their proposal, and discuss how they might help researchers better understand the mysteries of the brain. This interview has been edited and condensed.

When did you start to consider these might be two aspects of the same occurrence?

Maya Foster: This has been on my mind even before I came to this lab. I was reading a book — “Erosion: Essays of Undoing” by Terry Tempest Williams — and she talks about how human-made machinery like helicopters cause vibrations that interrupt the natural pulse of things and cause things like rock formations to fall apart. Relatedly, there are a lot of Indigenous populations that believe everything has a pulse. And that got me thinking of the brain and whether we have some type of resonance or vibration that can be disrupted.

Then I joined this lab and Dustin let me experiment with a lot of different things. During one of those experiments, I input some data into a particular analysis and the outputs looked wave-like, and patterns emerged and then repeated. That took me down a whole rabbit hole of research literature and there was a lot of evidence for this idea of wave-like patterns in brain states.

What are the benefits of considering brain states as wave-like?

Foster: I think it creates a synergy where both sides — the brain state folks and the brain wave folks — benefit by learning from each other. And maybe the gaps in knowledge we have now when it comes to how brain activity relates to behavior might be filled by both groups working together.

Dustin Scheinost: Brain waves are newer in this field and they’re complex. And any time you can take something new and relate it to something old — brain states in this case — it gives you a natural jumping off point. You can bring along everything you’ve learned so far. It’s kind of like not throwing the baby out with the bath water. We don’t need to drop brain states. They’ve informed us, but we can go in a different direction with them too.

How are you proposing researchers consider brain states and brain waves now?

Foster: Borrowing from physics, when you analyze light, it can be a discrete point — a photon — or it can be wave-like. And that’s one way we’re thinking about this. Similarly, depending on how you analyze brain states you can get static patterns, much like a photon, or you if you look at activity more dynamically, certain patterns start to occur more than once over time, kind of like a wave.

So we’re arguing that rather than a brain state being one single thing, it’s a collection of things, a collection of discrete patterns that emerge in time in a predictable way.

For example, if we measured four distinct patterns in brain activity as someone completed a cognitive task, a brain state could be that pattern one emerges, then pattern three, then two, then four, and that series might repeat over time. And when that repetition stops, that would be the end of that particular brain state.

You also draw comparisons to the musical technique known as “fugue.” How does that fit with how you’re visualizing these phenomena?

Foster: I’m a music person, so that’s where this came from. In a fugue, you have a basic melody and then that melody emerges later in the music in different forms and formats. For instance, the melody will play, then some other music comes in, then the melody returns with the same rhythm and time sequence but maybe it’s in a different key.

Fugues are cyclical and wave-like, they have distinct groups of notes, and there’s a systematic repetition and sometimes layering of the main melody. We’re arguing that brain states are also wave-like, have distinct patterns of brain activity, and display systematic repetition and layering of sequential patterns.

How are you hoping other researchers respond to your argument?

Foster: I would love feedback, honestly. There is evidence for what we’re proposing but when it comes to implementing these ideas going forward, it would be helpful to have a conversation about how that might work. There are a lot of different strategies and I’m interested in a broader conversation about how we as researchers might go about studying this.

What’s it like as someone who has been in this field for a while to have a student come in with a new idea like this?

Scheinost: You can get set in your ways as a researcher and you need new ideas, new creativity. Sometimes they may sound outlandish when you first hear them. But then you ruminate, and they start to take form. And it’s fun. That’s really where the fun of this job is, to hear new ideas and see how people discuss and debate them.

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PERSPECTIVE article

Supporting early scientific thinking through curiosity.

• Curry School of Education and Human Development, University of Virginia, Charlottesville, VA, United States

Curiosity and curiosity-driven questioning are important for developing scientific thinking and more general interest and motivation to pursue scientific questions. Curiosity has been operationalized as preference for uncertainty ( Jirout and Klahr, 2012 ), and engaging in inquiry-an essential part of scientific reasoning-generates high levels of uncertainty ( Metz, 2004 ; van Schijndel et al., 2018 ). This perspective piece begins by discussing mechanisms through which curiosity can support learning and motivation in science, including motivating information-seeking behaviors, gathering information in response to curiosity, and promoting deeper understanding through connection-making related to addressing information gaps. In the second part of the article, a recent theory of how to promote curiosity in schools is discussed in relation to early childhood science reasoning. Finally, potential directions for research on the development of curiosity and curiosity-driven inquiry in young children are discussed. Although quite a bit is known about the development of children’s question asking specifically, and there are convincing arguments for developing scientific curiosity to promote science reasoning skills, there are many important areas for future research to address how to effectively use curiosity to support science learning.

Scientific Thinking and Curiosity

Scientific thinking is a type of knowledge seeking involving intentional information seeking, including asking questions, testing hypotheses, making observations, recognizing patterns, and making inferences ( Kuhn, 2002 ; Morris et al., 2012 ). Much research indicates that children engage in this information-seeking process very early on through questioning behaviors and exploration. In fact, children are quite capable and effective in gathering needed information through their questions, and can reason about the effectiveness of questions, use probabilistic information to guide their questioning, and evaluate who they should question to get information, among other related skills (see Ronfard et al., 2018 for review). Although formal educational contexts typically give students questions to explore or steps to follow to “do science,” young children’s scientific thinking is driven by natural curiosity about the world around them, and the desire to understand it and generate their own questions about the world ( Chouinard et al., 2007 ; Duschl et al., 2007 ; French et al., 2013 ; Jirout and Zimmerman, 2015 ).

What Does Scientific Curiosity Look Like?

Curiosity is defined here as the desire to seek information to address knowledge gaps resulting from uncertainty or ambiguity ( Loewenstein, 1994 ; Jirout and Klahr, 2012 ). Curiosity is often seen as ubiquitous within early childhood. Simply observing children can provide numerous examples of the bidirectional link between curiosity and scientific reasoning, such as when curiosity about a phenomenon leads to experimentation, which, in turn, generates new questions and new curiosities. For example, an infant drops a toy to observe what will happen. When an adult stoops to pick it up, the infant becomes curious about how many times an adult will hand it back before losing interest. Or, a child might observe a butterfly over a period of time, and wonder why it had its wings folded or open at different points, how butterflies fly, why different butterflies are different colors, and so on (see Figure 1 ). Observations lead to theories, which may be immature, incomplete, or even inaccurate, but so are many early scientific theories. Importantly, theories can help identify knowledge gaps, leading to new instances of curiosity and motivating children’s information seeking to acquire new knowledge and, gradually, correct misconceptions. Like adults, children learn from their experiences and observations and use information about the probability of events to revise their theories ( Gopnik, 2012 ).

Figure 1. A child looks intently at a butterfly, becoming curious about the many things she wonders based on her observations.

Although this type of reasoning is especially salient in science, curiosity can manifest in many different types of information seeking in response to uncertainty, and is similar to critical thinking in other domains of knowledge and to active learning and problem solving more generally ( Gopnik, 2012 ; Klahr et al., 2013 ; Saylor and Ganea, 2018 ). The development of scientific thinking begins as the senses develop and begin providing information about the world ( Inhelder and Piaget, 1958 ; Gopnik et al., 1999 ). When they are not actively discouraged, children need no instruction to ask questions and explore, and the information they get often leads to further information seeking. In fact, observational research suggests that children can ask questions at the rate of more than 100 per hour ( Chouinard et al., 2007 )! Although the adults in a child’s life might tire of what seems like relentless questioning ( Turgeon, 2015 ), even young children can modify their beliefs and learn from the information they receive ( Ronfard et al., 2018 ). More generally, children seek to understand their world through active exploration, especially in response to recognizing a gap in their understanding ( Schulz and Bonawitz, 2007 ). The active choice of what to learn, driven by curiosity, can provide motivation and meaning to information and instill a lasting positive approach to learning in formal educational contexts.

How Does Curiosity Develop and Support Scientific Thinking?

There are several mechanisms through which children’s curiosity can support the development and persistence of scientific thinking. Three of these are discussed below, in sequence: that curiosity can (1) motivate information-seeking behavior, which leads to (2) question-asking and other information-seeking behaviors, which can (3) activate related previous knowledge and support deeper learning. Although we discuss these as independent, consecutive steps for the sake of clarity, it is much more likely that curiosity, question asking and information seeking, and cognitive processing of information and learning are all interrelated processes that support each other ( Oudeyer et al., 2016 ). For example, information seeking that is not a result of curiosity can lead to new questions, and as previous knowledge is activated it may influence the ways in which a child seeks information.

Curiosity as a Motivation for Information Seeking

Young children’s learning is driven by exploration to make sense of the world around them (e.g., Piaget, 1926 ). This exploration can result from curiosity ( Loewenstein, 1994 ; Jirout and Klahr, 2012 ) and lead to active engagement in learning ( Saylor and Ganea, 2018 ). In the example given previously, the child sees that some butterflies have open wings and some have closed wings, and may be uncertain about why, leading to more careful observations that provide potential for learning. Several studies demonstrate that the presence of uncertainty or ambiguity leads to higher engagement ( Howard-Jones and Demetriou, 2009 ) and more exploration and information seeking ( Berlyne, 1954 ; Lowry and Johnson, 1981 ; Loewenstein, 1994 ; Litman et al., 2005 ; Jirout and Klahr, 2012 ). For example, when children are shown ambiguous demonstrations for how a novel toy works, they prefer and play longer with that toy than with a new toy that was demonstrated without ambiguity ( Schulz and Bonawitz, 2007 ). Similar to ambiguity, surprising or unexpected observations can create uncertainty and lead to curiosity-driven questions or explanations through adult–child conversations ( Frazier et al., 2009 ; Danovitch and Mills, 2018 ; Jipson et al., 2018 ). This curiosity can promote lasting effects; Shah et al. (2018) show that young children’s curiosity, reported by parents at the start of kindergarten, relates to academic school readiness. In one of the few longitudinal studies including curiosity, research shows that parents’ promotion of curiosity early in childhood leads to science intrinsic motivation years later and science achievement in high school ( Gottfried et al., 2016 ). More generally, curiosity can provide a remedy to boredom, giving children a goal to direct their behavior and the motivation to act on their curiosity ( Litman and Silvia, 2006 ).

Curiosity as Support for Directing Information-Seeking Behavior

Gopnik et al. (2015) suggest that adults are efficient in their attention allocation, developed through extensive experience, but this attentional control comes at the cost of missing much of what is going on around them unrelated to their goals. Children have less experience and skill in focusing their attention, and more exploration-oriented goals, resulting in more open-ended exploratory behavior but also more distraction. Curiosity can help focus children’s attention on the specific information being sought (e.g., Legare, 2014 ). For example, when 7–9-year-old children completed a discovery-learning task in a museum, curiosity was related to more efficient learning-more curious children were quicker and learned more from similar exploration than less-curious children ( van Schijndel et al., 2018 ). Although children are quite capable of using questions to express curiosity and request specific information ( Berlyne, 1954 ; Chin and Osborne, 2010 ; Jirout and Zimmerman, 2015 ; Kidd and Hayden, 2015 ; Luce and Hsi, 2015 ), these skills can and should be strategically supported, as question asking plays a fundamental role in science and is important to develop ( Chouinard et al., 2007 ; Dewey, 1910 ; National Governors Association, 2010 ; American Association for the Advancement of Science [AAAS], 1993 ; among others). Indeed, the National Resource Council (2012) National Science Education Standards include question asking as the first of eight scientific and engineering practices that span all grade levels and content areas.

Children are proficient in requesting information from quite early ages ( Ronfard et al., 2018 ). Yet, there are limitations to children’s question asking; it can be “inefficient.” For example, to identify a target object from an array, young children often ask confirmation questions or make guesses rather than using more efficient “constraint-seeking” questions ( Mills et al., 2010 ; Ruggeri and Lombrozo, 2015 ). However, this behavior is observed in highly structured problem-solving tasks, during which children likely are not very curious. In fact, if the environment contains other things that children are curious about, it could be more efficient to use a simplistic strategy, freeing up cognitive resources for the true target of their curiosity. More research is needed to better understand children’s use of curiosity-driven questioning behavior as well as exploration, but naturalistic observations show that children do ask questions spontaneously to gain information, and that their questions (and follow-up questions) are effective in obtaining desired information ( Nelson et al., 2004 ; Kelemen et al., 2005 ; Chouinard et al., 2007 ).

Curiosity as Support for Deeper Learning

Returning to the definition of curiosity as information seeking to address knowledge gaps, becoming curious-by definition-involves the activation of previous knowledge, which enhances learning ( VanLehn et al., 1992 ; Conati and Carenini, 2001 ). The active learning that results from curiosity-driven information seeking involves meaningful cognitive engagement and constructive processing that can support deeper learning ( Bonwell and Eison, 1991 ; King, 1994 ; Loyens and Gijbels, 2008 ). The constructive process of seeking information to generate new thinking or new knowledge in response to curiosity is a more effective means of learning than simply receiving information ( Chi and Wylie, 2014 ). Even if information is simply given to a child as a result of their asking a question, the mere process of recognizing the gap in one’s knowledge to have a question activates relevant previous knowledge and leads to more effective storage of the new information within a meaningful mental representation; the generation of the question is a constructive process in itself. Further, learning more about a topic allows children to better recognize their related knowledge and information gaps ( Danovitch et al., 2019 ). This metacognitive reasoning supports learning through the processes of activating, integrating, and inferring involved in the constructive nature of curiosity-drive information seeking ( Chi and Wylie, 2014 ). Consistent with this theory, Lamnina and Chase (2019) showed that higher curiosity, which increased with the amount of uncertainty in a task, related to greater transfer of middle school students’ learning about specific science topics.

Promoting Curiosity in Young Children

Curiosity is rated by early childhood educators as “very important” or “essential” for school readiness and considered to be even more important than discrete academic skills like counting and knowing the alphabet ( Heaviside et al., 1993 ; West et al., 1993 ), behind only physical health and communication skills in importance ( Harradine and Clifford, 1996 ). Engel (2011 , 2013) finds that curiosity declines with development and suggests that understanding how to promote or at least sustain it is important. Although children’s curiosity is considered a natural characteristic that is present at birth, interactions with and responses from others can likely influence curiosity, both at a specific moment and context and as a more stable disposition ( Jirout et al., 2018 ). For example, previous work suggests that curiosity can be promoted by encouraging children to feel comfortable with and explore uncertainty ( Jirout et al., 2018 ); experiences that create uncertainty lead to higher levels of curious behavior (e.g., Bonawitz et al., 2011 ; Engel and Labella, 2011 ; Gordon et al., 2015 ).

One strategy for promoting curiosity is through classroom climate; children should feel safe and be encouraged to be curious and exploration and questions should be valued ( Pianta et al., 2008 ). This is accomplished by de-emphasizing being “right” or all-knowing, and instead embracing uncertainty and gaps in one’s own knowledge as opportunities to learn. Another strategy to promote curiosity is to provide support for the information-seeking behaviors that children use to act on their curiosity. There are several specific strategies that may promote children’s curiosity (see Jirout et al., 2018 , for additional strategies), including:

1. Encourage and provide opportunities for children to explore and “figure out,” emphasizing the value of the process (exploration) over the outcome (new knowledge or skills). Children cannot explore if opportunities are not provided to them, and they will not ask questions if they do not feel that their questions are welcomed. Even if opportunities and encouragement are provided, the fear of being wrong can keep children from trying to learn new things ( Martin and Marsh, 2003 ; Martin, 2011 ). Active efforts to discover or “figure out” are more effective at supporting learning than simply telling children something or having them practice learned procedures ( Schwartz and Martin, 2004 ). Children can explore when they have guidance and support to engage in think-aloud problem solving, instead of being told what to try or getting questions answered directly ( Chi et al., 1994 ).

2. Model curiosity for children, allowing them to see that others have things that they do not know and want to learn about, and that others also enjoy information-seeking activities like asking questions and researching information. Technology makes information seeking easier than it has ever been. For example, children are growing up surrounded by internet-connected devices (more than 8 per capita in 2018), and asking questions is reported to be one of the most frequent uses of smart speakers ( NPR-Edison Research Spring, 2019 ). Observing others seeking information as a normal routine can encourage children’s own question asking ( McDonald, 1992 ).

3. Children spontaneously ask questions, but adults can encourage deeper questioning by using explicit prompts and then supporting children to generate questions ( King, 1994 ; Rosenshine et al., 1996 ). This is different from asking “Do you have any questions?,” which may elicit a simple “yes” or “no” response from the child. Instead, asking, “What questions do you have?” is more likely to provide a cue for children to practice analyzing what they do not know and generating questions. The ability to evaluate one’s knowledge develops through practice, and scaffolding this process by helping children recognize questions to ask can effectively support development ( Kuhn and Pearsall, 2000 ; Chin and Brown, 2002 ).

4. Other methods to encourage curiosity include promoting and reinforcing children’s thinking about alternative ideas, which could also support creativity. Part of being curious is recognizing questions that can be asked, and if children understand that there are often multiple solutions or ways to do something they will be more likely to explore to learn “ how we know and why we believe; e.g., to expose science as a way of knowing” ( Duschl and Osborne, 2002 , p. 40). Children who learn to “think outside the box” will question what they and others know and better understand the dynamic nature of knowledge, supporting a curious mindset ( Duschl and Osborne, 2002 ).

Although positive interactions can promote and sustain curiosity in young children, curiosity can also be suppressed or discouraged through interactions that emphasize performance or a focus on explicit instruction ( Martin and Marsh, 2003 ; Martin, 2011 ; Hulme et al., 2013 ). Performance goals, which are goals that are focused on demonstrating the attainment of a skill, can lead to lower curiosity to avoid distraction or risk to achieving the goal ( Hulme et al., 2013 ). Mastery goals, which focus on understanding and the learning process, support learning for its own sake ( Ames, 1993 ). When children are older and attend school, they experience expectations that prioritize performance metrics over academic and intellectual exploration, such as through tests and state-standardized assessments, which discourages curiosity ( Engel, 2011 ; Jirout et al., 2018 ). In my own recent research, we observed a positive association between teachers’ use of mastery-focused language and their use of curiosity-promoting instructional practices in preschool math and science lessons ( Jirout and Vitiello, 2019 ). Among 5th graders, student ratings of teacher emphasis on standardized testing was associated with lower observed curiosity-promotion by teachers ( Jirout and Vitiello, 2019 ). It is likely that learning orientations influence children’s curiosity even before children begin formal schooling, and de-emphasizing performance is a way to support curiosity.

In summary, focusing on the process of “figuring out” something children do not know, modeling and explicitly prompting exploration and question asking, and supporting metacognitive and creative thinking are all ways to promote curiosity and support effective cognitive engagement during learning. These methods are consistent with inquiry-based and active learning, which both are grounded in constructivism and information gaps similar to the current operationalization of curiosity ( Jirout and Klahr, 2012 ; Saylor and Ganea, 2018 ; van Schijndel et al., 2018 ). Emphasizing performance, such as academic climates focused on teaching rote procedures and doing things the “correct” way to get the right answer, can suppress or discourage curiosity. Instead, creating a supportive learning climate and responding positively to curiosity are likely to further reinforce children’s information seeking, and to sustain their curiosity so that it can support scientific thinking and learning.

Conclusion: a Call for Research

In this article, I describe evidence from the limited existing research showing that curiosity is important and relates to science learning, and I suggest several mechanisms through which curiosity can support science learning. The general perspective presented here is that science learning can and should be supported by promoting curiosity, and I provide suggestions for promoting (and avoiding the suppression of) curiosity in early childhood. However, much more research is needed to address the complex challenge of educational applications of this work. Specifically, the suggested mechanisms through which curiosity promotes learning need to be studied to tease apart questions of directionality, the influence of related factors such as interest, the impact of context and learning domain on these relations, and the role of individual differences. Both the influence of curiosity on learning and effective ways to promote it likely change in interesting and important ways across development, and research is needed to understand this development-especially through studying change in individuals over time. Finally, it is important to acknowledge that learning does not happen in isolation, and one’s culture and environment have important roles in shaping one’s development. Thus, application of research on curiosity and science learning must include studies of the influence of social factors such as socioeconomic status and contexts, the influence of peers, teachers, parents, and others in children’s environments, and the many ways that culture may play a role, both in the broad values and beliefs instilled in children and the adults interacting with them, and in the influences of behavior expectations and norms. For example, parents across cultures might respond differently to children’s questions, so cross-cultural differences in questions likely indicate something other than differences in curiosity ( Ünlütabak et al., 2019 ). Although curiosity likely promotes science learning across cultures and contexts, the ways in which it does so and effective methods of promoting it may differ, which is an important area for future research to explore. Despite the benefits I present, curiosity seems to be rare or even absent from formal learning contexts ( Engel, 2013 ), even as children show curiosity about things outside of school ( Post and Walma van der Molen, 2018 ). Efforts to promote science learning should focus on the exciting potential for curiosity in supporting children’s learning, as promoting young children’s curiosity in science can start children on a positive trajectory for later learning.

Ethics Statement

Written informed consent was obtained from the individual(s) and/or minor(s)’ legal guardian/next of kin publication of any potentially identifiable images or data included in this article.

Author Contributions

JJ conceived of the manuscript topic and wrote the manuscript.

This publication was made possible through the support of grants from the John Templeton Foundation, the Spencer Foundation, and the Center for Curriculum Redesign. The opinions expressed in this publication are those of the author and do not necessarily reflect the views of the John Templeton Foundation or other funders.

Conflict of Interest

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

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Keywords : curiosity, scientific reasoning, scientific thinking, information seeking, exploration, learning

Citation: Jirout JJ (2020) Supporting Early Scientific Thinking Through Curiosity. Front. Psychol. 11:1717. doi: 10.3389/fpsyg.2020.01717

Received: 28 February 2020; Accepted: 23 June 2020; Published: 05 August 2020.

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Copyright © 2020 Jirout. 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) and the copyright owner(s) 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: Jamie J. Jirout, [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.

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Scientific Thinking and Critical Thinking in Science Education 

Two Distinct but Symbiotically Related Intellectual Processes

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Scientific thinking and critical thinking are two intellectual processes that are considered keys in the basic and comprehensive education of citizens. For this reason, their development is also contemplated as among the main objectives of science education. However, in the literature about the two types of thinking in the context of science education, there are quite frequent allusions to one or the other indistinctly to refer to the same cognitive and metacognitive skills, usually leaving unclear what are their differences and what are their common aspects. The present work therefore was aimed at elucidating what the differences and relationships between these two types of thinking are. The conclusion reached was that, while they differ in regard to the purposes of their application and some skills or processes, they also share others and are related symbiotically in a metaphorical sense; i.e., each one makes sense or develops appropriately when it is nourished or enriched by the other. Finally, an orientative proposal is presented for an integrated development of the two types of thinking in science classes.

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1 Introduction

In consulting technical reports, theoretical frameworks, research, and curricular reforms related to science education, one commonly finds appeals to scientific thinking and critical thinking as essential educational processes or objectives. This is confirmed in some studies that include exhaustive reviews of the literature in this regard such as those of Bailin ( 2002 ), Costa et al. ( 2020 ), and Santos ( 2017 ) on critical thinking, and of Klarh et al. ( 2019 ) and Lehrer and Schauble ( 2006 ) on scientific thinking. However, conceptualizing and differentiating between both types of thinking based on the above-mentioned documents of science education are generally difficult. In many cases, they are referred to without defining them, or they are used interchangeably to represent virtually the same thing. Thus, for example, the document A Framework for K-12 Science Education points out that “Critical thinking is required, whether in developing and refining an idea (an explanation or design) or in conducting an investigation” (National Research Council (NRC), 2012 , p. 46). The same document also refers to scientific thinking when it suggests that basic scientific education should “provide students with opportunities for a range of scientific activities and scientific thinking , including, but not limited to inquiry and investigation, collection and analysis of evidence, logical reasoning, and communication and application of information” (NRC, 2012 , p. 251).

A few years earlier, the report Science Teaching in Schools in Europe: Policies and Research (European Commission/Eurydice, 2006 ) included the dimension “scientific thinking” as part of standardized national science tests in European countries. This dimension consisted of three basic abilities: (i) to solve problems formulated in theoretical terms , (ii) to frame a problem in scientific terms , and (iii) to formulate scientific hypotheses . In contrast, critical thinking was not even mentioned in such a report. However, in subsequent similar reports by the European Commission/Eurydice ( 2011 , 2022 ), there are some references to the fact that the development of critical thinking should be a basic objective of science teaching, although these reports do not define it at any point.

The ENCIENDE report on early-year science education in Spain also includes an explicit allusion to critical thinking among its recommendations: “Providing students with learning tools means helping them to develop critical thinking , to form their own opinions, to distinguish between knowledge founded on the evidence available at a certain moment (evidence which can change) and unfounded beliefs” (Confederation of Scientific Societies in Spain (COSCE), 2011 , p. 62). However, the report makes no explicit mention to scientific thinking. More recently, the document “ Enseñando ciencia con ciencia ” (Teaching science with science) (Couso et al., 2020 ), sponsored by Spain’s Ministry of Education, also addresses critical thinking:

(…) with the teaching approach through guided inquiry students learn scientific content, learn to do science (procedures), learn what science is and how it is built, and this (...) helps to develop critical thinking , that is, to question any statement that is not supported by evidence. (Couso et al., 2020 , p. 54)

On the other hand, in referring to what is practically the same thing, the European report Science Education for Responsible Citizenship speaks of scientific thinking when it establishes that one of the challenges of scientific education should be: “To promote a culture of scientific thinking and inspire citizens to use evidence-based reasoning for decision making” (European Commission, 2015 , p. 14). However, the Pisa 2024 Strategic Vision and Direction for Science report does not mention scientific thinking but does mention critical thinking in noting that “More generally, (students) should be able to recognize the limitations of scientific inquiry and apply critical thinking when engaging with its results” (Organization for Economic Co-operation and Development (OECD), 2020 , p. 9).

The new Spanish science curriculum for basic education (Royal Decree 217/ 2022 ) does make explicit reference to scientific thinking. For example, one of the STEM (Science, Technology, Engineering, and Mathematics) competency descriptors for compulsory secondary education reads:

Use scientific thinking to understand and explain the phenomena that occur around them, trusting in knowledge as a motor for development, asking questions and checking hypotheses through experimentation and inquiry (...) showing a critical attitude about the scope and limitations of science. (p. 41,599)

Furthermore, when developing the curriculum for the subjects of physics and chemistry, the same provision clarifies that “The essence of scientific thinking is to understand what are the reasons for the phenomena that occur in the natural environment to then try to explain them through the appropriate laws of physics and chemistry” (Royal Decree 217/ 2022 , p. 41,659). However, within the science subjects (i.e., Biology and Geology, and Physics and Chemistry), critical thinking is not mentioned as such. Footnote 1 It is only more or less directly alluded to with such expressions as “critical analysis”, “critical assessment”, “critical reflection”, “critical attitude”, and “critical spirit”, with no attempt to conceptualize it as is done with regard to scientific thinking.

The above is just a small sample of the concepts of scientific thinking and critical thinking only being differentiated in some cases, while in others they are presented as interchangeable, using one or the other indistinctly to talk about the same cognitive/metacognitive processes or practices. In fairness, however, it has to be acknowledged—as said at the beginning—that it is far from easy to conceptualize these two types of thinking (Bailin, 2002 ; Dwyer et al., 2014 ; Ennis, 2018 ; Lehrer & Schauble, 2006 ; Kuhn, 1993 , 1999 ) since they feed back on each other, partially overlap, and share certain features (Cáceres et al., 2020 ; Vázquez-Alonso & Manassero-Mas, 2018 ). Neither is there unanimity in the literature on how to characterize each of them, and rarely have they been analyzed comparatively (e.g., Hyytinen et al., 2019 ). For these reasons, I believed it necessary to address this issue with the present work in order to offer some guidelines for science teachers interested in deepening into these two intellectual processes to promote them in their classes.

2 An Attempt to Delimit Scientific Thinking in Science Education

For many years, cognitive science has been interested in studying what scientific thinking is and how it can be taught in order to improve students’ science learning (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ). To this end, Kuhn et al. propose taking a characterization of science as argument (Kuhn, 1993 ; Kuhn et al., 2008 ). They argue that this is a suitable way of linking the activity of how scientists think with that of the students and of the public in general, since science is a social activity which is subject to ongoing debate, in which the construction of arguments plays a key role. Lehrer and Schauble ( 2006 ) link scientific thinking with scientific literacy, paying especial attention to the different images of science. According to those authors, these images would guide the development of the said literacy in class. The images of science that Leherer and Schauble highlight as characterizing scientific thinking are: (i) science-as-logical reasoning (role of domain-general forms of scientific reasoning, including formal logic, heuristic, and strategies applied in different fields of science), (ii) science-as-theory change (science is subject to permanent revision and change), and (iii) science-as-practice (scientific knowledge and reasoning are components of a larger set of activities that include rules of participation, procedural skills, epistemological knowledge, etc.).

Based on a literature review, Jirout ( 2020 ) defines scientific thinking as an intellectual process whose purpose is the intentional search for information about a phenomenon or facts by formulating questions, checking hypotheses, carrying out observations, recognizing patterns, and making inferences (a detailed description of all these scientific practices or competencies can be found, for example, in NRC, 2012 ; OECD, 2019 ). Therefore, for Jirout, the development of scientific thinking would involve bringing into play the basic science skills/practices common to the inquiry-based approach to learning science (García-Carmona, 2020 ; Harlen, 2014 ). For other authors, scientific thinking would include a whole spectrum of scientific reasoning competencies (Krell et al., 2022 ; Moore, 2019 ; Tytler & Peterson, 2004 ). However, these competences usually cover the same science skills/practices mentioned above. Indeed, a conceptual overlap between scientific thinking, scientific reasoning, and scientific inquiry is often found in science education goals (Krell et al., 2022 ). Although, according to Leherer and Schauble ( 2006 ), scientific thinking is a broader construct that encompasses the other two.

It could be said that scientific thinking is a particular way of searching for information using science practices Footnote 2 (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ; Vázquez-Alonso & Manassero-Mas, 2018 ). This intellectual process provides the individual with the ability to evaluate the robustness of evidence for or against a certain idea, in order to explain a phenomenon (Clouse, 2017 ). But the development of scientific thinking also requires metacognition processes. According to what Kuhn ( 2022 ) argues, metacognition is fundamental to the permanent control or revision of what an individual thinks and knows, as well as that of the other individuals with whom it interacts, when engaging in scientific practices. In short, scientific thinking demands a good connection between reasoning and metacognition (Kuhn, 2022 ). Footnote 3

From that perspective, Zimmerman and Klarh ( 2018 ) have synthesized a taxonomy categorizing scientific thinking, relating cognitive processes with the corresponding science practices (Table 1 ). It has to be noted that this taxonomy was prepared in line with the categorization of scientific practices proposed in the document A Framework for K-12 Science Education (NRC, 2012 ). This is why one needs to understand that, for example, the cognitive process of elaboration and refinement of hypotheses is not explicitly associated with the scientific practice of hypothesizing but only with the formulation of questions. Indeed, the K-12 Framework document does not establish hypothesis formulation as a basic scientific practice. Lederman et al. ( 2014 ) justify it by arguing that not all scientific research necessarily allows or requires the verification of hypotheses, for example, in cases of exploratory or descriptive research. However, the aforementioned document (NRC, 2012 , p. 50) does refer to hypotheses when describing the practice of developing and using models , appealing to the fact that they facilitate the testing of hypothetical explanations .

In the literature, there are also other interesting taxonomies characterizing scientific thinking for educational purposes. One of them is that of Vázquez-Alonso and Manassero-Mas ( 2018 ) who, instead of science practices, refer to skills associated with scientific thinking . Their characterization basically consists of breaking down into greater detail the content of those science practices that would be related to the different cognitive and metacognitive processes of scientific thinking. Also, unlike Zimmerman and Klarh’s ( 2018 ) proposal, Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal explicitly mentions metacognition as one of the aspects of scientific thinking, which they call meta-process . In my opinion, the proposal of the latter authors, which shells out scientific thinking into a broader range of skills/practices, can be more conducive in order to favor its approach in science classes, as teachers would have more options to choose from to address components of this intellectual process depending on their teaching interests, the educational needs of their students and/or the learning objectives pursued. Table 2 presents an adapted characterization of the Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal to address scientific thinking in science education.

3 Contextualization of Critical Thinking in Science Education

Theorization and research about critical thinking also has a long tradition in the field of the psychology of learning (Ennis, 2018 ; Kuhn, 1999 ), and its application extends far beyond science education (Dwyer et al., 2014 ). Indeed, the development of critical thinking is commonly accepted as being an essential goal of people’s overall education (Ennis, 2018 ; Hitchcock, 2017 ; Kuhn, 1999 ; Willingham, 2008 ). However, its conceptualization is not simple and there is no unanimous position taken on it in the literature (Costa et al., 2020 ; Dwyer et al., 2014 ); especially when trying to relate it to scientific thinking. Thus, while Tena-Sánchez and León-Medina ( 2022 ) Footnote 4 and McBain et al. ( 2020 ) consider critical thinking to be the basis of or forms part of scientific thinking, Dowd et al. ( 2018 ) understand scientific thinking to be just a subset of critical thinking. However, Vázquez-Alonso and Manassero-Mas ( 2018 ) do not seek to determine whether critical thinking encompasses scientific thinking or vice versa. They consider that both types of knowledge share numerous skills/practices and the progressive development of one fosters the development of the other as a virtuous circle of improvement. Other authors, such as Schafersman ( 1991 ), even go so far as to say that critical thinking and scientific thinking are the same thing. In addition, some views on the relationship between critical thinking and scientific thinking seem to be context-dependent. For example, Hyytine et al. ( 2019 ) point out that in the perspective of scientific thinking as a component of critical thinking, the former is often used to designate evidence-based thinking in the sciences, although this view tends to dominate in Europe but not in the USA context. Perhaps because of this lack of consensus, the two types of thinking are often confused, overlapping, or conceived as interchangeable in education.

Even with such a lack of unanimous or consensus vision, there are some interesting theoretical frameworks and definitions for the development of critical thinking in education. One of the most popular definitions of critical thinking is that proposed by The National Council for Excellence in Critical Thinking (1987, cited in Inter-American Teacher Education Network, 2015 , p. 6). This conceives of it as “the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action”. In other words, critical thinking can be regarded as a reflective and reasonable class of thinking that provides people with the ability to evaluate multiple statements or positions that are defensible to then decide which is the most defensible (Clouse, 2017 ; Ennis, 2018 ). It thus requires, in addition to a basic scientific competency, notions about epistemology (Kuhn, 1999 ) to understand how knowledge is constructed. Similarly, it requires skills for metacognition (Hyytine et al., 2019 ; Kuhn, 1999 ; Magno, 2010 ) since critical thinking “entails awareness of one’s own thinking and reflection on the thinking of self and others as objects of cognition” (Dean & Kuhn, 2003 , p. 3).

In science education, one of the most suitable scenarios or resources, but not the only one, Footnote 5 to address all these aspects of critical thinking is through the analysis of socioscientific issues (SSI) (Taylor et al., 2006 ; Zeidler & Nichols, 2009 ). Without wishing to expand on this here, I will only say that interesting works can be found in the literature that have analyzed how the discussion of SSIs can favor the development of critical thinking skills (see, e.g., López-Fernández et al., 2022 ; Solbes et al., 2018 ). For example, López-Fernández et al. ( 2022 ) focused their teaching-learning sequence on the following critical thinking skills: information analysis, argumentation, decision making, and communication of decisions. Even some authors add the nature of science (NOS) to this framework (i.e., SSI-NOS-critical thinking), as, for example, Yacoubian and Khishfe ( 2018 ) in order to develop critical thinking and how this can also favor the understanding of NOS (Yacoubian, 2020 ). In effect, as I argued in another work on the COVID-19 pandemic as an SSI, in which special emphasis was placed on critical thinking, an informed understanding of how science works would have helped the public understand why scientists were changing their criteria to face the pandemic in the light of new data and its reinterpretations, or that it was not possible to go faster to get an effective and secure medical treatment for the disease (García-Carmona, 2021b ).

In the recent literature, there have also been some proposals intended to characterize critical thinking in the context of science education. Table 3 presents two of these by way of example. As can be seen, both proposals share various components for the development of critical thinking (respect for evidence, critically analyzing/assessing the validity/reliability of information, adoption of independent opinions/decisions, participation, etc.), but that of Blanco et al. ( 2017 ) is more clearly contextualized in science education. Likewise, that of these authors includes some more aspects (or at least does so more explicitly), such as developing epistemological Footnote 6 knowledge of science (vision of science…) and on its interactions with technology, society, and environment (STSA relationships), and communication skills. Therefore, it offers a wider range of options for choosing critical thinking skills/processes to promote it in science classes. However, neither proposal refers to metacognitive skills, which are also essential for developing critical thinking (Kuhn, 1999 ).

3.1 Critical thinking vs. scientific thinking in science education: differences and similarities

In accordance with the above, it could be said that scientific thinking is nourished by critical thinking, especially when deciding between several possible interpretations and explanations of the same phenomenon since this generally takes place in a context of debate in the scientific community (Acevedo-Díaz & García-Carmona, 2017 ). Thus, the scientific attitude that is perhaps most clearly linked to critical thinking is the skepticism with which scientists tend to welcome new ideas (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ), especially if they are contrary to well-established scientific knowledge (Bell, 2009 ). A good example of this was the OPERA experiment (García-Carmona & Acevedo-Díaz, 2016a ), which initially seemed to find that neutrinos could move faster than the speed of light. This finding was supposed to invalidate Albert Einstein’s theory of relativity (the finding was later proved wrong). In response, Nobel laureate in physics Sheldon L. Glashow went so far as to state that:

the result obtained by the OPERA collaboration cannot be correct. If it were, we would have to give up so many things, it would be such a huge sacrifice... But if it is, I am officially announcing it: I will shout to Mother Nature: I’m giving up! And I will give up Physics. (BBVA Foundation, 2011 )

Indeed, scientific thinking is ultimately focused on getting evidence that may support an idea or explanation about a phenomenon, and consequently allow others that are less convincing or precise to be discarded. Therefore when, with the evidence available, science has more than one equally defensible position with respect to a problem, the investigation is considered inconclusive (Clouse, 2017 ). In certain cases, this gives rise to scientific controversies (Acevedo-Díaz & García-Carmona, 2017 ) which are not always resolved based exclusively on epistemic or rational factors (Elliott & McKaughan, 2014 ; Vallverdú, 2005 ). Hence, it is also necessary to integrate non-epistemic practices into the framework of scientific thinking (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ), practices that transcend the purely rational or cognitive processes, including, for example, those related to emotional or affective issues (Sinatra & Hofer, 2021 ). From an educational point of view, this suggests that for students to become more authentically immersed in the way of working or thinking scientifically, they should also learn to feel as scientists do when they carry out their work (Davidson et al., 2020 ). Davidson et al. ( 2020 ) call it epistemic affect , and they suggest that it could be approach in science classes by teaching students to manage their frustrations when they fail to achieve the expected results; Footnote 7 or, for example, to moderate their enthusiasm with favorable results in a scientific inquiry by activating a certain skepticism that encourages them to do more testing. And, as mentioned above, for some authors, having a skeptical attitude is one of the actions that best visualize the application of critical thinking in the framework of scientific thinking (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ).

On the other hand, critical thinking also draws on many of the skills or practices of scientific thinking, as discussed above. However, in contrast to scientific thinking, the coexistence of two or more defensible ideas is not, in principle, a problem for critical thinking since its purpose is not so much to invalidate some ideas or explanations with respect to others, but rather to provide the individual with the foundations on which to position themself with the idea/argument they find most defensible among several that are possible (Ennis, 2018 ). For example, science with its methods has managed to explain the greenhouse effect, the phenomenon of the tides, or the transmission mechanism of the coronavirus. For this, it had to discard other possible explanations as they were less valid in the investigations carried out. These are therefore issues resolved by the scientific community which create hardly any discussion at the present time. However, taking a position for or against the production of energy in nuclear power plants transcends the scope of scientific thinking since both positions are, in principle, equally defensible. Indeed, within the scientific community itself there are supporters and detractors of the two positions, based on the same scientific knowledge. Consequently, it is critical thinking, which requires the management of knowledge and scientific skills, a basic understanding of epistemic (rational or cognitive) and non-epistemic (social, ethical/moral, economic, psychological, cultural, ...) aspects of the nature of science, as well as metacognitive skills, which helps the individual forge a personal foundation on which to position themself in one place or another, or maintain an uncertain, undecided opinion.

In view of the above, one can summarize that scientific thinking and critical thinking are two different intellectual processes in terms of purpose, but are related symbiotically (i.e., one would make no sense without the other or both feed on each other) and that, in their performance, they share a fair number of features, actions, or mental skills. According to Cáceres et al. ( 2020 ) and Hyytine et al. ( 2019 ), the intellectual skills that are most clearly common to both types of thinking would be searching for relationships between evidence and explanations , as well as investigating and logical thinking to make inferences . To this common space, I would also add skills for metacognition in accordance with what has been discussed about both types of knowledge (Khun, 1999 , 2022 ).

In order to compile in a compact way all that has been argued so far, in Table 4 , I present my overview of the relationship between scientific thinking and critical thinking. I would like to point out that I do not intend to be extremely extensive in the compilation, in the sense that possibly more elements could be added in the different sections, but rather to represent above all the aspects that distinguish and share them, as well as the mutual enrichment (or symbiosis) between them.

4 A Proposal for the Integrated Development of Critical Thinking and Scientific Thinking in Science Classes

Once the differences, common aspects, and relationships between critical thinking and scientific thinking have been discussed, it would be relevant to establish some type of specific proposal to foster them in science classes. Table 5 includes a possible script to address various skills or processes of both types of thinking in an integrated manner. However, before giving guidance on how such skills/processes could be approached, I would like to clarify that while all of them could be dealt within the context of a single school activity, I will not do so in this way. First, because I think that it can give the impression that the proposal is only valid if it is applied all at once in a specific learning situation, which can also discourage science teachers from implementing it in class due to lack of time or training to do so. Second, I think it can be more interesting to conceive the proposal as a set of thinking skills or actions that can be dealt with throughout the different science contents, selecting only (if so decided) some of them, according to educational needs or characteristics of the learning situation posed in each case. Therefore, in the orientations for each point of the script or grouping of these, I will use different examples and/or contexts. Likewise, these orientations in the form of comments, although founded in the literature, should be considered only as possibilities to do so, among many others possible.

Motivation and predisposition to reflect and discuss (point i ) demands, on the one hand, that issues are chosen which are attractive for the students. This can be achieved, for example, by asking the students directly what current issues, related to science and its impact or repercussions, they would like to learn about, and then decide on which issue to focus on (García-Carmona, 2008 ). Or the teacher puts forward the issue directly in class, trying for it be current, to be present in the media, social networks, etc., or what they think may be of interest to their students based on their teaching experience. In this way, each student is encouraged to feel questioned or concerned as a citizen because of the issue that is going to be addressed (García-Carmona, 2008 ). Also of possible interest is the analysis of contemporary, as yet unresolved socioscientific affairs (Solbes et al., 2018 ), such as climate change, science and social justice, transgenic foods, homeopathy, and alcohol and drug use in society. But also, everyday questions can be investigated which demand a decision to be made, such as “What car to buy?” (Moreno-Fontiveros et al., 2022 ), or “How can we prevent the arrival of another pandemic?” (Ushola & Puig, 2023 ).

On the other hand, it is essential that the discussion about the chosen issue is planned through an instructional process that generates an environment conducive to reflection and debate, with a view to engaging the students’ participation in it. This can be achieved, for example, by setting up a role-play game (Blanco-López et al., 2017 ), especially if the issue is socioscientific, or by critical and reflective reading of advertisements with scientific content (Campanario et al., 2001 ) or of science-related news in the daily media (García-Carmona, 2014 , 2021a ; Guerrero-Márquez & García-Carmona, 2020 ; Oliveras et al., 2013 ), etc., for subsequent discussion—all this, in a collaborative learning setting and with a clear democratic spirit.

Respect for scientific evidence (point ii ) should be the indispensable condition in any analysis and discussion from the prisms of scientific and of critical thinking (Erduran, 2021 ). Although scientific knowledge may be impregnated with subjectivity during its construction and is revisable in the light of new evidence ( tentativeness of scientific knowledge), when it is accepted by the scientific community it is as objective as possible (García-Carmona & Acevedo-Díaz, 2016b ). Therefore, promoting trust and respect for scientific evidence should be one of the primary educational challenges to combating pseudoscientists and science deniers (Díaz & Cabrera, 2022 ), whose arguments are based on false beliefs and assumptions, anecdotes, and conspiracy theories (Normand, 2008 ). Nevertheless, it is no simple task to achieve the promotion or respect for scientific evidence (Fackler, 2021 ) since science deniers, for example, consider that science is unreliable because it is imperfect (McIntyre, 2021 ). Hence the need to promote a basic understanding of NOS (point iii ) as a fundamental pillar for the development of both scientific thinking and critical thinking. A good way to do this would be through explicit and reflective discussion about controversies from the history of science (Acevedo-Díaz & García-Carmona, 2017 ) or contemporary controversies (García-Carmona, 2021b ; García-Carmona & Acevedo-Díaz, 2016a ).

Also, with respect to point iii of the proposal, it is necessary to manage basic scientific knowledge in the development of scientific and critical thinking skills (Willingham, 2008 ). Without this, it will be impossible to develop a minimally serious and convincing argument on the issue being analyzed. For example, if one does not know the transmission mechanism of a certain disease, it is likely to be very difficult to understand or justify certain patterns of social behavior when faced with it. In general, possessing appropriate scientific knowledge on the issue in question helps to make the best interpretation of the data and evidence available on this issue (OECD, 2019 ).

The search for information from reliable sources, together with its analysis and interpretation (points iv to vi ), are essential practices both in purely scientific contexts (e.g., learning about the behavior of a given physical phenomenon from literature or through enquiry) and in the application of critical thinking (e.g., when one wishes to take a personal, but informed, position on a particular socio-scientific issue). With regard to determining the credibility of information with scientific content on the Internet, Osborne et al. ( 2022 ) propose, among other strategies, to check whether the source is free of conflicts of interest, i.e., whether or not it is biased by ideological, political or economic motives. Also, it should be checked whether the source and the author(s) of the information are sufficiently reputable.

Regarding the interpretation of data and evidence, several studies have shown the difficulties that students often have with this practice in the context of enquiry activities (e.g., Gobert et al., 2018 ; Kanari & Millar, 2004 ; Pols et al., 2021 ), or when analyzing science news in the press (Norris et al., 2003 ). It is also found that they have significant difficulties in choosing the most appropriate data to support their arguments in causal analyses (Kuhn & Modrek, 2022 ). However, it must be recognized that making interpretations or inferences from data is not a simple task; among other reasons, because their construction is influenced by multiple factors, both epistemic (prior knowledge, experimental designs, etc.) and non-epistemic (personal expectations, ideology, sociopolitical context, etc.), which means that such interpretations are not always the same for all scientists (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ). For this reason, the performance of this scientific practice constitutes one of the phases or processes that generate the most debate or discussion in a scientific community, as long as no consensus is reached. In order to improve the practice of making inferences among students, Kuhn and Lerman ( 2021 ) propose activities that help them develop their own epistemological norms to connect causally their statements with the available evidence.

Point vii refers, on the one hand, to an essential scientific practice: the elaboration of evidence-based scientific explanations which generally, in a reasoned way, account for the causality, properties, and/or behavior of the phenomena (Brigandt, 2016 ). In addition, point vii concerns the practice of argumentation . Unlike scientific explanations, argumentation tries to justify an idea, explanation, or position with the clear purpose of persuading those who defend other different ones (Osborne & Patterson, 2011 ). As noted above, the complexity of most socioscientific issues implies that they have no unique valid solution or response. Therefore, the content of the arguments used to defend one position or another are not always based solely on purely rational factors such as data and scientific evidence. Some authors defend the need to also deal with non-epistemic aspects of the nature of science when teaching it (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ) since many scientific and socioscientific controversies are resolved by different factors or go beyond just the epistemic (Vallverdú, 2005 ).

To defend an idea or position taken on an issue, it is not enough to have scientific evidence that supports it. It is also essential to have skills for the communication and discussion of ideas (point viii ). The history of science shows how the difficulties some scientists had in communicating their ideas scientifically led to those ideas not being accepted at the time. A good example for students to become aware of this is the historical case of Semmelweis and puerperal fever (Aragón-Méndez et al., 2019 ). Its reflective reading makes it possible to conclude that the proposal of this doctor that gynecologists disinfect their hands, when passing from one parturient to another to avoid contagions that provoked the fever, was rejected by the medical community not only for epistemic reasons, but also for the difficulties that he had to communicate his idea. The history of science also reveals that some scientific interpretations were imposed on others at certain historical moments due to the rhetorical skills of their proponents although none of the explanations would convincingly explain the phenomenon studied. An example is the case of the controversy between Pasteur and Liebig about the phenomenon of fermentation (García-Carmona & Acevedo-Díaz, 2017 ), whose reading and discussion in science class would also be recommended in this context of this critical and scientific thinking skill. With the COVID-19 pandemic, for example, the arguments of some charlatans in the media and on social networks managed to gain a certain influence in the population, even though scientifically they were muddled nonsense (García-Carmona, 2021b ). Therefore, the reflective reading of news on current SSIs such as this also constitutes a good resource for the same educational purpose. In general, according to Spektor-Levy et al. ( 2009 ), scientific communication skills should be addressed explicitly in class, in a progressive and continuous manner, including tasks of information seeking, reading, scientific writing, representation of information, and representation of the knowledge acquired.

Finally (point ix ), a good scientific/critical thinker must be aware of what they know, of what they have doubts about or do not know, to this end continuously practicing metacognitive exercises (Dean & Kuhn, 2003 ; Hyytine et al., 2019 ; Magno, 2010 ; Willingham, 2008 ). At the same time, they must recognize the weaknesses and strengths of the arguments of their peers in the debate in order to be self-critical if necessary, as well as to revising their own ideas and arguments to improve and reorient them, etc. ( self-regulation ). I see one of the keys of both scientific and critical thinking being the capacity or willingness to change one’s mind, without it being frowned upon. Indeed, quite the opposite since one assumes it to occur thanks to the arguments being enriched and more solidly founded. In other words, scientific and critical thinking and arrogance or haughtiness towards the rectification of ideas or opinions do not stick well together.

5 Final Remarks

For decades, scientific thinking and critical thinking have received particular attention from different disciplines such as psychology, philosophy, pedagogy, and specific areas of this last such as science education. The two types of knowledge represent intellectual processes whose development in students, and in society in general, is considered indispensable for the exercise of responsible citizenship in accord with the demands of today’s society (European Commission, 2006 , 2015 ; NRC, 2012 ; OECD, 2020 ). As has been shown however, the task of their conceptualization is complex, and teaching students to think scientifically and critically is a difficult educational challenge (Willingham, 2008 ).

Aware of this, and after many years dedicated to science education, I felt the need to organize my ideas regarding the aforementioned two types of thinking. In consulting the literature about these, I found that, in many publications, scientific thinking and critical thinking are presented or perceived as being interchangeable or indistinguishable; a conclusion also shared by Hyytine et al. ( 2019 ). Rarely have their differences, relationships, or common features been explicitly studied. So, I considered that it was a matter needing to be addressed because, in science education, the development of scientific thinking is an inherent objective, but, when critical thinking is added to the learning objectives, there arise more than reasonable doubts about when one or the other would be used, or both at the same time. The present work came about motivated by this, with the intention of making a particular contribution, but based on the relevant literature, to advance in the question raised. This converges in conceiving scientific thinking and critical thinking as two intellectual processes that overlap and feed into each other in many aspects but are different with respect to certain cognitive skills and in terms of their purpose. Thus, in the case of scientific thinking, the aim is to choose the best possible explanation of a phenomenon based on the available evidence, and it therefore involves the rejection of alternative explanatory proposals that are shown to be less coherent or convincing. Whereas, from the perspective of critical thinking, the purpose is to choose the most defensible idea/option among others that are also defensible, using both scientific and extra-scientific (i.e., moral, ethical, political, etc.) arguments. With this in mind, I have described a proposal to guide their development in the classroom, integrating them under a conception that I have called, metaphorically, a symbiotic relationship between two modes of thinking.

Critical thinking is mentioned literally in other of the curricular provisions’ subjects such as in Education in Civics and Ethical Values or in Geography and History (Royal Decree 217/2022).

García-Carmona ( 2021a ) conceives of them as activities that require the comprehensive application of procedural skills, cognitive and metacognitive processes, and both scientific knowledge and knowledge of the nature of scientific practice .

Kuhn ( 2021 ) argues that the relationship between scientific reasoning and metacognition is especially fostered by what she calls inhibitory control , which basically consists of breaking down the whole of a thought into parts in such a way that attention is inhibited on some of those parts to allow a focused examination of the intended mental content.

Specifically, Tena-Sánchez and León-Medina (2020) assume that critical thinking is at the basis of rational or scientific skepticism that leads to questioning any claim that does not have empirical support.

As discussed in the introduction, the inquiry-based approach is also considered conducive to addressing critical thinking in science education (Couso et al., 2020 ; NRC, 2012 ).

Epistemic skills should not be confused with epistemological knowledge (García-Carmona, 2021a ). The former refers to skills to construct, evaluate, and use knowledge, and the latter to understanding about the origin, nature, scope, and limits of scientific knowledge.

For this purpose, it can be very useful to address in class, with the help of the history and philosophy of science, that scientists get more wrong than right in their research, and that error is always an opportunity to learn (García-Carmona & Acevedo-Díaz, 2018 ).

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García-Carmona, A. Scientific Thinking and Critical Thinking in Science Education . Sci & Educ (2023). https://doi.org/10.1007/s11191-023-00460-5

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April 2, 2024

Eclipse Psychology: When the Sun and Moon Align, So Do We

How a total solar eclipse creates connection, unity and caring among the people watching

By Katie Weeman

Students observing a partial solar eclipse on June 21, 2020, in Lhokseumawe, Aceh Province, Indonesia.

NurPhoto/Getty Images

This article is part of a special report on the total solar eclipse that will be visible from parts of the U.S., Mexico and Canada on April 8, 2024.

It was 11:45 A.M. on August 21, 2017. I was in a grassy field in Glendo, Wyo., where I was surrounded by strangers turned friends, more than I could count—and far more people than had ever flocked to this town, population 210 or so. Golden sunlight blanketed thousands of cars parked in haphazard rows all over the rolling hills. The shadows were quickly growing longer, the air was still, and all of our faces pointed to the sky. As the moon progressively covered the sun, the light melted away, the sky blackened, and the temperature dropped. At the moment of totality, when the moon completely covered the sun , some people around me suddenly gasped. Some cheered; some cried; others laughed in disbelief.

Exactly 53 minutes later, in a downtown park in Greenville, S.C., the person who edited this story and the many individuals around him reacted in exactly the same ways.

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When a total solar eclipse descends—as one will across Mexico, the U.S. and Canada on April 8—everyone and everything in the path of totality are engulfed by deep shadow. Unlike the New Year’s Eve countdown that lurches across the globe one blocky time zone after another, the shadow of totality is a dark spot on Earth that measures about 100 miles wide and cruises steadily along a path, covering several thousand miles in four to five hours. The human experiences along that path are not isolated events any more than individual dominoes are isolated pillars in a formation. Once that first domino is tipped, we are all linked into something bigger—and unstoppable. We all experience the momentum and the awe together.

When this phenomenon progresses from Mexico through Texas, the Great Lakes and Canada on April 8, many observers will describe the event as life-changing, well beyond expectations. “You feel a sense of wrongness in those moments before totality , when your surroundings change so rapidly,” says Kate Russo, an author, psychologist and eclipse chaser. “Our initial response is to ask ourselves, ‘Is this an opportunity or a threat?’ When the light changes and the temperature drops, that triggers primal fear. When we have that threat response, our whole body is tuned in to taking in as much information as possible.”

Russo, who has witnessed 13 total eclipses and counting, has interviewed eclipse viewers from around the world. She continues to notice the same emotions felt by all. They begin with that sense of wrongness and primal fear as totality approaches. When totality starts, we feel powerful awe and connection to the world around us. A sense of euphoria develops as we continue watching, and when it’s over, we have a strong desire to seek out the next eclipse.

“The awe we feel during a total eclipse makes us think outside our sense of self. It makes you more attuned to things outside of you,” says Sean Goldy, a postdoctoral fellow at the department of psychiatry and behavioral sciences at Johns Hopkins University.

Goldy and his team analyzed Twitter data from nearly 2.9 million people during the 2017 total solar eclipse. They found that people within the path of totality were more likely to use not only language that expressed awe but also language that conveyed being unified and affiliated with others. That meant using more “we” words (“us” instead of “me”) and more humble words (“maybe” instead of “always”).

“During an eclipse, people have a broader, more collective focus,” Goldy says. “We also found that the more people expressed awe, the more likely they were to use those ‘we’ words, indicating that people who experience this emotion feel more connected with others.”

This connectivity ties into a sociological concept known as “collective effervescence,” Russo and Goldy say. When groups of humans come together over a shared experience, the energy is greater than the sum of its parts. If you’ve ever been to a large concert or sporting event, you’ve felt the electricity generated by a hive of humans. It magnifies our emotions.

I felt exactly that unified feeling in the open field in Glendo, as if thousands of us were breathing as one. But that’s not the only way people can experience a total eclipse.

During the 2008 total eclipse in Mongolia “I was up on a peak,” Russo recounts. “I was with only my husband and a close friend. We had left the rest of our 25-person tour group at the bottom of the hill. From that vantage point, when the shadow came sweeping in, there was not one man-made thing I could see: no power lines, no buildings or structures. Nothing tethered me to time: It could have been thousands of years ago or long into the future. In that moment, it was as if time didn’t exist.”

Giving us the ability to unhitch ourselves from time—to stop dwelling on time is a unique superpower of a total eclipse. In Russo’s work as a clinical psychologist, she notices patterns in our modern-day mentality. “People with anxiety tend to spend a lot of time in the future. And people with depression spend a lot of time in the past,” she says. An eclipse, time and time again, has the ability to snap us back into the present, at least for a few minutes. “And when you’re less anxious and worried, it opens you up to be more attuned to other people, feel more connected, care for others and be more compassionate,” Goldy says.

Russo, who founded Being in the Shadow , an organization that provides information about total solar eclipses and organizes eclipse events around the world, has experienced this firsthand. Venue managers regularly tell her that eclipse crowds are among the most polite and humble: they follow the rules; they pick up their garbage—they care.

Eclipses remind us that we are part of something bigger, that we are connected with something vast. In the hours before and after totality you have to wear protective glasses to look at the sun, to prevent damage to your eyes. But during the brief time when the moon blocks the last of the sun’s rays, you can finally lower your glasses and look directly at the eclipse. It’s like making eye contact with the universe.

“In my practice, usually if someone says, ‘I feel insignificant,’ that’s a negative thing. But the meaning shifts during an eclipse,” Russo says. To feel insignificant in the moon’s shadow instead means that your sense of self shrinks, that your ego shrinks, she says.

The scale of our “big picture” often changes after witnessing the awe of totality, too. “When you zoom out—really zoom out—it blows away our differences,” Goldy says. When you sit in the shadow of a celestial rock blocking the light of a star 400 times its size that burns at 10,000 degrees Fahrenheit on its surface, suddenly that argument with your partner, that bill sitting on your counter or even the differences among people’s beliefs, origins or politics feel insignificant. When we shift our perspective, connection becomes boundless.

You don’t need to wait for the next eclipse to feel this way. As we travel through life, we lose our relationship with everyday awe. Remember what that feels like? It’s the way a dog looks at a treat or the way my toddler points to the “blue sky!” outside his car window in the middle of rush hour traffic. To find awe, we have to surrender our full attention to the beauty around us. During an eclipse, that comes easily. In everyday life, we may need to be more intentional.

“Totality kick-starts our ability to experience wonder,” Russo says. And with that kick start, maybe we can all use our wonderment faculties more—whether that means pausing for a moment during a morning walk, a hug or a random sunset on a Tuesday. In the continental U.S., we won’t experience another total eclipse until 2044. Let’s not wait until then to seek awe and connection.