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How We Use Abstract Thinking

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

MoMo Productions / Getty Images

• How It Develops

Abstract thinking, also known as abstract reasoning, involves the ability to understand and think about complex concepts that, while real, are not tied to concrete experiences, objects, people, or situations.

Abstract thinking is considered a type of higher-order thinking, usually about ideas and principles that are often symbolic or hypothetical. This type of thinking is more complex than the type of thinking that is centered on memorizing and recalling information and facts.

Examples of Abstract Thinking

Examples of abstract concepts include ideas such as:

• Imagination

While these things are real, they aren't concrete, physical things that people can experience directly via their traditional senses.

You likely encounter examples of abstract thinking every day. Stand-up comedians use abstract thinking when they observe absurd or illogical behavior in our world and come up with theories as to why people act the way they do.

You use abstract thinking when you're in a philosophy class or when you're contemplating what would be the most ethical way to conduct your business. If you write a poem or an essay, you're also using abstract thinking.

With all of these examples, concepts that are theoretical and intangible are being translated into a joke, a decision, or a piece of art. (You'll notice that creativity and abstract thinking go hand in hand.)

Abstract Thinking vs. Concrete Thinking

One way of understanding abstract thinking is to compare it with concrete thinking. Concrete thinking, also called concrete reasoning, is tied to specific experiences or objects that can be observed directly.

Research suggests that concrete thinkers tend to focus more on the procedures involved in how a task should be performed, while abstract thinkers are more focused on the reasons why a task should be performed.

It is important to remember that you need both concrete and abstract thinking skills to solve problems in day-to-day life. In many cases, you utilize aspects of both types of thinking to come up with solutions.

Other Types of Thinking

Depending on the type of problem we face, we draw from a number of different styles of thinking, such as:

• Creative thinking : This involves coming up with new ideas, or using existing ideas or objects to come up with a solution or create something new.
• Convergent thinking : Often called linear thinking, this is when a person follows a logical set of steps to select the best solution from already-formulated ideas.
• Critical thinking : This is a type of thinking in which a person tests solutions and analyzes any potential drawbacks.
• Divergent thinking : Often called lateral thinking, this style involves using new thoughts or ideas that are outside of the norm in order to solve problems.

How Abstract Thinking Develops

While abstract thinking is an essential skill, it isn’t something that people are born with. Instead, this cognitive ability develops throughout the course of childhood as children gain new abilities, knowledge, and experiences.

The psychologist Jean Piaget described a theory of cognitive development that outlined this process from birth through adolescence and early adulthood. According to his theory, children go through four distinct stages of intellectual development:

• Sensorimotor stage : During this early period, children's knowledge is derived primarily from their senses.
• Preoperational stage : At this point, children develop the ability to think symbolically.
• Concrete operational stage : At this stage, kids become more logical but their understanding of the world tends to be very concrete.
• Formal operational stage : The ability to reason about concrete information continues to grow during this period, but abstract thinking skills also emerge.

This period of cognitive development when abstract thinking becomes more apparent typically begins around age 12. It is at this age that children become more skilled at thinking about things from the perspective of another person. They are also better able to mentally manipulate abstract ideas as well as notice patterns and relationships between these concepts.

Uses of Abstract Thinking

Abstract thinking is a skill that is essential for the ability to think critically and solve problems. This type of thinking is also related to what is known as fluid intelligence , or the ability to reason and solve problems in unique ways.

Fluid intelligence involves thinking abstractly about problems without relying solely on existing knowledge.

Abstract thinking is used in a number of ways in different aspects of your daily life. Some examples of times you might use this type of thinking:

• When you describe something with a metaphor
• When you talk about something figuratively
• When you come up with creative solutions to a problem
• When you analyze a situation
• When you notice relationships or patterns
• When you form a theory about why something happens
• When you think about a problem from another point of view

Research also suggests that abstract thinking plays a role in the actions people take. Abstract thinkers have been found to be more likely to engage in risky behaviors, where concrete thinkers are more likely to avoid risks.

Impact of Abstract Thinking

People who have strong abstract thinking skills tend to score well on intelligence tests. Because this type of thinking is associated with creativity, abstract thinkers also tend to excel in areas that require creativity such as art, writing, and other areas that benefit from divergent thinking abilities.

Abstract thinking can have both positive and negative effects. It can be used as a tool to promote innovative problem-solving, but it can also lead to problems in some cases:

• Bias : Research also suggests that it can sometimes promote different types of bias . As people seek to understand events, abstract thinking can sometimes cause people to seek out patterns, themes, and relationships that may not exist.
• Catastrophic thinking : Sometimes these inferences, imagined scenarios, and predictions about the future can lead to feelings of fear and anxiety. Instead of making realistic predictions, people may catastrophize and imagine the worst possible potential outcomes.
• Anxiety and depression : Research has also found that abstract thinking styles are sometimes associated with worry and rumination . This thinking style is also associated with a range of conditions including depression , anxiety, and post-traumatic stress disorder (PTSD) .

Conditions That Impact Abstract Thinking

The presence of learning disabilities and mental health conditions can affect abstract thinking abilities. Conditions that are linked to impaired abstract thinking skills include:

• Learning disabilities
• Schizophrenia
• Traumatic brain injury (TBI)

The natural aging process can also have an impact on abstract thinking skills. Research suggests that the thinking skills associated with fluid intelligence peak around the ages of 30 or 40 and begin to decline with age.

Tips for Reasoning Abstractly

While some psychologists believe that abstract thinking skills are a natural product of normal development, others suggest that these abilities are influenced by genetics, culture, and experiences. Some people may come by these skills naturally, but you can also strengthen these abilities with practice.

Some strategies that you might use to help improve your abstract thinking skills:

• Think about why and not just how : Abstract thinkers tend to focus on the meaning of events or on hypothetical outcomes. Instead of concentrating only on the steps needed to achieve a goal, consider some of the reasons why that goal might be valuable or what might happen if you reach that goal.
• Reframe your thinking : When you are approaching a problem, it can be helpful to purposefully try to think about the problem in a different way. How might someone else approach it? Is there an easier way to accomplish the same thing? Are there any elements you haven't considered?
• Consider the big picture : Rather than focusing on the specifics of a situation, try taking a step back in order to view the big picture. Where concrete thinkers are more likely to concentrate on the details, abstract thinkers focus on how something relates to other things or how it fits into the grand scheme of things.

Abstract thinking allows people to think about complex relationships, recognize patterns, solve problems, and utilize creativity. While some people tend to be naturally better at this type of reasoning, it is a skill that you can learn to utilize and strengthen with practice. 

It is important to remember that both concrete and abstract thinking are skills that you need to solve problems and function successfully. 

Gilead M, Liberman N, Maril A. From mind to matter: neural correlates of abstract and concrete mindsets . Soc Cogn Affect Neurosci . 2014;9(5):638-45. doi: 10.1093/scan/nst031

American Psychological Association. Creative thinking .

American Psychological Association. Convergent thinking .

American Psychological Association. Critical thinking .

American Psychological Association. Divergent thinking .

Lermer E, Streicher B, Sachs R, Raue M, Frey D. The effect of abstract and concrete thinking on risk-taking behavior in women and men . SAGE Open . 2016;6(3):215824401666612. doi:10.1177/2158244016666127

Namkoong J-E, Henderson MD. Responding to causal uncertainty through abstract thinking . Curr Dir Psychol Sci . 2019;28(6):547-551. doi:10.1177/0963721419859346

White R, Wild J. "Why" or "How": the effect of concrete versus abstract processing on intrusive memories following analogue trauma . Behav Ther . 2016;47(3):404-415. doi:10.1016/j.beth.2016.02.004

Williams DL, Mazefsky CA, Walker JD, Minshew NJ, Goldstein G. Associations between conceptual reasoning, problem solving, and adaptive ability in high-functioning autism . J Autism Dev Disord . 2014 Nov;44(11):2908-20. doi: 10.1007/s10803-014-2190-y

Oh J, Chun JW, Joon Jo H, Kim E, Park HJ, Lee B, Kim JJ. The neural basis of a deficit in abstract thinking in patients with schizophrenia . Psychiatry Res . 2015;234(1):66-73. doi: 10.1016/j.pscychresns.2015.08.007

Hartshorne JK, Germine LT. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span . Psychol Sci. 2015;26(4):433-43. doi:10.1177/0956797614567339

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

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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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Psychologily

What is Abstract Thinking? Understanding the Power of Creative Thought

When we think about thinking, we usually imagine it as a straightforward process of weighing options and making decisions. However, there is a more complex and abstract thinking type. Abstract thinking involves understanding and thinking about complex concepts not tied to concrete experiences, objects, people, or situations.

Abstract thinking is a type of higher-order thinking that usually deals with ideas and principles that are often symbolic or hypothetical. It is the ability to think about things that are not physically present and to look at the broader significance of ideas and information rather than the concrete details. Abstract thinkers are interested in the deeper meaning of things and the bigger picture. They can see patterns and connections between seemingly unrelated concepts and ideas. For example, when we listen to a piece of music, we may feel a range of emotions that are not directly related to the lyrics or melody. Abstract thinkers can understand and appreciate the complex interplay of elements that create this emotional response.

Understanding Abstract Thinking

Humans can think about concepts and ideas that are not physically present. This is known as abstract thinking. It is a type of higher-order thinking that involves processing often symbolic or hypothetical information.

Defining Abstract Thinking

Abstract thinking is a cognitive skill that allows us to understand complex ideas, make connections between seemingly unrelated concepts, and solve problems creatively. It is a way of thinking not tied to specific examples or situations. Instead, it involves thinking about the broader significance of ideas and information.

Abstract thinking differs from concrete thinking, which focuses on memorizing and recalling information and facts. Concrete thinking is vital for understanding the world, but abstract thinking is essential for problem-solving, creativity, and critical thinking.

Origins of Abstract Thinking

The origins of abstract thinking are partially clear, but it is believed to be a uniquely human ability. Some researchers believe that abstract thinking results from language and symbolic thought development. Others believe that it results from our ability to imagine and visualize concepts and ideas.

Abstract thinking is an essential skill that can be developed and strengthened with practice regardless of its origins. By learning to think abstractly, we can expand our understanding of the world and develop new solutions to complex problems.

Abstract thinking is a higher-order cognitive skill that allows us to think about concepts and ideas that are not physically present. We can improve our problem-solving, creativity, and critical thinking skills by developing our abstract thinking ability.

Importance of Abstract Thinking

Abstract thinking is a crucial skill that significantly impacts our daily lives. It allows us to understand complex concepts and think beyond what we see or touch. This section will discuss the benefits of abstract thinking in our daily lives and its role in problem-solving.

Benefits in Daily Life

Abstract thinking is essential for our personal growth and development. It enables us to think critically and creatively, which is necessary for making informed decisions. When we think abstractly, we can understand complex ideas and concepts, which helps us communicate more effectively with others.

Abstract thinking also helps us to be more adaptable and flexible in different situations. We can see things from different perspectives and find innovative solutions to problems. This skill is beneficial in today’s fast-paced world, where change is constant, and we need to adapt quickly.

Role in Problem Solving

Abstract thinking plays a crucial role in problem-solving. It allows us to approach problems from different angles and find creative solutions. When we can think abstractly, we can see the bigger picture and understand the underlying causes of a problem.

By using abstract thinking, we can also identify patterns and connections that may not be immediately apparent. This helps us to find solutions that are not only effective but also efficient. For example, a business owner who can think abstractly can identify the root cause of a problem and develop a solution that addresses it rather than just treating the symptoms.

Abstract thinking is a valuable skill with many benefits in our daily lives. It allows us to think critically and creatively, be more adaptable and flexible, and find innovative solutions to problems. By developing our abstract thinking skills, we can improve our personal and professional lives and positively impact the world around us.

Abstract Thinking Vs. Concrete Thinking

When it comes to thinking, we all have different approaches. Some of us tend to think more abstractly, while others tend to think more concretely. Abstract thinking and concrete thinking are two different styles of thought that can influence how we perceive and interact with the world around us.

Key Differences

The key difference between abstract and concrete thinking is the level of specificity involved in each style. Concrete thinking focuses on a situation’s immediate and tangible aspects, whereas abstract thinking is more concerned with the big picture and underlying concepts.

Concrete thinking is often associated with literal interpretations of information, while abstract thinking relates to symbolic and metaphorical interpretations. For example, if we describe a tree, someone who thinks concretely might describe its physical appearance and characteristics. In contrast, someone who thinks abstractly might explain its symbolic significance in nature.

The transition from Concrete to Abstract

While some people may naturally lean towards one style of thinking over the other, it is possible to transition from concrete to abstract thinking. This can be particularly useful in problem-solving and critical-thinking situations, where a more abstract approach may be needed to find a solution.

One way to make this transition is to focus on a situation’s underlying concepts and principles rather than just the immediate details. This can involve asking questions that explore the broader implications of a situation or looking for patterns and connections between seemingly unrelated pieces of information.

Abstract and concrete thinking are two different styles of thought that can influence how we perceive and interact with the world around us. While both styles have their strengths and weaknesses, transitioning between them can be valuable in many areas of life.

Development of Abstract Thinking

As we grow and learn, our ability to think abstractly develops. Age and education are two major factors that influence the development of abstract thinking.

Influence of Age

As we age, our ability to think abstractly improves. This is due to the development of our brain and cognitive abilities. According to Piaget’s theory of cognitive development , children progress through four stages of cognitive development, with the final stage being the formal operational stage. This stage is characterized by the ability to think abstractly and logically about hypothetical situations and concepts.

Role of Education

Education also plays a significant role in the development of abstract thinking. Through education, we are exposed to new ideas, concepts, and theories that challenge our existing knowledge and encourage us to think abstractly. Education also gives us the tools and skills to analyze and evaluate complex information and ideas.

In addition to traditional education, engaging in activities promoting abstract thinking can be beneficial. For example, participating in debates, solving puzzles, and playing strategy games can all help improve our abstract thinking skills.

The development of abstract thinking is a complex process influenced by age and education. By continually challenging ourselves to think abstractly and engaging in activities that promote abstract thinking, we can continue to improve our cognitive abilities and expand our knowledge and understanding of the world around us.

Challenges in Abstract Thinking

Abstract thinking can be a challenging cognitive process, especially for those not used to it. Here are some common misunderstandings and difficulties people may encounter when thinking abstractly.

Common Misunderstandings

One common misunderstanding about abstract thinking is that it is the same as creative thinking. While creativity can certainly involve abstract thinking, the two are not interchangeable. Abstract thinking consists of understanding and thinking about complex concepts not tied to concrete experiences, objects, people, or situations. Creative thinking, on the other hand, involves coming up with new and innovative ideas.

Another common misunderstanding is that abstract thinking is only helpful for people in certain fields, such as science or philosophy. Abstract thinking can benefit many different areas of life, from problem-solving at work to understanding complex social issues.

Overcoming Difficulties

One difficulty people may encounter when thinking abstractly is a lack of concrete examples or experiences to draw from. To overcome this, finding real-world examples of the concepts you are trying to understand can be helpful. For example, if you are trying to understand the concept of justice, you might look for examples of situations where justice was served or not served.

Another challenge people may encounter is focusing too much on details and needing more on the bigger picture. To overcome this, try to step back and look at the broader significance of the ideas and information you are working with. This can involve asking yourself questions like “What is the main point here?” or “How does this fit into the larger context?”

Abstract thinking can be a challenging but valuable cognitive process. By understanding common misunderstandings and overcoming difficulties, we can develop our ability to think abstractly and apply it in various aspects of our lives.

Frequently Asked Questions

How does abstract thinking differ from concrete thinking.

Abstract thinking is a type of thinking that involves the ability to think about concepts, ideas, and principles that are not necessarily tied to physical objects or experiences. Concrete thinking, on the other hand, is focused on the here and now, and is more concerned with the physical world and immediate experiences.

What are some examples of abstract thinking?

Examples of abstract thinking include the ability to understand complex ideas, to think creatively, to solve problems, to think critically, and to engage in philosophical discussions.

What is the significance of abstract thinking in psychiatry?

Abstract thinking is an important component of mental health and well-being. It allows individuals to think beyond the present moment and to consider different possibilities and outcomes. In psychiatry, the ability to engage in abstract thinking is often used as an indicator of cognitive functioning and overall mental health.

At what age does abstract thinking typically develop?

Abstract thinking typically develops during adolescence, around the age of 12 or 13. However, the ability to engage in abstract thinking can continue to develop throughout adulthood, with continued practice and exposure to new ideas and experiences.

What are the stages of abstract thought according to Piaget?

According to Piaget, there are four stages of abstract thought: the sensorimotor stage (birth to 2 years), the preoperational stage (2 to 7 years), the concrete operational stage (7 to 12 years), and the formal operational stage (12 years and up). During the formal operational stage, individuals are able to engage in abstract thinking and to think about hypothetical situations and possibilities.

What are some exercises to improve abstract thinking skills?

Some exercises that can help improve abstract thinking skills include engaging in philosophical discussions, solving puzzles and brain teasers, playing strategy games, and engaging in creative activities such as writing or painting. Additionally, exposing oneself to new ideas and experiences can help broaden one’s perspective and improve abstract thinking abilities.

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Abstract Thinking: What It Is, Examples And How To Develop It

The ability to formulate hypotheses and be able to test them is not a skill that has accompanied us throughout our lives. Our way of thinking changes with development, also accompanied and supported by the development of our own nervous system.

A child may know that if he presses a certain button the television will turn on, but what if it doesn’t turn on? Surely, go to the adult, who will be able to come up with various explanations for what may be happening. He will check if the remote has batteries, if they have run out, if the television has the light indicating that it is plugged into the power, etc.

Abstract thinking, often regarded as a cornerstone of human intelligence, plays a pivotal role in problem-solving and innovation. In today’s rapidly evolving world, where challenges are becoming increasingly complex, the ability to think abstractly is more crucial than ever before.

The adult, through development, has acquired the ability to think abstractly or formally. Do you want to know more about it? Continue reading this PsychologyFor article in which we talk to you about abstract thinking: what it is, examples and how to develop it.

Table of Contents

What is abstract thinking

He Abstract thinking either formal thinking consists of the ability to think independently of the reality that is shown to us in a concrete way< It allows the human being to think about different scenarios and possibilities among which, of course, is concrete reality.

In the simplified example that we have presented in the introduction, the child is not able to think beyond the reality in front of him, which is that the television does not turn on. The adult, however, may think further, establish hypotheses, test them and thus solve the problem.

Abstract thinking, contextualized in Piaget’s theory, appears in the last stage of development: the stage of formal operations. For Vygotsky, it is precisely this acquisition that marks the difference between the thinking of the child and the thinking of the adolescent.

Understanding Abstract Thinking

Abstract thinking refers to the mental process of contemplating ideas, concepts, and principles that are detached from specific instances or contexts. Unlike concrete thinking, which deals with tangible objects and observable phenomena, abstract thinking involves conceptualization, analysis, and synthesis of information at a higher level of abstraction. It enables individuals to grasp the underlying patterns, relationships, and implications inherent in diverse situations, thereby facilitating creative problem-solving and critical decision-making.

Significance of Abstract Thinking

The significance of abstract thinking lies in its ability to transcend the constraints of immediate reality and conventional wisdom. By fostering a deeper understanding of abstract concepts and principles, individuals can navigate complex scenarios with agility and insight. Abstract thinking empowers individuals to:

1. Foster Creativity and Innovation

Abstract thinking encourages divergent thought processes, allowing individuals to generate novel ideas, perspectives, and solutions. By breaking free from conventional constraints and exploring unconventional possibilities, abstract thinkers drive innovation and creativity across various fields, from technology and science to art and literature.

2. Enhance Problem-Solving Skills

Abstract thinking equips individuals with the analytical tools and mental flexibility needed to tackle multifaceted problems effectively. By discerning underlying patterns, identifying root causes, and envisioning alternative approaches, abstract thinkers can devise innovative solutions to complex challenges, driving progress and advancement.

3. Promote Strategic Planning

Abstract thinking enables individuals to envision long-term goals, anticipate future trends, and develop strategic plans to achieve desired outcomes. By synthesizing disparate information and discerning emerging patterns, abstract thinkers can formulate robust strategies that adapt to changing circumstances and seize opportunities for growth and success.

What age is abstracts thinking

Abstract thinking typically begins to emerge during early adolescence, around the ages of 11 to 14 years old, and continues to develop throughout adolescence and into adulthood. This cognitive ability allows individuals to think in terms of concepts, ideas, and hypothetical scenarios, rather than relying solely on concrete, tangible experiences. With abstract thinking, individuals can understand and analyze complex concepts, make predictions, think critically, and engage in creative problem-solving.

Practical Applications of Abstract Thinking

Abstract thinking finds applications across a wide range of domains, from scientific research and engineering to business management and artistic expression. Some practical applications include:

1. Scientific Discovery

In scientific research, abstract thinking plays a fundamental role in hypothesis formulation, experimental design, and theoretical modeling. Scientists leverage abstract concepts and mathematical frameworks to elucidate complex phenomena, advance knowledge, and drive technological innovation.

2. Business Strategy

In the business world, abstract thinking informs strategic decision-making, market analysis, and competitive positioning. Business leaders rely on abstract reasoning to identify emerging trends, assess competitive threats, and devise innovative strategies that drive sustainable growth and profitability.

3. Artistic Creation

In the realm of art and creativity, abstract thinking fuels artistic expression, aesthetic exploration, and conceptual innovation. Artists use abstract concepts, symbolism, and metaphorical imagery to evoke emotions, provoke thought, and challenge perceptions, fostering cultural enrichment and artistic diversity.

Cultivating Abstract Thinking Skills

While abstract thinking is often regarded as an innate ability, it can be cultivated and enhanced through deliberate practice and cognitive stimulation. Some strategies for cultivating abstract thinking skills include:

1. Engage in Divergent Thinking

Divergent thinking involves generating multiple solutions to a problem by exploring various perspectives, ideas, and possibilities. Engaging in activities such as brainstorming, mind mapping, and lateral thinking exercises can stimulate divergent thinking skills and foster creativity.

2. Explore Interdisciplinary Connections

Interdisciplinary learning exposes individuals to diverse fields of knowledge, fostering cross-disciplinary connections and insights. By exploring intersections between different disciplines, individuals can gain new perspectives, expand their intellectual horizons, and cultivate abstract thinking skills.

3. Practice Reflective Thinking

Reflective thinking involves introspection, analysis, and synthesis of information to derive deeper insights and understanding. By reflecting on past experiences, analyzing complex issues, and synthesizing disparate information, individuals can refine their abstract thinking skills and enhance their problem-solving abilities.

Phases of development and abstract thinking

As we have indicated, formal thinking is what characterizes the Piaget’s last stage of cognitive development Piagetian theory postulates that cognitive development occurs throughout several phases or stages, more or less lasting depending on each person but necessarily successive.

The acquisition of abstract thinking begins around the age of 11 (incipient formal stage) and is consolidated from the age of 14 or 15 (advanced formal stage). Although it is true that Piaget modifies his initial theories and indicates that it is at the age of 20 when this evolutionary acquisition is consolidated (Aguilar Villagrán, M., Navarro Guzmán, JI, López Pavón, JM and Alcalde Cuevas, C., 2002 ) (1)</sup.

Until this acquisition occurs in adolescence, the child has gone through several stages of development in which his or her way of thinking has been qualitatively different.

1. Sensory-motor stage

It covers from birth to two years of age and is linked to sensory and motor development. The baby’s thinking would be circumscribed “here and now”

2. Preoperational stage

This stage ranges from approximately 2 to 7 years old. At this stage arises the symbolic thinking , so that the child can think about events or objects that are not present at that moment. He may think about the ball you showed him a few days ago or the toy his schoolmate has and he liked it so much.

3. Stage of concrete operations

Although from 7 to 11 years old children are capable of doing complex mental operations (conservation tasks, classification, serialization, etc.) their way of thinking has a limitation, and that is that the child has to manipulate things or see them to be able to think about them. If you ask him to imagine them he will not give a correct answer. In the preoperational stage, therefore, they begin to use logic and mental operations but only for facts and objects in their environment, their concrete reality.

4. Formal operations stage

For Piaget, the most important characteristic of this new way of thinking would be the fact that being able to think in terms of possibilities and not just realities< Adolescents go beyond immediate reality and begin to discover that reality can be much broader than what is in front of them, which will significantly influence their behavior.

Following Sierra, P. and Brioso, A. (2006) (2) the adolescent differentiates between what is real and what is possible, necessarily using hypothetico-deductive reasoning and reasoning about verbal statements instead of reasoning about concrete objects.

This would be the last stage of Piagetian theory, however the existence of post-formal thought, subsequent to formal thought, has been proposed. This postformal thinking would go beyond formal reasoning that yields right or wrong results and would propose solutions relative to problems.

Examples of abstract thinking

In the introduction of this article we have presented a simplified example of abstract thinking in which the person is capable of thinking about hypotheses and possibilities beyond what concrete reality shows them.

• Deductive reasoning It is a clear example of abstract thinking. Trying to exemplify this type of reasoning, we can think “All people breathe. “My cousin is a person, therefore my cousin breathes.”
• Make hypotheses In a more ecological and less theoretical example, imagine that you have met a friend who is late. You write him a message and he doesn’t answer. Our abstract thinking will allow us to establish hypotheses about what could have happened: he forgot something and turned around, the bus was delayed, there is a traffic jam, he doesn’t want to answer us, a problem has arisen, etc.
• Create a work of art It is an example of obstructed thinking, whether it is the colors in a painting or the notes in a piece of music.
• Imagine the future : the future is something that we cannot touch or know, so it is part of abstract thinking. For example: making future plans or simply thinking about the future are examples of abstract thinking.
• Analyze the past : Leaving the present means using this type of thinking, so reflecting on the past is another example of abstract thinking.

Activities to develop abstract thinking

In general, any task that requires deductive reasoning or requires the person to think about various possibilities will trigger formal thinking mechanisms. For example:

• Solving mathematical problems : in these we must apply mathematical rules and formulas and, on many occasions, we need to think about the problem from different perspectives to find the solution, therefore, it is a good exercise in abstract reasoning.
• Solving riddles and riddles:  This abstract reasoning activity helps develop this type of thinking since to solve them we will have to go beyond their literal message.
• Resolution of syllogisms : we can offer two premises and request the conclusion.

Abstract thinking is a vital cognitive skill that empowers individuals to navigate complexity, foster innovation, and drive progress in an ever-changing world. By cultivating abstract thinking skills and embracing creative thinking, individuals can unlock new possibilities, overcome challenges, and shape a brighter future for themselves and society as a whole.

This article is merely informative, at PsychologyFor we do not have the power to make a diagnosis or recommend a treatment. We invite you to go to a psychologist to treat your particular case.

If you want to read more articles similar to Abstract thinking: what it is, examples and how to develop it we recommend that you enter our Cognitive Psychology category.

• Aguilar Villagrán, M., Navarro Guzmán, JI, López Pavón, JM and Alcalde Cuevas, c. (2002). Formal thinking and mathematical problem solving. Psychothema, 14 (2), 382-386.
• Sierra, P. and Brioso, A. (2006). Biological and Cognitive Changes During Adolescence. In Sierra, L. and Brioso, A. (2006). Developmental Psychology</i. Madrid: Sanz and Torres

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• Saldarriaga-Zambrano, PJ, Bravo-Cedeño, GR and Loor-Rivadeneira, M. (2016). Jean Piaget’s constructivist theory and its significance for contemporary pedagogy. Scientific Magazine Domain of Sciences, 2 (Extra 3), 127-137.

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• v.10; 2014 Oct

Development of abstract thinking during childhood and adolescence: The role of rostrolateral prefrontal cortex

Iroise dumontheil.

a Department of Psychological Sciences, Birkbeck, University of London, UK

b Institute of Cognitive Neuroscience, University College London, UK

• • Rostral prefrontal cortex (RPFC) supports self-generated, abstract thought processing.
• • Flexibly attending towards and processing abstract thoughts develop in adolescence.
• • RPFC activation becomes more specific to relational integration during development.
• • Prospective memory development remains to be further studied using neuroimaging.
• • Training of abstract thinking, e.g. reasoning, may have implication for education.

Rostral prefrontal cortex (RPFC) has increased in size and changed in terms of its cellular organisation during primate evolution. In parallel emerged the ability to detach oneself from the immediate environment to process abstract thoughts and solve problems and to understand other individuals’ thoughts and intentions. Rostrolateral prefrontal cortex (RLPFC) is thought to play an important role in supporting the integration of abstract, often self-generated, thoughts. Thoughts can be temporally abstract and relate to long term goals, or past or future events, or relationally abstract and focus on the relationships between representations rather than simple stimulus features. Behavioural studies have provided evidence of a prolonged development of the cognitive functions associated with RLPFC, in particular logical and relational reasoning, but also episodic memory retrieval and prospective memory. Functional and structural neuroimaging studies provide further support for a prolonged development of RLPFC during adolescence, with some evidence of increased specialisation of RLPFC activation for relational integration and aspects of episodic memory retrieval. Topics for future research will be discussed, such as the role of medial RPFC in processing abstract thoughts in the social domain, the possibility of training abstract thinking in the domain of reasoning, and links to education.

1. Introduction

Abstract thoughts can be broadly defined as thoughts that are self-generated and stimuli-independent, in contrast to stimulus-oriented, perceptually-derived, information. Beyond this definition, two particular forms of abstraction can be considered (see Nee et al., 2014 ). Abstraction can be defined temporally: abstract thoughts are those that relate to long term goals, or past or future events. Alternately, abstraction can be defined relationally: abstract thoughts are those that focus on the relationships between representations rather simple stimulus features. A subset of cognitive processes has particularly high requirements of abstract thoughts manipulation, either within a single temporal or relational domain, or across both. These include the retrieval of past thoughts and memories (e.g. episodic or source memory retrieval), the manipulation of current task-related or task-unrelated self-generated information (e.g. relational reasoning and problem solving or mindwandering respectively) and the processing of thoughts linked to the future (e.g. planning, multitasking, prospective memory). Interestingly, the most anterior part of the lateral prefrontal cortex, the rostrolateral prefrontal cortex (RLPFC), has been found to show increased activations in paradigms testing this whole range of cognitive functions (e.g. see Badre, 2008 , Burgess et al., 2007a , Ramnani and Owen, 2004 for review). The rostral prefrontal cortex (RPFC), as other parts of the frontal cortex and the temporal cortices, shows prolonged structural development during adolescence (e.g. see Dumontheil et al., 2008 for review). The relationship between abstract thoughts and RPFC, in particular the RLPFC, during late childhood and adolescence will be the topic of this review.

Adolescence starts at the onset of puberty and can be broadly defined as between the ages of 10 and 19 ( Sawyer et al., 2012 ). Although brain and behavioural changes during this period are less pronounced than during infancy and childhood, adolescence is nevertheless an important period of development in terms of the acquisition of higher cognitive skills, as well as the onset of mental disorders (see Dumontheil et al. (2008) for a discussion of RPFC and developmental disorders). Adolescence emerges as a critical phase of reorganisation of regulatory systems, and may also be a period of extended brain plasticity and thus a relevant target for interventions ( Steinberg, 2005 ).

The first section of this paper will focus on the association between lateral RPFC and the ability to attend to and manipulate abstract thoughts. I will then discuss the development of this ability during late childhood and adolescence and how structural and functional development of RPFC may underlie the behavioural changes observed during adolescence. I will then briefly relate these findings to studies of the development of medial RPFC function in social cognition tasks. Finally, I will discuss future avenues of research in this field as well as potential implications of these findings for education policy and practice. This review will focus on aspects of both relationally and temporally abstract thoughts ( Nee et al., 2014 ), as identified from the research on RLPFC function in adults. Although an effort was made to gather relevant evidence, this review is unlikely to be exhaustive and is biased towards those fields where more developmental neuroimaging research has currently been published.

Recently Ferrer et al. (2009) summarised the development of fluid reasoning, which can be considered as a type of abstract thinking. Here the goal is to perform a more extensive review of the development of abstract thinking more generally, including recent studies on the topic. Although some aspects of metacognition are relevant to the domain of abstract thought and reasoning, there has been until now little cognitive neuroscience research done with a developmental focus (see Fleming and Dolan, 2012 , Fleming et al., 2010 ) and thus metacognition will not be reviewed here (see Schneider, 2008 for a review of the development of meta-cognitive knowledge).

2. Rostral prefrontal cortex function

2.1. rostral prefrontal cortex: cytoarchitecture and subdivisions.

RPFC, which corresponds approximately to Brodmann area 10 (BA10), is a large brain region in humans and is thought to be subdivided into separate subregions distinct in terms of cellular organisation and function ( Christoff and Gabrieli, 2000 , Gilbert et al., 2006a , Gilbert et al., 2006b ). Two quite different types of cognitive ability have been associated with the RPFC. The lateral parts of RPFC (RLPFC) appear to support the ability to detach oneself from the environment and to elaborate, evaluate and maintain abstract rules and information, as it is involved in reasoning, problem solving, and more generally abstract thinking ( Amati and Shallice, 2007 , Christoff and Gabrieli, 2000 , Christoff et al., 2009b , Gilbert et al., 2006b , Koechlin et al., 2003 , Ramnani and Owen, 2004 ) (see below for further details). The medial aspect of RPFC, or medial prefrontal cortex (MPFC), is implicated in social cognition, that is, the understanding of other people's minds ( Amodio and Frith, 2006 , Blakemore, 2008 , Van Overwalle, 2009 ).

In the last decade, large scale magnetic resonance (MRI) studies have shown that the RPFC is one of the last brain regions to reach maturity in humans (see Dumontheil et al., 2008 for review). This region is also particularly interesting in terms of its cellular organisation and connection with other regions. RPFC is the only prefrontal region that is predominantly interconnected with supramodal cortex in the PFC ( Andersen et al., 1985 , Petrides and Pandya, 1999 ), anterior temporal cortex ( Amaral and Price, 1984 , Moran et al., 1987 ) and cingulate cortex ( Andersen et al., 1985 , Arikuni et al., 1994 , Bachevalier et al., 1997 , Morecraft and Van Hoesen, 1993 ). In addition, its projections to these other regions are broadly reciprocal ( Passingham, 2002 ; see Ramnani and Owen, 2004 for review). RPFC has a low cell density, which may indicate that this region in humans has more space available for connections both within this region and with other brain regions ( Semendeferi et al., 2011 , Semendeferi et al., 2001 ). RPFC also has a particularly high number of dendritic spines per cell, an indicator of the number of synaptic connections, which suggests that the computational properties of RPFC are more likely to involve the integration of inputs than those of comparable areas ( Ramnani and Owen, 2004 ).

In line with these findings, Amati and Shallice (2007) proposed that RPFC may support a novel type of cognitive computational process required for “abstract projectuality”, that may be behind the cognitive capacities specific to modern humans. They propose that this brain operation permits a fluent sequence of non-routine computational operations to occur over a prolonged timecourse. This qualitatively different type of brain operation may have emerged from increasing prefrontal cortical connectivity in the RPFC, induced by gradual (quantitative) genetic changes affecting RPFC structure and organisation over evolution ( Amati and Shallice, 2007 ). This model fits well with current theories of RLPFC function which will be detailed in the next section.

2.2. RLPFC and abstract thinking

A number of theories of the functional organisation of the frontal lobes have been proposed in the last decade based on neuroimaging and lesion data. The broad consensus is that the frontal cortex may possess a rostro-caudal organisation whereby more rostral regions support cognitive control involving progressively more abstract representations ( Azuar et al., 2014 , Badre and D’Esposito, 2007 , Badre and D’Esposito, 2009 , Badre, 2008 , Botvinick, 2008 , Christoff et al., 2009b , Koechlin and Jubault, 2006 , Koechlin and Summerfield, 2007 , Koechlin et al., 2003 , Petrides, 2005 ). In this organisation, posterior PFC supports the control and manipulation of temporally proximate, concrete action representations, while anterior PFC supports the control of temporally extended, abstract representations ( Badre, 2008 ). Fig. 1 , adapted from Badre (2008) , shows a representation of this organisation. Of interest here is the position of the RLPFC, at the top of this frontal lobe hierarchy, and the suggestion that this brain region is recruited when temporally extended, abstract representations are attended to or manipulated.

Sub-divisions of the frontal lobes. (a) Schematic representation of the major anatomical sub-divisions of the frontal lobes. Following a caudal to rostral direction, labelled areas include motor cortex, dorsal and ventral premotor cortices, dorsal and ventral aspects of anterior premotor cortex, ventrolateral prefrontal cortex (VLPFC), dorsolateral prefrontal cortex (DLPFC), and lateral frontopolar cortex, also termed rostrolateral prefrontal cortex (RLPFC). Boundaries and Brodmann areas (BA) are approximate. (b) Schematic representation of the rostro-caudal gradiant of the organisation of the prefrontal cortex. The consensus among diverse theoretical accounts of the organisation of the PFC is that progressively more anterior PFC regions support cognitive control of progressively more abstract and temporally extended representations (adapted from Badre, 2008 ).

RLPFC indeed shows increased blood oxygen level dependent (BOLD) signal in a number of tasks that require such aspects of cognition, including the retrieval of episodic or source memory (e.g. Dobbins et al., 2004 , Turner et al., 2008 ; see Gilbert et al., 2006b for review and Spaniol et al., 2009 for meta-analysis); prospective memory ( Barban et al., 2013 , Benoit et al., 2011 , Burgess et al., 2007b ); the manipulation of highly abstract information ( Christoff et al., 2009b ); the selection and maintenance of task rules ( Bengtsson et al., 2009 , Braver et al., 2003 , Dumontheil et al., 2011 , Sakai and Passingham, 2003 , Sakai and Passingham, 2006 ); sub-goal processing or branching ( Badre and D’Esposito, 2007 , Braver and Bongiolatti, 2002 , Koechlin et al., 2003 ); integration of information ( Badre and Wagner, 2004 , Wolfensteller and von Cramon, 2011 ); analogical and relational reasoning ( Bunge et al., 2009 , Geake and Hansen, 2005 , Hampshire et al., 2011 , Smith et al., 2007 , Volle et al., 2010 , Wendelken et al., 2008 , Wendelken et al., 2012 , Wright et al., 2008 ) – although note that medial dorsal RPFC has also been implicated in analogical reasoning ( Green et al., 2006 , Krawczyk, 2012 , Volle et al., 2010 ); reality monitoring ( Simons et al., 2008 ); and mind-wandering ( Christoff et al., 2004 , Christoff et al., 2009a , Dumontheil et al., 2010a , Schooler et al., 2011 ).

Lesion studies also provide supporting evidence for a role of RPFC in the control of temporally extended abstract representations, although, by their nature, these studies rarely distinguish between lateral and medial aspects of RPFC, and therefore between the social cognition and cognitive control aspects of RPFC function ( Burgess, 2000 , Burgess et al., 2009 , Gläscher et al., 2010 , Roca et al., 2010 , Shallice and Burgess, 1991 , Volle et al., 2011 ).

3. Behavioural studies of the development of abstract thinking

Abstract thinking encompasses a number of different cognitive processes, but one definition adopted here is that abstract thinking can be considered as the manipulation of self-generated thoughts, or thoughts that are not directly connected to the environment. A distinction is made between relationally and temporally abstract thoughts. As described above, neuroimaging and lesion studies in adults suggest that RLPFC is thought to be specifically involved in the elaboration, evaluation and maintenance of abstract rules ( Amati and Shallice, 2007 , Christoff and Gabrieli, 2000 , Christoff et al., 2009b , Koechlin et al., 2003 , Ramnani and Owen, 2004 ), as well as in the ability to flexibly control whether one selectively attends towards self-generated thoughts or the environment ( Burgess et al., 2007a ), whether this self-generated information is task-relevant, or task-irrelevant, i.e. when the mind wanders ( Christoff et al., 2004 , Christoff et al., 2009a , Dumontheil et al., 2010a ). A number of theorists have suggested that adolescents can operate at a new and more abstract level of thought because they can integrate the results of two different sorts of lower-order processing ( Case, 1985 , Fischer, 1980 , Halford, 1982 ). This new intellectual potential emerging in adolescence builds on the idea that children can progressively handle first one new abstract element, then two, and then multiple abstract elements simultaneously (see Marini and Case, 1994 , for review). Below are described behavioural studies investigating the development of the ability to flexibly attend towards self-generated thoughts, the development of the ability to reason logically and integrate relations or representations, and finally the development of the processing of self-generated thoughts that can be considered temporally abstract, and are related to past experiences (episodic memory) or future events (prospective memory). Although multitasking, or branching, has been a particular focus of neuroimaging and lesion research on RLPFC function in adults ( Badre and D’Esposito, 2007 , Braver and Bongiolatti, 2002 , Burgess, 2000 , Koechlin et al., 2003 ), this topic has not been specifically investigated in developmental psychology research.

3.1. Development of the flexible selection of self-generated thoughts

An important aspect of the manipulation of abstract thought resides in the ability to modulate the balance between cognition that is provoked by perceptual experience (stimulus-oriented, SO) and that which occurs in the absence of sensory input (self-generated, or stimulus-independent, SI) ( Burgess et al., 2007a ). In children, manipulation of SI thoughts has been studied in the context of fluid intelligence and relational reasoning ( Crone, 2009 , Wright et al., 2008 ; see below) and working memory (WM) tasks ( Crone et al., 2006 ), while the ability to resist distracting SO information has been studied in perceptual ( Booth et al., 2003 , Bunge et al., 2002 ) and WM tasks ( Olesen et al., 2007 ). In this latter study 13 year-old participants showed poorer accuracy than adults in visuospatial WM trials that included distraction relative to trials that did not.

In a recent study ( Dumontheil et al., 2010b ), we tested 179 female participants aged 7–27-year old on a single task (Alphabet task) that could be performed on the basis of either SO or SI information, without high working memory requirements ( Gilbert et al., 2005 , Gilbert et al., 2007 , Gilbert et al., 2008 ). Participants were asked to classify letters of the alphabet according to whether the upper case letter contained a curve or not. In SO blocks consecutive letters of the alphabet were presented on the screen, while in SI blocks either no letter (No-distractor condition) or distracting non-consecutive letters (Distractor condition) were presented on the screen. In SI blocks participants were asked to continue going through the alphabet sequence in their head and continue responding (see Fig. 2a ). Different patterns of development were observed for the different aspects of this task. Resistance to visual distractors exhibited small improvements with age, both in accuracy and speed of responding, while the manipulation of SI thoughts and switching between SI and SO thoughts showed steeper response speed improvements extending into late adolescence (see Fig. 2b ). This development in the speed of manipulating self-generated thoughts and in the speed of switching between perceptually-derived and self-generated thoughts may underlie improvements during adolescence in planning, reasoning and abstract thinking, abilities that rely on the manipulation of thoughts that are not directly derived from the environment ( Anderson et al., 2001 , De Luca et al., 2003 , Huizinga et al., 2006 , Rosso et al., 2004 ). Below is described in more detail the particular case of the development of reasoning.

Development of the flexible switching between selecting thoughts derived from the environment and abstract thoughts. (a) Alphabet task. Participants classify letters of the alphabet according to their shape (line or curve). When the letter is red, participants judge the letter presented on the screen (stimulus-oriented (SO) blocks). When the letter is blue (or when there is no letter) participants continue reciting the alphabet in their head and judge the shape of the letter in their head (stimulus-independent (SI) blocks), while ignoring the distracting letter presented on the screen (Distractor condition), or in the absence of a letter on the screen (No-distractor condition). Performance in the two types of blocks (SI vs. SO) and the two conditions (Distractor vs. No-distractor), and performance in switch trials (first trial of a SO or SI block) and subsequent trials (stay trials) were compared. (b) Behavioural results. The speed of responding in SI vs. SO, and in switch vs. stay trials continued to increase during adolescence. The speed of responding in the presence of Distractors also improved but followed a flatter linear developmental function (adapted from Dumontheil et al., 2010b ). (c) Functional MRI results. The main effect of switching between SO and SI conditions vs. a simple change of colour of the stimuli over the whole age range is presented (family-wise error corrected p < .05), highlighting the right superior RLPFC activation (top). RLPFC activity in this contrast is plotted against age (bottom). There was a significant decrease in activity during adolescence, which was not purely a consequence of differences in performance and brain structure between the participants and could reflect the maturation of neurocognitive strategies (see Dumontheil et al., 2010b ). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.2. Development of logical reasoning

Problem solving by analogy requires the transfer of previously acquired solutions or strategies from one context or situation to another. Preschoolers (e.g. Holyoak et al., 1984 ) and even infants (e.g. Chen et al., 1997 ) exhibit an ability to draw analogies and use a solution learned from a one problem to solve another problem. However older children are better able to detect the underlying similarities between the original problem and the novel problem situation (e.g. Chen and Daehler, 1992 , Daehler and Chen, 1993 , Holyoak et al., 1984 ; see Chen et al., 1997 for review). Experimental paradigms have tended to be action-based, requiring children to perform a particular action to achieve a goal. However, analogical reasoning is also assessed using verbal or pictorial stimuli in propositional analogy tasks ( Ferrer et al., 2009 ), for example asking children to match the sequence “bread: slice of bread:: orange:?” with one of the following options: slice of orange, slice of cake, squeezed oranges, orange balloon, orange basketball. The relational shift hypothesis proposes that young children interpret analogy and metaphor first in terms of object similarity, and then in terms of relational similarity. Support for this hypothesis is given for example by the observation that when relational similarity competes with object similarity, young children make object-similarity responses, while with increasing age/experience responses become in line with relational similarity ( Rattermann and Gentner, 1998 ). This relational shift is thought to be not simply age-determined, but knowledge-related, which means it can occur at different ages in different domains. However, adults continue to use both object commonalities and relational commonalities in processing comparisons (see Rattermann and Gentner (1998) for discussion). In a recent computational study, Morrison et al. (2011) propose that the development of analogical reasoning during childhood is best explained by a combination of improved information processing, in particular working memory (which supports the maintenance of a greater number of relations) and inhibitory control (which supports the resistance to distraction by object commonalities), in combination with knowledge accretion.

Subsequent developmental changes have been observed during adolescence. Marini and Case (1994) show that a capacity for abstract reasoning begins to emerge in both social and non-social domains about the age of 11 or 12 and that further development of this ability is constrained by the number of abstract elements that can be coordinated at one time, independent of the particular content of these abstract elements. The task used required participants to predict the movement of a beam where both the weight and distance from the centre were relevant factors to be combined, or to predict a character's behaviour based on personality traits abstracted from a scenario. Similarly, Hatcher et al. (1990) observed development of abstract thinking between ages 10, 13 and 17-year old, using the balance beam task and a verbal analogical reasoning task. Using conditional reasoning (if… then… statement) tasks, De Neys and Everaerts (2008) showed that improvements in conditional reasoning observed during adolescence were not only related to the start of the formal reasoning stage around age 12, but also depended on the ability to retrieve alternatives from memory and to inhibit these alternatives when necessary. The authors note that according to other studies (see De Neys and Everaerts, 2008 , for review) not all adolescents will show this ability to inhibit alternatives when they are irrelevant, leading to individual differences in conditional reasoning in adulthood.

These studies therefore suggest that logical reasoning depends on the interplay of the ability to maintain and manipulate information in working memory, the inhibition of irrelevant or incorrect alternatives, and domain-specific knowledge, in addition to the requirements of integrating multiple abstract representations.

3.3. Behavioural measures of relational reasoning development during adolescence

Although, as discussed above, relational processing can be recruited for analogical reasoning, a number of studies have focused more specifically on relational reasoning per se. The relational reasoning demands of a problem can be defined in terms of the number of dimensions, or sources of variation, that need to be considered simultaneously to reach a correct solution. Children under 5 years can solve 0- and 1-relational problems, but fail to solve 2-relational problems ( Halford et al., 1998 ). Early improvements in relational reasoning may reflect a shift from a focus on object similarity to relational similarity ( Rattermann and Gentner, 1998 ). Further improvements during childhood and adolescence may relate to increased relational knowledge or increased working memory capacity ( Crone et al., 2009 , Sternberg and Rifkin, 1979 ; see Richland et al., 2006 , for discussion). Indeed, Carpenter et al. (1990) argued that the processes leading to individual differences on relational reasoning tasks such as the Raven's matrices ( Raven, 1998 ) are primarily the ability to extract abstract relations and to dynamically manage a large set of problem-solving goals in working memory. Thus, for relational reasoning as for logical reasoning, working memory is thought to play an important role in supporting the maintenance of multiple abstract thoughts to allow their comparison and integration.

Prolonged developmental changes in relational reasoning into adolescence have been observed in a few behavioural studies (see also the next section on neuroimaging studies). For example, although their age groups were small, Rosso et al. (2004) showed that accuracy in the matrix reasoning section of the WAIS-III increased with age in the range 9–19-year old. We recently employed a relational reasoning task initially developed by Christoff et al. (2003) , to investigate relational reasoning development during adolescence in a large sample of healthy participants ( Dumontheil et al., 2010c , Experiment 1). The Shapes task required participants to assess whether two pairs of items, which could vary in shape and/or texture, differed or changed along the same dimension. The pairs of items could both show texture differences or both show shape differences, in which case participants were asked to response yes, i.e. the pairs change along the same dimension (match). Alternatively, one pair of items differed in texture while the other pair differed in shape, in which case participants were asked to respond no, i.e. the pairs change along different dimensions (no-match). One hundred and seventy nine female participants aged 7–27-year old participated in the study (same participant as Dumontheil et al. (2010b) ). When comparing the relational integration (or 2-relational) condition of the task to a condition requiring the processing of only 1-relation (either shape, or texture), the results showed a non-linear pattern of improvement in accuracy across age. After an early improvement in accuracy, with 9–11-year olds performing at adult levels, performance dipped in the 11–14-year olds and gradually improved again to adult levels throughout late adolescence. Further analysis of these data using a combined measure of reaction time over accuracy to take into account a potential speed-accuracy trade-off suggests that in fact 2-relational vs. 1-relational performance in this task improved progressively during late childhood and mid-adolescence, with a significant improvement between the 7–9 and 14–17 years old age groups on this combined measure.

3.4. Development of episodic memory

Episodic memory refers to memories for specific episodes previously experienced. Memories for such events are often accompanied by the phenomenal experience of recollective experience ( Tulving, 1983 ). Sander and colleagues have proposed that episodic memory relies on the combination of an associative and a strategic processing component ( Sander et al., 2012 ). Raj and Bell (2010) have reviewed the development of episodic memory formation in childhood extensively and similarly contrast binding and source memory to source monitoring. It is generally believed that by the age of 4 years, children have an episodic memory system in place ( Raj and Bell, 2010 ). The associative component, which relies primarily on mediotemporal and posterior brain regions (e.g. Simons and Spiers, 2003 ; see Raj and Bell, 2010 for review) is relatively mature by middle childhood ( Gathercole, 1998 , Rhodes et al., 2011 ). However, some studies still show continuing improvements in episodic memory performance between late childhood and adulthood ( DeMaster and Ghetti, 2013 , Lorsbach and Reimer, 2005 ), in particular in tasks requiring memory for combined features (e.g. objects and locations) ( Lorsbach and Reimer, 2005 ).

In contrast, the strategic component, which refers to top-down control processes involved in the organisation and monitoring of memory representations mainly relies on prefrontal brain regions ( Miller and Cohen, 2001 ), particularly for tasks requiring binding of feature information and source memory retrieval. This component shows more prolonged development in childhood, adolescence and until young adulthood. For example, in a longitudinal study following children between 4 and 10 years of age, different developmental timecourses were observed for the memory for individual items vs. a combination of source and facts ( Riggins, 2014 ). Overall, younger children perform worse than adolescents on source discrimination tasks, and adolescents perform themselves worse than adults ( De Chastelaine et al., 2007 , DeMaster and Ghetti, 2013 , Ghetti et al., 2010 ). Adults also perform better than children and adolescents on tasks requiring a recollection judgement, i.e. requiring the specific contextual details of a memory episode, but not in tasks requiring a recognition judgement, i.e. knowing that an item has been previously encountered ( Billingsley et al., 2002 , Ofen et al., 2007 ). Sander et al. (2012) showed that, similarly to adults, children and adolescents could benefit from mnemonic instruction and training in an episodic memory task, highlighting the role of strategy implementation in episodic memory performance.

Executive function (EF) abilities have been suggested to play a role in episodic memory performance. Indeed, higher EF scores are associated with better performance on source memory tests, and lower rates of source memory errors, particularly lower false alarm rates. Frontal lobe function may support the integration of item and source information, content and context, during encoding, and may also support contextual memory retrieval by guiding the search and monitoring processes and inhibition of feelings of familiarity (see Raj and Bell, 2010 for review). The specific role of RLPFC in episodic memory may be in supporting the coordination of search and monitoring processes during episodic memory retrieval ( Spaniol et al., 2009 ), with BOLD signal increases in RLPFC possibly specific to intentional rather than incidental retrieval ( Fletcher and Henson, 2001 , Simons and Spiers, 2003 ).

Little research has been done to investigate the role played by EF during episodic memory development. In young children (4 and 6 years old), Rajan et al. (2014) found that language ability, and a composite measure of EF (combining inhibitory control, working memory and set shifting) uniquely predicted fact and source memory retrieval, however when the EF measures were considered individually, the only significant association was that inhibitory control predicted source recall. Rhodes et al. (2011) found that 10 and 11-year old children, but not 8 and 9-year olds, showed a relationship between episodic memory and verbal working memory, which differed from the observed relationship between episodic memory and spatial working memory in adults, and thus suggested that the relationship between episodic memory and executive (frontal) components of episodic memory retrieval changed over the period of adolescence. Picard et al. (2012) also found that EF contributed to changes in temporal and spatial context aspects of episodic memory during adolescence. Ruffman et al. (2001) found that in children aged 6, 8 and 10 years old, working memory was related to accuracy in source monitoring judgements, while inhibitory control uniquely predicted false alarm rates.

3.5. Development of prospective memory

Prospective memory (PM) is the ability to “remember to remember”, and is particularly difficult when an individual is simultaneously engaged in other activities. Research suggests that active strategical monitoring is more likely to be required when the PM cues are non-focal, non-distinctive, when the task is non-demanding and non-absorbing, when high importance is given to the PM task and the interval retentions are short ( McDaniel and Einstein, 2007 ). Although a number of studies have now investigated the development of PM in childhood, fewer studies have investigated later development during adolescence ( McDaniel and Einstein, 2007 ).

Event-based PM can be observed in preschool aged children (e.g. Guajardo and Best, 2000 ), however performance tends to be poor when the ongoing task needs to be interrupted (e.g. Kliegel et al., 2008 ) or when the cue is non-focal, suggesting that children aged 5 or younger have not developed strategic monitoring processes or do not have the attentional resources to deploy them during ongoing task performance (see also McDaniel and Einstein, 2007 for review). Event-based PM continues to develop as children become more able to use external reminders to cue prospective remembering and to interrupt ongoing task performance when necessary ( Kliegel et al., 2008 ). Time-based PM requires greater strategic monitoring than event-based PM. Although time-based PM has also been observed in young children (5–7-year olds, Aberle and Kliegel, 2010 ), it tends overall to be associated with poorer performance than event-based PM (e.g. in 7–12-year-olds Yang et al., 2011 ). Time-based PM has been shown to continue to develop in late childhood and early adolescence ( Yang et al., 2011 ) as children become increasingly proficient at using time-checking strategies ( Kerns, 2000 , Mackinlay et al., 2009 , Voigt et al., 2011 ).

Developmental changes in PM performance are also observed further into adolescence, with more correct event-related PM responses made by adults than adolescents (aged 12 in Zöllig et al. (2007) ; aged 14 in Wang et al. (2006) ; but no difference observed with 13–14-year olds in Zimmermann and Meier (2006) ). In a large online study, Maylor and Logie (2010) found (using a single event-based PM trial) that performance peaked in late adolescence (16–19-year old) and that females outperform males in early adolescence. Ward et al. (2005) showed that adolescents detected more PM cues than children, with similar performance to adults, however they relied more than adults on a remembering strategy described as “Thought about all the time/looked out for the cues”, while adults used more frequently a strategy described as “Remembered only when saw the cues”. This indicates that to achieve a similar performance, adolescents needed to use a more active monitoring strategy than the adults. In a realistic time-based PM task requiring participants to remember to take baking cakes out of an oven while playing a video game, 14-year-olds were better than 10-year-old s , even though both age groups were able to deploy strategic clock-monitoring strategies ( Ceci and Bronfenbrenner, 1985 ). Consistent with the greater need for strategic monitoring, the development of PM abilities is mainly observed during adolescence when non-focal cues are used ( Wang et al., 2011 ).

The realisation of delayed intentions is thought to rely on a prospective component, the detection or recognition of prospective cues, but also a retrospective component, the retrieval of an intention from memory following the recognition of a prospective cue ( Simons et al., 2006 ). The retrospective component is likely to share many of the processes that support episodic memory, in particular the retrieval of contextual information from long-term memory. Zöllig et al. (2007) found that adolescents made more confusion errors than young adults, which the authors argue indicates that the retrospective component of PM is less efficient in adolescents. Similarly, Yang et al. (2011) report that 7–8-year-olds missed PM cues more often than 11–12-year olds, while 9–10-year olds showed a higher frequency of confusion (false-alarm and wrong responses) than 11–12-year olds suggesting differential developmental patterns of the PM and retrospective memory components. Maylor and Logie (2010) similarly observed earlier development of PM performance compared to retrospective memory performance in a lifespan study.

Successful PM is thought to rely on a range of other executive skills, however evidence is mixed regarding which aspects of EF are most relevant to PM development. A few studies have investigated this with time-based PM tasks. Aberle and Kliegel (2010) found that PM performance in 5–7-year olds was associated with processing speed and working memory. In older, 7–12-year old children, Mackinlay et al. (2009) found that the majority of the developmental changes in PM performance could be explained by planning and task switching performance measures, while Mäntylä et al. (2007) found children aged 8–12-year old achieved similar accuracy to adults in a time-based PM task by checking the clock more often, and that while in children inhibition and updating (within a single “supervision” factor), but not shifting, predicted clock monitoring frequency, in adults they predicted timing error.

To summarise, similarly to the investigations of logical and relational reasoning, these studies highlight the role of working memory in supporting temporally abstract thinking. In addition, good performance on prospective and episodic memory tasks may depend on the use of appropriate strategies, themselves dependent on the ability to extract and evaluate abstract information regarding task rules, goals and performance monitoring. It is this higher level of abstraction, either in the relational or temporal domain, which is thought to be specific to RLPFC ( Badre, 2008 ).

4. Functional neuroimaging studies of abstract thinking development

This section reviews the functional MRI findings on the development of abstract thinking during adolescence. The focus will first be on research on relationally abstract thinking, reviewing studies which have investigated the orientation of attention towards self-generated thoughts and the manipulation and integration of relations. Second, I will discuss findings related to the processing of temporally abstract thoughts, reviewing studies of episodic memory retrieval and prospective memory, although the evidence is more limited for the latter.

4.1. Neuroimaging study of the development of the flexible selection of self-generated thoughts

On the basis of studies in adults, Burgess et al. (2007a) have suggested that RPFC supports the flexible orientation of attention towards perceptually-derived information or self-generated thoughts. In a recent study, the Alphabet task described above, which contrasts SI and SO phases with very similar task requirements, was tested in a smaller group of participants aged 11–30 years old using functional MRI (fMRI). Two comparisons were performed using this task ( Dumontheil et al., 2010b ): SI vs. SO thought manipulation and switches between SO and SI phases versus switches of the colour of the letter stimuli. In this sample of 37 participants, the difference in performance between SI and SO trials did not change with age, however participants did become faster in the SO/SI switch trials with age. The comparison of SI vs. SO thought manipulation led to increased BOLD signal in a large fronto-parietal network of regions that extended into RLPFC bilaterally. Among this network, only the left anterior insula showed developmental changes, with a decrease in activation with age, which was independent of individual differences in performance. The comparison of SO/SI switches versus Colour switches led to a much smaller network of brain regions including the right superior RLPFC, precuneus and superior temporal gyrus ( Fig. 2c ). In this comparison only the RLPFC cluster showed a trend for a decrease in activation with age, similarly not accounted for by individual differences in performance ( Fig. 2c ).

4.2. Neuroimaging studies of visuospatial relational reasoning development

Neuroimaging studies in adults have shown that a fronto-parietal network of brain regions is recruited during relational integration, i.e. when solving 2-relational problems, with activation in RLPFC, and in particular left RLPFC, specific to relational integrational demands ( Bunge et al., 2009 , Christoff et al., 2003 , Smith et al., 2007 , Wendelken et al., 2012 ). Four recent studies have investigated the development of relational reasoning between late childhood and adolescence or adulthood using fMRI ( Crone et al., 2009 , Dumontheil et al., 2010c , Eslinger et al., 2009 , Wendelken et al., 2011 ). These four studies used paradigms of relational processing in the visuospatial domain. Dumontheil et al. (2010c) and Wendelken et al. (2011) used very similar tasks and compared 2-relational (i.e. relational integration), 1-relational, and fixation conditions. Crone et al. (2009) used problems derived from the Ravens Progressive Matrices ( Raven, 1998 ) and included an additional 0-relational condition and a simple orientation of arrows task as baseline. Eslinger et al. (2009) used coloured geometrical shape sequences as stimuli and compared 2-relational and 1-relational conditions.

In terms of behaviour, Crone et al. (2009) found that 8–12-year old made more errors, but were not slower, than 18–25-year olds in 2-relational than 1-relational trials; Dumontheil et al. (2010c, Experiment 2) found that 11–14-year olds responded faster than 14–18-year olds in 2-relational than 1-relational trials, but neither group differed from the adult group, and there was no age group difference in accuracy; Wendelken et al. (2011) did not observe age differences in 2-relational vs. 1-relational performance over the age range of 7–18-year old using age as a continuous variable; Eslinger et al. (2009) do not report analyses of performance changes in the 8–19-year age range they studied. Thus the performance findings are mixed in these studies and performance was typically included as a covariate in the analyses.

Neuroimaging results of the first three studies, with a particular focus on the RLPFC findings, are described in Fig. 3 . Crone et al. (2009) found increased specificity for 2-relational vs. 1-relational problems between childhood and adulthood in the left RLPFC ( Fig. 3a ) in the later part of the trial period, and increased specificity for 2-relational vs. 1-relational problems with age within the child group, aged 8–12-year old. Performance was not included as a covariate in these analyses, however the authors suggested that the fact that the left RLPFC in children showed increased BOLD signal in 2-relational trials compared to 1-relational in the initial part of the trial may be associated with the poorer performance observed in children in 2-relational trials. Dumontheil et al. (2010c) observed a trend for an increase in activation in the left RLPFC in 2-relational vs. 1-relational trials between early - and mid-adolescence, and a subsequent decreased activation in this region between mid-adolescence and adulthood ( Fig. 3b ). The early- to mid-adolescence increase did not remain when performance was included as covariates, while the mid-adolescence to adulthood increase was only partially accounted for by accuracy differences. Wendelken et al. (2011) found decrease activation with age in 1-relational trials in the left RLPFC, which led to increased activation in 2-relational vs. 1-relational trials between the ages of 6 and 18 years old ( Fig. 3c ). This developmental effect remained significant when performance was covaried. Finally, Eslinger et al. (2009) report increases with age between late childhood and adolescence in the parietal cortex bilaterally and decreases in age across large parts of the frontal cortex, but no specific findings in RLPFC. The development of the relational integration of semantic stimuli will be described below, before a possible general pattern of developmental change observed in these studies is discussed.

Increased specificity of left RLPFC activation for relational integration (2nd order vs. 1st order relational processing) during development. Although the three studies summarised here used slightly different tasks, methods and age groups, the overall pattern shows an increased specificity of left RLPFC activation, in particular between late childhood and mid-adolescence. (a) RLPFC activation observed in adult ( N = 17, age 18–25) and children ( N = 15, age 8–12) performing problems following the general form of the Raven Progressive Matrices test ( Raven, 1998 ), with a varying number of dimensions to be integrated. On the left are shown activations related to 1st order relational processing (REL-1 > REL-0) and relational integration (REL-2 > REL-1) in adults ( p < .001 uncorrected) and children ( p < .005 uncorrected) in the 8–16 s interval of a timecourse analysis. On the right are plotted the timecourses of activation from left RLPFC regions of interset in adults and children. In the later part of the timecourses, there was a significant interaction between age group and condition (grey highlight), with activations greater in REL-2 than REL-1 in adults, and greater in REL-1 than REL-0 in children (adapted from Crone et al., 2009 ). (b) Left RLPFC activation observed in three groups of children and adolescents (total N = 85) performing a task requiring 1st or 2nd order visuospatial relational processing. Analyses using age as a continuous variable show a significant decrease in left RLPFC associated with 1st-order relational processing only, resulting in a significant age × condition interaction (adapted from Wendelken et al., 2011 ). (c) Left hemisphere activation observed in a group of adult ( N = 13, age 22–30) and adolescent ( N = 24, age 11–18) participants performing a similar task to (b). In the left RLPFC, Relational > Control activation, i.e. that specific to 2nd vs. 1st order relational processing, increased marginally between early and mid-adolescence (#), while it decreased between mid-adolescence and adulthood (*) (adapted from Dumontheil et al., 2010a , Dumontheil et al., 2010b , Dumontheil et al., 2010c ).

4.3. Development of relational integration of semantic stimuli

Another study also investigated the development of relational integration, however the paradigm was an analogical reasoning task requiring the integration of semantic information ( Wright et al., 2008 ). Stimuli were pictures of objects. In the analogical condition participants were , for example , presented with a bee and a bee's nest, and a spider, and had to pick the correct matching object (a spider's web) among other items. In the control semantic condition the participant had to pick the most closely related object to a presented target object (e.g. a baseball for a baseball bat). A group of 6–13-year old children and a group of 19–26-year old adults participated in this study. The children/young adolescents were overall slower and made more errors than the adults, and also made disproportionally more errors in the analogical problems. In addition, children's RT was affected to a greater extent than adults by lure which were semantically vs. perceptually related to one of the stimulus items. Overall the comparison of analogical and semantic problems did not show increased BOLD signal in RLPFC. However, further analyses showed (1) increasing RLPFC activation with age in children both for semantic and analogical problems, and (2) in adulthood, greater RLPFC activation in the right RLPFC associated with greater accuracy in analogical problems. The authors argue this suggests that RLPFC is first increasingly involved in the processing of 1-relational (semantic) and 2-relational (analogical) problems, while in adulthood, its activation becomes more specific to relational integration, i.e. the analogical problems. In addition, Wright et al. (2008) similarly to Crone et al. (2009) observed timecourse differences in RLPFC activity between the children and the adults, with respectively later and more prolonged activation observed in children.

The use of a paradigm recruiting the manipulation of semantic relations raises the question of the role of verbal abilities in relational reasoning, including visuospatial reasoning. As discussed below, a recent study investigated the domain specificity of relational integration ( Wendelken et al., 2012 ), comparing visuo-spatial and semantic variants of the Shapes task described above. The results indicated that both tasks recruited left RLPFC specifically for the relational integration condition vs. the processing of two relations without integration. This left hemisphere-specificity of relational integration activity may be related to a verbal recoding during relational reasoning. In terms of development, it has been shown that after age 7 children tend to recode visuospatial or pictorial information in a verbal format in working memory tasks ( Conrad, 1971 , Flavell et al., 1966 ), and that these processes are related to their use of self-regulatory private speech ( Al-Namlah et al., 2006 ). This shift to phonological recoding has been suggested to be part of a general transition towards verbal mediation of cognitive processes ( Ford and Silber, 1994 , Hitch et al., 1991 ). Articulatory suppression has been shown to affect performance of executive functions tasks more broadly (e.g. in task switching ( Baddeley et al., 2001 ), or Tower of London tasks ( Wallace et al., 2009 )) and a diminished use of inner speech among individuals with autism spectrum disorders is thought to contribute to the executive dysfunction associated with these disorders ( Wallace et al., 2009 , Whitehouse et al., 2006 ). In addition, a large-scale lesion study in adults showed that performance deficits on the Raven's Colored Progressive Matrices, which is considered to be a non-verbal test of reasoning, were associated with lesions in temporal regions essential for language processing, as well as in the left inferior parietal lobule ( Baldo et al., 2010 ).

Therefore, current results suggest that relational reasoning in adults relies on verbal recoding of the relations and specific activations in the left RLPFC, however whether verbal recoding becomes more prevalent with age during relational reasoning, as in certain EF tasks, has not yet been investigated, and more research will be necessary to further explore these issues.

4.4. Increasing specificity of RLPFC activation for relational integration during development

A common overall pattern of the studies described above was of an increased activation in 2-relational problems vs. 1-relational problems between childhood and adolescence, which may be specific to the left RLPFC. However, this pattern of increased specialisation may be similar in a broader network of brain regions. Indeed, Crone et al. (2009) found that left dorsolateral prefrontal cortex (DLPFC) and left parietal cortex showed similar increased specialisation of activation for 2-relational trials vs. 1-relational trials when comparing children and adults. Wendelken et al. (2011) also found increased specialisation, although weaker, in bilateral intraparietal lobules, but not in the DLPFC. When comparing adolescents to adults Dumontheil et al. (2010c) did not find age effects in either DLPFC or parietal cortex. It is possible that only more sensitive analyses looking at BOLD signal timecourse or including a large number of children and adolescent participants may be able to pick up specialisation of brain activation in these regions.

It is as yet unclear how much this increased specialisation may relate to changes in accuracy and reaction times in 2-relational trials. However, the pattern suggests specialisation of left RLPFC, and potentially DLPFC and parietal cortex for relational integration compared to relational processing during adolescence. Only one of these studies compared later adolescence to adulthood and the findings showed decreased activation in the 2-relational vs. 1-relational comparison ( Dumontheil et al., 2010c ), which was partly related to accuracy differences between these age groups.

The pattern of increasing specialisation of brain activation for relational integration was driven in some studies by decreasing activation for relational processing, which highlights the complexity of investigating fMRI data developmentally. In particular, it is unclear whether increased activation (e.g. in WM task, Klingberg et al., 2002 ) or decreased activation (e.g. in response inhibition tasks, Tamm et al., 2002 ) reflect “more efficient” neural processing. One interpretation is that increased activation reflects greater specialisation of the brain region for a particular cognitive process, while decreased activation may reflect the fact that with more efficient neural processing in other brain regions or increased connectivity between regions, a particular brain region is no longer necessary for a particular cognitive process (e.g. RLPFC for the processing of single relations). In this context, as is true in general for fMRI studies, the specific contrast investigated is particularly relevant, for example whether one is contrasting relational integration (2-Rel) to relational processing (1-Rel) or to a fixation control condition. Although RLPFC did not show an increased BOLD signal during a Raven reasoning task at the corrected threshold used, a recent study in adults by Perfetti et al. (2009) speaks to the fact that lower performance or abilities overall may be associated with less specific brain activations in fronto-parietal regions. Comparing high and low fluid intelligence (gf) participants, Perfetti et al. (2009) found that while the high gf group showed increased fronto-parietal activation in the analytical (more complex) problems compared to the figural problems, the low gf group showed greater activations in the figural condition than the high gf group, and a tendency for the activations in the analytical condition to be lower than in the figural condition. In the visual analogy task described above, Wright et al. (2008) found that in adults the specificity of RLPFC activations for relational integration was positively correlated with accuracy on the task. In another study, it was shown that high gf participants showed greater parietal activations than low gf participants in a relational integration task ( Lee et al., 2006 ). This later result highlights the importance of processing in brain regions other than RLPFC for the performance of relational integration. The parietal cortex has been suggested to support the identification of the visuo-spatial relations that are the basis of relational integration ( Ferrer et al., 2009 ).

In summary, fMRI studies have demonstrated changes in RLPFC activation during adolescence during the manipulation and integration of self-generated thoughts and their relations. The overall pattern suggests increasing specialisation of activations in the left RLPFC in particular, but also in the DLPFC and parietal cortex, which are thought to support the processing of single relations. More work will be needed to assess how these observed functional changes relate to developmental changes in performance. One factor that has been proposed to play a role is brain structure, which will be discussed in Section 4.7.

4.5. RLPFC and episodic memory retrieval during development

RPFC has been suggested to play a role in the control, and possibly processing, of temporally extended representation ( Badre, 2008 , Fig. 1 ), as suggested by its increased activation during branching or multitasking ( Badre and D’Esposito, 2007 , Braver and Bongiolatti, 2002 , Koechlin et al., 2003 ), prospective memory ( Benoit et al., 2011 , Burgess et al., 2007b ), episodic memory, in particular episodic memory retrieval ( Dobbins et al., 2004 , Spaniol et al., 2009 , Turner et al., 2008 ) and mindwandering ( Christoff et al., 2009a , Christoff et al., 2004 , Dumontheil et al., 2010a , Schooler et al., 2011 ). Studies investigating the development of the neural correlates for episodic memory have tended to focus on the encoding phase of episodic memory, rather than episodic memory retrieval ( Chiu et al., 2006 , Ghetti et al., 2010 , Ofen et al., 2007 ). However a few very recent studies investigated episodic memory retrieval using fMRI and event-related potentials (ERPs).

Findings regarding the development of the neural correlates of episodic memory in the hippocampus have been mixed. In contrast, more consistent findings have been observed in the frontal and parietal cortices thought to support memory retrieval (see DeMaster et al., 2013 for review). Paz-Alonso et al. (2008) focused on the development of true and false recognition and tested children age 8 and 12-year old, and 19–23-year old adults. The results showed region-specific developmental changes in the MTL, bilateral DLPFC, posterior parietal cortex, and right RLPFC. Adults, but not children, exhibited strongest right RLPFC activation for hits and those trials where a semantically-related lure was correctly rejected, i.e., according to the authors, those conditions in which monitoring was both required (due to the presentation of semantically relevant stimuli), and successful (leading to a correct response) ( Fig. 4a ).

Developmental changes in RLPFC activation during episodic memory tasks. (a) Neural correlates of episodic memory retrieval. Top left: increased activation with age associated with hit trials compared to trials with correctly rejected semantically unrelated lures; top right: increased activation with age associated with trials where a semantically related (critical) lure vs. an unrelated lure is correctly identified; bottom: region of interest analysis suggesting that in adults right RLPFC is involved in the monitoring of performance during episodic memory retrieval, with greater activation associated to correctly recognised semantically relevant items (hits or critical lures). CR: correct rejections; FA: false alarms; aPFC: anterior prefrontal cortex (adapted from Paz-Alonso et al., 2008 ). (b) Region of interest analysis of left RLPFC activation during source memory retrieval. The condition × age group interaction was significant, revealing increased RLPFC activation for increasingly amount of recollected information (correct border = both drawing and colour were remembered (source memory); incorrect border = the drawing but not its border colour was remembered (item memory); Miss = error trial; correct rejection = drawing correctly identified as not presented before) in the adults, but not the children, who showed similar RLPFC recruitment across trial types (adapted from DeMaster and Ghetti, 2013 ). (c) Region of interest analysis of left RLPFC activation during source memory retrieval. The condition × age group interaction was significant, revealing increased RLPFC activation for increasingly amount of recollected information (correct spatial recall = both drawing and its location were remembered (source memory); incorrect spatial recall = the drawing but not its location was remembered (item memory); Miss = error trial; correct rejection = drawing correctly identified as not presented before) in the adults, with a difference between source and item memory in the 10–11-year olds, but activation for item memory only for the 8–9-year olds (adapted from DeMaster et al., 2013 ).

DeMaster and Ghetti (2013) scanned children aged 8–11-year old and adults aged 18–25-year old who were asked whether a drawing shown on the screen had been presented before or not (item memory) and what colour was the border of the drawing during its first presentation (context or source memory). Activations associated with successful retrieval across age groups were observed in the right MTL, left posterior parietal cortex, left RLPFC and precuneus. In the RLPFC activation was observed across conditions and was unspecific to successful retrieval in children, while in adults the activation was greater for trials where the colour-drawing pair was successfully remembered than when the drawing was recognised but the colour not remembered, and in turn these trials show greater activation than for drawings correctly recognised as new ( Fig. 4b ).

In a second study, DeMaster et al. (2013) used a spatial context (drawing presented on the left or right of the screen) rather than a colour border and scanned children aged 8–9 or 10–12 years old and adults. Similarly to their previous study, DeMaster et al. (2013) observed an age × condition interaction in the left RLPFC (with a similar but weaker pattern in the right RLPFC). Adults showed greater activation for correct than incorrect source memory retrieval, and more activation for incorrect source memory retrieval (but correct old item recognition) than for correctly rejected items (new items) ( Fig. 4c ). In 10–11-year-olds, only the comparison correct vs. incorrect source memory retrieval was significant, while in 8–9-year olds activation was greater for correctly recognised items than for items correctly identified as new ( Fig. 4c ). A similar pattern of developmental changes was observed in the left parietal cortex and precuneus, but differed in the insula and DLPFC. The similar pattern observed between the parietal cortex and RLPFC further reinforces the idea that these two regions interact strongly during abstract thinking, as suggested in the relational abstract thoughts studies described above and in Section 5 below. Although DeMaster et al. (2013) point out that these two regions have been associated with different cognitive processes in the past, they suggest that further work needs to be done to disentangle their role during episodic memory retrieval development.

Contrary to the three studies described above ( Fig. 4 ), Güler and Thomas (2013) did not observe developmental changes in RLPFC during episodic memory retrieval. However this study compared 9–10 and 12–13-year olds children and did not include an adult group, which may have limited the size of the developmental effect. In addition, the paradigm used was a paired-associate picture memory task rather than a source memory paradigm. Developmental differences in activation associated with successful recall were instead observed in a more posterior part of the left middle frontal gyrus (area 46/47), right middle temporal gyrus and cerebellum, left inferior parietal lobule and anterior cingulate gyrus ( Güler and Thomas, 2013 ).

To summarise, recent studies investigating episodic memory development using neuroimaging methods show prolonged development of the neural correlates of item and source memory retrieval between late childhood and adulthood, with evidence of increased sensitivity of RLPFC activation to specific components of episodic memory (e.g. source vs. item memory, old vs. new item) in adults compared to children.

4.6. Neuroimaging studies of episodic memory and prospective memory during development

Only two studies have investigated the neural correlates of PM development. Both studies used event-related PM paradigms and collected ERP data. Mattli et al. (2011) tested children (mean age 10.3 years) and younger adults (mean age 31.4 years) (as well as an older adult group not discussed here). The N300 component reflects greater negativity for PM hits than PM misses and ongoing activity trials over the occipito–parietal region of the scalp. It is therefore thought to be associated with the detection of an event-based PM cue in the environment. Mattli et al. (2011) observed no difference in N300 amplitude for PM hits versus ongoing trials between the age group, however while adults showed greater N300 amplitude for PM hits than PM misses, children did not. According to the authors, this suggests that in children cue detection was not necessarily associated with realisation of the intention, possibly reflecting failure of executive processes associated with switching or disengaging from the ongoing activity. Reversely, a parietal positivity discriminated between PM hits and misses in children, but not in adults. No difference between age group was found between a frontal positivity which also discriminated between PM hits and PM misses. In a study including adolescent participants, Zöllig et al. (2007) observed larger N300 amplitudes in adolescents than in adults when a PM intention had to be inhibited, and a larger parietal positivity between 600 and 800 ms when a PM intention had to be executed, as compared to ongoing trials. The latter effect is similar to that observed by Mattli et al. (2011) . Source analyses suggested differences in current density between adolescents and adults for PM execution in mostly posterior brain regions, while ongoing trials were associated with greater right middle frontal gyrus activations in adolescents, which may be associated with some sort of anticipatory processing ( Simons et al., 2006 ). However, adolescents also showed poorer performance in ongoing trials, limiting the inferences that can be made from these results. To summarise, very little neuroimaging research has been done to investigate the development of PM during late childhood and adolescence. Further work, including fMRI studies, will be necessary to inform our understanding of the role played by RLPFC during PM development.

5. Association between structural changes during development and abstract thinking

RLPFC undergoes substantial structural changes during adolescence (see Dumontheil et al., 2008 for review). Research on developmental changes in brain structure have tended to consist of whole-brain analyses and do not typically report analyses in anatomical subdivisons of the frontal cortex. Overall the results show increases in white matter volumes and decreases in grey matter volumes with age in the frontal cortex during adolescence ( Barnea-Goraly et al., 2005 , Giedd et al., 1999 , Shaw et al., 2008 , Sowell et al., 1999 , Sowell et al., 2004 , Tamnes et al., 2010 , Westlye et al., 2010 ). Behavioural and functional changes during development, and in particular late childhood and adolescence, are often interpreted as being a consequence of the structural changes that occur during this period ( Crone and Dahl, 2012 , Luna et al., 2010 , Spear, 2000 ). Decreases in functional activations are considered to reflect developmental reductions in grey matter volume, presumably related to synaptic pruning. Increases are thought to relate to improved and more localised task-specific processing, potentially facilitated by faster long-range connections due to increased axonal myelination and size ( Luna et al., 2010 ). Understanding the link between structural and functional changes is critical in understanding the mechanisms of neurocognitive development, yet very few studies have directly compared structural and functional data within the same individuals (e.g. Lu et al., 2009 , Olesen et al., 2003 , Van den Bos et al., 2012 ). The association between structural changes during development and relationally abstract thinking will be described below, presenting data from recent studies which attempt to integrate brain and behavioural measures. No studies to date have investigated associations between brain structure and temporally abstract thinking during development.

Cortical thickness of RLPFC, in particular in females (e.g. Narr et al., 2007 ), and during adolescence (e.g. Shaw et al., 2006 ), has been shown to be positively correlated with standardised intelligence quotient (IQ). IQ is typically measured using tests such as the Wechsler intelligence scales ( Wechsler, 1997 ), which include a variety of subtests testing verbal and performance intelligence. Some of these tests will require the manipulation of self-generated and abstract thoughts; however, it is as yet unclear whether this accounts for the observed link between RLPFC structure and IQ ( Narr et al., 2007 , Shaw et al., 2006 ). The finding by Shaw et al. (2006) that the developmental timecourse of cortical thickness changes was associated with IQ, rather than cortical thickness in early childhood or in adulthood, stresses the importance of studying developmental trajectories. However, very few research groups have the means to do so using large longitudinal samples and most of the data discussed below are cross-sectional.

Using the datasets described above, collected while participants performed the Alphabet and Shapes tasks ( Dumontheil et al., 2010b , Dumontheil et al., 2010c ), we aimed to test the hypothesis that decreases in functional BOLD signal during adolescence may reflect the concomitant local decreases in grey matter volume. To do so we extracted local grey and white matter volumes in the brain regions showing functional developmental changes and entered these data into multiple regression analyses. The results revealed that the decrease in superior RLPFC during switching between self-generated and perceptually-derived information was not accounted for by local structural changes ( Dumontheil et al., 2010b ). Analyses of the relational integration data from the Shapes task ( Dumontheil et al., 2010c ) provided a different picture, showing that the decreased BOLD signal between mid-adolescents and adults did not remain significant when local structural measures (and performance) were covaried. Further tests were performed to relate structural changes to the connectivity changes observed using dynamic causal modelling (DCM) ( Bazargani et al., 2014 ). Grey matter volume in RLPFC and fixed connectivity (i.e. connectivity in 1-relational trials) between frontal and insular regions were both found to decrease with age. RLPFC grey matter volume was further found to predict short-range fixed connectivity. However, no significant mediation of the effect of age on short-range fixed connectivity by RLPFC grey matter volume was observed ( Bazargani et al., 2014 ). RLPFC grey matter volume in addition predicted 2-relational vs. 1-relational accuracy ( Bazargani et al., 2014 ). In the other study of relational integration development in children and adolescent participants described above, increased functional selectivity in the left RLPFC was partly accounted for by cortical thinning in the left inferior parietal lobule ( Wendelken et al., 2011 ), with a positive correlation between inferior parietal lobule thickness and activation in the left RLPFC in 1-relational trials.

The first two sets of results, within the same participants, provide evidence for the complex relationships between developmental changes in task-related brain activity, performance and local changes in brain structure. Overall the results discussed above suggest that individual differences in grey matter, in RLPFC or the inferior parietal lobule, can play a role in the development of functional networks supporting relational integration. There is less evidence suggesting specific roles of individual differences or developmental changes in white matter in the development of relational reasoning. Indeed, a recent study has shown that developmental changes in whole-brain measures of white matter volume or fractional anisotropy predicted developmental improvements in visuospatial reasoning ability. However, this effect was mediated via processing speed and was not found to be specific to fronto-parietal white matter tracts ( Ferrer et al., 2013 ). This suggests that, contrary to grey matter volume, the influence of structural developmental changes in white matter on reasoning ability may not be region-specific.

6. Questions for future research

6.1. influence of puberty vs. chronological age.

The role of puberty in the developing adolescent brain ( Blakemore et al., 2010 , Crone and Dahl, 2012 ) and whether changes observed during adolescence are a consequence of chronological age or puberty levels has been the topic of a few recent studies investigating structural changes ( Goddings et al., 2014 ) and functional changes during a social cognition task ( Goddings et al., 2012 ). Although in this latter study the functional changes observed in the MPFC were related to age rather than puberty level (in contrast to the functional changes observed in the temporal cortex), very little is known about the effect of puberty stage on the development of abstract thinking and the lateral parts of the prefrontal cortex during adolescence. More generally, there is currently little evidence of gender differences in this age range in functional imaging data (e.g. Hatcher et al., 1990 , Wendelken et al., 2011 ), however the available data is limited as some studies only included participants of one gender (e.g. Dumontheil et al., 2010b , Dumontheil et al., 2010c ), and others did not test for potential gender differences (e.g. DeMaster and Ghetti, 2013 , Crone et al., 2009 ), likely because of sample size limitations. However, structural neuroimaging studies have shown that the RPFC is the region with the greatest difference in rates of cortical thinning between males and females between the ages of 9 and 22 years ( Raznahan et al., 2010 ), and that there are sex differences in the relationship between cortical thickness maturation in the RPFC and in the superior frontal cortex in the same age range ( Raznahan et al., 2011 ). These structural studies suggest investigating the possible consequences of these structural differences over chronological and pubertal development for RLPFC function maturation is warranted.

6.2. Investigation of the role of RLPFC in the development of temporally abstract thinking

As mentioned above, RLPFC has been implicated in prospective memory, episodic memory retrieval and mindwandering, i.e. cognitive processes associated with the manipulation of temporally extended abstract information. Although recent neuroimaging work has started to investigate the neural correlates of episodic memory retrieval, only a couple of ERP studies have investigated PM, and no research has been done on mindwandering development. Future research on these topics will broaden our understanding of the development of adolescents’ ability to retrieve past experience and think about the future, and how these abilities relate to the control of attention towards perceptually-derived vs. self-generated thoughts.

6.3. Abstract thinking in the social domain: the role of medial RPFC

Anatomical studies investigating the cytoarchitectonic properties of RPFC (e.g. Öngür et al., 2003 ) and meta-analyses of fMRI data ( Gilbert et al., 2006b , Van Overwalle, 2009 ) suggest a distinction between the medial and lateral aspects of RPFC. Activations along the medial wall have mainly been observed in social cognition tasks, in particular those involving theory of mind, or mentalising, i.e. our ability to understand our own and other people's mental states (except in the most polar part of Brodmann area 10, see Gilbert et al., 2006b , Van Overwalle, 2009 ). In some situations another person's intention may be quite apparent on the basis of their overt behaviour, and our own mental states or feelings may be salient via e.g. increased heart beat frequency, sweat or stomach-ache in response to stress. In such cases , mentalising would rely on perceptually-derived information. In other situations, one may need to retrieve from episodic memory past behaviour of a friend, or to retrieve social scripts and semantic information in order to judge how they should respond to a friend's comment or behave in a novel social situation. In such cases, one would need to manipulate and integrate self-generated information. Along these lines, Van Overwalle (2009) in his review describes MPFC “as a module that integrates social information across time and allows reflection and representation of traits and norms, and presumably also of intentionality, at a more abstract cognitive level”.

Of particular interest for further research would therefore be the functional relationship between RLPFC and MPFC during abstract thinking, and whether there is anything special about the reasoning and manipulation of social vs. non-social information. A couple of recent studies speak to this. In one study, the storage and manipulation of social information in working memory was associated with activations in both the typical lateral fronto-parietal network associated with working memory and regions of the social brain, including the MPFC and temporo-parietal junction ( Meyer et al., 2012 ). In contrast, the other study, using a relational reasoning task on social information (how pleasant or unpleasant the participant or a participant's friend finds a particular concept), did not observe greater medial PFC activation during relational integration compared to the manipulation of single relations, but did observe left RLPFC activation, consistent with the relational integration studies reported above ( Raposo et al., 2011 ). Note however that neither study included a non-social comparison condition, which would be needed to assess activation patterns that are specific to the manipulation of self-generated information of a social nature.

In terms of development, adolescents typically show increased MPFC activation during social cognition tasks ( Blakemore, 2008 , Crone and Dahl, 2012 ), although we recently showed that a pattern of increasing specialisation for perspective taking compared to the processing of social stimuli could be observed between adolescence and adulthood ( Dumontheil et al., 2012 ). Touching on the relationship between abstract thinking about social vs. non-social information, an older study reported complex links in participants aged 10, 13 and 17-year old between abstract reasoning and self- or other- mentalising measures, which were found to differ according to sex ( Hatcher et al., 1990 ). Finally, results of a recent qualitative study suggest that older teenagers coordinate an increasing number of psychological components while telling stories about their family and themselves, and in so doing, create increasingly abstract and coherent psychological profiles of themselves and others ( Mckeough and Malcolm, 2010 ). A better understanding of the link between abstract thinking and social cognition during development may thus inform our understanding of the development of the self-concept during adolescence.

7. Training studies and implications for education

Fluid intelligence can be defined as the use of deliberate mental operations to solve novel problems. These mental operations include drawing inferences, concept formation, classification, generating and testing hypothesis, identifying relations, comprehending implications, problem solving, extrapolating, and transforming information. Thus, fluid intelligence is tightly linked to abstract thinking and relational integration ( Ferrer et al., 2009 ). Fluid intelligence is thought to be an essential component of cognitive development ( Goswami, 1992 ) and the basis for acquisition of abilities in various domains during childhood and adolescence ( Blair, 2006 ; see Ferrer et al., 2009 for review). Fluid intelligence in childhood predicts achievements at school (e.g. in maths during early adolescence ( Primi et al., 2010 )), university and in cognitively demanding occupations ( Gottfredson, 1997 ). Fluid intelligence is therefore a predictor of learning , especially in novel and complex situations. Consequently, a better understanding of the development of abstract thinking and reasoning during late childhood and adolescence , both in terms of behaviour and neuroscience, may have implications for education.

Of particular relevance are recent studies assessing the training of abstract thinking or reasoning skills. A few studies have investigating fluid reasoning training during childhood. For example, computerised non-verbal reasoning training was shown to improve fluid intelligence in a large sample of 4-year olds ( Bergman Nutley et al., 2011 ), and fluid reasoning training emphasising planning and relational integration led to substantial improvement on performance IQ, but not speed of reasoning , in children aged 7–9-year old from low socioeconomic backgrounds ( Mackey et al., 2011 ). A couple of studies in young adults further report that students taking a US Law School Admissions Test (LSAT) course offering 70 h of reasoning training showed a strengthening in fronto-parietal and parietal-striatal resting state connectivity compared to matched control participants ( Mackey et al., 2013 ), as well as changes in white matter structure in the frontal and parietal lobes ( Mackey et al., 2012 ). Very little work has been done investigating training of reasoning in adolescents, although Chapman and Gamino (2008) have developed the Strategic Memory and Reasoning Training (SMART) programme, designed to improve top down reasoning skills. The aim of this programme is to teach children how to learn rather than what to learn, by supporting higher-order abstraction of meaning from incoming details and world knowledge, and there is promising evidence that this training programme leads to improved gist-reasoning and fact-learning ability ( Gamino et al., 2010 ).

Whether children and adolescents may benefit more from training than adults will be an important area of research. Relatively little is currently known about developmental differences in brain plasticity in response to training interventions, however research in this domain has greater potential for tailoring appropriate training interventions to different age groups (see Jolles and Crone, 2012 for discussion). Both childhood and adolescence may be “sensitive periods” for teaching, as significant brain reorganisation is taking place during these periods. Perhaps the aims of adolescents’ education might usefully include a focus on abilities that are controlled by the parts of the brain that undergo most change during adolescence, including those described in this review: abstract thinking and reasoning, and the ability to focus on one's own thoughts in spite of environmental distraction. However, training intervention may be limited by the current level of structural brain development and cognitive capacity (as pointed out in Jolles and Crone, 2012 ), in particular for those training interventions based on strategy rather than repeated performance.

8. Conclusion

Rostrolateral prefrontal cortex supports a wide range of cognitive processes, which may have in common their requirement of retrieval, maintenance, manipulation and/or integration of self-generated, or stimulus-independent thoughts, considered broadly here as abstract thoughts, either relationally abstract, or temporally abstract. This review focused on summarising the evidence from behavioural and neuroimaging studies of the development of RLPFC and its associated functions. Behavioural studies have shown prolonged changes in the speed and accuracy of attending towards and processing self-generated information, in particular in reasoning tasks. These developmental changes appear to build on working memory and inhibitory control functions, as well as the acquisition of domain-specific knowledge. This dependence on the maturation of other aspects of cognition, including working memory and inhibitory control, which are dependent on more posterior regions of the frontal cortex, reinforces the idea that the maturation of RLPFC function will be relatively more protracted. Certain aspects of episodic memory and prospective memory, namely those that rely on implementation of strategies for recollecting source memory, and for time-checking in prospective memory tasks also continue to develop during adolescence. Neuroimaging evidence suggests a possible developmental pattern of increasing specialisation of RLPFC for the integration of relational information, with complex relationships between developmental changes in structure, performance and brain activation, and increasing specialisation for the retrieval of source memory, and item memory information, compared to the processing of new items. A strong relationship between RLPFC and the parietal cortex was apparent across tasks, and further work, in particular using connectivity analyses, may inform our understanding of how the interplay between these brain regions permits the increasingly successful integration of relationally and temporally abstract thoughts over development. Future research could inform our understanding of development of reasoning and abstract thinking in the social domain, and whether functions associated with the RPFC could be trained, with potential benefits in the domain of education.

Acknowledgements

I thank Prof. Uta Frith for inviting this review and Prof. Sarah-Jayne Blakemore for her continuing support.

Available online 12 August 2014

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What is abstract thinking? 10 activities to improve your abstract thinking skills

Have you ever been in a meeting and proposed a unique solution to a problem? Or have you ever been faced with a difficult decision and thought about the potential consequences before making your choice?

These are examples of abstract thinking in action. Everyone uses abstract thinking in day-to-day life, but you may be wondering — what is abstract thinking?

Abstract thinking is the ability to comprehend ideas that aren't tangible or concrete. It's a crucial skill for problem-solving, creativity, and critical thinking — and the best part is that it can be developed and strengthened with practice.

In this article, we'll explore the concept of abstract thinking and offer some simple ways to become a stronger abstract thinker in everyday life. With some practice, you can become an expert problem-solver and use conceptual thinking to your advantage.

What is abstract thinking?

Abstract thinking is a cognitive process that allows us to think beyond observable information and deal with concepts, ideas, theories, and principles. By thinking outside of our existing knowledge, we can come up with solutions that aren't immediately obvious. This type of thinking is essential for problem-solving, decision-making, and critical thinking .

Abstract thinking enables us to generate new ideas, connect unrelated concepts, and look at the bigger picture. It also involves contemplating sentiments such as love, freedom, and compassion. These concepts aren’t concrete and can have different interpretations. By using abstract thinking, we can gain a deeper understanding of these concepts and their different meanings.

Abstract thinking is also crucial to creativity, innovation, and advanced problem-solving. It allows us to think beyond the surface level of a problem and come up with unique solutions. This can be especially important in fields such as science and technology, where new breakthroughs often require fresh perspectives and innovative thinking.

In addition, abstract thinking is a vital skill for personal development, enabling us to think beyond our immediate environment and beliefs and consider different perspectives. This allows individuals to make better decisions, be more receptive and open to change, and be more creative.

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Abstract vs. concrete thinking

We can best understand abstract thinking by knowing what it's not — concrete thinking. Concrete thinking is understanding and processing observable and directly experienced information. It's often associated with basic sensory and perceptual processes, such as recognizing a familiar face or identifying a physical object by its shape.

On the other hand, abstract thinking is the ability to understand and process information that isn’t directly observable or experienced. Abstract thinking is often associated with higher-level cognitive processes, such as decision making and critical thinking.

For example, if you’re asked what a chair looks like, concrete thinking would involve picturing it and what it's typically used for. By contrast, abstract thinking would involve considering what a chair could symbolize or how it could be used differently than what is traditionally accepted.

The two types of thinking aren’t mutually exclusive — instead, they complement each other in the cognitive process. We need concrete and abstract thinking skills to effectively process information and make informed decisions.

How is abstract thinking developed?

Abstract thinking is a cognitive process that develops over time, beginning in childhood and continuing into adulthood. The psychologist Jean Piaget , known for his theory of cognitive development, proposed that children go through different stages of mental growth. This begins with the sensorimotor stage, in which infants and young children learn through their senses and motor skills and develop concrete thinking skills. In their later years, they develop more advanced cognitive abilities, including abstract thinking.

During childhood, abstract thinking develops as children use the cognitive approach to learning to grasp new concepts and skills. They start to understand and manipulate abstract concepts such as numbers, time, and cause and effect. As they observe the world around them, they use what they know to make sense of what is happening and explore other possibilities.

A learning disability, mental health condition, or brain injury can, however, affect abstract thinking. Among these are psychological illnesses like schizophrenia , developmental disorders like autism, ADHD, and dyslexia, and physical illnesses like stroke, dementia, and traumatic brain injury. These individuals may have difficulty understanding and manipulating abstract concepts and require additional support to develop their abstract thinking skills.

As adults, we continue to refine our abstract thinking skills through practice. We can become adept at problem-solving and critical thinking by regularly engaging in activities that require abstract thought. These activities include brainstorming, reading, writing, playing board games, and exploring creative projects. Factors such as experience, education, and environment all play a role in the development of abstract thinking, and it's essential to continue challenging and exercising our cognitive learning skills to maintain and improve abstract thinking.

Why is it important to learn to think abstractly?

Thinking abstractly is a crucial skill that allows us to go beyond surface-level understanding and interpret the deeper meaning of concepts, ideas, and information. It enables us to see the big picture and make connections between seemingly unrelated ideas, which is a crucial thinking tool for problem solving and critical thinking. Additionally, learning to think abstractly can bring numerous benefits in our daily lives and in various fields such as science, technology, engineering, and mathematics (STEM).

For instance, abstract thinking enables us to process information quickly and efficiently on a daily basis. It helps us understand and interpret what people are saying and what is happening around us, which can lead to better decision-making. Abstract thinking is vital in STEM fields for innovation and progress, as it encourages creative thinking and the exploration of new ideas and perspectives.

Furthermore, abstract thinking helps us understand abstract concepts such as justice, freedom, and patriotism. By using analogies and other tools, we can consider what these words stand for, their implications in our world, and how they can be applied effectively in day-to-day life. In this way, abstract thinking helps us make sense of complex ideas and concepts and enables us to navigate the world with greater insight and understanding.

10 tips to improve your abstract thinking skills

Abstract thinking is crucial for problem-solving, creativity, and critical thinking. Fortunately, there are many ways to improve these skills in your everyday life.

1. Incorporate puzzles into your life

Solving puzzles is a great way to practice abstract reasoning and exercise your brain. Whether you enjoy crosswords, Sudoku, or jigsaw puzzles, solving these types of problems improves your ability to think abstractly by requiring you to think critically and strategically to find solutions to issues that aren’t immediately obvious.

2. Learn something new

Your mind engages in the information processing cycle when learning new things. Learning something new allows you to explore different perspectives and understand how the world works. You'll gain new knowledge and practice your abstract thinking skills as you process, store, and recall what you’ve learned.

3. Explore your creativity

Creative expression is another excellent way to exercise your abstract thinking skills. Creativity engages the right side of the brain , which is responsible for abstract thinking and creative problem-solving. Through drawing, painting, writing, or photography, exploring the creative process encourages you to think outside the box and develop new ideas.

4. Practice mindfulness

Mindfulness is the practice of purposely observing the present moment without judgment or bias. Practicing mindfulness can help you improve your abstract thinking by teaching you how to observe your thoughts, feelings, and emotions objectively and without judgment. As you think more deeply and analytically about what's happening in the present moment, you will further develop your abstract thinking skills.

5. Make a habit of reading

Books and articles on various topics can help you build your understanding of complex concepts and ideas. Reading enables you to develop your ability to connect different ideas and think critically about the material. You also have to use your imagination to visualize what you're reading, which helps to improve your creative thinking abilities. Annotating your reading can step this up a notch.

6. Travel somewhere new

Traveling to new places exposes you to new cultures and ways of thinking, which can help to expand your mind and improve your abstract thinking skills. Plus, when you're in a new place, you're forced to think on your feet as you figure out how to navigate the unfamiliar landscape. This helps to build up your problem-solving skills, which are essential for developing abstract thinking abilities.

7. Get more exercise

Exercise is not only beneficial for your physical health, but it can also be beneficial for your mental health . Exercise helps to increase oxygen flow to the brain, which can improve cognitive functioning and help you think more clearly. Exercise also increases the production of endorphins, which can improve your mood and make it easier to focus on what you're doing.

8. Practice critical thinking

Critical thinking involves using your reasoning skills to evaluate information objectively. By practicing critical thinking, you can develop your abstract thinking ability by learning to analyze information, identify patterns and connections, and draw logical conclusions. Additionally, critical thinking will help you become more aware of your own biases so that you can make unbiased decisions.

9. Embrace risk-taking

Taking risks and engaging in activities that make you uncomfortable can help you practice abstract thinking. Stepping outside of your comfort zone forces you to think differently and create solutions to complex problems. It also requires you to push yourself beyond what is familiar and take a leap of faith as you learn new things .

10. Take up a new hobby

Hobbies like painting, sculpting, and photography can help you practice abstract thinking by allowing you to explore new ideas and ways of looking at the world. These activities also require you to use your imagination and creativity to devise solutions that aren’t immediately obvious. It also makes you feel accomplished when you're done, which can boost your confidence and make you more open to taking risks in other aspects of life.

Enhance your abstract thinking skills

If you've wondered, "What is abstract thinking?" now you have a better understanding. Abstract thinking skills can benefit us in many areas. From problem solving to meaningful learning to critical thinking, it's a powerful tool that can enhance our ability to navigate daily challenges.

By incorporating activities that promote the abstract thinking process into our daily routine, we can improve our ability to grasp abstract ideas, improve our decision-making skills, and see the bigger picture. With practice and dedication, we can master the art of abstract thinking and unlock its full potential.

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How Our Brains Organize Abstract Scientific Concepts

String theory, quantum gravity, time dilation. why do these advanced physics concepts come easily to some of us but not others.

The trope that the human brain didn’t evolve to understand advanced physics is often applied in popular science.

After all, early humans’ priorities were constricted to comprehending and contending with predatory threats, learning how to feed themselves and their group, seeking out conditions that were favorable to the functioning of their bodies, and deciding who might make a good candidate to produce offspring with. Wrapping their heads around the intricacies of something like physics would then be an unnecessary, metabolically expensive task with no real benefit to human survival and reproduction, right?

Sure, the laws of classical physics, which relate to Newtonian concepts like velocity and momentum, seem to make sense to us because they describe the behavior of objects in the world as we experience them. When Isaac Newton declared that an object will only change its motion if a force acts upon it, we found this easy to understand because it relates directly to our perception of how things move and behave in the world.

But some post-classical concepts introduced by modern physics — such as entanglement, dark matter and the multiverse — seem to be at odds with what our common sense tells us about the world. And we often explain the counter-intuitiveness of ideas like non-local particle dynamics, wave-particle duality and the potential of a near infinite number of possible realities by invoking that popular trope: These concepts seem to be outside the pay grade of our specialized cognitive machinery.

Do As the Experts Do

Thinking about this stuff hurts. Well, at least for most of us. Why? For a while now, neuroscientists have investigated how a number of concepts, from falling apples to dark matter, are represented in the neural substrates of the brain.

Read More: Are Rocket Scientists and Brain Surgeons Any Smarter than the Average Person?

Using functional MRI, which tracks where blood is flowing in the brain, and other techniques, Robert Mason and his colleagues at Carnegie Mellon University’s Center for Cognitive Brain Imaging attempt to identify the neural activation patterns of abstract scientific concepts. And in particular, patterns associated with some of the more seemingly absurd ideas presented in post-classical physics.

When they asked trained physicists to think about specific physics concepts last year, the researchers found that “expert knowledge in physics has a neural trace that is measurable and is somewhat similar across experts,” Mason says. “We could identify concepts across individuals even when they were trained in different systems and had different first languages.”

There are some potential mind-boggling implications for what this research could mean in the context of education. “This might be a very science fiction idea, but we may be able to assess knowledge in students by comparing to the expert’s knowledge brain state because it is measurable and consistent across experts,” he adds.

Although these neural representations are consistent enough to be recognized across individuals, however, Mason stresses that the brain is a dynamic, context-dependent entity — and that there’s a lot of variation in how a brain may represent concepts over time and across different individuals.

“Every time we think of a concept, the brain will have some common trace that allows it to be identified in [functional] MRI in both individuals and across them, but there is likely also a response that may be context-dependent,” he says. “It is likely that even a simple concept does not have a single pattern of activity that is exactly the same every time it is encountered.”

Rethinking Thinking

While the number of participants in Mason’s study was relatively low (it's not easy recruiting expert physicists to participate in psychological studies), the data found is similar and consistent with a much larger set of investigations on the neurosemantics of concepts across different conceptual domains.

For instance, the nature of many post-classical concepts requires the consideration of alternative possible worlds, such as the multiverse concept that many comic book fans are familiar with. Regions of the brain that were associated with hypothetical or speculative reasoning in previous research played a significant role in the neural signatures of post-classical concepts that required this type of abstract thinking.

Additionally, post-classical concepts often require the unknown or non-observable to be brought into agreement with what’s already understood. This same process is often needed in the comprehension of an unfolding narrative; regions of the brain that activated when physicists thought about certain post-classical concepts in this study also activated when readers judged the coherence of a new story segment in a different one .

In a way, it seems as though the brain is reappropriating regions that may have been traditionally used to carry out more general conceptual tasks — with those original tasks sharing characteristics with newer, post-classical physics concepts.

“The way I think about it is that the brain has various regions specialized for different types of thought and perhaps with redundancies in various parts of the cortex,” Mason says. “It may be repurposing structures used for other tasks and thoughts, but it could also be true that some regions exist that can be tuned to newer thoughts as they emerge and become consistently used by the individual.”  

Throughout history, our minds have accommodated abstract ideas in the realms of philosophy, morality, storytelling — where nuanced concepts couldn’t be reduced to simple visual representations and where we were faced with contradictions at every turn.

Perhaps our brains have had, for a while now, the tools to represent advanced concepts like those found in post-classical physics. Perhaps our characterization of the brain as not being adequate in its ability to encode for complex theories doesn’t give our brain the credit it deserves as a malleable, adaptive entity. It certainly gives us a lot to think about.

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Scientific Thinking and Critical Thinking in Science Education 

Two Distinct but Symbiotically Related Intellectual Processes

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• Published: 05 September 2023

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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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Education is not the learning of facts, but the training of the mind to think. Albert Einstein

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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The art and science of abstract thinking

Anne-Laure Le Cunff

What is something we only become capable of doing after age eleven, that helps us solve complex problems and write poetry, but needs to be yielded carefully? That’s abstract thinking, a powerful tool for creativity and innovation which anyone can learn how to use better.

The difference between concrete and abstract thinking

Concrete thinking is closely related to experiences that can be directly observed. It involves everyday, tangible facts and physical objects. On the other hand, abstract thinking is a higher-order reasoning skill. It deals with conceptual ideas, patterns, and theories.

For instance, thinking about the Statue of Liberty is a concrete thought, but thinking about what it represents — the idea of liberty — is an abstract thought. Listing the names of everyone on the team who are working on a specific project is concrete thinking, but questioning whether this is the best team for the project is abstract thinking.

Another way to put it is that concrete thinking asks how whereas abstract thinking asks why . In the words of researchers from Tel-Aviv University: “Focusing on the means required to achieve a specific goal ultimately entails transforming an abstract idea into a concrete action and thus primes a concretizing mindset; likewise, focusing on the purpose of an action primes an abstracting mindset.” 

According to famous psychologist Jean Piaget, it is not until around eleven years old that children become able to think abstractly and to use metacognition . Before that age, we are only able to think logically about objects we can physically manipulate. Our ability to think abstractly keeps on expanding as we grow up, but most people take this ability for granted, and very few proactively practice their abstract reasoning skills.

Three concrete ways to practice abstract thinking

It is possible to improve your abstract reasoning skills.

• Reframe the question. Go from “how?” to “why?” in order to take a step-back and tap into your abstract reasoning skills. For example, if you feel stuck trying to write a blog post, ask yourself: why am I writing this, who is this for, what exactly am I trying to achieve? This higher-order approach may help you discover a fresh angle to tackle your project.
• Look for patterns. Instead of looking at each concrete element in isolation, practice networked thinking to uncover abstract patterns and underlying dynamics in the relationship between those elements. Don’t be afraid to use your imagination. Sometimes patterns can be hard to detect, but the simple process of looking for them will help you improve your abstract reasoning skills.
• Take inspiration from abstract thinkers. Philosophers, artists, and scientists are great abstract thinkers. Like a philosopher, examine the nature of ideas such as success, reality, or community. Like a poet, go from concrete thinking to abstract thinking by using metaphors, simile, analogies, and symbolism. Like a scientist, formulate a theory by going from the particular to the general. Is the concrete event you are currently observing an occurrence of a wider phenomenon? Could you test your hypothesis?

Abstract thinking is essential in order to solve complex problems, come up with innovative ideas, and collaborate with other people. It allows us to analyse situations, understand new concepts, formulate theories, and to put things in perspective.

Without abstract thinking, we would not be able to grasp concepts such as friendship, hope, democracy, imagination, success, wisdom, happiness, or even love. However, while it’s a powerful tool to add to your thinking toolbox, it should not be the only tool, and it should be used wisely.

A balancing game

As with any powerful tool, abstract thinking can be a double-edged sword. First, abstract thinking without concrete thinking amounts to imagination without execution. Creativity requires an ambidextrous mindset which balances exploration and exploitation. Once you have figured out why an idea needs to see the light of day, you need to think about how you will make it happen. In other words, you need to go from abstract thinking to concrete thinking.

It can also be dangerous for your mental health to always default to abstract thinking, especially when thinking about past events. Psychology researchers explain that “abstract rumination is characteristic of depressed individuals, as is the tendency to experience post-decisional regret.” It is particularly true of thinking about traumatic events, where concrete thinking has been found to be much more helpful than abstract thinking.

Despite these caveats, abstract thinking skills are particularly helpful in situations that require thinking outside the box, uncovering hidden patterns, and generating innovative ideas. Just make sure you are balancing it with concrete thinking and monitoring your thought patterns so abstract thinking doesn’t turn into abstract rumination.

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Abstractly Thinking: Unleashing Your Creative Potential

Peeling back the layers of our mind, we often stumble upon a concept that’s as intriguing as it is elusive – abstract thinking. It’s a term many have heard, yet few truly understand. I’m here to bridge that gap, to shed light on this profound form of cognition and illustrate how it shapes our understanding of the world around us.

At its core, abstract thinking is the ability to move beyond concrete and physical reality, allowing us to ponder complex concepts, imagine possibilities, and analyze information in a broader sense. We’ll delve into its significance in problem-solving scenarios, creative pursuits, and even social interactions.

In essence, abstract thinking isn’t just about lofty ideas or philosophical musings; it’s an integral part of our everyday lives. Whether you’re solving a tricky math problem or brainstorming innovative solutions at work – chances are you’re employing this powerful cognitive process more often than you realize.

Understanding the Concept of Abstract Thinking

Ever wondered how your mind jumps from pondering over a cup of coffee to contemplating the cosmos? That’s abstract thinking in action. It’s our brain’s way of connecting the dots between unrelated concepts, enabling us to think beyond physical or present scenarios.

Diving deeper into this concept, abstract thinking is essentially the ability to understand concepts that are real, such as freedom or vulnerability, but which are not directly tied to concrete physical objects and experiences. It’s what allows us to analyze information and apply it to different contexts. For instance, if you’ve never visited a desert before, your brain can still use abstract thinking to imagine what being there might feel like.

Now let’s talk numbers. A study by Cambridge University found that children begin developing abstract reasoning skills around age 11-12. But this doesn’t mean they’re absent in younger kids! They just manifest differently – through imaginative play and curiosity.

Here are some key points about abstract thinking:

• It involves seeing beyond what’s obvious.
• Emotions like love and fear fall under its domain.
• Problem-solving often requires a strong grasp of abstraction.

Abstract thought is crucial for advanced learning and creative innovation. Without it, we’d be unable to anticipate future events or appreciate art forms like poetry and metaphors – which aren’t literally true but convey great truth all the same.

So next time you find yourself daydreaming about space travel while sipping on your morning latte, know that it’s your amazing capacity for abstract thought at work!

The Science Behind Abstract Thinking

I’ve always been fascinated by the way our brains work, particularly when it comes to abstract thinking. You see, abstract thinking is a critical part of human cognition. It’s what allows us to comprehend complex concepts, solve problems that don’t have clear-cut solutions, and adapt to new circumstances.

In fact, neuroscience has shown that different parts of our brain are involved in abstract thinking. For example:

• The prefrontal cortex (PFC) plays a key role in planning and decision making.
• The parietal lobe contributes to our understanding of numbers and spatial relationships.
• And the temporal lobe helps us recognize patterns.

Isn’t it incredible how all these different components come together to enable this higher order thinking? But let’s delve deeper into the role of each part.

The Prefrontal Cortex (PFC), often associated with executive functions like impulse control and long-term planning, is pivotal in abstract reasoning. When we’re faced with an unfamiliar situation or problem, it’s the PFC that steps up to help us strategize a solution.

Similarly important is the parietal lobe. This region handles numerical comprehension and spatial awareness – both crucial for abstraction. After all, if you can’t understand numbers or visualize space correctly, tackling mathematical equations or architectural designs could prove an uphill battle!

Lastly but not leastly – there’s the temporal lobe! It helps us identify patterns in information – which again is essential for abstract thought. Whether we’re trying to decipher code or spot trends in data sets – you bet it’s thanks mainly to this hardworking part of our brain!

So next time you find yourself pondering deeply over an intricate problem, take a moment to thank your brain for its abstract thinking prowess. The science behind it is truly mind-boggling!

How Abstractly Thinking Affects Creativity

Diving right into the heart of the matter, I can’t emphasize enough how significantly abstract thinking influences our creativity. It’s like unlocking a secret door in our minds that allows us to perceive things from a unique perspective.

When we think abstractly, we’re not just focusing on what meets the eye. We’re delving deeper and considering all possible dimensions, interpretations, and connections. This encourages us to break free from traditional boundaries and explore uncharted territories of thought. For example, an artist might be inspired by a simple leaf falling from a tree – but instead of merely replicating this scene on canvas, they could use it as a metaphor for change or transition.

Abstract thought also fosters problem-solving skills. Rather than viewing problems as concrete obstacles with only one solution, abstract thinkers see them as challenges with multiple potential outcomes. In fact, research has shown that people who frequently engage in abstract thinking are more likely to find innovative solutions to complex problems.

It’s worth noting too that abstract thinking isn’t just beneficial for artists or inventors; it’s valuable in virtually every field or profession:

• Marketing Professionals : They leverage their ability to think beyond product features and focus on symbolic meanings and emotional connections.
• Engineers : They often design solutions based on conceptual models before translating them into physical prototypes.
• Teachers : By employing abstract concepts during instruction, they can help students develop critical thinking skills.

So there you have it! Abstract thinking is more than just daydreaming or getting lost in your thoughts – it’s about seeing possibilities where others see dead ends; it’s about transforming ordinary ideas into extraordinary creations.

Role of Abstract Thought in Problem Solving

Diving into the world of abstract thought, it’s fascinating to see how it plays a significant role in problem-solving. Now you might ask, “What’s so special about thinking abstractly?” Well, let me break this down for you.

Abstract thinking is like taking a bird’s eye view of problems. It allows us to look beyond details and perceive situations from varied perspectives. When we’re faced with complex issues that can’t be solved using traditional or straightforward methods, that’s where abstract thinking comes into play.

For instance, consider trying to solve a jigsaw puzzle. If you focus solely on individual pieces (concrete thinking), it would be difficult to visualize the complete picture. But if you step back and think about how the pieces fit together as part of a larger image (abstract thinking), suddenly things start making sense!

Interestingly, research indicates that abstract thinkers are better at finding innovative solutions to problems. According to a study by Darya Zabelina and Michael Robinson at North Dakota State University:

These findings show that abstract thinkers could potentially lead the way in fields where problem-solving skills are crucial, such as engineering or management consulting.

The power of abstraction also shines through when brainstorming potential solutions for problems – known as ‘divergent thinking’. By generating numerous ideas without dwelling on their feasibility initially,

• We broaden our horizon
• Potential unconventional solutions come into light
• We challenge our conventional ways of doing things

So whether it’s piecing together puzzles or coming up with groundbreaking business strategies, applying an abstract approach can truly revolutionize the way we tackle problems!

Advantages and Disadvantages of Abstract Thinking

Dive into the world of abstract thinking, and you’ll find a realm full of possibilities. It’s like exploring an endless ocean, brimming with mystery and intrigue. But like every coin that has two sides, abstract thinking comes with its own set of advantages and disadvantages.

Let’s start on a positive note— the benefits it brings to the table. A knack for abstract thought can drastically improve problem-solving skills. Why? Because it allows you to look beyond just the surface level details. You can step back, assess different angles, connecting patterns that aren’t immediately apparent.

Another perk is enhanced creativity. Abstract thinkers are often the ones who come up with innovative ideas or out-of-the-box solutions because they’re not confined by traditional boundaries or norms.

Yet another benefit is empathy towards others’ perspectives. Since abstract thinking involves understanding concepts from various viewpoints, it enables better comprehension of diverse beliefs and feelings.

Here’s a quick recap:

• Enhanced problem-solving skills
• Increased creativity
• Improved empathy

But let’s not forget there are some downsides too! For one, abstract thinking might lead you down paths where concrete answers are hard to find—it can be more about exploration than resolution.

It may also result in overthinking—a common pitfall for many abstract thinkers—where analysis paralysis sets in as you get lost in your own thoughts.

Lastly, people who think abstractly might struggle with tasks requiring detailed focus since their mind tends to dwell on overarching concepts rather than minute particulars.

To summarize these points:

• Potential for overthinking
• Difficulty finding definitive answers
• Struggles with detail-oriented tasks

So there we have it—the upsides and downsides of abstract thinking laid bare! Like any cognitive ability, how well it serves largely depends on how effectively we harness this power within us.

Techniques to Improve Your Ability to Think Abstractly

Ever wondered how you could get better at abstract thinking? Don’t sweat it. I’ve got some tips and tricks that might just help. Firstly, let’s clear the air – abstract thinking isn’t an overnight skill. It takes practice, persistence, and a bit of creativity.

First up on my list is brainstorming sessions. Whether it’s solo or in a group, brainstorming can stimulate your brain and allow you to think outside the box. The trick is not to shoot down any ideas initially – no matter how crazy they appear. You’ll be surprised how often these “wild” ideas spark innovative solutions.

Next up is this little trick I like to call ‘the perspective switch’. Simply put, try looking at problems from a different angle or point of view. This technique forces you to step out of your comfort zone and expands your horizon of thoughts.

Let’s not forget about puzzles and games! Believe it or not, playing chess or solving Rubik’s cubes can do wonders for your abstract reasoning skills. These activities train your brain to see patterns and connections which are vital in building strong abstract thinking abilities.

Finally, there’s nothing like good old reading. Diving into philosophy books or complex novels helps expose you to intricate concepts – enhancing both comprehension skills and abstract thought processes.

Remember folks – Rome wasn’t built in a day! Developing your ability for abstract thinking will take time but with patience and consistent practice using these techniques, I’m confident that anyone can enhance their capacity for conceptual understanding over time.

Examples Demonstrating the Use of Abstract Thought

Diving straight into our first example, let’s consider planning a vacation. This seemingly simple task is brimming with abstract thought. I’m thinking about concepts like relaxation or adventure, and then deciding what those mean for me personally. Does relaxation mean lounging on a beach or exploring a new city? There’s no concrete answer because it all depends on my personal perspective.

Moving onto another instance, ponder over the last book you read. When we read, we’re constantly using abstract thought to interpret symbols (words) and construct an understanding in our minds. We’re not just decoding text; we’re creating worlds, imagining characters’ emotions, predicting outcomes – all without concrete references.

Moreover, the world of mathematics serves as an excellent playground for abstract thought too! Let’s take algebra for instance – it’s based entirely around manipulating symbols according to rules to solve problems. When I look at an equation like x + 2 = 5, there’s nothing tangible about ‘x’. Yet by applying rules of algebraic manipulation, I can solve that ‘x’ equals 3.

Let’s also remember how often we use metaphors and similes in daily conversation – they’re perfect examples of abstraction in language! When I say something like “Time is a thief”, there isn’t really some burglar named Time sneaking around stealing hours from us. Instead, it’s expressing the abstract idea that time passes quickly and is irreversible.

Finally yet importantly are moral decisions which are deeply rooted in abstract thinking. Deciding right from wrong isn’t often black-and-white since these judgments are largely based on societal constructs or personal beliefs which aren’t physically present or observable.

So next time when you’re reading your favorite novel or solving that tricky math problem – just remember – you’re beautifully demonstrating your capacity for abstract thought!

Conclusion: Embracing the Power of Abstractly Thinking

It’s been quite a journey, hasn’t it? We’ve delved deep into the realm of abstract thinking and unearthed its true potential. If there’s one thing I hope you’ll take away from this article, it’s that embracing abstract thinking isn’t just about being more creative or solving problems more effectively—it’s about transforming how we view and interact with the world.

This might sound like an exaggeration, but consider this: every great invention or innovation in history has been born from someone looking beyond the obvious. From Einstein’s theory of relativity to Steve Jobs’ iPhone, abstract thinking is at the heart of progress.

But let me be clear—I’m not saying everyone needs to be an Einstein or a Jobs. What I am saying is that anyone can harness the power of abstract thinking in their everyday lives. Whether you’re trying to solve a tricky problem at work, make sense of complex issues in your personal life, or even explore new hobbies and passions—abstract thinking can open up new pathways and possibilities.

Here are some key insights from our exploration:

• Abstract thoughts are universal : Regardless of age, culture, or background—we all have the capacity for abstract thought.
• It improves problem-solving : Abstract thinkers tend to find unique solutions by viewing problems from different perspectives.
• Encourages creativity : The ability to think outside-the-box often leads to innovative ideas and fresh perspectives.
• Promotes understanding : By focusing on underlying principles rather than surface details—abstract thinking aids in grasping complex concepts.

So don’t shy away from abstraction; embrace it! Try seeing things not just as they are—but what they could be. And who knows? You might surprise yourself with where your thoughts lead you next!

In closing, remember that like any skill—abstract thinking takes practice. So start small, keep an open mind—and most importantly—enjoy the journey. Because in the end, it’s not just about reaching a destination—it’s about experiencing new ways of thinking and seeing along the way.

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The development of the reasoning brain and how to foster logical reasoning skills

Early childhood development / Effective lifelong learning / Learning mathematics

Executive summary

Learning to reason logically is necessary for the growth of critical and scientific thinking in children. Yet, both psychological and neural evidence indicates that logical reasoning is hard even for educated adults. Here, we examine the factors that scaffold the emergence of logical reasoning in children. Evidence suggests that the development of reasoning with concrete information can be accounted for by the development of both world knowledge and self-regulation. The transition from concrete to abstract reasoning, however, is a challenge for children. Children’s development of reasoning may be supported by encouraging both divergent thinking and reasoning at levels of abstraction that are just above reasoners’ current levels, alongside activities in which children reason with others.

Introduction

It is often argued that one of the most fundamental goals of education is to nurture critical thinking, that is, to teach children to employ good reasoning skills when developing their beliefs. Therefore, fostering logical reasoning should be an important goal for education: Children should learn to provide logical reasons for their opinions and should be able to distinguish between good and bad arguments. This is likely to be important for their effective exercise of citizenship as adults. For example, logical reasoning could tell you that it is unwarranted to conclude “All Muslims are terrorists” from the assertions “All the 9/11 perpetrators are Muslims” and “All the 9/11 perpetrators are terrorists.” Yet, many educated adults still draw such a conclusion, most likely because fear and bias can overcome rational thinking. This suggests that logical reasoning is hard even for educated adults, a conclusion that is supported by a wealth of psychological studies. Perhaps the most striking demonstration of the difficulty of logical reasoning was discovered by the psychologist Peter Wason in 1966 1 . Wason designed a task in which he presented participants with four playing cards, each with a letter on one side and a number on the other side. For example, the cards could be as follow:

A         B         2          3

Participants were then shown the conditional rule “If a card has the letter A on one side, then it has the number 2 on the other side.” The task consisted of selecting those cards that had to be turned over to discover whether the rule was true or false. Since Wason’s study, that task has been performed many times, and the results are always the same. Most people select either the A card alone or sometimes both the cards A and 2. However, very few adults, even highly educated, typically choose the 3 card. This is despite the fact that discovering what is on the other side of the 3 card is necessary to evaluate whether the rule is true or false (i.e., if there is an A on the other side of the 3, the rule is false). This reasoning failure has puzzled psychologists for decades because it questions the long-standing assumption that human beings are inherently rational. Why is it so hard for participants to select the 3 card? Neuroscience research suggests that it is because it is much more difficult for the brain to focus on the elements that are absent from the rule (e.g., 3) than on the elements that are present (e.g., A) 2 . Thus, selecting the 3 card requires much more extensive brain activation in several brain regions (primarily involved in attention and concentration) to overcome that tendency (see Figure 1). So, how can we get people to activate more of their reasoning brain and act more rationally on this task? One of the first ideas that comes to mind would be to teach them logic. Cheng and colleagues 3 have tested this. The researchers presented the Wason selection task to college students before and after they took a whole-semester introductory class in logic (about 40 hours of lectures). Surprisingly, they found no difference in the students’ poor performance between the beginning and the end of the semester. In other words, a whole semester of learning about logic did not help students make any less error on the task! What, then, can train the reasoning brain? To answer that question, it is interesting to turn to what we know about the development of logical reasoning in children.

Figure 1. The reasoning brain. Location of the brain regions (in red, blue, and white) that are activated when participants reason with elements that are not present in the rule in the Wason card task. Activations are displayed on pictures of the brain taken using a magnetic resonance imaging scanner. (Reproduced from Ref. 2 )

The development of concrete logical reasoning in children

It is clear that even young children can use some logical reasoning when concrete information is involved. For instance, most 6-year-olds can draw the conclusion “The person is hurt” from the statements “If the person breaks his arm, the person will be hurt” and “The person breaks his arm.” However, the reasoning abilities of young children are limited. For example, many 6-year-olds would also draw the conclusion “The person broke his arm” from the statements “If the person breaks his arm, the person will be hurt” and “The person is hurt.” This, however, is an invalid conclusion because there may be many other reasons why a person could be hurt. Children will progressively understand this and will make this type of reasoning error less and less as they get older. By the time they reach the end of elementary school, most children are able to refrain from concluding “The person broke his arm” from the statements “If the person breaks his arm, the person will be hurt” and “The person is hurt” 4 . Critically, this increased reasoning ability is mirrored by an increase in the ability to think about alternate causes for a given consequence. For example, older children are much more able than younger children to think about the many other reasons why someone would be hurt, like getting sick, breaking a leg, cutting a finger, etc. In other words, better reasoning ability with age is associated with a better ability to consider alternatives from stored knowledge. Clearly, however, children differ in terms of what they know about the world. This predicts that those who have better world knowledge and can think about more alternatives should be better reasoners than the others. And this is exactly what has been shown in several studies 4 .

Interestingly, the importance of world knowledge for reasoning has a paradoxical effect: It can make children poorer reasoners on some occasions. For example, children who can think about a lot of alternatives would be less inclined to draw the logically valid conclusion “The person will be tired” from the statements “If a person goes to sleep late, then he will be tired” and “The person goes to sleep late.” This is because a child with significant world knowledge can think of several circumstances that would make the conclusion unwarranted, such as waking up later the next day. Thus, more world knowledge needs to be associated with more ability to suppress the alternatives that might come to mind if the task requires it. This self-regulation ability relies on a part of the brain that also massively develops during childhood, i.e., the prefrontal cortex (see Figure 2). Overall, then, the development of concrete logical reasoning in children can be largely accounted for by the development of both world knowledge and self-regulation skills that are associated with the frontal cortex.

Figure 2. The prefrontal cortex. Location of the prefrontal cortex on a 3D rendering of the human brain. Polygon data were generated by Database Center for Life Science(DBCLS),  distributed under a CC-BY-SA-2.1-jp license.

From concrete to abstract reasoning

There is, however, an important difference between the reasoning skills described above and the task developed by Peter Wason about the four cards. What we just described relates to reasoning with very concrete information, whereas the card task involves reasoning with purely abstract information. Abstract reasoning is difficult because it requires one to manipulate information without any referent in the real world. Knowledge is of no help. In fact, neuroscience research indicates that abstract and concrete reasoning rely on two different parts of the brain 5 (see Figure 3). The ability to reason logically with an abstract premise is generally only found during late adolescence 4 . Transitioning from concrete to abstract reasoning may require extensive practice with concrete reasoning. With mastery, children may extract from the reasoning process abstract strategies that could be applied to abstract information. A recent study, however, suggests a trick to help facilitate this transition in children 6 . The researchers discovered that abstract reasoning in 12- to 15-year-olds is much improved when these adolescents are previously engaged in a task in which they have to reason with information that is concrete but empirically false, such as “If a shirt is rubbed with mud, then the shirt will be clean.” No such effect was observed when adolescents are asked to reason with concrete information that is empirically true, such as “If a shirt is washed with detergent, then the shirt will be clean.” Therefore, reasoning with information that contradicts what we know about the world might constitute an intermediary step in transitioning from concrete to abstract reasoning.

Figure 3. Brain regions activated when reasoning with concrete (left) and abstract (right) information. Activations are displayed on pictures of the brain taken using a magnetic resonance imaging scanner. (Reproduced from Ref. 5 )

What can we do to foster logical reasoning skills?

What, then, can we do to help foster the development of logical reasoning skills in children? The research described above suggests several potentially fruitful ways. First, it is clear that the development of concrete reasoning—the very first type of reasoning children can engage in—relies on an increased ability to think about counter-examples for a given statement. This implies that knowledge about the world is critical to the emergence of logical reasoning in children, at least when concrete information is involved. Therefore, all activities that would expand such world knowledge (e.g., reading informational books, learning new vocabulary, exploring new environments and places) are likely to be beneficial to the development of children’s reasoning skills. Second, it is important to consider that the more world knowledge a child possesses, the more he/she will need to juggle with this knowledge. For example, generating counter-examples when solving a reasoning problem will require maintaining pieces of information in memory for a short period of time, a type of memory called working memory . World knowledge can also sometimes be detrimental to reasoning and needs to be inhibited , such as when recognizing that the conclusion “The person will be tired” logically follows from the statements “If a person goes to sleep late, then he will be tired” and “The person goes to sleep late” (even if one might think of several conditions that would make the conclusion untrue based on what we know about the world). Fostering these types of self-regulation skills (working memory and inhibition) should thus be beneficial to the development of logical reasoning. Several studies suggest that these functions could be promoted by targeting children’s emotional and social development, such as in curricula involving social pretend play (requiring children to act out of character and adjusting to improvisation of others), self-discipline, orderliness, and meditation exercises 7 . Studies also indicate positive effects of various physical activities emphasizing self-control and mindfulness, such as yoga or traditional martial arts 7 . Third, studies indicate that the transition from concrete to abstract reasoning occurring around adolescence is challenging. Although more research is needed in this domain, one promising way to help this transition is by encouraging children’s thinking about alternatives with content that contradicts what they know about the world (e.g., “If a shirt is rubbed with mud, then the shirt will be clean”). In sum, as stated by Henry Markovits, “the best way to encourage the development of more abstract ways of logical reasoning is to gradually encourage both divergent thinking and reasoning at levels of abstraction that are just above reasoners’ current levels” 4 .

Fostering the development of logical reasoning should be an important goal of education. Yet, studies indicate that logical reasoning is hard even for educated adults and relies on the activation of an extensive network of brain regions. Neuroscience studies also demonstrate that reasoning with concrete information involves brain regions that qualitatively differ from those involved in reasoning with more abstract information, explaining why transitioning from concrete to abstract reasoning is challenging for children. We nonetheless reviewed here the more recent research on the development of reasoning skills and suggest several important factors that scaffold children’s reasoning abilities, such as world knowledge and self-regulation functions. On a final note, it is important to consider that logical reasoning is not something that we always do on our own, isolated from our peers. In fact, some have argued that the very function of reasoning is to argue with our peers (i.e., to find the best arguments to convince others and to evaluate arguments made by others) 8 . This idea is interesting from an educational point of view because it suggests that reasoning with others might be easier than reasoning in isolation—a hypothesis validated by several studies. For example, performance on the card task developed by Peter Wason is much higher when participants solve it as a group rather than alone 8 . Therefore, encouraging activities in which children reason with others might also be a fruitful avenue for stimulating the reasoning brain.

• Wason, P. C. Reasoning. In New Horizons in Psychology (ed. Foss, B. M.). (Penguin: Harmondsworth, 1966).
• Prado, J., & Noveck, I. A. Overcoming perceptual features in logical reasoning: A parametric functional magnetic resonance imaging study. J Cogn Neurosci . 19(4): 642-57 (2007).
• Cheng, P. W. et al. Pragmatic versus syntactic approaches to training deductive reasoning. Cogn Psychol . 18(3): 293-328 (1986).
• Markovits, H. How to develop a logical reasoner. In The Developmental Psychology of Reasoning and Decision-Making (ed. Markovits, H.) 148-164. (Psychology Press: Hove, UK, 2014).
• Goel, V. Anatomy of deductive reasoning. Trends Cogn. Sci. (Reg. Ed.) 11(10): 435-41 (2007).
• Markovits, H., & Lortie-Forgues, H. Conditional reasoning with false premises facilitates the transition between familiar and abstract reasoning. Child Development 82(2): 646-660 (2011).
• Diamond, A., & Lee, K. Interventions shown to aid executive function development in children 4 to 12 years old. Science 333(6045): 959-964 (2011).
• Mercier, H., & Sperber, D. Why do humans reason? Arguments for an argumentative theory. Behav Brain Sci . 34(2): 57-74; discussion 74-111 (2011).

Why Do We Love Thinking About Schrödinger’s Cat?

In physics, the whole point of the thought experiment is that it’s absurd. But in literature, it’s been used to explore all sorts of ideas and possibilities.

We all know Schrödinger ’ s Cat, that unfortunate feline trapped in a box in a strange, indeterminate state. But why did a few lines in an old physics paper spark so many people’s imaginations? Literary scholar Marie-Laure Ryan explores .

In 1929, Austrian physicist Erwin Schrödinger came up with a groundbreaking equation that could accurately predict the behavior of a system of subatomic particles. It also showed that at the tiny quantum scale things behaved much differently than in the big world we can see. It suggested that people might only be able to understand the behavior of electrons as probabilities. Perhaps, some physicists argued, two incompatible states somehow existed simultaneously until an event known as “wave-function collapse,” when they took one path or the other. Schrödinger found this idea unacceptable.

“He is known to have said that he hated his equation for its consequences for the nature of reality,” Ryan writes.

It’s in this spirit that he created his famous thought experiment, which was only a short paragraph in a 1935 paper. The idea is that the cat’s fate depends on the behavior of a subatomic particle, so it remains both alive and dead until the collapse of the wave function. The point, Schrödinger wrote, was that this “ridiculous” scenario should discourage us from “naively accepting as valid a ‘blurred model’” of reality.

In the decades since Schrödinger created the cat parable, many people beyond the world of physics have adapted it for their own purposes. In the short story “Schrödinger’s Cat” by Ursula K. Le Guin, a talking dog attempts to place a cat in a deadly contraption for the “scientific” purpose of determining that, in the end, the cat “will be dead, or not dead.” Ryan writes that the story “leaves the reader’s mind in a dizzying superposition of possible but partial interpretations. The only global meaning that can arise from this superposition is a radical loss of certainty.”

Another short story, “Schrödinger’s Plague,” by Greg Bear, hinges on the idea—more popular among new-age thinkers than physicists —that the collapse of the wave function in quantum mechanics is caused by conscious observation. In the story, a scientist causes a deadly virus to exist in the superimposed states of “released” and “not released” as long as no one knows about the experiment. This results in a series of murders and suicides designed to prevent the knowledge from leaking.

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A more frequent treatment of quantum mechanics in literature relies on the “ many worlds ” interpretation, in which a particle actually takes both of its two possible paths, leading to the creation of separate universes. Here, the cat is alive in one version of the world and dead in another.

Whichever angle a writer takes on Schrödinger’s cat, Ryan argues that the topic poses the challenge of applying logic from the subatomic level in narrative formats normally concerned with macro-level interactions between people and their environments. And that challenge, she suggests, is exactly why writers find it so appealing.

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