the cognitive problem solving

Cognitive Behavioral Therapy

Solving problems the cognitive-behavioral way, problem solving is another part of behavioral therapy..

Posted February 2, 2022 | Reviewed by Ekua Hagan

• What Is Cognitive Behavioral Therapy?
• Find a therapist who practices CBT
• Problem-solving is one technique used on the behavioral side of cognitive-behavioral therapy.
• The problem-solving technique is an iterative, five-step process that requires one to identify the problem and test different solutions.
• The technique differs from ad-hoc problem-solving in its suspension of judgment and evaluation of each solution.

As I have mentioned in previous posts, cognitive behavioral therapy is more than challenging negative, automatic thoughts. There is a whole behavioral piece of this therapy that focuses on what people do and how to change their actions to support their mental health. In this post, I’ll talk about the problem-solving technique from cognitive behavioral therapy and what makes it unique.

The problem-solving technique

While there are many different variations of this technique, I am going to describe the version I typically use, and which includes the main components of the technique:

The first step is to clearly define the problem. Sometimes, this includes answering a series of questions to make sure the problem is described in detail. Sometimes, the client is able to define the problem pretty clearly on their own. Sometimes, a discussion is needed to clearly outline the problem.

The next step is generating solutions without judgment. The "without judgment" part is crucial: Often when people are solving problems on their own, they will reject each potential solution as soon as they or someone else suggests it. This can lead to feeling helpless and also discarding solutions that would work.

The third step is evaluating the advantages and disadvantages of each solution. This is the step where judgment comes back.

Fourth, the client picks the most feasible solution that is most likely to work and they try it out.

The fifth step is evaluating whether the chosen solution worked, and if not, going back to step two or three to find another option. For step five, enough time has to pass for the solution to have made a difference.

This process is iterative, meaning the client and therapist always go back to the beginning to make sure the problem is resolved and if not, identify what needs to change.

Advantages of the problem-solving technique

The problem-solving technique might differ from ad hoc problem-solving in several ways. The most obvious is the suspension of judgment when coming up with solutions. We sometimes need to withhold judgment and see the solution (or problem) from a different perspective. Deliberately deciding not to judge solutions until later can help trigger that mindset change.

Another difference is the explicit evaluation of whether the solution worked. When people usually try to solve problems, they don’t go back and check whether the solution worked. It’s only if something goes very wrong that they try again. The problem-solving technique specifically includes evaluating the solution.

Lastly, the problem-solving technique starts with a specific definition of the problem instead of just jumping to solutions. To figure out where you are going, you have to know where you are.

One benefit of the cognitive behavioral therapy approach is the behavioral side. The behavioral part of therapy is a wide umbrella that includes problem-solving techniques among other techniques. Accessing multiple techniques means one is more likely to address the client’s main concern.

Salene M. W. Jones, Ph.D., is a clinical psychologist in Washington State.

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48 Problem Solving

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

• Published: 03 June 2013
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Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined. The major cognitive processes in problem solving are representing, planning, executing, and monitoring. The major kinds of knowledge required for problem solving are facts, concepts, procedures, strategies, and beliefs. Classic theoretical approaches to the study of problem solving are associationism, Gestalt, and information processing. Current issues and suggested future issues include decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific thinking, everyday thinking, and the cognitive neuroscience of problem solving. Common themes concern the domain specificity of problem solving and a focus on problem solving in authentic contexts.

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

Definition of Problem Solving

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

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

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

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

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

Definition of Problem

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

Types of Problems

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

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

Cognitive Processes in Problem Solving

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

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

Knowledge for Problem Solving

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

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

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

Historical Approaches to Problem Solving

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

Early Conceptions

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

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

Associationism

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

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

Gestalt Psychology

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

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

Information Processing

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

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

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

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

Classic Issues in Problem Solving

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

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

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

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

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

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

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

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

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

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

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

Current and Future Issues in Problem Solving

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

Decision Making

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

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

Intelligence and Creativity

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

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

Teaching of Thinking Skills

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

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

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

Expert Problem Solving

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

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

Analogical Reasoning

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

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

Mathematical and Scientific Problem Solving

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

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

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

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

Everyday Thinking

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

Cognitive Neuroscience of Problem Solving

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

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

Future Directions

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

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Further Reading

Baron, J. ( 2008 ). Thinking and deciding (4th ed). New York: Cambridge University Press.

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

Holyoak, K. J. , & Morrison, R. G. ( 2005 ). The Cambridge handbook of thinking and reasoning . New York: Cambridge University Press.

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

Sternberg, R. J. , & Ben-Zeev, T. ( 2001 ). Complex cognition: The psychology of human thought . New York: Oxford University Press.

Weisberg, R. W. ( 2006 ). Creativity . New York: Wiley.

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Cognitive Approach in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Cognitive psychology is the scientific study of the mind as an information processor. It concerns how we take in information from the outside world, and how we make sense of that information.

Cognitive psychology focuses on studying mental processes, including how people perceive, think, remember, learn, solve problems, and make decisions.

Cognitive psychologists try to build up cognitive models of the information processing that goes on inside people’s minds, including perception, attention, language, memory, thinking, and consciousness.

Cognitive psychology became of great importance in the mid-1950s. Several factors were important in this:

• Dissatisfaction with the behaviorist approach in its simple emphasis on external behavior rather than internal processes.
• The development of better experimental methods.
• Comparison between human and computer processing of information . Using computers allowed psychologists to try to understand the complexities of human cognition by comparing it with computers and artificial intelligence.

The emphasis of psychology shifted away from the study of conditioned behavior and psychoanalytical notions about the study of the mind, towards the understanding of human information processing using strict and rigorous laboratory investigation.

Summary Table

Theoretical assumptions.

Mediational processes occur between stimulus and response:

The behaviorists approach only studies external observable (stimulus and response) behavior that can be objectively measured.

They believe that internal behavior cannot be studied because we cannot see what happens in a person’s mind (and therefore cannot objectively measure it).

However, cognitive psychologists regard it as essential to look at the mental processes of an organism and how these influence behavior.

Cognitive psychology assumes a mediational process occurs between stimulus/input and response/output. 

These are mediational processes because they mediate (i.e., go-between) between the stimulus and the response. They come after the stimulus and before the response.

Instead of the simple stimulus-response links proposed by behaviorism, the mediational processes of the organism are essential to understand. Without this understanding, psychologists cannot have a complete understanding of behavior.

The mediational (i.e., mental) event could be memory , perception , attention or problem-solving, etc. 

For example, the cognitive approach suggests that problem gambling is a result of maladaptive thinking and faulty cognitions. These both result in illogical errors being drawn, for example gamblers misjudge the amount of skill involved with ‘chance’ games so are likely to participate with the mindset that the odds are in their favour so they may have a good chance of winning.

Therefore, cognitive psychologists say that if you want to understand behavior, you must understand these mediational processes.

Psychology should be seen as a science:

The cognitive approach believes that internal mental behavior can be scientifically studied using controlled experiments . They use the results of their investigations as the basis for making inferences about mental processes. 

Cognitive psychology uses laboratory experiments that are highly controlled so they avoid the influence of extraneous variables. This allows the researcher to establish a causal relationship between the independent and dependent variables.

Cognitive psychologists measure behavior that provides information about cognitive processes (e.g., verbal protocols of thinking aloud). They also measure physiological indicators of brain activity, such as neuroimages (PET and fMRI).

For example, brain imaging fMRI and PET scans map areas of the brain to cognitive function, allowing the processing of information by centers in the brain to be seen directly. Such processing causes the area of the brain involved to increase metabolism and “light up” on the scan.

These controlled experiments are replicable, and the data obtained is objective (not influenced by an individual’s judgment or opinion) and measurable. This gives psychology more credibility.

Replicability is a crucial concept of science as it ensures that people can validate research by repeating the experiment to ensure that an accurate conclusion has been reached.

Without replicability, a scientific finding may be invalid as it cannot be falsified. Additionally, scientific research relies on the peer review of research to ensure that the research is justifiable.

Without replicability, it would be impossible to justify the accuracy of the research. 

Humans are information processors:

Cognitive psychology has been influenced by developments in computer science and analogies are often made between how a computer works and how we process information.

Information processing in humans resembles that in computers, and is based on transforming information, storing and processing information, and retrieving information from memory.

Information processing models of cognitive processes such as memory and attention assume that mental processes follow a linear sequence.

For example:

• Input processes are concerned with the analysis of the stimuli.
• Storage processes cover everything that happens to stimuli internally in the brain and can include coding and manipulation of the stimuli.
• Output processes are responsible for preparing an appropriate response to a stimulus.

This has led to models which show information flowing through the cognitive system, such as the multi-store model of memory.

Information Processing Paradigm

The cognitive approach began to revolutionize psychology in the late 1950s and early 1960s to become the dominant approach (i.e., perspective) in psychology by the late 1970s. Interest in mental processes was gradually restored through the work of Jean Piaget and Edward Tolman .

Tolman was a ‘soft behaviorist’. His book Purposive Behavior in Animals and Man in 1932 described research that behaviorism found difficult to explain. The behaviorists’ view was that learning occurred due to associations between stimuli and responses.

However, Tolman suggested that learning was based on the relationships formed amongst stimuli. He referred to these relationships as cognitive maps.

But the arrival of the computer gave cognitive psychology the terminology and metaphor it needed to investigate the human mind.

The start of the use of computers allowed psychologists to try to understand the complexities of human cognition by comparing it with something simpler and better understood, i.e., an artificial system such as a computer.

The use of the computer as a tool for thinking about how the human mind handles information is known as the computer analogy. Essentially, a computer codes (i.e., changes) information, stores information, uses information and produces an output (retrieves info).

The idea of information processing was adopted by cognitive psychologists as a model of how human thought works.

The information processing approach is based on several assumptions, including:

• Information made available from the environment is processed by a series of processing systems (e.g., attention, perception, short-term memory);
• These processing systems transform, or alter the information in systematic ways;
• The aim of research is to specify the processes and structures that underlie cognitive performance;
• Information processing in humans resembles that in computers.

The Role of Schemas

Schemas can often affect cognitive processing (a mental framework of beliefs and expectations developed from experience). As you get older, these become more detailed and sophisticated.

A schema is a “packet of information” or cognitive framework that helps us organize and interpret information. They are based on our previous experience.

Schemas help us to interpret incoming information quickly and effectively; this prevents us from being overwhelmed by the vast amount of information we perceive in our environment.

However, it can also lead to distortion of this information as we select and interpret environmental stimuli using schemas that might not be relevant.

This could be the cause of inaccuracies in areas such as eyewitness testimony. It can also explain some errors we make when perceiving optical illusions.

History of Cognitive Psychology

• Kohler (1925) published a book called, The Mentality of Apes . In it, he reported observations which suggested that animals could show insightful behavior. He rejected behaviorism in favour of an approach which became known as Gestalt psychology .
• Norbert Wiener (1948) published Cybernetics: or Control and Communication in the Animal and the Machine, introducing terms such as input and output.
• Tolman (1948) work on cognitive maps – training rats in mazes, showed that animals had an internal representation of behavior.
• Birth of Cognitive Psychology often dated back to George Miller’s (1956) “ The Magical Number 7 Plus or Minus 2 : Some Limits on Our Capacity for Processing Information.” Milner argued that short-term memory could only hold about seven pieces of information, called chunks.
• Newell and Simon’s (1972) development of the General Problem Solver.
• In 1960, Miller founded the Center for Cognitive Studies at Harvard with the famous cognitivist developmentalist, Jerome Bruner.
• Ulric Neisser (1967) publishes “ Cognitive Psychology” , which marks the official beginning of the cognitive approach.
• Process models of memory Atkinson & Shiffrin’s (1968) Multi-Store Model .
• The cognitive approach is highly influential in all areas of psychology (e.g., biological, social, neuroscience, developmental, etc.).

Issues and Debates

Free will vs. determinism.

The position of the cognitive approach is unclear as it argues, on the one hand, the way we process information is determined by our experience (schemas).

On the other hand in, the therapy derived from the approach (CBT) argues that we can change the way we think.

Nature vs. Nurture

The cognitive approach takes an interactionist view of the debate as it argues that our behavior is influenced by learning and experience (nurture), but also by some of our brains’ innate capacities as information processors e.g., language acquisition (nature).

Holism vs. Reductionism

The cognitive approach tends to be reductionist as when studying a variable, it isolates processes such as memory from other cognitive processes.

However, in our normal life, we would use many cognitive processes simultaneously, so it lacks validity.

Idiographic vs. Nomothetic

It is a nomothetic approach as it focuses on establishing theories on information processing that apply to all people.

Critical Evaluation

B.F. Skinner criticizes the cognitive approach as he believes that only external stimulus-response behavior should be studied as this can be scientifically measured.

Therefore, mediation processes (between stimulus and response) do not exist as they cannot be seen and measured. Due to its subjective and unscientific nature, Skinner continues to find problems with cognitive research methods, namely introspection (as used by Wilhelm Wundt).

Humanistic psychologist Carl Rogers believes that the use of laboratory experiments by cognitive psychology has low ecological validity and creates an artificial environment due to the control over variables . Rogers emphasizes a more holistic approach to understanding behavior.

The cognitive approach uses a very scientific method which are controlled and replicable, so the results are reliable. However, experiments lack ecological validity because of the artificiality of the tasks and environment, so it might not reflect the way people process information in their everyday life.

For example, Baddeley (1966) used lists of words to find out the encoding used by LTM, however, these words had no meaning to the participants, so the way they used their memory in this task was probably very different than they would have done if the words had meaning for them. This is a weakness as the theories might not explain how memory works outside the laboratory.

These are used to study rare conditions which provide an insight on the working of some mental processes i.e. Clive Wearing, HM. Although case studies deal with very small sample so the results cannot be generalized to the wider population as they are influenced by individual characteristics, they allow us to study cases which could not be produced experimentally because of ethical and practical reasons.

The information processing paradigm of cognitive psychology views the minds in terms of a computer when processing information. However, although there are similarities between the human mind and the operations of a computer (inputs and outputs, storage systems, the use of a central processor), the computer analogy has been criticized by many.

The approach is reductionist as it does not consider emotions and motivation, which influence the processing of information and memory. For example, according to the Yerkes-Dodson law anxiety can influence our memory.

Such machine reductionism (simplicity) ignores the influence of human emotion and motivation on the cognitive system and how this may affect our ability to process information.

Behaviorism assumes that people are born a blank slate (tabula rasa) and are not born with cognitive functions like schemas , memory or perception .

The cognitive approach does not always recognize physical ( biological psychology ) and environmental (behaviorist approach) factors in determining behavior.

Cognitive psychology has influenced and integrated with many other approaches and areas of study to produce, for example, social learning theory , cognitive neuropsychology, and artificial intelligence (AI).

Another strength is that the research conducted in this area of psychology very often has applications in the real world.

For example, cognitive behavioral therapy (CBT) has been very effective in treating depression (Hollon & Beck, 1994), and moderately effective for anxiety problems (Beck, 1993). CBT’s basis is to change how the person processes their thoughts to make them more rational or positive.

By highlighting the importance of cognitive processing, the cognitive approach can explain mental disorders such as depression, where Beck argues that it is the negative schemas we hold about the self, the world, and the future which lead to depression rather than external events.

Atkinson, R. C., & Shiffrin, R. M. (1968). Chapter: Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. The psychology of learning and motivation (Volume 2). New York: Academic Press. pp. 89–195.

Beck, A. T, & Steer, R. A. (1993). Beck Anxiety Inventory Manual. San Antonio: Harcourt Brace and Company.

Hollon, S. D., & Beck, A. T. (1994). Cognitive and cognitive-behavioral therapies. In A. E. Bergin & S.L. Garfield (Eds.), Handbook of psychotherapy and behavior change (pp. 428—466) . New York: Wiley.

Köhler, W. (1925). An aspect of Gestalt psychology. The Pedagogical Seminary and Journal of Genetic Psychology, 32(4) , 691-723.

Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review , 63 (2): 81–97.

Neisser, U (1967). Cognitive psychology . Appleton-Century-Crofts: New York

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

Tolman, E. C., Hall, C. S., & Bretnall, E. P. (1932). A disproof of the law of effect and a substitution of the laws of emphasis, motivation and disruption. Journal of Experimental Psychology, 15(6) , 601.

Tolman E. C. (1948). Cognitive maps in rats and men . Psychological Review. 55, 189–208

Wiener, N. (1948). Cybernetics or control and communication in the animal and the machine . Paris, (Hermann & Cie) & Camb. Mass. (MIT Press).

Further Reading

• Why Your Brain is Not a Computer
• Cognitive Psychology Historial Development

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7 Module 7: Thinking, Reasoning, and Problem-Solving

This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure out the solution to many problems, because you feel capable of using logic to argue a point, because you can evaluate whether the things you read and hear make sense—you do not need any special training in thinking. But this, of course, is one of the key barriers to helping people think better. If you do not believe that there is anything wrong, why try to fix it?

The human brain is indeed a remarkable thinking machine, capable of amazing, complex, creative, logical thoughts. Why, then, are we telling you that you need to learn how to think? Mainly because one major lesson from cognitive psychology is that these capabilities of the human brain are relatively infrequently realized. Many psychologists believe that people are essentially “cognitive misers.” It is not that we are lazy, but that we have a tendency to expend the least amount of mental effort necessary. Although you may not realize it, it actually takes a great deal of energy to think. Careful, deliberative reasoning and critical thinking are very difficult. Because we seem to be successful without going to the trouble of using these skills well, it feels unnecessary to develop them. As you shall see, however, there are many pitfalls in the cognitive processes described in this module. When people do not devote extra effort to learning and improving reasoning, problem solving, and critical thinking skills, they make many errors.

As is true for memory, if you develop the cognitive skills presented in this module, you will be more successful in school. It is important that you realize, however, that these skills will help you far beyond school, even more so than a good memory will. Although it is somewhat useful to have a good memory, ten years from now no potential employer will care how many questions you got right on multiple choice exams during college. All of them will, however, recognize whether you are a logical, analytical, critical thinker. With these thinking skills, you will be an effective, persuasive communicator and an excellent problem solver.

The module begins by describing different kinds of thought and knowledge, especially conceptual knowledge and critical thinking. An understanding of these differences will be valuable as you progress through school and encounter different assignments that require you to tap into different kinds of knowledge. The second section covers deductive and inductive reasoning, which are processes we use to construct and evaluate strong arguments. They are essential skills to have whenever you are trying to persuade someone (including yourself) of some point, or to respond to someone’s efforts to persuade you. The module ends with a section about problem solving. A solid understanding of the key processes involved in problem solving will help you to handle many daily challenges.

7.1. Different kinds of thought

7.2. Reasoning and Judgment

7.3. Problem Solving

READING WITH PURPOSE

Remember and understand.

By reading and studying Module 7, you should be able to remember and describe:

• Concepts and inferences (7.1)
• Procedural knowledge (7.1)
• Metacognition (7.1)
• Characteristics of critical thinking:  skepticism; identify biases, distortions, omissions, and assumptions; reasoning and problem solving skills  (7.1)
• Reasoning:  deductive reasoning, deductively valid argument, inductive reasoning, inductively strong argument, availability heuristic, representativeness heuristic  (7.2)
• Fixation:  functional fixedness, mental set  (7.3)
• Algorithms, heuristics, and the role of confirmation bias (7.3)
• Effective problem solving sequence (7.3)

By reading and thinking about how the concepts in Module 6 apply to real life, you should be able to:

• Identify which type of knowledge a piece of information is (7.1)
• Recognize examples of deductive and inductive reasoning (7.2)
• Recognize judgments that have probably been influenced by the availability heuristic (7.2)
• Recognize examples of problem solving heuristics and algorithms (7.3)

Analyze, Evaluate, and Create

By reading and thinking about Module 6, participating in classroom activities, and completing out-of-class assignments, you should be able to:

• Use the principles of critical thinking to evaluate information (7.1)
• Explain whether examples of reasoning arguments are deductively valid or inductively strong (7.2)
• Outline how you could try to solve a problem from your life using the effective problem solving sequence (7.3)

7.1. Different kinds of thought and knowledge

• Take a few minutes to write down everything that you know about dogs.
• Do you believe that:
• Psychic ability exists?
• Hypnosis is an altered state of consciousness?
• Magnet therapy is effective for relieving pain?
• Aerobic exercise is an effective treatment for depression?
• UFO’s from outer space have visited earth?

On what do you base your belief or disbelief for the questions above?

Of course, we all know what is meant by the words  think  and  knowledge . You probably also realize that they are not unitary concepts; there are different kinds of thought and knowledge. In this section, let us look at some of these differences. If you are familiar with these different kinds of thought and pay attention to them in your classes, it will help you to focus on the right goals, learn more effectively, and succeed in school. Different assignments and requirements in school call on you to use different kinds of knowledge or thought, so it will be very helpful for you to learn to recognize them (Anderson, et al. 2001).

Factual and conceptual knowledge

Module 5 introduced the idea of declarative memory, which is composed of facts and episodes. If you have ever played a trivia game or watched Jeopardy on TV, you realize that the human brain is able to hold an extraordinary number of facts. Likewise, you realize that each of us has an enormous store of episodes, essentially facts about events that happened in our own lives. It may be difficult to keep that in mind when we are struggling to retrieve one of those facts while taking an exam, however. Part of the problem is that, in contradiction to the advice from Module 5, many students continue to try to memorize course material as a series of unrelated facts (picture a history student simply trying to memorize history as a set of unrelated dates without any coherent story tying them together). Facts in the real world are not random and unorganized, however. It is the way that they are organized that constitutes a second key kind of knowledge, conceptual.

Concepts are nothing more than our mental representations of categories of things in the world. For example, think about dogs. When you do this, you might remember specific facts about dogs, such as they have fur and they bark. You may also recall dogs that you have encountered and picture them in your mind. All of this information (and more) makes up your concept of dog. You can have concepts of simple categories (e.g., triangle), complex categories (e.g., small dogs that sleep all day, eat out of the garbage, and bark at leaves), kinds of people (e.g., psychology professors), events (e.g., birthday parties), and abstract ideas (e.g., justice). Gregory Murphy (2002) refers to concepts as the “glue that holds our mental life together” (p. 1). Very simply, summarizing the world by using concepts is one of the most important cognitive tasks that we do. Our conceptual knowledge  is  our knowledge about the world. Individual concepts are related to each other to form a rich interconnected network of knowledge. For example, think about how the following concepts might be related to each other: dog, pet, play, Frisbee, chew toy, shoe. Or, of more obvious use to you now, how these concepts are related: working memory, long-term memory, declarative memory, procedural memory, and rehearsal? Because our minds have a natural tendency to organize information conceptually, when students try to remember course material as isolated facts, they are working against their strengths.

One last important point about concepts is that they allow you to instantly know a great deal of information about something. For example, if someone hands you a small red object and says, “here is an apple,” they do not have to tell you, “it is something you can eat.” You already know that you can eat it because it is true by virtue of the fact that the object is an apple; this is called drawing an  inference , assuming that something is true on the basis of your previous knowledge (for example, of category membership or of how the world works) or logical reasoning.

Procedural knowledge

Physical skills, such as tying your shoes, doing a cartwheel, and driving a car (or doing all three at the same time, but don’t try this at home) are certainly a kind of knowledge. They are procedural knowledge, the same idea as procedural memory that you saw in Module 5. Mental skills, such as reading, debating, and planning a psychology experiment, are procedural knowledge, as well. In short, procedural knowledge is the knowledge how to do something (Cohen & Eichenbaum, 1993).

Metacognitive knowledge

Floyd used to think that he had a great memory. Now, he has a better memory. Why? Because he finally realized that his memory was not as great as he once thought it was. Because Floyd eventually learned that he often forgets where he put things, he finally developed the habit of putting things in the same place. (Unfortunately, he did not learn this lesson before losing at least 5 watches and a wedding ring.) Because he finally realized that he often forgets to do things, he finally started using the To Do list app on his phone. And so on. Floyd’s insights about the real limitations of his memory have allowed him to remember things that he used to forget.

All of us have knowledge about the way our own minds work. You may know that you have a good memory for people’s names and a poor memory for math formulas. Someone else might realize that they have difficulty remembering to do things, like stopping at the store on the way home. Others still know that they tend to overlook details. This knowledge about our own thinking is actually quite important; it is called metacognitive knowledge, or  metacognition . Like other kinds of thinking skills, it is subject to error. For example, in unpublished research, one of the authors surveyed about 120 General Psychology students on the first day of the term. Among other questions, the students were asked them to predict their grade in the class and report their current Grade Point Average. Two-thirds of the students predicted that their grade in the course would be higher than their GPA. (The reality is that at our college, students tend to earn lower grades in psychology than their overall GPA.) Another example: Students routinely report that they thought they had done well on an exam, only to discover, to their dismay, that they were wrong (more on that important problem in a moment). Both errors reveal a breakdown in metacognition.

The Dunning-Kruger Effect

In general, most college students probably do not study enough. For example, using data from the National Survey of Student Engagement, Fosnacht, McCormack, and Lerma (2018) reported that first-year students at 4-year colleges in the U.S. averaged less than 14 hours per week preparing for classes. The typical suggestion is that you should spend two hours outside of class for every hour in class, or 24 – 30 hours per week for a full-time student. Clearly, students in general are nowhere near that recommended mark. Many observers, including some faculty, believe that this shortfall is a result of students being too busy or lazy. Now, it may be true that many students are too busy, with work and family obligations, for example. Others, are not particularly motivated in school, and therefore might correctly be labeled lazy. A third possible explanation, however, is that some students might not think they need to spend this much time. And this is a matter of metacognition. Consider the scenario that we mentioned above, students thinking they had done well on an exam only to discover that they did not. Justin Kruger and David Dunning examined scenarios very much like this in 1999. Kruger and Dunning gave research participants tests measuring humor, logic, and grammar. Then, they asked the participants to assess their own abilities and test performance in these areas. They found that participants in general tended to overestimate their abilities, already a problem with metacognition. Importantly, the participants who scored the lowest overestimated their abilities the most. Specifically, students who scored in the bottom quarter (averaging in the 12th percentile) thought they had scored in the 62nd percentile. This has become known as the  Dunning-Kruger effect . Many individual faculty members have replicated these results with their own student on their course exams, including the authors of this book. Think about it. Some students who just took an exam and performed poorly believe that they did well before seeing their score. It seems very likely that these are the very same students who stopped studying the night before because they thought they were “done.” Quite simply, it is not just that they did not know the material. They did not know that they did not know the material. That is poor metacognition.

In order to develop good metacognitive skills, you should continually monitor your thinking and seek frequent feedback on the accuracy of your thinking (Medina, Castleberry, & Persky 2017). For example, in classes get in the habit of predicting your exam grades. As soon as possible after taking an exam, try to find out which questions you missed and try to figure out why. If you do this soon enough, you may be able to recall the way it felt when you originally answered the question. Did you feel confident that you had answered the question correctly? Then you have just discovered an opportunity to improve your metacognition. Be on the lookout for that feeling and respond with caution.

concept :  a mental representation of a category of things in the world

Dunning-Kruger effect : individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

inference : an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

metacognition :  knowledge about one’s own cognitive processes; thinking about your thinking

Critical thinking

One particular kind of knowledge or thinking skill that is related to metacognition is  critical thinking (Chew, 2020). You may have noticed that critical thinking is an objective in many college courses, and thus it could be a legitimate topic to cover in nearly any college course. It is particularly appropriate in psychology, however. As the science of (behavior and) mental processes, psychology is obviously well suited to be the discipline through which you should be introduced to this important way of thinking.

More importantly, there is a particular need to use critical thinking in psychology. We are all, in a way, experts in human behavior and mental processes, having engaged in them literally since birth. Thus, perhaps more than in any other class, students typically approach psychology with very clear ideas and opinions about its subject matter. That is, students already “know” a lot about psychology. The problem is, “it ain’t so much the things we don’t know that get us into trouble. It’s the things we know that just ain’t so” (Ward, quoted in Gilovich 1991). Indeed, many of students’ preconceptions about psychology are just plain wrong. Randolph Smith (2002) wrote a book about critical thinking in psychology called  Challenging Your Preconceptions,  highlighting this fact. On the other hand, many of students’ preconceptions about psychology are just plain right! But wait, how do you know which of your preconceptions are right and which are wrong? And when you come across a research finding or theory in this class that contradicts your preconceptions, what will you do? Will you stick to your original idea, discounting the information from the class? Will you immediately change your mind? Critical thinking can help us sort through this confusing mess.

But what is critical thinking? The goal of critical thinking is simple to state (but extraordinarily difficult to achieve): it is to be right, to draw the correct conclusions, to believe in things that are true and to disbelieve things that are false. We will provide two definitions of critical thinking (or, if you like, one large definition with two distinct parts). First, a more conceptual one: Critical thinking is thinking like a scientist in your everyday life (Schmaltz, Jansen, & Wenckowski, 2017).  Our second definition is more operational; it is simply a list of skills that are essential to be a critical thinker. Critical thinking entails solid reasoning and problem solving skills; skepticism; and an ability to identify biases, distortions, omissions, and assumptions. Excellent deductive and inductive reasoning, and problem solving skills contribute to critical thinking. So, you can consider the subject matter of sections 7.2 and 7.3 to be part of critical thinking. Because we will be devoting considerable time to these concepts in the rest of the module, let us begin with a discussion about the other aspects of critical thinking.

Let’s address that first part of the definition. Scientists form hypotheses, or predictions about some possible future observations. Then, they collect data, or information (think of this as making those future observations). They do their best to make unbiased observations using reliable techniques that have been verified by others. Then, and only then, they draw a conclusion about what those observations mean. Oh, and do not forget the most important part. “Conclusion” is probably not the most appropriate word because this conclusion is only tentative. A scientist is always prepared that someone else might come along and produce new observations that would require a new conclusion be drawn. Wow! If you like to be right, you could do a lot worse than using a process like this.

A Critical Thinker’s Toolkit 

Now for the second part of the definition. Good critical thinkers (and scientists) rely on a variety of tools to evaluate information. Perhaps the most recognizable tool for critical thinking is  skepticism (and this term provides the clearest link to the thinking like a scientist definition, as you are about to see). Some people intend it as an insult when they call someone a skeptic. But if someone calls you a skeptic, if they are using the term correctly, you should consider it a great compliment. Simply put, skepticism is a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided. People from Missouri should recognize this principle, as Missouri is known as the Show-Me State. As a skeptic, you are not inclined to believe something just because someone said so, because someone else believes it, or because it sounds reasonable. You must be persuaded by high quality evidence.

Of course, if that evidence is produced, you have a responsibility as a skeptic to change your belief. Failure to change a belief in the face of good evidence is not skepticism; skepticism has open mindedness at its core. M. Neil Browne and Stuart Keeley (2018) use the term weak sense critical thinking to describe critical thinking behaviors that are used only to strengthen a prior belief. Strong sense critical thinking, on the other hand, has as its goal reaching the best conclusion. Sometimes that means strengthening your prior belief, but sometimes it means changing your belief to accommodate the better evidence.

Many times, a failure to think critically or weak sense critical thinking is related to a  bias , an inclination, tendency, leaning, or prejudice. Everybody has biases, but many people are unaware of them. Awareness of your own biases gives you the opportunity to control or counteract them. Unfortunately, however, many people are happy to let their biases creep into their attempts to persuade others; indeed, it is a key part of their persuasive strategy. To see how these biases influence messages, just look at the different descriptions and explanations of the same events given by people of different ages or income brackets, or conservative versus liberal commentators, or by commentators from different parts of the world. Of course, to be successful, these people who are consciously using their biases must disguise them. Even undisguised biases can be difficult to identify, so disguised ones can be nearly impossible.

Here are some common sources of biases:

• Personal values and beliefs.  Some people believe that human beings are basically driven to seek power and that they are typically in competition with one another over scarce resources. These beliefs are similar to the world-view that political scientists call “realism.” Other people believe that human beings prefer to cooperate and that, given the chance, they will do so. These beliefs are similar to the world-view known as “idealism.” For many people, these deeply held beliefs can influence, or bias, their interpretations of such wide ranging situations as the behavior of nations and their leaders or the behavior of the driver in the car ahead of you. For example, if your worldview is that people are typically in competition and someone cuts you off on the highway, you may assume that the driver did it purposely to get ahead of you. Other types of beliefs about the way the world is or the way the world should be, for example, political beliefs, can similarly become a significant source of bias.
• Racism, sexism, ageism and other forms of prejudice and bigotry.  These are, sadly, a common source of bias in many people. They are essentially a special kind of “belief about the way the world is.” These beliefs—for example, that women do not make effective leaders—lead people to ignore contradictory evidence (examples of effective women leaders, or research that disputes the belief) and to interpret ambiguous evidence in a way consistent with the belief.
• Self-interest.  When particular people benefit from things turning out a certain way, they can sometimes be very susceptible to letting that interest bias them. For example, a company that will earn a profit if they sell their product may have a bias in the way that they give information about their product. A union that will benefit if its members get a generous contract might have a bias in the way it presents information about salaries at competing organizations. (Note that our inclusion of examples describing both companies and unions is an explicit attempt to control for our own personal biases). Home buyers are often dismayed to discover that they purchased their dream house from someone whose self-interest led them to lie about flooding problems in the basement or back yard. This principle, the biasing power of self-interest, is likely what led to the famous phrase  Caveat Emptor  (let the buyer beware) .  

Knowing that these types of biases exist will help you evaluate evidence more critically. Do not forget, though, that people are not always keen to let you discover the sources of biases in their arguments. For example, companies or political organizations can sometimes disguise their support of a research study by contracting with a university professor, who comes complete with a seemingly unbiased institutional affiliation, to conduct the study.

People’s biases, conscious or unconscious, can lead them to make omissions, distortions, and assumptions that undermine our ability to correctly evaluate evidence. It is essential that you look for these elements. Always ask, what is missing, what is not as it appears, and what is being assumed here? For example, consider this (fictional) chart from an ad reporting customer satisfaction at 4 local health clubs.

Clearly, from the results of the chart, one would be tempted to give Club C a try, as customer satisfaction is much higher than for the other 3 clubs.

There are so many distortions and omissions in this chart, however, that it is actually quite meaningless. First, how was satisfaction measured? Do the bars represent responses to a survey? If so, how were the questions asked? Most importantly, where is the missing scale for the chart? Although the differences look quite large, are they really?

Well, here is the same chart, with a different scale, this time labeled:

Club C is not so impressive any more, is it? In fact, all of the health clubs have customer satisfaction ratings (whatever that means) between 85% and 88%. In the first chart, the entire scale of the graph included only the percentages between 83 and 89. This “judicious” choice of scale—some would call it a distortion—and omission of that scale from the chart make the tiny differences among the clubs seem important, however.

Also, in order to be a critical thinker, you need to learn to pay attention to the assumptions that underlie a message. Let us briefly illustrate the role of assumptions by touching on some people’s beliefs about the criminal justice system in the US. Some believe that a major problem with our judicial system is that many criminals go free because of legal technicalities. Others believe that a major problem is that many innocent people are convicted of crimes. The simple fact is, both types of errors occur. A person’s conclusion about which flaw in our judicial system is the greater tragedy is based on an assumption about which of these is the more serious error (letting the guilty go free or convicting the innocent). This type of assumption is called a value assumption (Browne and Keeley, 2018). It reflects the differences in values that people develop, differences that may lead us to disregard valid evidence that does not fit in with our particular values.

Oh, by the way, some students probably noticed this, but the seven tips for evaluating information that we shared in Module 1 are related to this. Actually, they are part of this section. The tips are, to a very large degree, set of ideas you can use to help you identify biases, distortions, omissions, and assumptions. If you do not remember this section, we strongly recommend you take a few minutes to review it.

skepticism :  a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

bias : an inclination, tendency, leaning, or prejudice

• Which of your beliefs (or disbeliefs) from the Activate exercise for this section were derived from a process of critical thinking? If some of your beliefs were not based on critical thinking, are you willing to reassess these beliefs? If the answer is no, why do you think that is? If the answer is yes, what concrete steps will you take?

7.2 Reasoning and Judgment

• What percentage of kidnappings are committed by strangers?
• Which area of the house is riskiest: kitchen, bathroom, or stairs?
• What is the most common cancer in the US?
• What percentage of workplace homicides are committed by co-workers?

An essential set of procedural thinking skills is  reasoning , the ability to generate and evaluate solid conclusions from a set of statements or evidence. You should note that these conclusions (when they are generated instead of being evaluated) are one key type of inference that we described in Section 7.1. There are two main types of reasoning, deductive and inductive.

Deductive reasoning

Suppose your teacher tells you that if you get an A on the final exam in a course, you will get an A for the whole course. Then, you get an A on the final exam. What will your final course grade be? Most people can see instantly that you can conclude with certainty that you will get an A for the course. This is a type of reasoning called  deductive reasoning , which is defined as reasoning in which a conclusion is guaranteed to be true as long as the statements leading to it are true. The three statements can be listed as an  argument , with two beginning statements and a conclusion:

Statement 1: If you get an A on the final exam, you will get an A for the course

Statement 2: You get an A on the final exam

Conclusion: You will get an A for the course

This particular arrangement, in which true beginning statements lead to a guaranteed true conclusion, is known as a  deductively valid argument . Although deductive reasoning is often the subject of abstract, brain-teasing, puzzle-like word problems, it is actually an extremely important type of everyday reasoning. It is just hard to recognize sometimes. For example, imagine that you are looking for your car keys and you realize that they are either in the kitchen drawer or in your book bag. After looking in the kitchen drawer, you instantly know that they must be in your book bag. That conclusion results from a simple deductive reasoning argument. In addition, solid deductive reasoning skills are necessary for you to succeed in the sciences, philosophy, math, computer programming, and any endeavor involving the use of logic to persuade others to your point of view or to evaluate others’ arguments.

Cognitive psychologists, and before them philosophers, have been quite interested in deductive reasoning, not so much for its practical applications, but for the insights it can offer them about the ways that human beings think. One of the early ideas to emerge from the examination of deductive reasoning is that people learn (or develop) mental versions of rules that allow them to solve these types of reasoning problems (Braine, 1978; Braine, Reiser, & Rumain, 1984). The best way to see this point of view is to realize that there are different possible rules, and some of them are very simple. For example, consider this rule of logic:

therefore q

Logical rules are often presented abstractly, as letters, in order to imply that they can be used in very many specific situations. Here is a concrete version of the of the same rule:

I’ll either have pizza or a hamburger for dinner tonight (p or q)

I won’t have pizza (not p)

Therefore, I’ll have a hamburger (therefore q)

This kind of reasoning seems so natural, so easy, that it is quite plausible that we would use a version of this rule in our daily lives. At least, it seems more plausible than some of the alternative possibilities—for example, that we need to have experience with the specific situation (pizza or hamburger, in this case) in order to solve this type of problem easily. So perhaps there is a form of natural logic (Rips, 1990) that contains very simple versions of logical rules. When we are faced with a reasoning problem that maps onto one of these rules, we use the rule.

But be very careful; things are not always as easy as they seem. Even these simple rules are not so simple. For example, consider the following rule. Many people fail to realize that this rule is just as valid as the pizza or hamburger rule above.

if p, then q

therefore, not p

Concrete version:

If I eat dinner, then I will have dessert

I did not have dessert

Therefore, I did not eat dinner

The simple fact is, it can be very difficult for people to apply rules of deductive logic correctly; as a result, they make many errors when trying to do so. Is this a deductively valid argument or not?

Students who like school study a lot

Students who study a lot get good grades

Jane does not like school

Therefore, Jane does not get good grades

Many people are surprised to discover that this is not a logically valid argument; the conclusion is not guaranteed to be true from the beginning statements. Although the first statement says that students who like school study a lot, it does NOT say that students who do not like school do not study a lot. In other words, it may very well be possible to study a lot without liking school. Even people who sometimes get problems like this right might not be using the rules of deductive reasoning. Instead, they might just be making judgments for examples they know, in this case, remembering instances of people who get good grades despite not liking school.

Making deductive reasoning even more difficult is the fact that there are two important properties that an argument may have. One, it can be valid or invalid (meaning that the conclusion does or does not follow logically from the statements leading up to it). Two, an argument (or more correctly, its conclusion) can be true or false. Here is an example of an argument that is logically valid, but has a false conclusion (at least we think it is false).

Either you are eleven feet tall or the Grand Canyon was created by a spaceship crashing into the earth.

You are not eleven feet tall

Therefore the Grand Canyon was created by a spaceship crashing into the earth

This argument has the exact same form as the pizza or hamburger argument above, making it is deductively valid. The conclusion is so false, however, that it is absurd (of course, the reason the conclusion is false is that the first statement is false). When people are judging arguments, they tend to not observe the difference between deductive validity and the empirical truth of statements or conclusions. If the elements of an argument happen to be true, people are likely to judge the argument logically valid; if the elements are false, they will very likely judge it invalid (Markovits & Bouffard-Bouchard, 1992; Moshman & Franks, 1986). Thus, it seems a stretch to say that people are using these logical rules to judge the validity of arguments. Many psychologists believe that most people actually have very limited deductive reasoning skills (Johnson-Laird, 1999). They argue that when faced with a problem for which deductive logic is required, people resort to some simpler technique, such as matching terms that appear in the statements and the conclusion (Evans, 1982). This might not seem like a problem, but what if reasoners believe that the elements are true and they happen to be wrong; they will would believe that they are using a form of reasoning that guarantees they are correct and yet be wrong.

deductive reasoning :  a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

argument :  a set of statements in which the beginning statements lead to a conclusion

deductively valid argument :  an argument for which true beginning statements guarantee that the conclusion is true

Inductive reasoning and judgment

Every day, you make many judgments about the likelihood of one thing or another. Whether you realize it or not, you are practicing  inductive reasoning   on a daily basis. In inductive reasoning arguments, a conclusion is likely whenever the statements preceding it are true. The first thing to notice about inductive reasoning is that, by definition, you can never be sure about your conclusion; you can only estimate how likely the conclusion is. Inductive reasoning may lead you to focus on Memory Encoding and Recoding when you study for the exam, but it is possible the instructor will ask more questions about Memory Retrieval instead. Unlike deductive reasoning, the conclusions you reach through inductive reasoning are only probable, not certain. That is why scientists consider inductive reasoning weaker than deductive reasoning. But imagine how hard it would be for us to function if we could not act unless we were certain about the outcome.

Inductive reasoning can be represented as logical arguments consisting of statements and a conclusion, just as deductive reasoning can be. In an inductive argument, you are given some statements and a conclusion (or you are given some statements and must draw a conclusion). An argument is  inductively strong   if the conclusion would be very probable whenever the statements are true. So, for example, here is an inductively strong argument:

• Statement #1: The forecaster on Channel 2 said it is going to rain today.
• Statement #2: The forecaster on Channel 5 said it is going to rain today.
• Statement #3: It is very cloudy and humid.
• Statement #4: You just heard thunder.
• Conclusion (or judgment): It is going to rain today.

Think of the statements as evidence, on the basis of which you will draw a conclusion. So, based on the evidence presented in the four statements, it is very likely that it will rain today. Will it definitely rain today? Certainly not. We can all think of times that the weather forecaster was wrong.

A true story: Some years ago psychology student was watching a baseball playoff game between the St. Louis Cardinals and the Los Angeles Dodgers. A graphic on the screen had just informed the audience that the Cardinal at bat, (Hall of Fame shortstop) Ozzie Smith, a switch hitter batting left-handed for this plate appearance, had never, in nearly 3000 career at-bats, hit a home run left-handed. The student, who had just learned about inductive reasoning in his psychology class, turned to his companion (a Cardinals fan) and smugly said, “It is an inductively strong argument that Ozzie Smith will not hit a home run.” He turned back to face the television just in time to watch the ball sail over the right field fence for a home run. Although the student felt foolish at the time, he was not wrong. It was an inductively strong argument; 3000 at-bats is an awful lot of evidence suggesting that the Wizard of Ozz (as he was known) would not be hitting one out of the park (think of each at-bat without a home run as a statement in an inductive argument). Sadly (for the die-hard Cubs fan and Cardinals-hating student), despite the strength of the argument, the conclusion was wrong.

Given the possibility that we might draw an incorrect conclusion even with an inductively strong argument, we really want to be sure that we do, in fact, make inductively strong arguments. If we judge something probable, it had better be probable. If we judge something nearly impossible, it had better not happen. Think of inductive reasoning, then, as making reasonably accurate judgments of the probability of some conclusion given a set of evidence.

We base many decisions in our lives on inductive reasoning. For example:

Statement #1: Psychology is not my best subject

Statement #2: My psychology instructor has a reputation for giving difficult exams

Statement #3: My first psychology exam was much harder than I expected

Judgment: The next exam will probably be very difficult.

Decision: I will study tonight instead of watching Netflix.

Some other examples of judgments that people commonly make in a school context include judgments of the likelihood that:

• A particular class will be interesting/useful/difficult
• You will be able to finish writing a paper by next week if you go out tonight
• Your laptop’s battery will last through the next trip to the library
• You will not miss anything important if you skip class tomorrow
• Your instructor will not notice if you skip class tomorrow
• You will be able to find a book that you will need for a paper
• There will be an essay question about Memory Encoding on the next exam

Tversky and Kahneman (1983) recognized that there are two general ways that we might make these judgments; they termed them extensional (i.e., following the laws of probability) and intuitive (i.e., using shortcuts or heuristics, see below). We will use a similar distinction between Type 1 and Type 2 thinking, as described by Keith Stanovich and his colleagues (Evans and Stanovich, 2013; Stanovich and West, 2000). Type 1 thinking is fast, automatic, effortful, and emotional. In fact, it is hardly fair to call it reasoning at all, as judgments just seem to pop into one’s head. Type 2 thinking , on the other hand, is slow, effortful, and logical. So obviously, it is more likely to lead to a correct judgment, or an optimal decision. The problem is, we tend to over-rely on Type 1. Now, we are not saying that Type 2 is the right way to go for every decision or judgment we make. It seems a bit much, for example, to engage in a step-by-step logical reasoning procedure to decide whether we will have chicken or fish for dinner tonight.

Many bad decisions in some very important contexts, however, can be traced back to poor judgments of the likelihood of certain risks or outcomes that result from the use of Type 1 when a more logical reasoning process would have been more appropriate. For example:

Statement #1: It is late at night.

Statement #2: Albert has been drinking beer for the past five hours at a party.

Statement #3: Albert is not exactly sure where he is or how far away home is.

Judgment: Albert will have no difficulty walking home.

Decision: He walks home alone.

As you can see in this example, the three statements backing up the judgment do not really support it. In other words, this argument is not inductively strong because it is based on judgments that ignore the laws of probability. What are the chances that someone facing these conditions will be able to walk home alone easily? And one need not be drunk to make poor decisions based on judgments that just pop into our heads.

The truth is that many of our probability judgments do not come very close to what the laws of probability say they should be. Think about it. In order for us to reason in accordance with these laws, we would need to know the laws of probability, which would allow us to calculate the relationship between particular pieces of evidence and the probability of some outcome (i.e., how much likelihood should change given a piece of evidence), and we would have to do these heavy math calculations in our heads. After all, that is what Type 2 requires. Needless to say, even if we were motivated, we often do not even know how to apply Type 2 reasoning in many cases.

So what do we do when we don’t have the knowledge, skills, or time required to make the correct mathematical judgment? Do we hold off and wait until we can get better evidence? Do we read up on probability and fire up our calculator app so we can compute the correct probability? Of course not. We rely on Type 1 thinking. We “wing it.” That is, we come up with a likelihood estimate using some means at our disposal. Psychologists use the term heuristic to describe the type of “winging it” we are talking about. A  heuristic   is a shortcut strategy that we use to make some judgment or solve some problem (see Section 7.3). Heuristics are easy and quick, think of them as the basic procedures that are characteristic of Type 1.  They can absolutely lead to reasonably good judgments and decisions in some situations (like choosing between chicken and fish for dinner). They are, however, far from foolproof. There are, in fact, quite a lot of situations in which heuristics can lead us to make incorrect judgments, and in many cases the decisions based on those judgments can have serious consequences.

Let us return to the activity that begins this section. You were asked to judge the likelihood (or frequency) of certain events and risks. You were free to come up with your own evidence (or statements) to make these judgments. This is where a heuristic crops up. As a judgment shortcut, we tend to generate specific examples of those very events to help us decide their likelihood or frequency. For example, if we are asked to judge how common, frequent, or likely a particular type of cancer is, many of our statements would be examples of specific cancer cases:

Statement #1: Andy Kaufman (comedian) had lung cancer.

Statement #2: Colin Powell (US Secretary of State) had prostate cancer.

Statement #3: Bob Marley (musician) had skin and brain cancer

Statement #4: Sandra Day O’Connor (Supreme Court Justice) had breast cancer.

Statement #5: Fred Rogers (children’s entertainer) had stomach cancer.

Statement #6: Robin Roberts (news anchor) had breast cancer.

Statement #7: Bette Davis (actress) had breast cancer.

Judgment: Breast cancer is the most common type.

Your own experience or memory may also tell you that breast cancer is the most common type. But it is not (although it is common). Actually, skin cancer is the most common type in the US. We make the same types of misjudgments all the time because we do not generate the examples or evidence according to their actual frequencies or probabilities. Instead, we have a tendency (or bias) to search for the examples in memory; if they are easy to retrieve, we assume that they are common. To rephrase this in the language of the heuristic, events seem more likely to the extent that they are available to memory. This bias has been termed the  availability heuristic   (Kahneman and Tversky, 1974).

The fact that we use the availability heuristic does not automatically mean that our judgment is wrong. The reason we use heuristics in the first place is that they work fairly well in many cases (and, of course that they are easy to use). So, the easiest examples to think of sometimes are the most common ones. Is it more likely that a member of the U.S. Senate is a man or a woman? Most people have a much easier time generating examples of male senators. And as it turns out, the U.S. Senate has many more men than women (74 to 26 in 2020). In this case, then, the availability heuristic would lead you to make the correct judgment; it is far more likely that a senator would be a man.

In many other cases, however, the availability heuristic will lead us astray. This is because events can be memorable for many reasons other than their frequency. Section 5.2, Encoding Meaning, suggested that one good way to encode the meaning of some information is to form a mental image of it. Thus, information that has been pictured mentally will be more available to memory. Indeed, an event that is vivid and easily pictured will trick many people into supposing that type of event is more common than it actually is. Repetition of information will also make it more memorable. So, if the same event is described to you in a magazine, on the evening news, on a podcast that you listen to, and in your Facebook feed; it will be very available to memory. Again, the availability heuristic will cause you to misperceive the frequency of these types of events.

Most interestingly, information that is unusual is more memorable. Suppose we give you the following list of words to remember: box, flower, letter, platypus, oven, boat, newspaper, purse, drum, car. Very likely, the easiest word to remember would be platypus, the unusual one. The same thing occurs with memories of events. An event may be available to memory because it is unusual, yet the availability heuristic leads us to judge that the event is common. Did you catch that? In these cases, the availability heuristic makes us think the exact opposite of the true frequency. We end up thinking something is common because it is unusual (and therefore memorable). Yikes.

The misapplication of the availability heuristic sometimes has unfortunate results. For example, if you went to K-12 school in the US over the past 10 years, it is extremely likely that you have participated in lockdown and active shooter drills. Of course, everyone is trying to prevent the tragedy of another school shooting. And believe us, we are not trying to minimize how terrible the tragedy is. But the truth of the matter is, school shootings are extremely rare. Because the federal government does not keep a database of school shootings, the Washington Post has maintained their own running tally. Between 1999 and January 2020 (the date of the most recent school shooting with a death in the US at of the time this paragraph was written), the Post reported a total of 254 people died in school shootings in the US. Not 254 per year, 254 total. That is an average of 12 per year. Of course, that is 254 people who should not have died (particularly because many were children), but in a country with approximately 60,000,000 students and teachers, this is a very small risk.

But many students and teachers are terrified that they will be victims of school shootings because of the availability heuristic. It is so easy to think of examples (they are very available to memory) that people believe the event is very common. It is not. And there is a downside to this. We happen to believe that there is an enormous gun violence problem in the United States. According the the Centers for Disease Control and Prevention, there were 39,773 firearm deaths in the US in 2017. Fifteen of those deaths were in school shootings, according to the Post. 60% of those deaths were suicides. When people pay attention to the school shooting risk (low), they often fail to notice the much larger risk.

And examples like this are by no means unique. The authors of this book have been teaching psychology since the 1990’s. We have been able to make the exact same arguments about the misapplication of the availability heuristics and keep them current by simply swapping out for the “fear of the day.” In the 1990’s it was children being kidnapped by strangers (it was known as “stranger danger”) despite the facts that kidnappings accounted for only 2% of the violent crimes committed against children, and only 24% of kidnappings are committed by strangers (US Department of Justice, 2007). This fear overlapped with the fear of terrorism that gripped the country after the 2001 terrorist attacks on the World Trade Center and US Pentagon and still plagues the population of the US somewhat in 2020. After a well-publicized, sensational act of violence, people are extremely likely to increase their estimates of the chances that they, too, will be victims of terror. Think about the reality, however. In October of 2001, a terrorist mailed anthrax spores to members of the US government and a number of media companies. A total of five people died as a result of this attack. The nation was nearly paralyzed by the fear of dying from the attack; in reality the probability of an individual person dying was 0.00000002.

The availability heuristic can lead you to make incorrect judgments in a school setting as well. For example, suppose you are trying to decide if you should take a class from a particular math professor. You might try to make a judgment of how good a teacher she is by recalling instances of friends and acquaintances making comments about her teaching skill. You may have some examples that suggest that she is a poor teacher very available to memory, so on the basis of the availability heuristic you judge her a poor teacher and decide to take the class from someone else. What if, however, the instances you recalled were all from the same person, and this person happens to be a very colorful storyteller? The subsequent ease of remembering the instances might not indicate that the professor is a poor teacher after all.

Although the availability heuristic is obviously important, it is not the only judgment heuristic we use. Amos Tversky and Daniel Kahneman examined the role of heuristics in inductive reasoning in a long series of studies. Kahneman received a Nobel Prize in Economics for this research in 2002, and Tversky would have certainly received one as well if he had not died of melanoma at age 59 in 1996 (Nobel Prizes are not awarded posthumously). Kahneman and Tversky demonstrated repeatedly that people do not reason in ways that are consistent with the laws of probability. They identified several heuristic strategies that people use instead to make judgments about likelihood. The importance of this work for economics (and the reason that Kahneman was awarded the Nobel Prize) is that earlier economic theories had assumed that people do make judgments rationally, that is, in agreement with the laws of probability.

Another common heuristic that people use for making judgments is the  representativeness heuristic (Kahneman & Tversky 1973). Suppose we describe a person to you. He is quiet and shy, has an unassuming personality, and likes to work with numbers. Is this person more likely to be an accountant or an attorney? If you said accountant, you were probably using the representativeness heuristic. Our imaginary person is judged likely to be an accountant because he resembles, or is representative of the concept of, an accountant. When research participants are asked to make judgments such as these, the only thing that seems to matter is the representativeness of the description. For example, if told that the person described is in a room that contains 70 attorneys and 30 accountants, participants will still assume that he is an accountant.

inductive reasoning :  a type of reasoning in which we make judgments about likelihood from sets of evidence

inductively strong argument :  an inductive argument in which the beginning statements lead to a conclusion that is probably true

heuristic :  a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

availability heuristic :  judging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

representativeness heuristic:   judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

Type 1 thinking : fast, automatic, and emotional thinking.

Type 2 thinking : slow, effortful, and logical thinking.

• What percentage of workplace homicides are co-worker violence?

Many people get these questions wrong. The answers are 10%; stairs; skin; 6%. How close were your answers? Explain how the availability heuristic might have led you to make the incorrect judgments.

• Can you think of some other judgments that you have made (or beliefs that you have) that might have been influenced by the availability heuristic?

7.3 Problem Solving

• Please take a few minutes to list a number of problems that you are facing right now.
• Now write about a problem that you recently solved.
• What is your definition of a problem?

Mary has a problem. Her daughter, ordinarily quite eager to please, appears to delight in being the last person to do anything. Whether getting ready for school, going to piano lessons or karate class, or even going out with her friends, she seems unwilling or unable to get ready on time. Other people have different kinds of problems. For example, many students work at jobs, have numerous family commitments, and are facing a course schedule full of difficult exams, assignments, papers, and speeches. How can they find enough time to devote to their studies and still fulfill their other obligations? Speaking of students and their problems: Show that a ball thrown vertically upward with initial velocity v0 takes twice as much time to return as to reach the highest point (from Spiegel, 1981).

These are three very different situations, but we have called them all problems. What makes them all the same, despite the differences? A psychologist might define a  problem   as a situation with an initial state, a goal state, and a set of possible intermediate states. Somewhat more meaningfully, we might consider a problem a situation in which you are in here one state (e.g., daughter is always late), you want to be there in another state (e.g., daughter is not always late), and with no obvious way to get from here to there. Defined this way, each of the three situations we outlined can now be seen as an example of the same general concept, a problem. At this point, you might begin to wonder what is not a problem, given such a general definition. It seems that nearly every non-routine task we engage in could qualify as a problem. As long as you realize that problems are not necessarily bad (it can be quite fun and satisfying to rise to the challenge and solve a problem), this may be a useful way to think about it.

Can we identify a set of problem-solving skills that would apply to these very different kinds of situations? That task, in a nutshell, is a major goal of this section. Let us try to begin to make sense of the wide variety of ways that problems can be solved with an important observation: the process of solving problems can be divided into two key parts. First, people have to notice, comprehend, and represent the problem properly in their minds (called  problem representation ). Second, they have to apply some kind of solution strategy to the problem. Psychologists have studied both of these key parts of the process in detail.

When you first think about the problem-solving process, you might guess that most of our difficulties would occur because we are failing in the second step, the application of strategies. Although this can be a significant difficulty much of the time, the more important source of difficulty is probably problem representation. In short, we often fail to solve a problem because we are looking at it, or thinking about it, the wrong way.

problem :  a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

problem representation :  noticing, comprehending and forming a mental conception of a problem

Defining and Mentally Representing Problems in Order to Solve Them

So, the main obstacle to solving a problem is that we do not clearly understand exactly what the problem is. Recall the problem with Mary’s daughter always being late. One way to represent, or to think about, this problem is that she is being defiant. She refuses to get ready in time. This type of representation or definition suggests a particular type of solution. Another way to think about the problem, however, is to consider the possibility that she is simply being sidetracked by interesting diversions. This different conception of what the problem is (i.e., different representation) suggests a very different solution strategy. For example, if Mary defines the problem as defiance, she may be tempted to solve the problem using some kind of coercive tactics, that is, to assert her authority as her mother and force her to listen. On the other hand, if Mary defines the problem as distraction, she may try to solve it by simply removing the distracting objects.

As you might guess, when a problem is represented one way, the solution may seem very difficult, or even impossible. Seen another way, the solution might be very easy. For example, consider the following problem (from Nasar, 1998):

Two bicyclists start 20 miles apart and head toward each other, each going at a steady rate of 10 miles per hour. At the same time, a fly that travels at a steady 15 miles per hour starts from the front wheel of the southbound bicycle and flies to the front wheel of the northbound one, then turns around and flies to the front wheel of the southbound one again, and continues in this manner until he is crushed between the two front wheels. Question: what total distance did the fly cover?

Please take a few minutes to try to solve this problem.

Most people represent this problem as a question about a fly because, well, that is how the question is asked. The solution, using this representation, is to figure out how far the fly travels on the first leg of its journey, then add this total to how far it travels on the second leg of its journey (when it turns around and returns to the first bicycle), then continue to add the smaller distance from each leg of the journey until you converge on the correct answer. You would have to be quite skilled at math to solve this problem, and you would probably need some time and pencil and paper to do it.

If you consider a different representation, however, you can solve this problem in your head. Instead of thinking about it as a question about a fly, think about it as a question about the bicycles. They are 20 miles apart, and each is traveling 10 miles per hour. How long will it take for the bicycles to reach each other? Right, one hour. The fly is traveling 15 miles per hour; therefore, it will travel a total of 15 miles back and forth in the hour before the bicycles meet. Represented one way (as a problem about a fly), the problem is quite difficult. Represented another way (as a problem about two bicycles), it is easy. Changing your representation of a problem is sometimes the best—sometimes the only—way to solve it.

Unfortunately, however, changing a problem’s representation is not the easiest thing in the world to do. Often, problem solvers get stuck looking at a problem one way. This is called  fixation . Most people who represent the preceding problem as a problem about a fly probably do not pause to reconsider, and consequently change, their representation. A parent who thinks her daughter is being defiant is unlikely to consider the possibility that her behavior is far less purposeful.

Problem-solving fixation was examined by a group of German psychologists called Gestalt psychologists during the 1930’s and 1940’s. Karl Dunker, for example, discovered an important type of failure to take a different perspective called  functional fixedness . Imagine being a participant in one of his experiments. You are asked to figure out how to mount two candles on a door and are given an assortment of odds and ends, including a small empty cardboard box and some thumbtacks. Perhaps you have already figured out a solution: tack the box to the door so it forms a platform, then put the candles on top of the box. Most people are able to arrive at this solution. Imagine a slight variation of the procedure, however. What if, instead of being empty, the box had matches in it? Most people given this version of the problem do not arrive at the solution given above. Why? Because it seems to people that when the box contains matches, it already has a function; it is a matchbox. People are unlikely to consider a new function for an object that already has a function. This is functional fixedness.

Mental set is a type of fixation in which the problem solver gets stuck using the same solution strategy that has been successful in the past, even though the solution may no longer be useful. It is commonly seen when students do math problems for homework. Often, several problems in a row require the reapplication of the same solution strategy. Then, without warning, the next problem in the set requires a new strategy. Many students attempt to apply the formerly successful strategy on the new problem and therefore cannot come up with a correct answer.

The thing to remember is that you cannot solve a problem unless you correctly identify what it is to begin with (initial state) and what you want the end result to be (goal state). That may mean looking at the problem from a different angle and representing it in a new way. The correct representation does not guarantee a successful solution, but it certainly puts you on the right track.

A bit more optimistically, the Gestalt psychologists discovered what may be considered the opposite of fixation, namely  insight . Sometimes the solution to a problem just seems to pop into your head. Wolfgang Kohler examined insight by posing many different problems to chimpanzees, principally problems pertaining to their acquisition of out-of-reach food. In one version, a banana was placed outside of a chimpanzee’s cage and a short stick inside the cage. The stick was too short to retrieve the banana, but was long enough to retrieve a longer stick also located outside of the cage. This second stick was long enough to retrieve the banana. After trying, and failing, to reach the banana with the shorter stick, the chimpanzee would try a couple of random-seeming attempts, react with some apparent frustration or anger, then suddenly rush to the longer stick, the correct solution fully realized at this point. This sudden appearance of the solution, observed many times with many different problems, was termed insight by Kohler.

Lest you think it pertains to chimpanzees only, Karl Dunker demonstrated that children also solve problems through insight in the 1930s. More importantly, you have probably experienced insight yourself. Think back to a time when you were trying to solve a difficult problem. After struggling for a while, you gave up. Hours later, the solution just popped into your head, perhaps when you were taking a walk, eating dinner, or lying in bed.

fixation :  when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

functional fixedness :  a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

mental set :  a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

insight :  a sudden realization of a solution to a problem

Solving Problems by Trial and Error

Correctly identifying the problem and your goal for a solution is a good start, but recall the psychologist’s definition of a problem: it includes a set of possible intermediate states. Viewed this way, a problem can be solved satisfactorily only if one can find a path through some of these intermediate states to the goal. Imagine a fairly routine problem, finding a new route to school when your ordinary route is blocked (by road construction, for example). At each intersection, you may turn left, turn right, or go straight. A satisfactory solution to the problem (of getting to school) is a sequence of selections at each intersection that allows you to wind up at school.

If you had all the time in the world to get to school, you might try choosing intermediate states randomly. At one corner you turn left, the next you go straight, then you go left again, then right, then right, then straight. Unfortunately, trial and error will not necessarily get you where you want to go, and even if it does, it is not the fastest way to get there. For example, when a friend of ours was in college, he got lost on the way to a concert and attempted to find the venue by choosing streets to turn onto randomly (this was long before the use of GPS). Amazingly enough, the strategy worked, although he did end up missing two out of the three bands who played that night.

Trial and error is not all bad, however. B.F. Skinner, a prominent behaviorist psychologist, suggested that people often behave randomly in order to see what effect the behavior has on the environment and what subsequent effect this environmental change has on them. This seems particularly true for the very young person. Picture a child filling a household’s fish tank with toilet paper, for example. To a child trying to develop a repertoire of creative problem-solving strategies, an odd and random behavior might be just the ticket. Eventually, the exasperated parent hopes, the child will discover that many of these random behaviors do not successfully solve problems; in fact, in many cases they create problems. Thus, one would expect a decrease in this random behavior as a child matures. You should realize, however, that the opposite extreme is equally counterproductive. If the children become too rigid, never trying something unexpected and new, their problem solving skills can become too limited.

Effective problem solving seems to call for a happy medium that strikes a balance between using well-founded old strategies and trying new ground and territory. The individual who recognizes a situation in which an old problem-solving strategy would work best, and who can also recognize a situation in which a new untested strategy is necessary is halfway to success.

Solving Problems with Algorithms and Heuristics

For many problems there is a possible strategy available that will guarantee a correct solution. For example, think about math problems. Math lessons often consist of step-by-step procedures that can be used to solve the problems. If you apply the strategy without error, you are guaranteed to arrive at the correct solution to the problem. This approach is called using an  algorithm , a term that denotes the step-by-step procedure that guarantees a correct solution. Because algorithms are sometimes available and come with a guarantee, you might think that most people use them frequently. Unfortunately, however, they do not. As the experience of many students who have struggled through math classes can attest, algorithms can be extremely difficult to use, even when the problem solver knows which algorithm is supposed to work in solving the problem. In problems outside of math class, we often do not even know if an algorithm is available. It is probably fair to say, then, that algorithms are rarely used when people try to solve problems.

Because algorithms are so difficult to use, people often pass up the opportunity to guarantee a correct solution in favor of a strategy that is much easier to use and yields a reasonable chance of coming up with a correct solution. These strategies are called  problem solving heuristics . Similar to what you saw in section 6.2 with reasoning heuristics, a problem solving heuristic is a shortcut strategy that people use when trying to solve problems. It usually works pretty well, but does not guarantee a correct solution to the problem. For example, one problem solving heuristic might be “always move toward the goal” (so when trying to get to school when your regular route is blocked, you would always turn in the direction you think the school is). A heuristic that people might use when doing math homework is “use the same solution strategy that you just used for the previous problem.”

By the way, we hope these last two paragraphs feel familiar to you. They seem to parallel a distinction that you recently learned. Indeed, algorithms and problem-solving heuristics are another example of the distinction between Type 1 thinking and Type 2 thinking.

Although it is probably not worth describing a large number of specific heuristics, two observations about heuristics are worth mentioning. First, heuristics can be very general or they can be very specific, pertaining to a particular type of problem only. For example, “always move toward the goal” is a general strategy that you can apply to countless problem situations. On the other hand, “when you are lost without a functioning gps, pick the most expensive car you can see and follow it” is specific to the problem of being lost. Second, all heuristics are not equally useful. One heuristic that many students know is “when in doubt, choose c for a question on a multiple-choice exam.” This is a dreadful strategy because many instructors intentionally randomize the order of answer choices. Another test-taking heuristic, somewhat more useful, is “look for the answer to one question somewhere else on the exam.”

You really should pay attention to the application of heuristics to test taking. Imagine that while reviewing your answers for a multiple-choice exam before turning it in, you come across a question for which you originally thought the answer was c. Upon reflection, you now think that the answer might be b. Should you change the answer to b, or should you stick with your first impression? Most people will apply the heuristic strategy to “stick with your first impression.” What they do not realize, of course, is that this is a very poor strategy (Lilienfeld et al, 2009). Most of the errors on exams come on questions that were answered wrong originally and were not changed (so they remain wrong). There are many fewer errors where we change a correct answer to an incorrect answer. And, of course, sometimes we change an incorrect answer to a correct answer. In fact, research has shown that it is more common to change a wrong answer to a right answer than vice versa (Bruno, 2001).

The belief in this poor test-taking strategy (stick with your first impression) is based on the  confirmation bias   (Nickerson, 1998; Wason, 1960). You first saw the confirmation bias in Module 1, but because it is so important, we will repeat the information here. People have a bias, or tendency, to notice information that confirms what they already believe. Somebody at one time told you to stick with your first impression, so when you look at the results of an exam you have taken, you will tend to notice the cases that are consistent with that belief. That is, you will notice the cases in which you originally had an answer correct and changed it to the wrong answer. You tend not to notice the other two important (and more common) cases, changing an answer from wrong to right, and leaving a wrong answer unchanged.

Because heuristics by definition do not guarantee a correct solution to a problem, mistakes are bound to occur when we employ them. A poor choice of a specific heuristic will lead to an even higher likelihood of making an error.

algorithm :  a step-by-step procedure that guarantees a correct solution to a problem

problem solving heuristic :  a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

confirmation bias :  people’s tendency to notice information that confirms what they already believe

An Effective Problem-Solving Sequence

You may be left with a big question: If algorithms are hard to use and heuristics often don’t work, how am I supposed to solve problems? Robert Sternberg (1996), as part of his theory of what makes people successfully intelligent (Module 8) described a problem-solving sequence that has been shown to work rather well:

• Identify the existence of a problem.  In school, problem identification is often easy; problems that you encounter in math classes, for example, are conveniently labeled as problems for you. Outside of school, however, realizing that you have a problem is a key difficulty that you must get past in order to begin solving it. You must be very sensitive to the symptoms that indicate a problem.
• Define the problem.  Suppose you realize that you have been having many headaches recently. Very likely, you would identify this as a problem. If you define the problem as “headaches,” the solution would probably be to take aspirin or ibuprofen or some other anti-inflammatory medication. If the headaches keep returning, however, you have not really solved the problem—likely because you have mistaken a symptom for the problem itself. Instead, you must find the root cause of the headaches. Stress might be the real problem. For you to successfully solve many problems it may be necessary for you to overcome your fixations and represent the problems differently. One specific strategy that you might find useful is to try to define the problem from someone else’s perspective. How would your parents, spouse, significant other, doctor, etc. define the problem? Somewhere in these different perspectives may lurk the key definition that will allow you to find an easier and permanent solution.
• Formulate strategy.  Now it is time to begin planning exactly how the problem will be solved. Is there an algorithm or heuristic available for you to use? Remember, heuristics by their very nature guarantee that occasionally you will not be able to solve the problem. One point to keep in mind is that you should look for long-range solutions, which are more likely to address the root cause of a problem than short-range solutions.
• Represent and organize information.  Similar to the way that the problem itself can be defined, or represented in multiple ways, information within the problem is open to different interpretations. Suppose you are studying for a big exam. You have chapters from a textbook and from a supplemental reader, along with lecture notes that all need to be studied. How should you (represent and) organize these materials? Should you separate them by type of material (text versus reader versus lecture notes), or should you separate them by topic? To solve problems effectively, you must learn to find the most useful representation and organization of information.
• Allocate resources.  This is perhaps the simplest principle of the problem solving sequence, but it is extremely difficult for many people. First, you must decide whether time, money, skills, effort, goodwill, or some other resource would help to solve the problem Then, you must make the hard choice of deciding which resources to use, realizing that you cannot devote maximum resources to every problem. Very often, the solution to problem is simply to change how resources are allocated (for example, spending more time studying in order to improve grades).
• Monitor and evaluate solutions.  Pay attention to the solution strategy while you are applying it. If it is not working, you may be able to select another strategy. Another fact you should realize about problem solving is that it never does end. Solving one problem frequently brings up new ones. Good monitoring and evaluation of your problem solutions can help you to anticipate and get a jump on solving the inevitable new problems that will arise.

Please note that this as  an  effective problem-solving sequence, not  the  effective problem solving sequence. Just as you can become fixated and end up representing the problem incorrectly or trying an inefficient solution, you can become stuck applying the problem-solving sequence in an inflexible way. Clearly there are problem situations that can be solved without using these skills in this order.

Additionally, many real-world problems may require that you go back and redefine a problem several times as the situation changes (Sternberg et al. 2000). For example, consider the problem with Mary’s daughter one last time. At first, Mary did represent the problem as one of defiance. When her early strategy of pleading and threatening punishment was unsuccessful, Mary began to observe her daughter more carefully. She noticed that, indeed, her daughter’s attention would be drawn by an irresistible distraction or book. Fresh with a re-representation of the problem, she began a new solution strategy. She began to remind her daughter every few minutes to stay on task and remind her that if she is ready before it is time to leave, she may return to the book or other distracting object at that time. Fortunately, this strategy was successful, so Mary did not have to go back and redefine the problem again.

Pick one or two of the problems that you listed when you first started studying this section and try to work out the steps of Sternberg’s problem solving sequence for each one.

a mental representation of a category of things in the world

an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

knowledge about one’s own cognitive processes; thinking about your thinking

individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

Thinking like a scientist in your everyday life for the purpose of drawing correct conclusions. It entails skepticism; an ability to identify biases, distortions, omissions, and assumptions; and excellent deductive and inductive reasoning, and problem solving skills.

a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

an inclination, tendency, leaning, or prejudice

a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

a set of statements in which the beginning statements lead to a conclusion

an argument for which true beginning statements guarantee that the conclusion is true

a type of reasoning in which we make judgments about likelihood from sets of evidence

an inductive argument in which the beginning statements lead to a conclusion that is probably true

fast, automatic, and emotional thinking

slow, effortful, and logical thinking

a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

udging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

noticing, comprehending and forming a mental conception of a problem

when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

a sudden realization of a solution to a problem

a step-by-step procedure that guarantees a correct solution to a problem

The tendency to notice and pay attention to information that confirms your prior beliefs and to ignore information that disconfirms them.

a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

Introduction to Psychology Copyright © 2020 by Ken Gray; Elizabeth Arnott-Hill; and Or'Shaundra Benson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Cognition in Psychology

How People Think and What's Involved in This Process

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

Daniel B. Block, MD, is an award-winning, board-certified psychiatrist who operates a private practice in Pennsylvania.

Verywell / Laura Porter

Definition of Cognition

• Improvement Tips

Frequently Asked Questions

Cognition is a term referring to the mental processes involved in gaining knowledge and comprehension. Some of the many different cognitive processes include thinking, knowing, remembering, judging, and problem-solving .

These are higher-level functions of the brain and encompass language, imagination, perception, and planning. Cognitive psychology is the field of psychology that investigates how people think and the processes involved in cognition. 

What is an example of cognition?

Cognition includes all of the conscious and unconscious processes involved in thinking, perceiving, and reasoning. Examples of cognition include paying attention to something in the environment, learning something new, making decisions, processing language, sensing and perceiving environmental stimuli, solving problems, and using memory. 

History of the Study of Cognition

The study of how humans think dates back to the time of ancient Greek philosophers Plato and Aristotle.

Philosophical Origins

Plato's approach to the study of the mind suggested that people understand the world by first identifying basic principles buried deep inside themselves, then using rational thought to create knowledge. This viewpoint was later advocated by philosophers such as Rene Descartes and linguist Noam Chomsky. It is often referred to as rationalism.

Aristotle, on the other hand, believed that people acquire knowledge through their observations of the world around them. Later thinkers such as John Locke and B.F. Skinner also advocated this point of view, which is often referred to as empiricism.

Early Psychology

During the earliest days of psychology—and for the first half of the 20th century—psychology was largely dominated by psychoanalysis , behaviorism , and humanism .

Eventually, a formal field of study devoted solely to the study of cognition emerged as part of the "cognitive revolution" of the 1960s. This field is known as cognitive psychology.

The Emergence of Cognitive Psychology

One of the earliest definitions of cognition was presented in the first textbook on cognitive psychology, which was published in 1967. According to Ulric Neisser, a psychologist and the book's author, cognition is "those processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used."

Types of Cognitive Processes

There are many different types of cognitive processes. They include:

• Attention : Attention is a cognitive process that allows people to focus on a specific stimulus in the environment.
• Language : Language and language development are cognitive processes that involve the ability to understand and express thoughts through spoken and written words. This allows us to communicate with others and plays an important role in thought.
• Learning : Learning requires cognitive processes involved in taking in new things, synthesizing information, and integrating it with prior knowledge.
• Memory : Memory is an important cognitive process that allows people to encode, store, and retrieve information. It is a critical component in the learning process and allows people to retain knowledge about the world and their personal histories.
• Perception : Perception is a cognitive process that allows people to take in information through their senses, then utilize this information to respond and interact with the world.
• Thought : Thought is an essential part of every cognitive process. It allows people to engage in decision-making , problem-solving, and higher reasoning.

Hot Cognition vs. Cold Cognition

Some split cognition into two categories: hot and cold. Hot cognition refers to mental processes in which emotion plays a role, such as reward-based learning . Conversely, cold cognition refers to mental processes that don't involve feelings or emotions, such as working memory .

What Can Affect Cognition?

It is important to remember that these cognitive processes are complex and often imperfect. Some of the factors that can affect or influence cognition include:

Research indicates that as we age, our cognitive function tends to decline. Age-related cognitive changes include processing things more slowly, finding it harder to recall past events, and a failure to remember information that was once known (such as how to solve a particular math equation or historical information).

Attention Issues

Selective attention is a limited resource, so there are a number of things that can make it difficult to focus on everything in your environment. Attentional blink , for example, happens when you are so focused on one thing that you completely miss something else happening right in front of you.

Cognitive Biases

Cognitive biases are systematic errors in thinking related to how people process and interpret information about the world. Confirmation bias is one common example that involves only paying attention to information that aligns with your existing beliefs while ignoring evidence that doesn't support your views. 

Some studies have connected cognitive function with certain genes. For example, a 2020 study published in Brain Communications found that a person's level of brain-derived neurotrophic factor (BDNF), which is 30% determined by heritability, can impact the rate of brain neurodegeneration, a condition that ultimately impacts cognitive function.

Memory Limitations

Short-term memory is surprisingly brief, typically lasting just 20 to 30 seconds, whereas long-term memory can be stable and enduring, with memories lasting years and even decades. Memory can also be fragile and fallible. Sometimes we forget and other times we are subject to misinformation effects that may even lead to the formation of false memories .

Uses of Cognition

Cognitive processes affect every aspect of life, from school to work to relationships. Some specific uses for these processes include the following.

Learning New Things

Learning requires being able to take in new information, form new memories, and make connections with other things that you already know. Researchers and educators use their knowledge of these cognitive processes to create instructive materials to help people learn new concepts .

Forming Memories

Memory is a major topic of interest in the field of cognitive psychology. How we remember, what we remember, and what we forget reveal a great deal about how cognitive processes operate.

While people often think of memory as being much like a video camera—carefully recording, cataloging, and storing life events away for later recall—research has found that memory is much more complex.

Making Decisions

Whenever people make any type of a decision, it involves making judgments about things they have processed. This might involve comparing new information to prior knowledge, integrating new information into existing ideas, or even replacing old knowledge with new knowledge before making a choice.

Impact of Cognition

Our cognitive processes have a wide-ranging impact that influences everything from our daily life to our overall health.

Perceiving the World

As you take in sensations from the world around you, the information that you see, hear, taste, touch, and smell must first be transformed into signals that the brain can understand. The perceptual process allows you to take in this sensory information and convert it into a signal that your brain can recognize and act upon.

Forming Impressions

The world is full of an endless number of sensory experiences . To make meaning out of all this incoming information, it is important for the brain to be able to capture the fundamentals. Events are reduced to only the critical concepts and ideas that we need.

Filling in the Gaps

In addition to reducing information to make it more memorable and understandable, people also elaborate on these memories as they reconstruct them. In some cases, this elaboration happens when people are struggling to remember something . When the information cannot be recalled, the brain sometimes fills in the missing data with whatever seems to fit.

Interacting With the World

Cognition involves not only the things that go on inside our heads but also how these thoughts and mental processes influence our actions. Our attention to the world around us, memories of past events, understanding of language, judgments about how the world works, and abilities to solve problems all contribute to how we behave and interact with our surrounding environment.

Tips for Improving Cognition

Cognitive processes are influenced by a range of factors, including genetics and experiences. While you cannot change your genes or age, there are things that you can do to protect and maximize your cognitive abilities:

• Stay healthy . Lifestyle factors such as eating a nutritious diet and getting regular exercise can have a positive effect on cognitive functioning.  
• Think critically . Question your assumptions and ask questions about your thoughts, beliefs, and conclusions.
• Stay curious and keep learning . A great way to flex your cognitive abilities is to keep challenging yourself to learn more about the world.
• Skip multitasking . While it might seem like doing several things at once would help you get done faster, research has shown it actually decreases both productivity and work quality.

Thinking is an important component, but cognition also encompasses unconscious and perceptual processes as well. In addition to thinking, cognition involves language, attention, learning, memory, and perception.

People utilize cognitive skills to think, learn, recall, and reason. Five important cognitive skills include short-term memory, logic, processing speed, attention, and spatial recognition.

American Psychological Association. Cognition .

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Sgarbi M.  The Aristotelian Tradition and the Rise of British Empiricism: Logic and Epistemology in the British Isles (1570–1689) .

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Li S, Weinstein G, Zare H, et al. The genetics of circulating BDNF: Towards understanding the role of BDNF in brain structure and function in middle and old ages . Brain Commun . 2020;2(2):fcaa176. doi:10.1093/braincomms/fcaa176

Weinsten Y. How long is short-term memory: Shorter than you might think . Duke Undergraduate Education.

Leding J, Antonio L. Need for cognition and discrepancy detection in the misinformation effect . J Cognitive Psychol . 2019;31(4):409-415. doi:10.1080/20445911.2019.1626400

Scheiter K, Schubert C, Schuler A. Self-regulated learning from illustrated text: Eye movement modelling to support use and regulation of cognitive processes during learning from multimedia . Brit J Educ Psychol . 2017;88(1):80-94. doi:10.1111/bjep.12175

Toppi J, Astolfi L, Risetti M, et al. Different topological properties of EEG-derived networks describe working memory phases as revealed by graph theoretical analysis . Front Hum Neurosci . 2018;11:637. doi:10.3389/fnhum.2017.00637

Mather G. Foundations of sensation and perception .

Sousa D.  How the brain learns .

Houben S, Otgaar H, Roelofs J, Merckelbach H. EMDR and false memories: A response to Lee, de Jongh, and Hase (2019) . Clin Psycholog Sci . 2019;7(3):405-6. doi:10.1177/2167702619830392

Schwarzer R. Self-efficacy: Thought control of action .

Imaoka M, Nakao H, Nakamura M, et al. Effect of multicomponent exercise and nutrition support on the cognitive function of older adults: A randomized controlled trial . Clin Interv Aging . 2019;14:2145-53. doi:10.2147/CIA.S229034

Petroutsatou K, Sifiniadis A. Exploring the consequences of human multitasking in industrial automation projects: A tool to mitigate impacts - Part II . Organiz Techn Manage Construct . 2018;10(1):1770-1777. doi:10.2478/otmcj-2016-0031

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Revlin R. Cognition: Theory and Practice .

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

Cognitive Remediation Therapy: 13 Exercises & Worksheets

This can result in concentration, organizational, and planning difficulties that impact their quality of life and independent living.

Cognitive Remediation Therapy (CRT) helps by increasing awareness of intellectual difficulties and improving thinking skills. While originally designed for people with thinking problems associated with schizophrenia, it has also proven successful for those with other diagnoses (Bristol Mental Health, n.d.).

CRT works by encouraging a range of exercises and activities that challenge memory, flexible thinking, planning, and concentration problems.

This article explores CRT and its potential to help clients and includes techniques, activities, and worksheets to build effective therapy sessions.

Before you continue, we thought you might like to download our three Positive CBT Exercises for free . These science-based exercises will provide you with detailed insight into Positive CBT and give you the tools to apply it in your therapy or coaching.

This Article Contains:

What is cognitive remediation therapy (crt), how does cognitive remediation work, 8 techniques for your sessions, 7 exercises, activities, & games, 6 helpful worksheets and manuals, implementing online crt programs, 3 best software programs for helping your clients, a take-home message.

“Cognitive remediation is a behavioral treatment for people who are experiencing cognitive impairments that interfere with daily functioning” (Medalia, Revheim, & Herlands, 2009, p. 1).

Successful cognitive functions, including memory, attention, visual-spatial analysis, and abstract reasoning, are vital for engaging with tasks, the environment, and healthy relationships.

CRT improves cognitive processing and psychosocial functioning through behavioral training and increasing individual confidence in people with mental health disorders (Corbo & Abreu, 2018). Training interventions focus on the skills and supports required to “improve the success and satisfaction people experience in their chosen living, learning, working, and social environments” (Medalia et al., 2009, p. 2).

Exercises typically focus on specific cognitive functions, where tasks are repeated (often on a computer) at increasing degrees of difficulty. For example:

• Paying attention
• Remembering
• Being organized
• Planning skills
• Problem-solving
• Processing information

Based on the principles of errorless learning and targeted reinforcement exercises , interventions involve memory, motor dexterity, and visual reading tasks. Along with improving confidence in personal abilities, repetition encourages thinking about solving tasks in multiple ways (Corbo & Abreu, 2018).

While initially targeted for patients with schizophrenia, CRT is an effective treatment for other mental health conditions , including mood and eating disorders (Corbo & Abreu, 2018).

CRT is particularly effective when the cognitive skills and support interventions reflect the individual’s self-selected rehabilitation goals. As a result, cognitive remediation relies on collaboration, assessing client needs, and identifying appropriate opportunities for intervention (Medalia et al., 2009).

Cognitive remediation vs cognitive rehabilitation

CRT is one of several skill-training psychiatric rehabilitation interventions. And yet, cognitive remediation is not the same as cognitive rehabilitation (Tchanturia, 2015).

Cognitive rehabilitation typically targets neurocognitive processes damaged because of injury or illness and involves a series of interventions designed to retrain previously learned cognitive skills along with compensatory strategies (Tsaousides & Gordon, 2009).

While initially done in person, they can subsequently be performed remotely as required (Corbo & Abreu, 2018; Bristol Mental Health, n.d.).

Well-thought-out educational software provides multisensory feedback and positive reinforcement while supporting success, choice, and control of the learning process. Its design can target either specific cognitive functions or non-specific learning skills and mechanisms (Medalia et al., 2009).

CRT successfully uses the brain’s neuroplasticity and is often more effective in younger age groups who haven’t experienced the effects of long-term psychosis. It works by increasing activation and connectivity patterns within and across several brain regions involved in working memory and high-order executive functioning (Corbo & Abreu, 2018).

The Neuropsychological Educational Approach to Cognitive Remediation (NEAR) is one of several approaches that provide highly individualized learning opportunities. It allows each client to proceed at their own pace on tasks selected and designed to engage them and address their cognitive needs (Medalia et al., 2009).

NEAR and other CRT techniques are influenced by learning theory and make use of the following (Medalia et al., 2009):

• Errorless learning Encouraging the client to learn progressively, creating a positive experience without relying on trial and error.
• Shaping and positive feedback Reinforcing behaviors that approximate target behaviors (such as good timekeeping) and offering rewards (for example, monthly certificates for attendance).
• Prompting Using open-ended questions that guide the client toward the correct response.
• Modeling Demonstrating how to solve a problem.
• Generalizing Learning how to generalize learned skills to other situations.
• Bridging Understanding how to apply skills learned inside a session outside  in everyday life.

Encouraging intrinsic motivation (doing the tasks for the satisfaction of doing them rather than for external rewards) and task engagement are also essential aspects of successful CRT programs (Medalia et al., 2009).

Therapy is most effective when it successfully supports clients as they transfer learning skills into the real world.

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Cognitive remediation techniques must be selected according to the skills and needs of the client and typically fall into one of three major intervention categories (Medalia et al., 2009):

• Planning exercises, such as planning a trip to the beach to practice cognitive strategies
• Cueing and sequencing , such as adding signs or placing reminder notes at home to encourage completing everyday tasks (for example, brushing teeth)

Such techniques rely on several key principles, including “(1) teaching new, efficient, information processing strategies; (2) aiding the transfer of cognitive gains to the real world; and (3) modifying the local environment” (Medalia et al., 2009, p. 5).

• Restorative approaches Directly target cognitive deficits by repeating task practices and gradually increasing difficulty and complexity; along with regular feedback, they encourage accurate and high levels of performance.

Practice is often organized hierarchically, as follows:

• Elementary aspects of sensory processing (for example, improving auditory processing speed and accuracy)
• High-order memory and problem-solving skills (including executive functioning and verbal skills)

This technique assumes a degree of neuroplasticity that, with training, results in a greater degree of accuracy in sensory representations, improved cognitive strategies for grouping stimuli into more meaningful groups, and better recall.

• Repetition and reaching for increasing levels of task difficulty
• Modeling other people’s positive behavior
• Role-play  to re-enact experienced or imagined behavior from different perspectives
• Corrective feedback to improve and correct unwanted or unhelpful behavior

Complex social cognitive processes are typically broken down into elemental skills for repetitive practice, role-play, and corrective feedback.

Professor Dame Til Wykes: cognitive remediation therapy

It is vital that activities within CRT are interesting and engaging for clients. They must foster the motivation required to persevere to the end of the task or game.

The following three games and puzzles are particularly valuable for children and adolescents (modified from Tchanturia, 2015):

SET is a widely available card game that practices matching based on color, shape, shading, etc.

Clients must shift their thinking to identify multiple ways of categorizing and grouping cards, then physically sort them based on their understanding.

It may be helpful to begin with a limited set of cards to reduce the likelihood of the clients becoming overwhelmed by the game or finding it less enjoyable.

2. Rush Hour

Rush Hour is another fun game that balances problem-solving skills with speed.

Puzzles start simple and increase in complexity, with additional elements involved. Skills developed include problem-solving and abstract thinking, and the game requires a degree of perseverance.

Other activities require no specialist equipment and yet can be highly engaging and support clients in learning transferable skills (modified from Tchanturia, 2015).

• Bigger picture thinking This involves the client picturing a shape in their minds or looking at one out of sight of the therapist. They then describe the shape (without naming it), while the therapist attempts to draw it according to the instructions. This practice is helpful with clients who get overwhelmed by detail and cannot see the bigger picture.
• Word searches Word searches encourage the client to focus on relevant information and ignore everything else – an essential factor in central coherence. Such puzzles also challenge memory, concentration, and attention.
• Last word response Last word response is a challenging verbal game promoting cognitive flexibility. The first player makes up and says a sentence out loud. Each subsequent player makes up a new sentence, starting with the last word of the previous player’s sentence. For example, ‘ I like cheese’ may be followed by the next player saying, ‘ Cheese is my favorite sandwich ingredient ,’ etc.
• Dexterity Using your non-dominant hand once a week (for example, combing your hair or brushing your teeth) stimulates different parts of your brain, creating alternative patterns of neuron firing and strengthening cognitive functions.

The following therapy worksheets help structure Cognitive Remediation Therapy sessions and ensure that the needs of clients are met using appropriately targeted CRT interventions (modified from Medalia et al., 2009; Medalia & Bowie, 2016):

Client referral to CRT

The Cognitive Remediation Therapy Referral Form captures valuable information when a client is referred from another agency or therapist so that the new therapist can identify and introduce the most appropriate CRT interventions. The form includes information such as:

Primary reasons

Secondary reasons

• Self-confidence
• Working with others
• Time management
• Goal-directed activities

Cognitive Appraisal for CRT

The Cognitive Appraisal for CRT form is helpful for identifying and recording areas of cognitive processing that cause difficulty for the client and require focus during Cognitive Remediation Therapy sessions.

Clients are scored on their degree of difficulty with the following:

• Paying attention during conversation
• Maintaining concentration in meetings
• Completing tasks once started
• Starting tasks
• Planning and organizing tasks and projects
• Reasoning and solving problems

Software Appraisal for CRT

The Software Appraisal for CRT form helps assess which software would be most helpful in a specific Cognitive Remediation Therapy session. It provides valuable input for tailoring treatment to the needs of the client.

For example:

• Level of reading ability required
• Cognitive deficits addressed by the software
• What is the multimedia experience like?
• How much input is required by the therapist?

Appraisal records become increasingly important as more software is acquired for clients with various cognitive deficits from multiple backgrounds.

Software Usage for CRT

The Software Usage for CRT form helps keep track of the software clients have tried and how effectively it supports them as they learn, develop, and overcome cognitive deficits.

The client considers the software they use and whether they practiced the following areas of cognition:

• Concentration
• Processing speed
• Multitasking
• Logic and reasoning
• Organization
• Fast responses
• Working memory

Thought Tracking During Cognitive Remediation Therapy

Thought Tracking During Cognitive Remediation Therapy is valuable for identifying and recording the client’s goals for that day’s Cognitive Remediation Therapy session and understanding how it relates to their overall treatment goals.

Planning to Meet Goals in CRT

The Planning to Meet Goals in CRT worksheet is for clients requiring support and practice in planning, goal-setting, and goal achievement.

Working with the client, answer the following prompts:

• What goal or project are you working toward?
• What date should it be completed by?
• Are there any obstacles to overcome to complete the goal?
• Are there any additional resources required?
• Then consider the steps needed to achieve the goal.

Other free resources

Happy Neuron provides several other free resources that are available for download .

Consider the five Cs when selecting online CRT programs (modified from Medalia et al., 2009):

• Cognitive – What target deficits are being addressed?
• Client – What interests and level of functioning does the client have?
• Computer – What computing requirements and compatibility factors need to be considered?
• Context – Does the software use real-world or fantasy activities and environments? Are they age and cognitive ability appropriate?
• Choice – Is the learner given choice and options to adapt the activity to their preferences?

Once you’ve ordered the software, give it a thorough review to understand when it is most appropriate to use and with whom.

For online CRT programs to be effective as teaching tools and activities, they should include the following features (modified from Medalia et al., 2009, p. 53):

• Intrinsically motivating
• Active use of information
• Multisensory strategies
• Frequent feedback
• Control over the learning process
• Positive reinforcement
• Application of newly acquired skills in appropriate contexts
• Errorless learning – challenging yet not frustrating

Therapists must become familiar with each program’s content and processes so that targeted deficits are fully understood and clients are engaged without confusion or risk of failure.

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A great deal of software “targets different skills and offers a variety of opportunities for contextualization and personalization” (Medalia et al., 2009, p. 43).

We focus on three suppliers of extensive CRT software resources below (recommended by Medalia et al., 2009).

1. Happy Neuron

Happy Neuron provides a wide variety of online brain training exercises and activities to stimulate cognitive functioning in the following areas:

• Visual-spatial

When you’re performing well, the exercises become increasingly difficult.

The exercises are grouped into the following areas:

• Brain speed
• People skills
• Intelligence

3. Games for the Brain

Cognitive difficulties, such as challenges with paying attention, planning, remembering, and problem-solving, can further compound and exacerbate mental health issues

While initially created for schizophrenia, CRT is also valuable for other mental health problems, including eating and mood disorders. Treatments are effective in one-to-one and group sessions, and lessons can be transferred to the outside world, providing crucial gains for a client’s mental wellbeing and social interaction.

Through repeated and increasingly challenging skill-based interventions, CRT benefits cognitive functioning and provides confidence gains to its users. The treatment adheres to learning theory principles and targets specific brain processing areas such as motor dexterity, memory, and visual-spatial perception, along with higher-order functioning.

Involving clients in treatment choices increases the likelihood of ongoing perseverance, engagement, and motivation as activities repeat with increasing degrees of difficulty.

This article offers a valuable starting point for exploring CRT and its benefits, with several worksheets and forms to encourage effective treatment.

We hope you enjoyed reading this article. For more information, don’t forget to download our three Positive CBT Exercises for free .

• Bristol Mental Health. (n.d.). Cognitive remediation therapy: Improving thinking skills . Retrieved December 15, 2021, from http://www.awp.nhs.uk/media/424704/cognitive-remediation-therapy-022019.pdf
• Corbo, M., & Abreu, T. (2018). Cognitive remediation therapy: EFPT psychotherapy guidebook . Retrieved December 15, 2021, from https://epg.pubpub.org/pub/05-cognitive-remediation-therapy/release/3
• Medalia, A., & Bowie, C. R. (2016). Cognitive remediation to improve functional outcomes . Oxford University Press.
• Medalia, A., Revheim, N., & Herlands, T. (2009). Cognitive remediation for psychological disorders: Therapist guide . Oxford University Press.
• Tchanturia, K. (2015). Cognitive remediation therapy (CRT) for eating and weight disorders . Routledge.
• Tsaousides, T., & Gordon, W. A. (2009). Cognitive rehabilitation following traumatic brain injury: Assessment to treatment. Mount Sinai Journal of Medicine: A Journal of Translational and Personalized Medicine , 76 (2), 173-181.

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To my surprise this is a treatment that has not been discussed in the area I live and work. I just stumbled upon this when I was researching cognitive impairments with schizophrenia. I currently work on a team with multiple mental health professionals that go out into the community, to work with people diagnosed with Schizophrenia. It seems like most of what we do is manage and monitor symptoms. Are you aware of anyone or any agency in Buffalo, NY that uses this method of treatment? I am trying to figure out how to get trained and use it in practice, if that is possible. Any help will be greatly appreciated.

This looks like the treatment my daughter needs. She has struggled for years with the cognitive problems associated with depression. How do we find a therapist near us who can use these techniques?

I’m sorry to read that your daughter is struggling. You can find a directory of licensed therapists here (and note that you can change the country setting in the top-right corner). You’ll also find that there are a range of filters to help you drill down to the type of support you need: https://www.psychologytoday.com/us/therapists

I hope you find the help you need.

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National Research Council (US) Committee on the Assessment of 21st Century Skills. Assessing 21st Century Skills: Summary of a Workshop. Washington (DC): National Academies Press (US); 2011.

Assessing 21st Century Skills: Summary of a Workshop.

• Hardcopy Version at National Academies Press

2 Assessing Cognitive Skills

As described in Chapter 1 , the steering committee grouped the five skills identified by previous efforts ( National Research Council, 2008 , 2010 ) into the broad clusters of cognitive skills, interpersonal skills, and intrapersonal skills. Based on this grouping, two of the identified skills fell within the cognitive cluster: nonroutine problem solving and systems thinking. The definition of each, as provided in the previous report ( National Research Council, 2010 , p. 3), appears below:

• Nonroutine problem solving: A skilled problem solver uses expert thinking to examine a broad span of information, recognize patterns, and narrow the information to reach a diagnosis of the problem. Moving beyond diagnosis to a solution requires knowledge of how the information is linked conceptually and involves metacognition—the ability to reflect on whether a problem-solving strategy is working and to switch to another strategy if it is not working ( Levy and Murnane, 2004 ). It includes creativity to generate new and innovative solutions, integrating seemingly unrelated information, and entertaining possibilities that others may miss ( Houston, 2007 ).
• Systems thinking: The ability to understand how an entire system works; how an action, change, or malfunction in one part of the system affects the rest of the system; adopting a “big picture” perspective on work ( Houston, 2007 ). It includes judgment and decision making, systems analysis, and systems evaluation as well as abstract reasoning about how the different elements of a work process interact ( Peterson et al., 1999 ).

After considering these definitions, the committee decided a third cognitive skill, critical thinking, was not fully represented. The committee added critical thinking to the list of cognitive skills, since competence in critical thinking is usually judged to be an important component of both skills ( Mayer, 1990 ). Thus, this chapter focuses on assessments of three cognitive skills: problem solving, critical thinking, and systems thinking.

• DEFINING THE CONSTRUCT

One of the first steps in developing an assessment is to define the construct and operationalize it in a way that supports the development of assessment tasks. Defining some of the constructs included within the scope of 21st century skills is significantly more challenging than defining more traditional constructs, such as reading comprehension or mathematics computational skills. One of the challenges is that the definitions tend to be both broad and general. To be useful for test development, the definition needs to be specific so that there can be a shared conception of the construct for use by those writing the assessment questions or preparing the assessment tasks.

This set of skills also generates debate about whether they are domain general or domain specific. A predominant view in the past has been that critical thinking and problem-solving skills are domain general: that is, that they can be learned without reference to any specific domain and, further, once they are learned, can be applied in any domain. More recently, psychologists and learning theorists have argued for a domain-specific conception of these skills, maintaining that when students think critically or solve problems, they do not do it in the absence of subject matter: instead, they think about or solve a problem in relation to some topic. Under a domain-specific conception, the learner may acquire these skills in one domain as he or she acquires expertise in that domain, but acquiring them in one domain does not necessarily mean the learner can apply them in another.

At the workshop, Nathan Kuncel, professor of psychology with University of Minnesota, and Eric Anderman, professor of educational psychology with Ohio State University, discussed these issues. The sections below summarize their presentations and include excerpts from their papers, 1 dealing first with the domain-general and domain-specific conceptions of critical thinking and problem solving and then with the issue of transferring skills from one domain to another.

Critical Thinking: Domain-Specific or Domain-General

It is well established, Kuncel stated, that foundational cognitive skills in math, reading, and writing are of central importance and that students need to be as proficient as possible in these areas. Foundational cognitive abilities, such as verbal comprehension and reasoning, mathematical knowledge and skill, and writing skills, are clearly important for success in learning in college as well as in many aspects of life. A recent study documents this. Kuncel and Hezlett (2007) examined the body of research on the relationships between traditional measures of verbal and quantitative skills and a variety of outcomes. The measures of verbal and quantitative skills included scores on six standardized tests—the GRE, MCAT, LSAT, GMAT, MAT, and PCAT. 2 The outcomes included performance in graduate school settings ranging from Ph.D. programs to law school, medical school, business school, and pharmacy programs. Figure 2-1 shows the correlations between scores on the standardized tests and the various outcome measures, including (from bottom to top) first-year graduate GPA (1st GGPA), cumulative graduate GPA (GGPA), qualifying or comprehensive examination scores, completion of the degree, estimate of research productivity, research citation counts, faculty ratings, and performance on the licensing exam for the profession. For instance, the top bar shows a correlation between performance on the MCAT and performance on the licensing exam for physicians of roughly .65, the highest of the correlations reported in this figure. The next bar indicates the correlation between performance on the LSAT and performance on the licensing exam for lawyers is roughly .35. Of the 34 correlations shown in the figure, all but 11 are over .30. Kuncel characterized this information as demonstrating that verbal and quantitative skills are important predictors of success based on a variety of outcome measures, including performance on standardized tests, whether or not people finish their degree program, how their performance is evaluated by faculty, and their contribution to the field.

Correlations between scores on standardized tests and academic and job outcome measures. SOURCE: Kuncel and Hezlett (2007). Reprinted with permission of American Association for the Advancement of Science.

Kuncel has also studied the role that broader abilities have in predicting future outcomes. A more recent review ( Kuncel and Hezlett, 2010 ) examined the body of research on the relationships between measures of general cognitive ability (historically referred to as IQ) and job outcomes, including performance in high, medium, and low complexity jobs; training success in civilian and military settings; how well leaders perform on objective measures; and evaluations of the creativity of people’s work. Figure 2-2 shows the correlations between performance on a measure of general cognitive ability and these outcomes. All of the correlations are above .30, which Kuncel characterized as demonstrating a strong relationship between general cognitive ability and job performance across a variety of performance measures. Together, Kuncel said, these two reviews present a body of evidence documenting that verbal and quantitative skills along with general cognitive ability are predictive of college and career performance.

Correlations between measures of cognitive ability and job performance. SOURCE: Kuncel and Hezlett (2011). Copyright 2010 by Sage Publications. Reprinted with permission of Sage Publications.

Kuncel noted that other broader skills, such as critical thinking or analytical reasoning, may also be important predictors of performance, but he characterizes this evidence as inconclusive. In his view, the problems lie both with the conceptualization of the constructs as domain-general (as opposed to domain-specific) as well as with the specific definition of the construct. He finds the constructs are not well defined and have not been properly validated. For instance, a domain-general concept of the construct of “critical thinking” is often indistinguishable from general cognitive ability or general reasoning and learning skills. To demonstrate, Kuncel presented three definitions of critical thinking that commonly appear in the literature:

• “[Critical thinking involves] cognitive skills or strategies that increase the probability of a desirable outcome—in the long run, critical thinkers will have more desirable outcomes than ‘noncritical’ thinkers. . . . Critical thinking is purposeful, reasoned, and goal-directed. It is the kind of thinking involved in solving problems, formulating inferences, calculating likelihoods, and making decisions” ( Halpern, 1998 , pp. 450–451).
• “Critical thinking is reflective and reasonable thinking that is focused on deciding what to believe or do” ( Ennis, 1985 , p. 45).
• “Critical thinking [is] the ability and willingness to test the validity of propositions” ( Bangert-Drowns and Bankert, 1990 , p. 3).

He characterizied these definitions both very general and very broad. For instance, Halpern’s definition essentially encompasses all of problem solving, judgment, and cognition, he said. Others are more specific and focus on a particular class of tasks (e.g., Bangert-Drowns and Bankert, 1990 ). He questioned the extent to which critical thinking so conceived is distinct from general cognitive ability (or general intelligence).

Kuncel conducted a review of the literature for empirical evidence of the validity of the construct of critical thinking. The studies in the review examined the relationships between various measures of critical thinking and measures of general intelligence and expert performance. He looked for two types of evidence—convergent validity evidence 3 and discriminant validity 4 evidence.

Kuncel found several analyses of the relationships among different measures of critical thinking (see Bondy et al., 2001 ; Facione, 1990 ; and Watson and Glaser, 1994 ). The assessments that were studied included the Watson-Glaser Critical Thinking Appraisal (WGCTA), the Cornell Critical Thinking Test (CCTT), the California Critical Thinking Skills Test (CCTST), and the California Critical Thinking Disposition Inventory (CCTDI). The average correlation among the measures was .41. Considering that all of these tests purport to be measures of the same construct, Kuncel judged this correlation to be low. For comparison, he noted a correlation of .71 between two subtests of the SAT intended to measure critical thinking (the SAT-critical reading test and the SAT-writing test).

With regard to discriminant validity, Kuncel conducted a literature search that yielded 19 correlations between critical-thinking skills and traditional measures of cognitive abilities, such as the Miller Analogies Test and the SAT ( Adams et al., 1999 ; Bauer and Liang, 2003 ; Bondy et al., 2001 ; Cano and Martinez, 1991 ; Edwards, 1950 ; Facione et al., 1995 , 1998 ; Spector et al., 2000 ; Watson and Glaser, 1994 ). He separated the studies into those that measured critical-thinking skills and those that measured critical-thinking dispositions (i.e., interest and willingness to use one’s critical-thinking skills). The average correlation between general cognitive ability measures and critical-thinking skills was .48, and the average correlation between general cognitive ability measures and critical-thinking dispositions was .21.

Kuncel summarized these results as demonstrating that different measures of critical thinking show lower correlations with each other (i.e., average of .41) than they do with traditional measures of general cognitive ability (i.e., average of .48). Kuncel judges that these findings provide little support for critical thinking as a domain-general construct distinct from general cognitive ability. Given this relatively weak evidence of convergent and discriminant validity, Kuncel argued, it is important to determine if critical thinking is correlated differently than cognitive ability with important outcome variables like grades or job performance. That is, do measures of critical-thinking skills show incremental validity beyond the information provided by measures of general cognitive ability?

Kuncel looked at two outcome measures: grades in higher education and job performance. With regard to higher education, he examined data from 12 independent samples with 2,876 subjects ( Behrens, 1996 ; Gadzella et al., 2002 , 2004 ; Kowalski and Taylor, 2004 ; Taube, 1997 ; Williams, 2003 ). Across these studies, the average correlation between critical-thinking skills and grades was .27 and between critical-thinking dispositions and grades was .24. To put these correlations in context, the SAT has an average correlation with 1st year college GPA between .26 to .33 for the individual scales and .35 when the SAT scales are combined ( Kobrin et al., 2008 ). 5

There are very limited data that quantify the relationship between critical-thinking measures and subsequent job performance. Kuncel located three studies with the Watson-Glaser Appraisal ( Facione and Facione, 1996 , 1997 ; Giancarlo, 1996 ). They yielded an average correlation of .32 with supervisory ratings of job performance (N = 293).

Kuncel described these results as “mixed” but not supporting a conclusion that assessments of critical thinking are better predictors of college and job performance than other available measures. Taken together with the convergent and discriminant validity results, the evidence to support critical thinking as an independent construct distinct from general cognitive ability is weak.

Kuncel believes these correlational results do not tell the whole story, however. First, he noted, a number of artifactual issues may have contributed to the relatively low correlation among different assessments of critical thinking, such as low reliability of the measures themselves, restriction in range, different underlying definitions of critical thinking, overly broad definitions that are operationalized in different ways, different kinds of assessment tasks, and different levels of motivation in test takers.

Second, he pointed out, even though two tests correlate highly with each other, they may not measure the same thing. That is, although the critical-thinking tests correlate .48, on average, with cognitive ability measures, it does not mean that they measure the same thing. For example, a recent study ( Kuncel and Grossbach, 2007 ) showed that ACT and SAT scores are highly predictive of nursing knowledge. But, obviously, individuals who score highly on a college admissions test do not have all the knowledge needed to be a nurse. The constructs may be related but not overlap entirely.

Kuncel explained that one issue with these studies is they all conceived of critical thinking in its broadest sense and as a domain-general construct. He said this conception is not useful, and he summarized his meta-analysis findings as demonstrating little evidence that critical thinking exists as a domain-general construct distinct from general cognitive ability. He highlighted the fact that some may view critical thinking as a specific skill that, once learned, can be applied in many situations. For instance, many in his field of psychology mention the following as specific critical-thinking skills that students should acquire: understanding the law of large numbers, understanding what it means to affirm the consequent, being able to make judgments about sample bias, understanding control groups, and understanding Type I versus Type II errors. However, Kuncel said many tasks that require critical thinking would not make use of any of these skills.

In his view, the stronger argument is for critical thinking as a domain-specific construct that evolves as the person acquires domain-specific knowledge. For example, imagine teaching general critical-thinking skills that can be applied across all reasoning situations to students. Is it reasonable, he asked, to think a person can think critically about arguments for different national economic policies without understanding macro-economics or even the current economic state of the country? At one extreme, he argued, it seems clear that people cannot think critically about topics for which they have no knowledge, and their reasoning skills are intimately tied to the knowledge domain. For instance, most people have no basis for making judgments about how to conduct or even prioritize different experiments for CERN’s Large Hadron Collider. Few people understand the topic of particle physics sufficiently to make more than trivial arguments or decisions. On the other hand, perhaps most people could try to make a good decision about which among a few medical treatments would best meet their needs.

Kuncel also talked about the kinds of statistical and methodological reasoning skills learned in different disciplines. For instance, chemists, engineers, and physical scientists learn to use these types of skills in thinking about the laws of thermodynamics that deal with equilibrium, temperature, work, energy, and entropy. On the other hand, psychologists learn to use these skills in thinking about topics such as sample bias and self-selection in evaluating research findings. Psychologists who are adept at thinking critically in their own discipline would have difficulty thinking critically about problems in the hard sciences, unless they have specific subject matter knowledge in the discipline. Likewise, it is difficult to imagine that a scientist highly trained in chemistry could solve a complex problem in psychology without knowing some subject matter in psychology.

Kuncel said it is possible to train specific skills that aid in making good judgments in some situations, but the literature does not demonstrate that it is possible to train universally effective critical thinking skills. He noted, “I think you can give people a nice toolbox with all sorts of tools they can apply to a variety of tasks, problems, issues, decisions, citizenship questions, and learning those things will be very valuable, but I dissent on them being global and trainable as a global skill.”

Transfer from One Context to Another

There is a commonplace assumption, Eric Anderman noted in his presentation, that learners readily transfer the skills they have learned in one course or context to situations and problems that arise in another. Anderman argued research on human learning does not support this assumption. Research suggests such transfer seldom occurs naturally, particularly when learners need to transfer complex cognitive strategies from one domain to another ( Salomon and Perkins, 1989 ). Transfer is only likely to occur when care is taken to facilitate that transfer: that is, when students are specifically taught strategies that facilitate the transfer of skills learned in one domain to another domain ( Gick and Holyoak, 1983 ).

For example, Anderman explained, students in a mathematics class might be taught how to solve a problem involving the multiplication of percentages (e.g., 4.79% × 0.25%). The students then might encounter a problem in their social studies courses that involves calculating compounded interest (such as to solve a problem related to economics or banking). Although the same basic process of multiplying percentages might be necessary to solve both problems, it is unlikely that students will naturally, on their own, transfer the skills learned in the math class to the problem encountered in the social studies class.

In the past, Anderman said, there had been some notion that critical-thinking and problem-solving skills could be taught independent of context. For example, teaching students a complex language such as Latin, a computer programming language such as LOGO, or other topics that require complex thinking might result in an overall increase in their ability to think critically and problem solve.

Both Kuncel and Anderman maintained that the research does not support this idea. Instead, the literature better supports a narrower definition in which critical thinking is considered a finite set of specific skills. These skills are useful for effective decision making for many, but by no means all, tasks or situations. Their utility is further curtailed by task-specific knowledge demands. That is, a decision maker often has to have specific knowledge to make more than trivial progress with a problem or decision.

Anderman highlighted four important messages emerging from recent research. First, research documents that it is critical that students learn basic skills (such as basic arithmetic skills like times tables) so the skills become automatic. Mastery of these skills is required for the successful learning of more complex cognitive skills. Second, the use of general practices intended to improve students’ thinking are not usually successful as a means of improving their overall cognitive abilities. The research suggests students may become more adept in the specific skill taught, but this does not transfer to an overall increase in cognitive ability. Third, when general problem-solving strategies are taught, they should be taught within meaningful contexts and not as simply rote algorithms to be memorized. Finally, educators need to actively teach students to transfer skills from one context to another by helping students to recognize that the solution to one type of problem may be useful in solving a problem with similar structural features ( Mayer and Wittrock, 1996 ).

He noted that instructing students in general problem-solving skills can be useful but more elaborate scaffolding and domain-specific applications of these skills are often necessary. Whereas general problem-solving and critical-thinking strategies can be taught, research indicates these skills will not automatically or naturally transfer to other domains. Anderman stressed that educators and trainers must recognize that 21st century skills should be taught within specific domains; if they are taught as general skills, he cautioned, then extreme care must be taken to facilitate the transfer of these skills from one domain to another.

• ASSESSMENT EXAMPLES

The workshop included examples of four different types of assessments of critical-thinking and problem-solving skills—one that will be used to make international comparisons of achievement, one used to license lawyers, and two used for formative purposes (i.e., intended to support instructional decision making). The first example was the computerized problem-solving component of the Programme for International Student Assessment (PISA). This assessment is still under development but is scheduled for operational administration in 2012. 6 Joachim Funke, professor of cognitive, experimental, and theoretical psychology with the Heidelberg University in Germany, discussed this assessment.

The second example was the Multistate Bar Exam, a paper-and-pencil test that consists of both multiple-choice and extended-response components. This test is used to qualify law students for practice in the legal profession. Susan Case, director of testing with the National Conference of Bar Exams, made this presentation.

The two formative assessments both make use of intelligent tutors, with assessments embedded into instruction modules. The “Auto Tutor” described by Art Graesser, professor of psychology with the University of Memphis, is used in instructing high school and higher education students in critical thinking skills in science. The Auto Tutor is part of a system Graesser has developed called Operation ARIES! (Acquiring Research Investigative and Evaluative Skills). The “Packet Tracer,” described by John Beherns, director of networking academy learning systems development with Cisco, is intended for individuals learning computer networking skills.

Problem Solving on PISA

For the workshop, Joachim Funke supplied the committee with the draft framework for PISA (see Organisation for Economic Co-operation and Development, 2010 7 ) and summarized this information in his presentation. 8 The summary below is based on both documents.

PISA, Funke explained, defines problem solving as an individual’s capacity to engage in cognitive processing to understand and resolve problem situations where a solution is not immediately obvious. The definition includes the willingness to engage with such situations in order to achieve one’s potential as a constructive and reflective citizen ( Organisation for Co-operation and Development, 2010 , p. 12). Further, the PISA 2012 assessment of problem-solving competency will not test simple reproduction of domain-based knowledge, but will focus on the cognitive skills required to solve unfamiliar problems encountered in life and lying outside traditional curricular domains. While prior knowledge is important in solving problems, problem-solving competency involves the ability to acquire and use new knowledge or to use old knowledge in a new way to solve novel problems. The assessment is concerned with nonroutine problems, rather than routine ones (i.e., problems for which a previously learned solution procedure is clearly applicable). The problem solver must actively explore and understand the problem and either devise a new strategy or apply a strategy learned in a different context to work toward a solution. Assessment tasks center on everyday situations, with a wide range of contexts employed as a means of controlling for prior knowledge in general.

The key domain elements for PISA 2012 are as follows:

• The problem context: whether it involves a technological device or not, and whether the focus of the problem is personal or social
• The nature of the problem situation: whether it is interactive or static (defined below)
• The problem-solving processes: the cognitive processes involved in solving the problem

The PISA 2012 framework (pp. 18–19) defines four processes that are components of problem solving. The first involves information retrieval. This process requires the test taker to quickly explore a given system to find out how the relevant variables are related to each other. The test taker must explore the situation, interact with it, consider the limitations or obstacles, and demonstrate an understanding of the given information. The objective is for the test taker to develop a mental representation of each piece of information presented in the problem. In the PISA framework, this process is referred to as exploring and understanding.

The second process is model building, which requires the test taker to make connections between the given variables. To accomplish this, the examinee must sift through the information, select the information that is relevant, mentally organize it, and integrate it with relevant prior knowledge. This requires the test taker to represent the problem in some way and formulate hypotheses about the relevant factors and their interrelationships. In the PISA framework, this dimension is called representing and formulating.

The third process is called forecasting and requires the active control of a given system. The framework defines this process as setting goals, devising a strategy to carry them out, and executing the plan. In the PISA framework, this dimension is called planning and executing.

The fourth process is monitoring and reflecting. The framework defines this process as checking the goal at each stage, detecting unexpected events, taking remedial action if necessary, and reflecting on solutions from different perspectives by critically evaluating assumptions and alternative solutions.

Each of these processes requires the use of reasoning skills, which the framework describes as follows ( Organisation for Economic Co-operation and Development, 2010 , p. 19):

In understanding a problem situation, the problem solver may need to distinguish between facts and opinion, in formulating a solution, the problem solver may need to identify relationship between variables, in selecting a strategy, the problem solver may need to consider cause and effect, and in communicating the results, the problem solver may need to organize information in a logical manner. The reasoning skills associated with these processes are embedded within problem solving. They are important in the PISA context since they can be taught and modeled in classroom instruction (e.g., Adey et al., 2007 ; Klauer and Phye, 2008 ).

For any given test taker, the test lasts for 40 minutes. PISA is a survey-based assessment that uses a balanced rotation design. A total of 80 minutes of material is organized into four 20-minute clusters, with each student taking two clusters.

The items are grouped into units around a common stimulus that describes the problem. Reading and numeracy demands are kept to a minimum. The tasks all consist of authentic stimulus items, such as refueling a moped, playing on a handball team, mixing a perfume, feeding cats, mixing elements in a chemistry lab, taking care of a pet, and so on. Funke noted that the different contexts for the stimuli are important because test takers might be motivated differentially and might be differentially interested depending on the context. The difficulty of the items is manipulated by increasing the number of variables or the number of relations that the test taker has to deal with.

PISA 2012 is a computer-based test in which items are presented by computer and test takers respond on the computer. Approximately three-quarters of the items are in a format that the computer can score (simple or complex multiple-choice items). The remaining items are constructed-response, and test takers enter their responses into text boxes.

Scoring of the items is based on the processes that the test taker uses to solve the problem and involves awarding points for the use of certain processes. For information retrieval, the focus is on identifying the need to collect baseline data (referred to in PISA terminology as identifying the “zero round”) and the method of manipulating one variable at a time (referred to in PISA terminology as “varying one thing at a time” or VOTAT). Full credit is awarded if the subject uses VOTAT strategy and makes use of zero rounds. Partial credit is given if the subject uses VOTAT but does not make use of zero rounds.

For model building, full credit is awarded if the generated model is correct. If one or two errors are present in the model, partial credit is given. If more than two errors are present, then no credit is awarded.

For forecasting, full credit is given if the target goals are reached. Partial credit is given if some progress toward the target goals can be registered, and no credit is given if there is no progress toward target goals at all.

PISA items are classified as static versus interactive. In static problems, all the information the test taker needs to solve the problem is presented at the outset. In contrast, interactive problems require the test taker to explore the problem to uncover important relevant information ( Organisation for Economic Co-operation and Development, 2010 , p. 15). Two sample PISA items appear in Box 2-1 .

Sample Problem-Solving Items for PISA 2012. Digital Watch–interactive: A simulation of a digital watch is presented. The watch is controlled by four buttons, the functions of which are unknown to the student at the outset of the problems. The (more...)

Funke and his colleagues have conducted analyses to evaluate the construct validity of the assessment. They have examined the internal structure of the assessment using structural equation modeling, which evaluates the extent to which the items measure the dimensions they are intended to measure. The results indicate the three dimensions are correlated with each other. Model Building and Forecasting correlate at .77; Forecasting and Information Retrieval correlate at .71; and Information Retrieval and Model Building correlate at .75. Funke said that the results also document that the items “load on” the three dimensions in the way the test developers hypothesized. He indicated some misfit related to the items that measure Forecasting, and he attributes this to the fact that the Forecasting items have a skewed distribution. However, the fit of the model does not change when these items are removed.

Funke reported results from studies of the relationship between test performance and other variables, including school achievement and two measures of problem solving on the PISA German National Extension on Complex Problem Solving. The latter assessment, called HEIFI, measures knowledge about a system and the control of the system separately. Scores on the PISA Model Building dimension are statistically significant (p < .05) related to school achievement (r = .64) and to scores on the HEIFI knowledge component (r = .48). Forecasting is statistically significant (p < .05) related to both of the HEIFI scores (r = .48 for HEIFI knowledge and r = .36 for HEIFI control). Information Retrieval is statistically significant (p < .05) related to HEIFI control (r = .38). The studies also show that HEIFI scores are not related to school achievement.

Funke closed by discussing the costs associated with the assessment. He noted it is not easy to specify the costs because in a German university setting, many costs are absorbed by the department and its equipment. Funke estimates that development costs run about $13 per unit, 9 plus $6.5 for the Cognitive Labs used to pilot test and refine the items. 10 The license for the Computer Based Assessment (CBA) Item-builder and the execution environment is given for free for scientific use from DIPF 11 Frankfurt.

The Bar Examination for Lawyers 12

The Bar examination is administered by each jurisdiction in the United States as one step in the process to license lawyers. The National Council of Bar Examiners (NCBE) develops a series of three exams for use by the jurisdictions. Jurisdictions may use any or all of these three exams or may administer locally developed exam components if they wish. The three major components developed by the NCBE include the Multi-state Bar Exam (MBE), the Multi-state Essay Exam (MEE), and the Multi-state Performance Test (MPT). All are paper-and-pencil tests. Examinees pay to take the test, and the costs are $54 for the MBE, $20 for the MEE, and $20 for the MPT.

Susan Case, who has spent her career working on licensing exams—first the medical licensing exam for physicians and then the bar exam for lawyers—noted the Bar examination is like other tests used to award professional licensure. The focus of the test is on the extent to which the test taker has the knowledge and skills necessary to be licensed in the profession on the day of the test. The test is intended to ensure the newly licensed professional knows what he/she needs to know to practice law. The test is not designed to measure the curriculum taught in law schools, but what licensed professionals need to know. When they receive the credential, lawyers are licensed to practice in all fields of law. This is analogous to medical licensing in which the licensed professional is eligible to practice any kind of medicine.

The Bar exam includes both multiple-choice and constructed-response components. Both require examinees to be able to gather and synthesize information and apply their knowledge to the given situation. The questions generally follow a vignette that describes a case or problem and asks the examinee to determine the issues to resolve before advising the client or to determine other information needed in order to proceed. For instance, what questions should be asked next? What is the best strategy to implement? What is the best defense? What is the biggest obstacle to relief? The questions may require the examinee to synthesize the law and the facts to predict outcomes. For instance, is the ordinance constitutional? Should a conviction be overturned?

The purpose of the MBE is to assess the extent to which an examinee can apply fundamental legal principles and legal reasoning to analyze a given pattern of facts. The questions focus on the understanding of legal principles rather than memorization of local case or statutory law. The MBE consists of 60 multiple-choice questions and lasts a full day.

A sample question follows:

A woman was told by her neighbor that he planned to build a new fence on his land near the property line between their properties. The woman said that, although she had little money, she would contribute something toward the cost. The neighbor spent $2,000 in materials and a day of his time to construct the fence. The neighbor now wants her to pay half the cost of the materials. Is she liable for this amount?

The purpose of the MEE is to assess the examinee’s ability to (1) identify legal issues raised by a hypothetical factual situation; (2) separate material that is relevant from that which is not; (3) present a reasoned analysis of the relevant issues in a clear, concise, and well-organized composition; and (4) demonstrate an understanding of the fundamental legal principles relevant to the probable resolution of the issues raised by the factual situation.

The MEE lasts for 6 hours and consists of nine 30-minute questions. An excerpt from a sample question follows:

The CEO/chairman of the 12-member board of directors (the Board) of a company plus three other members of the Board are senior officers of the company. The remaining eight members of the Board are wholly independent directors. Recently, the Board decided to hire a consulting firm to market a new product . . . The CEO disclosed to the Board that he had a 25% partnership interest in the consulting firm. The CEO stated that he would not be involved in any work to be performed by the consulting firm. He knew but did not disclose to the Board that the consulting firm’s proposed fee for this consulting assignment was substantially higher than it normally charged for comparable work . . . The Board discussed the relative merits of the two proposals for 10 minutes. The Board then voted unanimously (CEO abstaining) to hire the consulting firm . . . Did the CEO violate his duty of loyalty to his company? Explain. Assuming the CEO breached his duty of loyalty to his company, does he have any defense to liability? Explain. Did the other directors violate their duty of care? Explain.

The purpose of the MPT is to assess fundamental lawyering skills in realistic situations by asking the candidate to complete a task that a beginning lawyer should be able to accomplish. The MPT requires applicants to sort detailed factual materials; separate relevant from irrelevant facts; analyze statutory, case, and administrative materials for relevant principles of law; apply relevant law to the facts in a manner likely to resolve a client’s problem; identify and resolve ethical dilemmas; communicate effectively in writing; and complete a lawyering task within time constraints.

Each task is completely self-contained and includes a file, a library, and a task to complete. The task might deal with a car accident, for example, and therefore might include a file with pictures of the accident scene and depositions from the various witnesses, as well as a library with relevant case law. Examinees are given 90 minutes to complete each task.

For example, in a case involving a slip and fall in a store, the task might be to prepare an initial draft of an early dispute resolution for a judge. The draft should candidly discuss the strengths and weaknesses of the client’s case. The file would contain the instructional memo from the supervising attorney, the local rule, the complaint, an investigator’s report, and excerpts of the depositions of the plaintiff and a store employee. The library would include a jury instruction concerning the premises liability with commentary on contributory negligence.

The MBE is a multiple-choice test and thus scored by machine. However, the other two components require human scoring. The NCBE produces the questions and the grading guidelines for the MEE and MPT, but the essays and performance tests are scored by the jurisdictions themselves. The scorers are typically lawyers who are trained during grading seminars held at the NCBE offices, after the exam is administered. At this time, they review sample papers and receive training on how to apply the scoring guidelines in a consistent fashion.

Each component of the Bar examination (MBE, MEE, MPT) is intended to assess different skills. The MBE focuses on breadth of knowledge, the MEE focuses on depth of knowledge, and the MPT focuses on the ability to demonstrate practical skills. Together, the three formats cover the different types of tasks that a new lawyer needs to do.

Determinations about weighting the three components are left to the jurisdictions; however, the NCBE urges them to weight the MBE score by 50 percent and the MEE and MPT by 25 percent each. The recommendation is an attempt to balance a number of concerns, including authenticity, psychometric considerations, logistical issues, and economic concerns. The recommendation is to award the highest weight to the MBE because it is the most psychometrically sound. The reliability of scores on the MBE is generally over .90, much higher than scores on the other portions, and the MBE is scaled and equated across time. The recommended weighting helps to ensure high decision consistency and comparability of pass/fail decisions across administrations.

Currently the MBE is used by all but three jurisdictions (Louisiana, Washington, and Puerto Rico). The essay exam is used by 27 jurisdictions, and the performance test is used by 34 jurisdictions.

Test Development

Standing test development committees that include practicing lawyers, judges, and lawyers on staff with law schools write the test questions. The questions are reviewed by outside experts, pretested on appropriate populations, analyzed and revised, and professionally edited before operational use. Case said the test development procedures for the Bar exam are analogous to those used for the medical licensure exams.

Operation ARIES! (Acquiring Research Investigative and Evaluative Skills)

The summary below is based on materials provided by Art Graesser, including his presentation 13 and two background papers he supplied to the committee ( Graesser et al., 2010 ; Millis et al., in press ).

Operation ARIES! is a tutorial system with a formative assessment component intended for high school and higher education students, Graesser explained. It is designed to teach and assess critical thinking about science. The program operates in a game environment intended to be engaging to students. The system includes an “Auto Tutor,” which makes use of animated characters that converse with students. The Auto Tutor is able to hold conversations with students in natural language, interpret the student’s response, and respond in a way that is adaptive to the student’s response. The designers have created a science fiction setting in which the game and exercises operate. In the game, alien creatures called “Fuaths” are disguised as humans. The Fuaths disseminate bad science through various media outlets in an attempt to confuse humans about the appropriate use of the scientific method. The goal for the student is to become a “special agent of the Federal Bureau of Science (FBS), an agency with a mission to identify the Fuaths and save the planet” ( Graesser et al., 2010 , p. 328).

The system addresses scientific inquiry skills, developing research ideas, independent and dependent variables, experimental control, the sample, experimenter bias, and relation of data to theory. The focus is on use of these skills in the domains of biology, chemistry, and psychology. The system helps students to learn to evaluate evidence intended to support claims. Some examples of the kinds of research questions/claims that are evaluated include the following:

From Biology

• Do chemical and organic pesticides have different effects on food quality?
• Does milk consumption increase bone density?

From Chemistry

• Does a new product for winter roads prevent water from freezing?
• Does eating fish increase blood mercury levels?

From Psychology

• Does using cell phones hurt driving?
• Is a new cure for autism effective?

The system includes items in real-life formats, such as articles, advertisements, blogs, and letters to the editor, and makes use of different types of media where it is common to see faulty claims.

Through the system, the student encounters a story told by video, combined with communications received by e-mail, text message, and updates. The student is engaged through the Auto Tutor, which involves a “tutor agent” that serves as a narrator, and a “student agent” that serves in different roles, depending on the skill level of the student.

The system makes use of three kinds of modules—interactive training, case studies, and interrogations. The interactive training exchanges begin with the student reading an e-book, which provides the requisite information used in later modules. After each chapter, the student responds to a set of multiple-choice questions intended to assess the targeted skills. The text is interactive in that it involves “trialogs” (three-way conversations) between the primary agent, the student agent, and the actual (human) student. It is adaptive in that the strategy used is geared to the student’s performance. If the student is doing poorly, the two auto-tutor agents carry on a conversation that promotes vicarious learning: that is, the tutor agent and the student agent interact with each other, and the human student observes. If the student is performing at an intermediate level, normal tutoring occurs in which the student carries on a conversational exchange with the tutor agent. If the student is doing very well, he or she may be asked to teach the student agent, under the notion that the act of teaching can help to perfect one’s skills.

In the case study modules, the student is expected to apply what he or she has learned. The case study modules involve some type of flawed science, and the student is to identify the flaws by applying information learned from the interactive text in the first module. The student responds by verbally articulating the flaws, and the system makes use of advances in computational linguistics to analyze the meaning of the response. The researchers adopted the case study approach because it “allows learners to encode and discover the rich source of constraints and interdependencies underlying the target elements (flaws) within the cases. [Prior] cases provide a knowledge base for assessing new cases and help guide reasoning, problem solving, interpretation and other cognitive processes” ( Millis et al., in press , p. 17).

In the interrogation modules, insufficient information is provided, so students must ask questions. Research is presented in an abbreviated fashion, such as through headlines, advertisements, or abstracts. The student is expected to identify the relevant questions to ask and to learn to discriminate good research from flawed research. The storyline is advanced by e-mails, dialogues, and videos that are interspersed among the learning activities.

Through the three kinds of modules, the system interweaves a variety of key principles of learning that Graesser said have been shown to increase learning. These include

• Self-explanation (where the learner explains the material to another student, such as the automated student)
• Immediate feedback (through the tutoring system)
• Multimedia effects (which tend to engage the student)
• Active learning (in which students actually participate in solving a problem)
• Dialog interactivity (in which students learn by engaging in conversations and tutorial dialogs)
• Multiple, real-life examples (intended to help students transfer what they learn in one context to another context and to real world situations)

Graesser closed by saying that he and his colleagues are beginning to collect data from evaluation studies to examine the effects of the Auto Tutor. Research has focused on estimating changes in achievement before and after use of the system, and, to date, the results are promising.

Packet Tracer

The summary below is based on materials provided by John Behrens, including his presentation 14 and a background paper he forwarded in preparation for the workshop ( Behrens et al., in press ).

To help countries around the world train their populations in networking skills, Cisco created the Networking Academy. The academy is a public/private partnership through which Cisco provides free online curricula and assessments. Behrens pointed out that in order to become adept with networking, students need both a conceptual understanding of networking and the skills to apply this knowledge to real situations. Thus, hands-on practice and assessment on real equipment are important components of the academy’s instructional program. Cisco also wants to provide students with time for out-of-class practice and opportunities to explore on their own using online equipment that is not typically available in the average classroom setting. In the Networking Academy, students work with an online instructor, and they proceed through an established curriculum that incorporates numerous interactive activities.

Behrens talked specifically about a new program Cisco has developed called “Packet Tracer,” a computer package that uses simulations to provide instruction and includes an interactive and adaptable assessment component. Cisco has incorporated Packet Tracer activities into the curricula for training networking professionals. Through this program, instructors and students can construct their own activities, and students can explore problems on their own. In Cisco’s Networking Academy, assessments can be student-initiated or instructor-initiated. Student-initiated assessments are primarily embedded in the curriculum and include quizzes, interactive activities, and “challenge labs,” which are a feature of Packet Tracer. The student-initiated assessments are designed to provide feedback to the student to help his or her learning. They use a variety of technologies ranging from multiple-choice questions (in the quizzes) to complex simulations (in the challenge labs). Before the development of Packet Tracer, the instructor-initiated assessments consisted either of hands-on exams with real networking equipment or multiple-choice exams in the online assessment system. Packet Tracer provides more simulation-based options, and also includes detailed reporting and grade-book integration features.

Each assessment consists of one extensive network configuration or troubleshooting activity that may require up to 90 minutes to complete. Access to the assessment is associated with a particular curricular unit, and it may be re-accessed repeatedly based on instructor authorization. The system provides simulations of a broad range of networking devices and networking protocols, including features set around the Cisco IOS (Internet Operating System). Instructions for tasks can be presented through HTML-formatted text boxes that can be preauthored, stored, and made accessible by the instructor at the appropriate time.

Behrens presented an example of a simulated networking problem in which the student needs to obtain the appropriate cable. To complete this task, the student must determine what kind of cable is needed, where on the computer to plug it in, and how to connect it. The student’s performance is scored, and his or her interactions with the problem are tracked in a log. The goal is not to simply assign a score to the student’s performance but to provide detailed feedback to enhance learning and to correct any misinterpretations. The instructors can receive and view the log in order to evaluate how well the student understands the tasks and what needs to be done.

Packet Tracer can simulate a broad range of devices and networking protocols, including a wide range of PC facilities covering communication cards, power functionality, web browsers, and operating system configurations. The particular devices, configurations, and problem states are determined by the author of the task (e.g., the instructor) in order to address whatever proficiencies the chapter, course, or instruction targets. When icons of the devices are touched in the simulator, more detailed pictures are presented with which the student can interact. The task author can program scoring rules into the system. Students can be observed trying and discarding potential solutions based on feedback from the game resulting in new understandings. The game encourages students to engage in problem-solving steps (such as problem identification, solution generation, and solution testing). Common incorrect strategies can be seen across recordings.

For Kuncel’s presentation, see http://www7 ​.national-academies ​.org/bota/21st ​_Century_Workshop_Kuncel.pdf . For Kuncel’s paper, see http://www7 ​.national-academies ​.org/bota/21st ​_Century_Workshop_Kuncel_Paper.pdf . For Anderman’s presentation, see http://www7 ​.national-academies ​.org/bota/21st ​_Century_Workshop_Anderman.pdf . For Anderman’s paper, see http: ​//nrc51/xpedio/groups ​/dbasse/documents ​/webpage/060387~1.pdf [August 2011].

Respectively, the Graduate Record Exam, Medical College Admission Test, Law School Admission Test, Graduate Management Admission Test, Miller Analogies Test, and Pharmacy College Admission Test.

Convergent validilty indicates the degree to which an operationalized construct is similar to other operationalized constructs that it theoretically should also be similar to. For instance, to show the convergent validity of a test of critical thinking, the scores on the test can be correlated with scores on other tests that are also designed to measure critical thinking. High correlations between the test scores would be evidence of convergent validity.

Discriminant validity evaluates the extent to which a measure of an operationalized construct differs from measures of other operationalized constructs that it should differ from. In the present context, the interest is in verifying that critical thinking is a construct distinct from general intelligence and expert performance. Thus, discriminant validity would be examined by evaluating the patterns of correlations between and among scores on tests of critical thinking and scores on tests of the other two constructs (general intelligence and expert performance).

It is important to note that when corrected for restriction in range, these coefficients increase to .47 to .51 for individual scores and .51 for the combined score.

For a full description of the PISA program, see http://www ​.oecd.org/pages ​/0,3417,en_32252351 ​_32235731_1_1_1_1_1,00.html [August 2011].

Available at http://www ​.oecd.org/dataoecd ​/8/42/46962005.pdf [August 2011].

Available at http://www7 ​.national-academies ​.org/bota/21st ​_Century_Workshop_Funke.pdf [August 2011].

A unit consists of stimulus materials, instructions, and the associated questions.

Costs are in American dollars.

DIPF stands for the Deutsches Institut für Internationale Pädagogische Forschung, which translates to the German Institute for Educational Research and Educational Information.

The summary is based on a presentation by Susan Case, see http://www7 ​.nationalacademies ​.org/bota/21st ​_Century_Workshop_Case.pdf [August 2011].

For Graesser’s presentation, see http: ​//nrc51/xpedio/groups ​/dbasse/documents ​/webpage/060267~1.pdf [August 2011].

For Behrens’ presentation, see http://www7 ​.national-academies ​.org/bota/21st ​_Century_Workshop_Behrens.pdf [August 2011].

• Cite this Page National Research Council (US) Committee on the Assessment of 21st Century Skills. Assessing 21st Century Skills: Summary of a Workshop. Washington (DC): National Academies Press (US); 2011. 2, Assessing Cognitive Skills.
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'It Turned Everything Around': How This CEO Conquered His ADHD Using Brain Boosters Dan Freed, founder and CEO of Thesis, found mental clarity with nootropics. Now, he is sharing his formulas with the world.

By Jon Bier • Apr 2, 2024

Key Takeaways

• Dan Freed is Founder and CEO of Thesis, a nootropics brand
• ADHD caused him to drop out of school and become a world-class chef
• After he discovered nootropics, he was no longer dependent on pharmaceutical stimulants

Opinions expressed by Entrepreneur contributors are their own.

When he was eight years old, Dan Freed could not sit still in school.

"I remember coming home and bouncing off the walls. I was getting suspended. I'm in the principal's office. They're sending me to a psychiatrist," Freed says. "People thought I was stupid, lazy, or unmotivated. I started to believe it."

Doctors later diagnosed Freed with ADHD and prescribed the stimulant Adderall. This helped him focus, but also left him depressed and irritable.

It wasn't until he was an adult that Freed discovered nootropics , nutrient compounds shown to support motivation, creativity, mood, memory, and cognitive processing.

"Nootropics turned everything around for me and helped me form the positive habits that I built my success on," he says.

Freed is now the founder and CEO of Thesis , a leading nootropics company that makes specially formulated blends to enhance an individual's personal neurochemistry. I spoke with him recently on my podcast One Day with Jon Bier about his personal and professional journey.

From chef to CEO

Early in Freed's career, he became a saucier at Michelin-starred restaurants and an esteemed chef worldwide. He credits part of his success to his ADHD.

"It was my superpower," he says. "As a chef, you're constantly in the weeds doing five things at once, but I was able to concentrate on so many different things."

But the work was also burning him out. He dreamed of taking the GMAT and going back to school. While working as an executive sous chef on a cruise ship, he learned how to run a successful operation from his boss, a former investment banker.

"One day, he said to me, 'If you're able to do all of this without an education, imagine what you could do with one," Freed recalls.

Around this time, he discovered nootropics, which were a total game changer.

"I scored in the 99th percentile on the GMAT and earned a master's degree from Yale and INSEAD," he says. "For years, I was always on and off Adderall, Vyvanse, Concerta, every couple of months," he says. "But nootropics were the first thing where I'm like, hey, this is sustainable. It doesn't have these side effects. I was able to make meaningful progress and be more productive and creative."

Related: How to Upgrade Your Brain to Boost Focus and Productivity

Solving a problem

Freed's newfound appreciation for nootropics changed the trajectory of his life, but it wasn't without its challenges.

For one, he realized early on that nootropics weren't a one-size-fits-all solution. He couldn't find a product in the market that was the perfect formulation for him.

"I was constantly experimenting with different ingredients, dosages, combinations to find what worked," Freed says. "When I found something that worked, it was much better than anything I could find on the market. And that was when it clicked: How can we get this tailored experience?"

A business idea was born.

Freed and his team tried out various combinations of different dosages and ingredients on a test group of 2,500 people until they got the desired results. The result was Thesis--nootropic blends formulated for different outcomes, including logic, creativity, motivation, energy, clarity, and confidence.

Freed says, "Nootropics is a data science problem. The solution is to get a massive amount of customer data on how they respond to different ingredients and formulations."

New product line

On the podcast, Freed also talks about a new product line they have developed called Stasis, which he describes as "supplements that are synergistic with stimulants."

Unlike Thesis, which is tailored to replace the effects of pharmaceutical stimulants such as Adderall, Stasis is designed to complement these drugs.

"We're able to find the right ingredients for the right person based on their genetics and their goals and knowing this person's taking a stimulant that very predictably altered their brain chemistry," he explains.

So far, customers have given the product exceptional reviews.

"I've been formulating for almost a decade now, and I've never seen such a positive response from any product," Freed says.

Related: How Leaders Can Help Employees With ADHD Succeed in Remote Work

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Examples from customer support, management consulting, professional writing, legal analysis, and software and technology.

The interactive, conversational, analytical, and generative features of GenAI offer support for creativity, problem-solving, and processing and digestion of large bodies of information. Therefore, these features can act as cognitive resources for knowledge workers. Moreover, the capabilities of GenAI can mitigate various hindrances to effective performance that knowledge workers may encounter in their jobs, including time pressure, gaps in knowledge and skills, and negative feelings (such as boredom stemming from repetitive tasks or frustration arising from interactions with dissatisfied customers). Empirical research and field observations have already begun to reveal the value of GenAI capabilities and their potential for job crafting.

There is an expectation that implementing new and emerging Generative AI (GenAI) tools enhances the effectiveness and competitiveness of organizations. This belief is evidenced by current and planned investments in GenAI tools, especially by firms in knowledge-intensive industries such as finance, healthcare, and entertainment, among others. According to forecasts, enterprise spending on GenAI will increase by two-fold in 2024 and grow to $151.1 billion by 2027 .

• Maryam Alavi is the Elizabeth D. & Thomas M. Holder Chair & Professor of IT Management, Scheller College of Business, Georgia Institute of Technology .

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After completing this 2-hour course, the learner will be able to describe how social studies and using nature support children's cognitive development. Social studies experiences should reflect the range of the children's cultural groups. The learner will be able to give activity examples from these two areas.

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Opinion | Should we have age limits or cognitive tests…

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Opinion | Should we have age limits or cognitive tests for politicians?

With recent questions being raised about the mental competence of presidential candidates and congresspeople, it’s worth thinking about what should be done about it or whether we should do anything about it at all. An age limit has been proposed although it’s far from clear if this is something we should implement. Since part of the reason we’re considering whether to implement such a measure is to exclude individuals like Biden and Trump, I’ll assume that the limit would be somewhere around 75. Not much of what I say here depends on whether this is the optimal age limit.

An unintended consequence of an upper limit is that we would also be excluding many individuals who are perfectly capable of carrying out the duties of office. I personally know many people over the age of 75 who are about as mentally sound as anyone. I’d actually vote for some of them if they were on the ballot.

While this is not an unfounded concern, current candidates must be over the age of 35 to run for the presidency. To some this might justify an upper age limit. The thought behind the lower limit is that we think that age correlates with traits like wisdom and experience. We want our presidents to have certain characteristics, such as experience, and we want them to lack others, such as the impulsiveness that comes with young age.

We might apply similar reasoning to justify an upper limit. Certain diseases like dementia and general cognitive decline correlate with age. So the justification for a 75 year limit is similar to the lower limit currently in effect: being a president is extremely difficult so for the sake of the public good, we need to make sure that candidates are able to perform the required duties.

There is a practical argument here: an age limit is the best we can do. We use the age limit as a broad protection against a president having a certain trait, just like we use the age minimum. Of course, there are many capable, intelligent, and ethical individuals under 35. But generally speaking perhaps, we might think that we get more experienced and wise individuals after 35.

One problem with this is that people over the age of 80 only have a one in six chance of developing dementia. While this may seem high, this means of course that there’s a five in six chance that they won’t. The great majority of individuals in the 65 to 80 age range will not have dementia, which casts doubt on the justification for the age limit, particularly when we note that the rate of cognitive problems is dependent on the present and future state of medicine.

We are trying to account for mental decline, so a more direct proposal would be to implement yearly mandatory cognitive tests. Trump submitted himself to a cognitive test, which he apparently passed with flying colors – yeah, we’re going to need an independent office to design and administer the exam.

This would allow us to address our concerns with individuals like our current presidential candidates while also avoiding discriminating against the elderly who are perfectly fit to serve. Implementing mental fitness tests would present several challenges. How would that test be designed and where should we set the threshold for a passing grade?

It’s clear that there are many concerning features of both an age limit and a mental fitness test. They both suffer from their own threshold problem. But we don’t have to throw our hands up about this. The threshold problem with an age limit appears to be more vicious than the threshold problem with cognitive tests.

An age limit casts a wider net and has the potential to exclude many more capable individuals. There is no perfect solution to this problem and there will be ambiguous cases where competent candidates are excluded either way. But we cannot allow compromised individuals to hold such powerful positions. At least a cognitive test for holding office directly targets the problem we’re trying to solve – it’s about mental acuity, not age.

Rafael Perez is a doctoral candidate in philosophy at the University of Rochester. You can reach him at [email protected].

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