theories of problem solving psychology

7.3 Problem-Solving

Learning objectives.

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

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

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

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

PROBLEM-SOLVING STRATEGIES

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

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

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

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

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

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

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

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

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

Additional Problem Solving Strategies :

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

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

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

Missionary-Cannibal Problem

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

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

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

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

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

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

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

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

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

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

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

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

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

Solving puzzles.

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

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

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

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

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

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

Pitfalls to problem solving.

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

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

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

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

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

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

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

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

References:

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

Review Questions:

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

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

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

a. divide and conquer

b. means-end analysis

d. experiment

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

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

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

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

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

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

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

a. functional fixedness

c. working memory

Critical Thinking Questions:

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

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

Personal Application Question:

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

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

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

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

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

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

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

heuristic:  mental shortcut that saves time when solving a problem

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

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

problem-solving strategy:  method for solving problems

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

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

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

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In This Article Expand or collapse the "in this article" section Problem Solving and Decision Making

Introduction.

• General Approaches to Problem Solving
• Representational Accounts
• Problem Space and Search
• Working Memory and Problem Solving
• Domain-Specific Problem Solving
• The Rational Approach
• Prospect Theory
• Dual-Process Theory
• Cognitive Heuristics and Biases

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Problem Solving and Decision Making by Emily G. Nielsen , John Paul Minda LAST REVIEWED: 26 June 2019 LAST MODIFIED: 26 June 2019 DOI: 10.1093/obo/9780199828340-0246

Problem solving and decision making are both examples of complex, higher-order thinking. Both involve the assessment of the environment, the involvement of working memory or short-term memory, reliance on long term memory, effects of knowledge, and the application of heuristics to complete a behavior. A problem can be defined as an impasse or gap between a current state and a desired goal state. Problem solving is the set of cognitive operations that a person engages in to change the current state, to go beyond the impasse, and achieve a desired outcome. Problem solving involves the mental representation of the problem state and the manipulation of this representation in order to move closer to the goal. Problems can vary in complexity, abstraction, and how well defined (or not) the initial state and the goal state are. Research has generally approached problem solving by examining the behaviors and cognitive processes involved, and some work has examined problem solving using computational processes as well. Decision making is the process of selecting and choosing one action or behavior out of several alternatives. Like problem solving, decision making involves the coordination of memories and executive resources. Research on decision making has paid particular attention to the cognitive biases that account for suboptimal decisions and decisions that deviate from rationality. The current bibliography first outlines some general resources on the psychology of problem solving and decision making before examining each of these topics in detail. Specifically, this review covers cognitive, neuroscientific, and computational approaches to problem solving, as well as decision making models and cognitive heuristics and biases.

General Overviews

Current research in the area of problem solving and decision making is published in both general and specialized scientific journals. Theoretical and scholarly work is often summarized and developed in full-length books and chapter. These may focus on the subfields of problem solving and decision making or the larger field of thinking and higher-order cognition.

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• The Psychology of Problem Solving

The Psychology of Problem Solving

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Book description

Problems are a central part of human life. The Psychology of Problem Solving organizes in one volume much of what psychologists know about problem solving and the factors that contribute to its success or failure. There are chapters by leading experts in this field, including Miriam Bassok, Randall Engle, Anders Ericsson, Arthur Graesser, Keith Stanovich, Norbert Schwarz, and Barry Zimmerman, among others. The Psychology of Problem Solving is divided into four parts. Following an introduction that reviews the nature of problems and the history and methods of the field, Part II focuses on individual differences in, and the influence of, the abilities and skills that humans bring to problem situations. Part III examines motivational and emotional states and cognitive strategies that influence problem solving performance, while Part IV summarizes and integrates the various views of problem solving proposed in the preceding chapters.

‘A good book on any subject should summarise the current state of knowledge, and point to the important areas where further work is needed, and this book does both. Overall, this is a very stimulating collection, which all researchers in problem solving will wish to consult.’

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Frontmatter pp i-iv

• Get access Check if you have access via personal or institutional login Log in Register

Contents pp v-vi

Contributors pp vii-viii, preface pp ix-xii.

• By Janet E. Davidson , Associate Professor of Psychology, Lewis & Clark College, Robert J. Sternberg , IBM Professor of Psychology and Education, Yale University; Director, Yale Center for the Psychology of Abilities, Competencies and Expertise (PACE Center)

PART I - INTRODUCTION pp 1-2

1 - recognizing, defining, and representing problems pp 3-30.

• By Jean E. Pretz , Yale University, Adam J. Naples , Yale University, Robert J. Sternberg , Yale University

2 - The Acquisition of Expert Performance as Problem Solving: Construction and Modification of Mediating Mechanisms through Deliberate Practice pp 31-84

• By K. Anders Ericsson , Florida State University

PART II - RELEVANT ABILITIES AND SKILLS pp 85-86

3 - is success or failure at solving complex problems related to intellectual ability pp 87-126.

• By Dorit Wenke , Humboldt-University at Berlin, Peter A. Frensch , Humboldt-University at Berlin

4 - Creativity: A Source of Difficulty in Problem Solving pp 127-148

• By Todd I. Lubart , Université René Descartes, Paris, Christophe Mouchiroud , Université René Descartes, Paris

5 - Insights about Insightful Problem Solving pp 149-175

• By Janet E. Davidson , Associate Professor of Psychology, Lewis & Clark College

6 - The Role of Working Memory in Problem Solving pp 176-206

• By David Z. Hambrick , Michigan State University, Randall W. Engle , Georgia Institute of Technology

7 - Comprehension of Text in Problem Solving pp 207-230

• By Shannon Whitten , The University of Memphis, Arthur C. Graesser , The University of Memphis

PART III - STATES AND STRATEGIES pp 231-232

8 - motivating self-regulated problem solvers pp 233-262.

• By Barry J. Zimmerman , Graduate School and University Center, City University of New York, Magda Campillo , Graduate School and University Center, City University of New York

9 - Feeling and Thinking: Implications for Problem Solving pp 263-290

• By Norbert Schwarz , University of Michigan, Ian Skurnik , University of Michigan

10 - The Fundamental Computational Biases of Human Cognition: Heuristics That (Sometimes) Impair Decision Making and Problem Solving pp 291-342

• By Keith E. Stanovich , University of Toronto

11 - Analogical Transfer in Problem Solving pp 343-370

• By Miriam Bassok , University of Washington

PART IV - CONCLUSION AND INTEGRATION pp 371-372

12 - problem solving – large/small, hard/easy, conscious/nonconscious, problem-space/problem-solver: the issue of dichotomization pp 373-384.

• By Kenneth Kotovsky , Carnegie Mellon University

Index pp 385-394

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Theory of Problem Solving

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Teaching of General Psychology: Problem Solving

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• David Gibson 5 ,
• Dirk Ifenthaler 5 , 6 &
• Samuel Greiff 7  

Part of the book series: Springer International Handbooks of Education ((SIHE))

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This chapter defines problem solving and its research history. In addition to this, it introduces data science approaches to research on problem solving for psychology students, educators, and researchers. The chapter describes four new core content and topical areas on the immediate horizon: data science, Internet of things, network analyses, and artificial intelligence. The chapter elucidates implications for data science education in general psychology, focusing on research in problem solving and on how problem solving can be taught in higher education.

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Gibson, D., Ifenthaler, D., Greiff, S. (2022). Teaching of General Psychology: Problem Solving. In: Zumbach, J., Bernstein, D., Narciss, S., Marsico, G. (eds) International Handbook of Psychology Learning and Teaching. Springer International Handbooks of Education. Springer, Cham. https://doi.org/10.1007/978-3-030-26248-8_8-1

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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?
• Take our Your Mental Health Today Test
• 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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Jerome Bruner’s Theory Of Learning And Cognitive Development

Saul Mcleod, PhD

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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.

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Jerome Bruner believed that children construct knowledge and meaning through active experience with the world around them. He emphasized the role of culture and language in cognitive development, which occurs in a spiral fashion with children revisiting basic concepts at increasing levels of complexity and abstraction.

Bruner’s Ideas

• Like  Ausubel (and other cognitive psychologists), Bruner sees the learner as an active agent; emphasizing the importance of existing schemata in guiding learning.
• Bruner argues that students should discern for themselves the structure of subject content – discovering the links and relationships between different facts, concepts and theories (rather than the teacher simply telling them).
• Bruner (1966) hypothesized that the usual course of intellectual development moves through three stages: enactive, iconic, and symbolic, in that order. However, unlike Piaget’s stages, Bruner did not contend that these stages were necessarily age-dependent, or invariant.
• Piaget and, to an extent, Ausubel, contended that the child must be ready, or made ready, for the subject matter. But Bruner contends just the opposite. According to his theory, the fundamental principles of any subject can be taught at any age, provided the material is converted to a form (and stage) appropriate to the child.
• The notion of a “spiral curriculum” embodies Bruner’s ideas by “spiraling” through similar topics at every age, but consistent with the child’s stage of thought.
• His spiral curriculum revisits basic ideas repeatedly, building upon them into more complex, abstract concepts over time in a developmentally appropriate sequence.
• The aim of education should be to create autonomous learners (i.e., learning to learn).
• Cognitive growth involves an interaction between basic human capabilities and “culturally invented technologies that serve as amplifiers of these capabilities.”
• These culturally invented technologies include not just obvious things such as computers and television, but also more abstract notions such as the way a culture categorizes phenomena, and language itself.
• Bruner would likely agree with  Vygotsky  that language serves to mediate between environmental stimuli and the individual’s response.

Three Modes of Representation

Modes of representation are how information or knowledge is stored and encoded in memory.

Rather than neat age-related stages (like Piaget), the modes of representation are integrated and only loosely sequential as they “translate” into each other.

Bruner (1966) was concerned with how knowledge is represented and organized through different modes of thinking (or representation).

In his research on the cognitive development of children,  Jerome Bruner proposed three modes of representation:

• Enactive representation (action-based)
• Iconic representation (image-based)
• Symbolic representation (language-based)

Bruner’s constructivist theory suggests it is effective when faced with new material to follow a progression from enactive to iconic to symbolic representation; this holds true even for adult learners.

Bruner’s work also suggests that a learner even of a very young age is capable of learning any material so long as the instruction is organized appropriately, in sharp contrast to the beliefs of Piaget and other stage theorists.

Enactive Mode (0-1 year)

In the  enactive mode , knowledge is stored primarily in the form of motor responses. This mode is used within the first year of life (corresponding with Piaget’s sensorimotor stage ).

Thinking is based entirely on physical actions , and infants learn by doing, rather than by internal representation (or thinking).

It involves encoding physical action-based information and storing it in our memory. For example, in the form of movement as muscle memory, a baby might remember the action of shaking a rattle.

And this is not just limited to children. Many adults can perform a variety of motor tasks (typing, sewing a shirt, operating a lawn mower) that they would find difficult to describe in iconic (picture) or symbolic (word) form.

This mode continues later in many physical activities, such as learning to ride a bike.

Iconic Mode (1-6 years)

Information is stored as sensory images (icons), usually visual ones, like pictures in the mind. For some, this is conscious; others say they don’t experience it.

This may explain why, when we are learning a new subject, it is often helpful to have diagrams or illustrations to accompany the verbal information.

Thinking is also based on using other mental images (icons), such as hearing, smell or touch.

Symbolic Mode (7 years onwards)

This develops last. In the  symbolic stage , knowledge is stored primarily as language, mathematical symbols, or in other symbol systems.

This mode is acquired around six to seven years old (corresponding to Piaget’s concrete operational stage ).

In the symbolic stage, knowledge is stored primarily as words, mathematical symbols, or other symbol systems, such as music.

Symbols are flexible in that they can be manipulated, ordered, classified, etc., so the user isn’t constrained by actions or images (which have a fixed relation to that which they represent).

According to Bruner’s taxonomy, these differ from icons in that symbols are “arbitrary.” For example, the word “beauty” is an arbitrary designation for the idea of beauty in that the word itself is no more inherently beautiful than any other word.

The Importance of Language

Language is important for the increased ability to deal with abstract concepts.

Bruner argues that language can code stimuli and free an individual from the constraints of dealing only with appearances, to provide a more complex yet flexible cognition.

The use of words can aid the development of the concepts they represent and can remove the constraints of the “here & now” concept.

Bruner views the infant as an intelligent & active problem solver from birth, with intellectual abilities basically similar to those of the mature adult.

Educational Implications

Education should aim to create autonomous learners (i.e., learning to learn).

For Bruner (1961), the purpose of education is not to impart knowledge, but instead to facilitate a child’s thinking and problem-solving skills which can then be transferred to a range of situations. Specifically, education should also develop symbolic thinking in children.

In 1960 Bruner’s text, The Process of Education was published. The main premise of Bruner’s text was that students are active learners who construct their own knowledge.

Bruner (1960) opposed Piaget’s notion of readiness . He argued that schools waste time trying to match the complexity of subject material to a child’s cognitive stage of development.

This means students are held back by teachers as certain topics are deemed too difficult to understand and must be taught when the teacher believes the child has reached the appropriate stage of cognitive maturity .

The Spiral Curriculum

Bruner (1960) adopts a different view and believes a child (of any age) is capable of understanding complex information:

“We begin with the hypothesis that any subject can be taught effectively in some intellectually honest form to any child at any stage of development.” (p. 33)

Bruner (1960) explained how this was possible through the concept of the spiral curriculum. This involved information being structured so that complex ideas can be taught at a simplified level first, and then re-visited at more complex levels later on.

The underlying principle in this is that the student should review particular concepts at over and over again during their educative experience; each time building and their understanding and requiring more sophisticated cognitive strategies (and thus increase the sophistication of their understanding).

Therefore, subjects would be taught at levels of gradually increasing difficultly (hence the spiral analogy). Ideally, teaching his way should lead to children being able to solve problems by themselves.

Bruner argues that, as children age, they are capable of increasingly complex modes of representation (basically, ways of thinking) – and the spiral curriculum should be sensitive to this development;

• Initially, children learn better using an  enactive  mode of representation (i.e. they learn better through “doing things” such as physical and manual tasks) – for instance, the concept of addition might be first taught by asking the child to combine piles of beads and counting the results.
• As they grow older – and more familiar with subject content – pupils become more confident in using an  iconic  mode of representation; they are able to perform tasks by imagining concrete pictures in their heads. To continue the above example; as the child becomes more confident with addition, they should be able to imagine the beads in order to complete additions (without physically needing to manipulate the piles).
• Finally, students become capable of more abstract,  symbolic  modes of representation; without the need for either physical manipulation or mental imagery. Consequently, at this point, the student should have little problem with completing a series of written calculations; of numbers which are higher than is possible by “imagining beads”.

Discovery Learning Theory

Bruner (1960) developed the concept of Discovery Learning – arguing that students should “not be presented with the subject matter in its final form, but rather are required to organize it themselves…[requiring them] to discover for themselves relationships that exist among items of information”.

Bruner (1961) proposes that learners construct their own knowledge and do this by organizing and categorizing information using a coding system.

Bruner believed that the most effective way to develop a coding system is to discover it rather than being told by the teacher.

The concept of discovery learning implies that students construct their own knowledge for themselves (also known as a constructivist approach ).

The result is an extremely active form of learning, in which the students are always engaged in tasks, finding patterns or solving puzzles – and in which they constantly need to exercise their existing schemata , reorganizing and amending these concepts to address the challenges of the task.

The role of the teacher should not be to teach information by rote learning, but instead to facilitate the learning process. This means that a good teacher will design lessons that help students discover the relationship between bits of information.

To do this a teacher must give students the information they need, but without organizing for them. The use of the spiral curriculum can aid the process of discovery learning .

For example, in teaching a particular concept, the teacher should present the set of instances that will best help learners develop an appropriate model of the concept. The teacher should also model the inquiry process. Bruner would likely not contend that all learning should be through discovery.

For example, it seems pointless to have children “discover” the names of the U.S. Presidents, or important dates in history.

Bruner’s theory is probably clearest when illustrated with practical examples. The instinctive response of a teacher to the task of helping a primary-school child understand the concept of odd and even numbers, for instance, would be to explain the difference to them.

However, Bruner would argue that understanding of this concept would be much more genuine if the child discovered the difference for themselves; for instance, by playing a game in which they had to share various numbers of beads fairly between themselves and their friend.

Discovery is not just an instructional technique, but an important learning outcome in itself. Schools should help learners develop their own ability to find the “recurrent regularities” in their environment.

Bruner would likely not contend that all learning should be through discovery. For example, it seems pointless to have children “discover” the names of the U.S. Presidents, or important dates in history.

Scaffolding Theory

On the surface, Bruner’s emphasis on the learner discovering subject content for themselves seemingly absolves the teacher of a great deal of work.

In practice, however, his model requires the teacher to be actively involved in lessons; providing cognitive scaffolding which will facilitate learning on the part of the student.

On the one hand, this involves the selection and design of appropriate stimulus materials and activities which the student can understand and complete – however Bruner also advocates that the teacher should circulate the classroom and work with individual students, performing six core “functions” (Wood, Bruner and Ross: 1976):

• Recruitment : ensuring that the student is interested in the task, and understands what is required of them.
• Reducing degrees of freedom : helping the student make sense of the material by eliminating irrelevant directions and thus reducing the “trial and error” aspect of learning.
• Direction Maintenance : ensuring that the learner is on-task and interest is maintained – often by breaking the ultimate aim of the task into “sub-aims” which are more readily understood and achieved.
• Marking critical features : highlighting relevant concepts or processes and pointing out errors.
• Frustration Control : stopping students from “giving up” on the task.
• Demonstration : providing models for imitation or possible (partial solution).

In this context, Bruner’s model might be better described as guided discovery learning; as the teacher is vital in ensuring that the acquisition of new concepts and processes is successful.

Bruner and Vygotsky

Both Bruner and Vygotsky emphasize a child’s environment, especially the social environment, more than Piaget did. Both agree that adults should play an active role in assisting the child’s learning.

Bruner, like Vygotsky, emphasized the social nature of learning, citing that other people should help a child develop skills through the process of scaffolding.

“[Scaffolding] refers to the steps taken to reduce the degrees of freedom in carrying out some task so that the child can concentrate on the difficult skill she is in the process of acquiring” (Bruner, 1978, p. 19).

He was especially interested in the characteristics of people whom he considered to have achieved their potential as individuals.

The term scaffolding first appeared in the literature when Wood, Bruner, and Ross described how tutors” interacted with a preschooler to help them solve a block reconstruction problem (Wood et al., 1976).

The concept of scaffolding is very similar to Vygotsky’s notion of the zone of proximal development , and it’s not uncommon for the terms to be used interchangeably.

Scaffolding involves helpful, structured interaction between an adult and a child with the aim of helping the child achieve a specific goal.

The purpose of the support is to allow the child to achieve higher levels of development by:

• Simplifying the task or idea.
• Motivating and encouraging the child.
• Highlighting important task elements or errors.
• Giving models that can be imitated.

Bruner and Piaget

There are similarities between Piaget and Bruner, but a significant difference is that Bruner’s modes are not related in terms of which presuppose the one that precedes it. While sometimes one mode may dominate in usage, they coexist.

Bruner states that the level of intellectual development determines the extent to which the child has been given appropriate instruction together with practice or experience.

So – the right way of presentation and explanation will enable a child to grasp a concept usually only understood by an adult. His theory stresses the role of education and the adult.

Although Bruner proposes stages of cognitive development, he doesn’t see them as representing different separate modes of thought at different points of development (like Piaget).

Instead, he sees a gradual development of cognitive skills and techniques into more integrated “adult” cognitive techniques.

Bruner views symbolic representation as crucial for cognitive development, and since language is our primary means of symbolizing the world, he attaches great importance to language in determining cognitive development.

• Children are innately PRE-ADAPTED to learning
• Children have a NATURAL CURIOSITY
• Children’s COGNITIVE STRUCTURES develop over time
• Children are ACTIVE participants in the learning process
• Cognitive development entails the acquisition of SYMBOLS
• Social factors, particularly language, were important for cognitive growth. These underpin the concept of ‘scaffolding’.
• The development of LANGUAGE is a cause not a consequence of cognitive development
• You can SPEED-UP cognitive development. You don’t have to wait for the child to be ready
• The involvement of ADULTS and MORE KNOWLEDGEABLE PEERS makes a big difference

Bruner, J. S. (1957). Going beyond the information given. New York: Norton.

Bruner, J. S. (1960). The Process of education. Cambridge, Mass.: Harvard University Press.

Bruner, J. S. (1961). The act of discovery. Harvard Educational Review , 31, 21-32.

Bruner, J. S. (1966). Toward a theory of instruction , Cambridge, Mass.: Belkapp Press.

Bruner, J. S. (1973). The relevance of education . New York: Norton.

Bruner, J. S. (1978). The role of dialogue in language acquisition. In A. Sinclair, R., J. Jarvelle, and W. J.M. Levelt (eds.) The Child’s Concept of Language. New York: Springer-Verlag.

Wood, D. J., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychiatry and Psychology , 17(2), 89-100.

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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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• Published: 01 August 2024

An Integrated theory of false insights and beliefs under psychedelics

• H. T. McGovern   ORCID: orcid.org/0000-0002-4050-6300 1 , 2 ,
• H. J. Grimmer 1 ,
• M. K. Doss 3 ,
• B. T. Hutchinson 4 ,
• C. Timmermann 5 ,
• A. Lyon 6 ,
• P. R. Corlett   ORCID: orcid.org/0000-0002-5368-1992 7 , 8 &
• R. E. Laukkonen 9  

Communications Psychology volume  2 , Article number:  69 ( 2024 ) Cite this article

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Psychedelics are recognised for their potential to re-orient beliefs. We propose a model of how psychedelics can, in some cases, lead to false insights and thus false beliefs. We first review experimental work on laboratory-based false insights and false memories. We then connect this to insights and belief formation under psychedelics using the active inference framework. We propose that subjective and brain-based alterations caused by psychedelics increases the quantity and subjective intensity of insights and thence beliefs, including false ones. We offer directions for future research in minimising the risk of false and potentially harmful beliefs arising from psychedelics. Ultimately, knowing how psychedelics may facilitate false insights and beliefs is crucial if we are to optimally leverage their therapeutic potential.

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Introduction.

When an idea or problem solution appears, it can produce a distinct and powerful phenomenology—a feeling of profound understanding and truth known as an insight moment 1 , 2 , 3 . This largely ineffable experience appears to play a pivotal role in the development and adjustment of beliefs and although often associated with verifiably correct discoveries and adaptive personal growth 4 , 5 , insight phenomenology can be triggered by unrelated or misleading information 6 , 7 and for objectively incorrect problem solutions 8 . Insight moments are also a defining feature of psychedelic experiences 9 , 10 , and could underlie the profound belief changes seen during and after psychedelic drug use 11 , 12 . Thus, psychedelics are increasingly recognised for their potential to restructure maladaptive beliefs underlying mental illness 13 , 14 , 15 . However, given the fallibility of insight moments, how can we ensure that psychedelic insights align with desired outcomes, or simply with reality itself? Here, we discuss the different theoretical frameworks of insight, belief change, and the neuropharmacology of psychedelics and present an integrated model for how psychedelics can engender false or maladaptive insights, which have not yet been addressed in the literature — which we term False Insights and Beliefs Under Psychedelics (FIBUS).

Insight and the psychedelic experience

Insight, defined as the sudden appearance of a problem solution in consciousness, has a long history in psychological research 1 , 16 , 17 , 18 , 19 , 20 , 21 . Insight moments—also known as “Aha!” moments—are a special type of problem-solving process where a problem-solver achieves a sudden and complete mental restructuring of a problem 1 , 2 , 3 accompanied by a distinct rush of satisfaction, surprise, and confidence 22 , 23 , 24 , 25 , 26 . A substantial literature suggests that insights are often associated with correct solutions to problems, at least when using constrained and artificial stimuli 27 , 28 , 29 , 30 . Insight experiences have been observed in several recent studies on recreational psychedelic use and psychedelic assisted therapy 31 , 32 , 33 , 34 , suggesting that subjective experiences of insight play a key role psychedelic assisted therapy 9 , 35 , 36 . These findings have garnered excitement in the field of psychedelic research, as insight moments have a long tradition of research and have generally been found to predict accuracy in problem-solving experiments 22 , 29 . Indeed, psychedelic assisted therapy trials have reported many instances of insight moments during psychedelic experiences precipitating positive changes to mind and behaviour such as smoking cessation 9 , 31 , 37 , 38 , 39 , 40 , potentially making them a crucial lever for clinical improvement 32 , 41 , 42

The eureka heuristic

This varied body of evidence, therefore, generally supports a link between insight moments and what we deem to be “true”, either in the narrow sense of objectively correct problem solutions or broader, idiosyncratic changes in perspective and beliefs that are “verified” by their connection to positive outcomes, relative to one’s goals or values. However, recent research has shown that this link may be explained as a metacognitive process wherein insight phenomenology guides the selection of ideas—the “truth” of which depends more critically on one’s prior information. Laukkonen and colleagues 5 (2023) propose The Eureka Heuristic—the theory that feelings of insight play a heuristic role in guiding epistemic decision-making about which ideas to trust by imbuing them with a sense of obviousness. Usually, this sense aligns with reality, as other heuristics are often grounded in statistical norms, making them a generally sensible shortcut. However, as with other heuristics, The Eureka Heuristic can fail under conditions that violate these statistical norms.

False insights and metacognitive illusions

Indeed, while often correct, a proportion of insight moments are false 22 , 43 and false insights can be experimentally induced 8 . For example, Grimmer et al 8 . had participants read a series of words with high semantic similarity (e.g., Remember, Significant, Honour, Tribute, Memorial), before solving anagrams that were visually similar to another word sharing the same semantic space to the studied list, (e.g., MEMUNOMT tends to be incorrectly solved as MONUMENT instead of MOMENTUM). Participants had more false insights for the anagrams that were visually similar (to a primed associate) compared to a range of controls. The accuracy of insight moments can thus partly depend on the relevance of one’s prior information, the ease (or fluency) with which it is processed, and whether the available information to the problem-solver encourages accurate or inaccurate associations.

Another downfall of using feelings of insight as a guide for truth can be seen when insight phenomenology can be misattributed, making irrelevant facts and worldviews feel true 6 , 7 . Across several studies, Laukkonen and colleagues 6 , 7 , presented participants with propositions (e.g., “there is no such thing as free will”) or factual statements (e.g., “lobsters can be trained to understand verbal commands”) containing anagrams (e.g. the word “lobsters” was scrambled). When participants solved the anagram via an insight moment, the irrelevant insight was temporally associated with a worldview, and participants’ belief in the worldviews and statements were stronger than when no insight moment was reported. Insight phenomenology thus appears to contaminate participants’ judgements during claim evaluation.

In the episodic memory literature, there is a similar concept of misattribution that can produce memory distortions 44 . Like insight feelings, fluency driven by semantic activation or repetition enhances feelings of familiarity that can be misattributed to novel stimuli, resulting in false memories 45 , 46 , 47 , 48 . A parallel between the episodic memory literature and psychedelic literature is that the feeling of knowing from familiarity 49 has been referred to as “noetic consciousness,” and the undeniable sense of knowledge produced by psychedelics has been referred to as the “noetic quality” 36 . The noetic quality has also been linked to the experiences of acquiring knowledge in a seemingly unmediated fashion during spiritual or religious experiences 50 , 51 . Lastly, the noetic quality is closely linked to feelings of ‘truthiness’ as described above, or ‘obviousness’ as it is often called in insight research.

However, the impact of insights goes beyond the moment of their occurrence —they may also recursively reinforce certain beliefs. For example, an individual may ‘do their own research’ about what caused the twin-towers to collapse. An insight moment at an early stage of research that makes an unfounded claim appear true could lead an individual down unproductive research pathways, and recursively induce further misleading insights. Along similar lines, false insights can also become entrenched via unfounded plausibility 8 , 52 , 53 . Again, parallels can be found in the episodic memory literature. Emotionally positive memories tend to engage more associative and semantic processes 54 , 55 that can result in memory distortions 56 , 57 , and negative valence can attenuate fluency-driven false memories 46 . Intriguingly, the noetic quality tends to occur with positive affect during psychedelic-induced mystical-like states 58 .

Although the interaction between positive valence and false beliefs is thought to be evolutionarily adaptive 59 , 60 , Laukkonen et al 5 . proposed these related processes may mutually reinforce each other. When a false insight occurs, the positive affect accompanying the insight affirms the other beliefs the individual has—a process through which an agent can form a model increasingly out of touch with reality 61 . The strong link between insight phenomenology, belief, and accuracy, may hold particular importance in the context of psychedelic use, as many users cite a desire for knowledge and understanding in their motivation for taking psychedelics. As interest in psychedelics has grown, the mechanisms behind these phenomena have been mapped within the now dominant computational paradigm known as active inference or predictive processing, a framework that unpacks potential mechanisms of the power of insights in determining belief formation over time. Below, we discuss the broad principles of predictive processing and outline a related theory of how psychedelics change beliefs via this process, before synthesising these perspectives into a novel model of how psychedelics can induce (false) insights and (false) beliefs – which we have termed FIBUS.

Active inference: a neurocomputational understanding of insight, ideas and beliefs

An increasingly popular view conceives of the brain as an inference machine 62 , 63 , 64 that infers the most likely cause of sensory data so that it can optimally infer their hidden causes (i.e., what “out there” is causing these electrical signals?) These predictions (i.e., guesses about the cause of sense data) are compared to likelihoods (i.e., clarity or precision of sense data and its weighting) to arrive at posteriors (i.e., the updated prediction about the cause of sense data following the comparison between priors and likelihoods). If sense data refute priors, this solicits a prediction error – a signal informing the brain it must update its prior with respect to the stimulus at hand, or to look elsewhere for confirmatory data (i.e., active inference 63 ). In this way, the brain seeks to continually minimise prediction error 64 , 65 (see refs. 64 , 65 for conceptual overview).

For example, if agent A thinks that the only red fruit is an apple, they will expect that the red fruit in their hand is an apple. In other words, their prior (i.e., existing expectation) is that the red fruit-like objects are most likely to be apples. If they were given another red fruit to hold (e.g., a tomato),’ A’ may notice textural differences and be told that this is another type of red fruit. This departure between A’s prior that the only red fruits are apple, and the sensory data at hand (i.e., likelihood) that tells them they are holding another type of red fruit, would solicit this prediction error signal. This prediction error signal would thus inform ‘A’s internal model of the world that the initial prior was incorrect, allowing A to change the model such that ‘red fruit’ can include apples and tomatoes. Via this process, beliefs evolve in a continuous trade-off between priors, likelihoods, and posteriors (which then inform priors). Beliefs can thus be conceived as the priors carried into each sensory encounter and are equivalent to probability distributions of possible sensory encounters, evolving alongside the agent’s interactions with the world. This is the definition of belief we adopt hereafter.

Predictive processing assumes the process of prediction error minimization occurs along a hierarchical neural architecture 66 . Hierarchically higher cortical regions encode complex concepts pertaining to longer timescales and higher abstraction 66 , 67 , 68 , 69 , 70 . In contrast, lower-level sensory regions (e.g., visual cortex; see refs. 67 , 71 , 72 , 73 ) typically encode more domain-specific, concrete information pertaining to shorter timescales 66 , 74 . Predictions are carried down the hierarchy from higher-level cortices (e.g., associative areas such as frontal cortex) to lower-level cortices (i.e., early columns of the visual system such as V1) to narrow the range of explanations for sense data 67 . In contrast, prediction errors propagate up the hierarchy such that if a prediction error cannot be explained by the next level, where more complex abstractions are encoded, it is carried to the next level 64 , 67 . This process repeats until the prediction error reaches a level where it can be sufficiently explained, and the brain updates its predictions to maintain an accurate (generative) world model. This scheme allows the brain to continually refine its model of self and world for adaptive actions that aid survival 66 , 70 , 75 .

Underwriting much of the trade-off between priors, likelihoods and posteriors is precision, an index of how narrow each probability distribution is 76 . Higher precision distributions are narrower, representing increased confidence or clarity. The precision of priors and likelihoods is traded off to arrive at a posterior distribution that appropriately weighs them 77 , 78 . To illustrate, imagine Agent X carries a strong but false prediction (i.e., high precision) that the red ball they are holding in their hand is a tomato. In this scenario, it is a cold night and X is wearing gloves. Therefore, the grainy, low-precision sensory data is incapable of updating the strong expectation that “this is a tomato,” leaving the prior intact that the red ball in X’s hand is a tomato. In another scenario, X carries a lower-precision, weaker expectation about the identity of the red ball that is met with strong sensory evidence because now it is daytime, and the gloves are off. This high precision evidence (or likelihood) suggests the red ball is an apple – making it more likely X will revise their weak prior and arrive at the posterior that the red ball is instead an apple. Here, we are referring to the fact that this process allows the agent to arrive a precise posterior distribution over models, allowing the agent to optimally navigate its sensory landscape and thus their environment.

With respect to the brain, more generalized and coarse-grained representations (based on many observations) are thought to be encoded in domain-general cortex 70 . During perception, predictions cascade down to sensory regions of cortex which encode more specified and faster-updating predictions. Prediction errors ascend this cortico-perceptual hierarchy until the error can be explained by the level above. In the event the error cannot be explained at the next level, it ascends all the way up to the most coarse-grained level such that the agent’s model is updated to account for this new contingency 67 , 79 . In the example above, if we assume the agent has observed many times that red circular shapes are tomatoes, they will have a relatively stronger prediction that the red ball is a tomato and be less susceptible to updating this belief when the sensory data was unclear (assuming they do not actually find out that the red ball is not a tomato).

Greater (environmental) statistical regularities often accompany increased prior precision and should therefore be accompanied by strong contradictory empirical data to be refuted and updated. For example, a black sky almost always means it is night, and only strong evidence, such as knowledge of a solar eclipse occurring, enables one to suspend the belief that the black sky they are observing is not evidence of it being night time. The difference in outcomes to which these scenarios underscore is a process called precision weighting , the relative weighting of priors and likelihoods during the perceptual process, given the context and prior learning. The clarity of sensory data and the relative confidence in expectations thus play a crucial role in how beliefs evolve. Reliance on sense data or likelihoods, based on their precision, is crucial for determining which beliefs are ultimately reached, with the higher-precision distribution typically being more influential.

Insight, beliefs, and active inference

Friston et al. 63 posited an account of insight experiences nested in the active inference and predictive processing frameworks. Under this view, refinement or modification of one’s generative model (i.e., world model) need not rely on new information—a process deemed fact-free learning . Fact-free learning occurs via a process of Bayesian model reduction, wherein the brain arrives at models providing more parsimony of sense data already accrued, rather than continued sampling. Fact-free learning is said to be metaphorically similar to the way that a “sculpture is revealed by the artful removal of stone” 63 .

Such learning is proposed to occur implicitly via simplification of one’s model, in states where people are not actively taking in sensory information, such as in sleep 80 , or states of interoceptive reflection 81 . The key point is that via Bayesian model reduction, no further sensory sampling is necessary for refining and updating beliefs about the matter at hand.

An extension to Friston et al‘s 63 model of insight has been proposed that considers the experiential quality of insight and its effects higher in the cortical hierarchy 5 . According to the ‘Eureka Heuristic’, we experience feelings of insight because they help ‘highlight’ which ideas we should trust in light of past learning. In other words, insight moments play a role in heuristically selecting ideas from the stream of consciousness by capturing attention, inducing confidence, and boosting drive to act on them. In an uncertain world where time is limited, ideas cannot always be evaluated analytically. The feeling of insight plays a key role in permitting quick and efficient action on novel ideas (e.g., when running from a lion on the savannah).

This view is consistent with work suggesting that insight moments increase confidence 25 , 26 , can be misattributed 6 , 7 , lead to beliefs that are difficult to forget 82 , and are resistant to revision 28 . In some ways, insights can be considered a fast-track to semantic memory, bypassing the slow training process between the hippocampus and cortex that typically give rise to semantic memories imbued with noetic consciousness. The Eureka Heuristic also includes computational mechanisms that extend the Bayesian reduction account above, and helps us understand the recursive, reinforcing role that insights may play during a psychedelic experience. We summarise the model below.

The Eureka Heuristic proposes that when the implicit process of Bayesian reduction results in a novel (i.e., updated) model, it necessarily solicits a prediction error at a higher-order conscious level of abstraction, under the hypothesis that precisely held beliefs enter consciousness. This prediction error in turn can change one’s model at a conscious level, making it possible to have a reportable insight (and meta-awareness of an insight having occurred). In other words, while Bayesian reduction inherently reduces global prediction errors across the system, a certain amount of time is required to share this information via ascending prediction errors.

Crucially, since many ideas can appear in the mind, it is only the ideas that have high expected precision (i.e., subjective confidence in the ideas)­—thus feeling insightful—which are selected and meaningfully impact beliefs. This is analogous to the way that an organism must infer both the action policy and (dopaminergic) confidence in it 63 , 83 . Similarly, organisms infer both the content of ideas as well as their (dopaminergic) insightfulness.

Prediction errors ascending the cortical hierarchy can be ascribed higher or lower precision weighting, as can predictions that descend the cortical hierarchy (although it is worth noting that this may not always be the case, as lower level prediction errors may not invariably propagate to higher levels if the loss of accuracy is accompanied by complexity reduction). Notably, insight experiences have all the neural characteristics of a higher-order prediction error (e.g., restructuring and insight is associated with the event related potential component N320 84 , 85 , 86 . More precise prediction errors (i.e., very sharp departures from predictions with strong evidence) are thought to enact a larger influence in (Bayesian) belief updating, such that they bear increased weighting compared to the agent’s priors. Increasing the precision weighting of prediction errors is thought to be instantiated by higher synaptic gain (i.e., the inhibitory or excitatory strength of connections between neurons) 5 , 87 , 88 . One way synaptic gain is instantiated is the up-regulation of dopamine, through which belief updates and thus confidence in belief updates are thought to occur 87 , 88 , 89 , 90 .

Like the construct of precision, the feeling of insight is thought to be implemented through dopamine and has been linked to the reward system 91 , 92 . Just like precision, insight experiences are associated with attentional capture 30 , 93 , higher confidence and (phenomenological) pleasure 22 , 25 , and seem to map onto the dopaminergic reward system 91 , 92 . Moreover, in contrast to norepinephrine, which is thought to retain the dependency of episodic memories on the hippocampus, dopamine is thought to facilitate the integration of episodic memories into cortical semantic networks 94 . Precision also drives model selection, just like insight drives the selection of new ideas 5 , 12 . Thus, what we call ‘insight experiences’ map extraordinarily well to the computational construct of a precise prediction error at an abstract level. Put simply, insights are a surprising inner event (prediction error) imbued with noeticism given what one knows (high expected precision), thus permitting idea selection and action.

We note that dual process theories provide a framework for understanding cognition as a binary of ‘conscious, deliberate, effortful’ and ‘unconscious, rapid, and largely involuntary’ thought 95 . However, more recent work has expanded upon this concept and empirical findings (such as the phenomenology of insight occurring in traditionally analytic problems 96 ) and identified the need for a more comprehensive view of cognition as occurring as a hierarchy, with “system 1” and “system 2” effectively existing on opposite ends of a continuum encompassing all of conscious thought, unconscious judgement and decision-making, as well as even ‘lower-order processes’ such as perception and emotion” 4 . Indeed, insight can occur across both types of thinking 25 , 26 , 81 , 97 , 98 . One of the key deviations that PP takes from these earlier theories is it emphasises these lower-level processes as occurring mostly prior to (outside of) conscious awareness. This is also a key component of our argument, as predictive processing can be applied to phenomena that have until now been thought of as completely “conscious” such as belief, insight, and even cognitive dissonance theory.

Psychedelics and belief change: two possible pathways

We have thus far covered the notion of insight and the key role that it can play in updating beliefs via predictive processing. Similarly, predictive processing has been suggested to explain belief change under psychedelics in an influential theory known as Relaxed Beliefs Under psychedelics or REBUS 99 . We discuss how REBUS effects from psychedelics can result in belief changes. Following the description of REBUS and how it can drive belief change, we then describe an alternative pathway to psychedelic-facilitated belief change that does not rely on the assumptions of REBUS. After introducing these relevant theories, we will then present our integrative account for how belief change under psychedelics can engender false beliefs drawing upon components of each.

REBUS and belief change

Psychedelic substances, such as LSD, psilocybin, and DMT, primarily act as agonists at the brain’s 5-HT 2A serotonin receptors, which are widely distributed throughout the brain, particularly in regions associated with high-level cognition, such as the prefrontal cortex, and sensory processing, like the visual cortex 100 . When psychedelics bind to 5-HT 2A receptors, they cause increased excitation of neurons, leading to altered patterns of neural activity and communication. This heightened excitation is thought to contribute to the profound perceptual, cognitive, and emotional effects of psychedelics 101 . In addition to their actions on 5-HT 2A receptors, psychedelics can also influence other neurotransmitter systems, such as dopamine and glutamate, which further modulate neural activity and contribute to their complex effects 102 . Neuroimaging studies have shown that psychedelics induce changes in brain connectivity, reducing the connectivity of the default mode network (DMN), a group of brain regions involved in self-referential processing and inner thought 103 , 104 . This disruption of the DMN is hypothesized to underlie the “ego dissolution” and sense of unity often reported during psychedelic experiences 103 , 105 . Simultaneously, psychedelics enhance connectivity between other brain networks, potentially facilitating novel associations, insights, and perspectives. The combination of receptor-level effects, neurotransmitter modulation, and large-scale network changes induced by psychedelics is thought to create a unique brain state that supports profound alterations in consciousness, perception, and cognition 99 , 101 . Below, Fig.  1 provides an overview of the neuropharmacology of hallucinogenic substances mode of action.

Cortical regions that comprise the DMN (medial prefrontal cortex, posterior cingulate cortex, angular gyrus, and precuneus) are shaded in purple. These DMN regions include the densest expression of 5-HT2A receptors, which psychedelic drugs bind to, resulting in disrupted functioning of the DMN. We note this is a simplified portrait (see de Vos et al.105 for a detailed overview) (created with BioRender.com).

REBUS proposes that psychedelics facilitate belief change via a two-step process 99 (Carhart-Harris & Friston, 2019). First, psychedelics disproportionately diminish the precision weighting of high-level priors (e.g., reducing confidence in ‘beliefs’ in the colloquial sense, formalised as higher variance probability distributions) that otherwise constrain lower levels (e.g., perception). This assumption of disproportionate higher-level effects is due to the densest distribution of 5-HT 2A receptors found in certain association cortices, including parts of the default mode network (DMN), proposed to be the top of the brain’s hierarchy (note, however, that 5-HT 2A receptors are also densely distributed in visual and auditory cortices 106 ). By relaxing the DMN’s constraints on the rest of the brain, the brain’s hierarchy of information processing is thought to be “flattened” or less controlled and constrained by higher-order abstraction and ‘freer’ to change according to new input (note that by higher order, and in a more technical sense, we are referring to beliefs about plausibility’s of a set of models that are updated). A secondary consequence of the system being unable to rely on prior assumptions is the relatively increased precision weighting of sensory data, resulting in novel input becoming more likely to impinge on high-level beliefs. An agent under psychedelics may therefore consider any number of alternative hypotheses about the causes of sensory data, perhaps rapidly, and revise higher-order beliefs that were held in a sober state. Particularly under high doses, psychedelics can produce a collapse of complex assumptions such as one’s sense of self, one’s membership to a group, and typical knowledge about the world, coinciding with the DMN’s role in self-referential processing 107 , social processing 108 , and semantic memory 109 .

The REBUS hypothesis is supported by neuronal, behavioural, and clinical data. Evidence suggests that psychedelics reduce top-down connectivity and dampen the power of backward travelling waves (i.e., signature of neural activity traveling across cortex, suggesting a decreased activity between higher and lower levels in the brain) 110 , 111 , both suggested mechanisms for the influence of priors on brain activity 112 . After the acute psychedelic experience, there are documented changes to metaphysical beliefs, particularly away from a physicalist worldview (we note this does not provide evidence exclusively for REBUS- but just that psychedelics can seemingly change beliefs) 113 , 114 , 115 . Finally, qualitative studies from clinical trials suggest that revision of self-related beliefs (arising from REBUS processes) may underpin positive psychological changes 10 , 40 , 116 . Whilst these effects may be beneficial as a metaphorical ‘reset’ if one holds an array of maladaptive beliefs, there is no guarantee that relaxing one’s hard-earned abstract understanding results in positive change.

Alternate pathways to belief revision under psychedelics

Outside of the active inference or computational frameworks, psychedelics may impact beliefs via effects on fluency and relative weighting of hippocampal and cortically dependent memories. This pathway to belief revision, which we will term a ‘memory systems account’ does not preclude REBUS effects, but we highlight this account since there may be differing predictions on the specific brain-based substrates to belief changes.

Feelings of insight and familiarity can come from fluency manipulations such as semantic priming (e.g., seeing the word whisker could activate the category of cat) 8 , 45 , which can be enhanced by psilocybin 117 . Moreover, although psychedelics impair the formation of hippocampally-dependent recollection memories (e.g., remembering/recollecting where or when an event took place), they spare or even enhance formation of cortically dependent memories that solicit feelings of familiarity (e.g., knowing a face, without who the individual was or where they met them 118 ).

Typically, hippocampal recollection may constrain the interpretation of noetic feelings driven by fluency/familiarity. If one can explicitly recall semantically relevant words or the multiple repetitions of a stimulus’ presentation, they may be better able to understand the source of their noeticism and not misattribute it to irrelevant stimuli. For example, a person might continually observe they are in a friend’s house one evening and can thus attribute this fact to explaining why they keep remembering the presence of their friend, instead of mistakenly attributing this feeling to the fact they saw their friendship bracelet that reminded them of their friend. In contrast, non-drug studies have found that when recollection fails and familiarity is high, presque vu (illusory feelings of insight) can emerge 119 , as well as other phenomena sometimes reported under psychedelics such as déjà vu 120 and premonition 121 .

In models of memory systems, the hippocampus is thought to “train” the cortex over time such that greater statistical regularities between episodic memories are what eventually become semi-permanent semantic memories (e.g., one may no longer have memory for every instance they had pizza or even the first time they had pizza, but they have learned what a pizza is) 122 . The hippocampus may even constrain what the cortex can learn by providing contextual information that biases cortical processing 123 . Some work suggests that conditions in which hippocampal activity is relatively disconnected from the cortex such as during rapid eye movement sleep is important to the instantiation of new cortical information 124 . High-level beliefs can be thought of as semantic memories not necessarily shared by others (e.g., “I am a bad person”), as they are typically slowly learned over time, difficult to revise (it would be hard to forget what a pizza is) and represented by association cortices including the DMN but especially the anterior temporal lobe 125 . In fact, the anterior temporal lobe is an important site for insight learning 93 , 126 , 127 , familiarity 128 , 129 , 130 , semantic priming 131 , the illusory truth effect 132 , and the formation of beliefs such as prejudice 133 .

By reducing the constraints of recent hippocampal memory (i.e., impairments of forming recollections) via inhibitory 5-HT 2A receptors in entorhinal cortex (i.e., the input to the hippocampus) and the hippocampus itself 134 , 135 , 136 , 137 and facilitating cortical processing (i.e., fluency) via excitatory 5-HT 2A receptors in the cortex, psychedelics may be able to revise semantic stores supporting high-level beliefs. Less constraints may provide greater exploration of a conceptual search space allowing one to reach veridical insights. However, noetic feelings arising from aberrant semantic activation could also be misattributed to unrelated or bizarre ideas produced by psychedelics, resulting in false insights.

REBUS proposes that the hippocampus is one of the regions that becomes less constrained by the cortex under psychedelics and thus should increase its influence on the cortex, especially the DMN 99 . In contrast, this memory systems account predicts that typically the cortex is constrained by the hippocampus and that under psychedelics, the cortex becomes free of such constraints. It has been found that psilocybin attenuates hippocampal-DMN coupling 138 and hippocampal glutamate, which is predictive of feelings of insight, but not necessarily veridical insights 139 . Nonetheless, all accounts converge on the general notion that psychedelics change beliefs, even if mechanisms are debated. We now turn our attention back to insight. Crucially, we suggest that it may play a key role in entrenchment of new beliefs following psychedelics.

Psychedelic-induced insights: a possible pathway to false beliefs

Considering the theories of insight and belief change under psychedelics discussed thus far, we now turn to the pressing issue identified at the outset—the possibility that psychedelics could engender false beliefs. Although psychedelics show promise as tools for engendering insight and therapeutic belief change, the neurocomputational perspective on insight and belief change in general suggests that psychedelics could also elicit false beliefs under some circumstances. For instance, psychedelics could merely increase the frequency of belief changes, orthogonal to accuracy, with the utility of these belief changes depending on the accuracy of one’s prior information at the time of restructuring. Give the evidence that insight moments can often be wrong or misleading due to cognitive or environmental factors 4 , 8 , 52 , 53 , 140 , a higher frequency of insights (both true and false) could also increase the probability of psychedelic-induced maladaptive, or potentially false, insights 11 . We now sketch a candidate framework for how psychedelics engender belief changes via soliciting insight moments.

Our proposal is as follows: Psychedelics imbue a decreased ability to make sense of sensory data, leading to an increased number of insight moments and noetic feelings. Following the experience, the person may be left with a lack of detailed memory, but an increased noetic confidence in the insight moments encountered during the experience. Crucially, the increased quantity of insights and acute malleability may leave one vulnerable to empirically false, misleading, or maladaptive insights, alongside the prospect of obtaining valuable new perspectives.

Note that our focus on false beliefs is not because we believe that psychedelics only solicit incorrect ideas, but because the potential for false beliefs under psychedelics have been somewhat overlooked 11 . Secondly, if psychedelics do hold potential to change deeply held beliefs—as they are believed to 99 —and some proportion of these are likely to be false but feel profoundly true and motivating, there are major consequences to consider. Given the renaissance that is currently underway, mass adoption of psychedelic use both clinically and beyond have an important epistemic task to address: how do we improve the likelihood that the insights and subsequent belief changes engendered by psychedelics result in beliefs that move one closer to reality? Below, Box  1 provides a summary of the similarities and differences between these models, including our proposed model.

Box 1 Outline of the overlap and departure between REBUS model and the model of psychedelic belief change forwarded here: FIBUS. Overlap between REBUS and FIBUS is bolded, and differences in the FIBUS model are non-bolded

False insights and beliefs under psychedelics (FIBUS): towards a theoretical account

Based on our prior discussion of precision, model reduction, and fact-free learning, we propose a process for how insight-derived belief changes under psychedelics may reorganise belief structures – which we have coined FIBUS. We do not just account for the mechanism of insight and belief change under psychedelics, but also articulate how insights can be true or false, with clear implications for future studies (see below) involving psychedelics. We propose this process approximates four steps as follows, drawing on all the research thus-far reviewed.

Figure  2 illustrates the process by which psychedelic induced insights may engender false beliefs. First, increased agonism of serotonin 5-HT 2A receptors results in decreased precision weighting of priors, such that priors now no longer characteristically constrain perception and cognition (See 141 , 142 , 143 for in-depth discussion on serotonin, dopamine, and precision). Second, this collapse in the perceptual-belief landscape results in novel thoughts and perceptions that then increase the incidence of prediction errors (e.g., through new sensory input or via ‘fact-free learning’). These predictions errors facilitate new ways of interpreting sensory data and generate new ideas and perspectives. Third, the decreased precision weighting of prior beliefs affords increased precision to the novel input passed to higher levels (i.e., everything feels more insightful because it is not constrained by prior belief). This increased precision weighting is thought to be implemented via dopaminergic release (note this is a secondary release not directly facilitated by drug effects; see 5 , 87 , 88 , 91 , 92 , 144 ), affording higher precision to the insights encountered in step two. Fourth, this increased precision weighting given to the insights makes them more likely to feature in model selection (i.e., the feeling of insight has an unusually strong effect on belief updating).

The red distributions represent prediction errors, and green distributions represent predictions. Higher levels encode more domain general, complex abstractions (i.e., high-level beliefs). Lower levels, such as the sensory cortex, encode simpler and domain-specific concepts (i.e., low levels, such as edge detectors 172 ). High levels send descending predictions, while prediction errors ascend the hierarchy until they are explained away by higher levels. Insights (i.e., prediction errors) arrived at during the trip are afforded higher precision due to decreased precision weighting of priors, rendering insights and consequent beliefs higher precision thereafter — a process that we have termed FIBUS.

This process, we suggest, can describe how beliefs arising from psychedelic insights can become entrenched in working models thereafter. Such entrenchment may be especially true for psychedelics that also activate dopamine receptors such as LSD, which tends to have more lingering effects on perception 145 . Critically, we suggest that while this process can impart adaptive, meaningful, and lasting belief changes, it can also facilitate false insights (and hence false beliefs). Below, we discuss implications for this model, with a particular eye toward how practitioners and researchers alike may consider, test, and optimally refine these dynamics to ensure optimal treatment protocol.

A model of psychedelic insight and belief change, and its implications

We suggest that psychedelics can provide a genesis for false beliefs as follows. First, REBUS effects (or other mechanisms of decreased precision weighting) may induce belief relaxation, including those that are true, and increase precision weighting of novel dopaminergically modulated insights. The insights afforded by increased precision subsequently bear disproportionate weighting on model building. In some instances, ‘fact-free’ learning may therefore be occurring primarily with respect to erroneous or embellished sensory data. Moreover, the insights may then play a recursive role of preferencing and entrenching ideas consistent with the new beliefs. Concurrently, the impairment of hippocampally modulated recollection may lead to a decreased ability to remember veridical details of the experience, while cortically facilitated memory encoding leads to increased semantic aberrance and noeticism. In the experience, one encounters 1) an impaired apparatus to make sense of incoming sensory data, 2) increased insight moments, and 3) increased feelings of familiarity irrespective of accuracy. After the experience, one is left with a lack of detail of memory, but an enhanced noetic sense about the insight moments of the experience. Psychedelics may thus facilitate insights and increase the perceived novelty in new ideas and original thoughts 11 , 139 . However, nowhere does this experience preference accuracy, or a necessary nudge toward more adaptive beliefs characteristic of improved mental health, leading us to describe this process as one of False Insights and Beliefs Under psychedelics (FIBUS). Below, we outline considerations that accompany this empirically derived model and proposal. We divide our considerations between the acute and post-acute phases.

Acute effects

A potential downside of (higher order) belief relaxation is that some adaptive priors that typically constrain perceptual inferences may also be relaxed in the process leading to false insights and hence false beliefs. This amplifies the oft-cited importance of set and setting, which are crucial predictors of the psychedelic experience 146 . With respect to setting, misleading contextual information could serve to increase the possibility of false insights. Psychedelics may serve as amplifiers of environmental influence rather than pushing someone toward one set of views over another 147 . A recent study found that changes in metaphysical beliefs following a psychedelic experience were mediated by a range of factors (i.e., age, personality traits, suggestibility) reinforcing the notion that non-pharmacological factors play an important role in adopting novel beliefs 11 , 115 .

Aspects typically emphasised in the clinical setting, such as safety and control, may additionally provide patients overly precise priors of felt safety or control in a non-clinical setting, where psychedelic consumption may not be safe nor well controlled. If psychedelics do acutely enhance fluency (ease of retrieval in memory 148 ), this may result in an exaggerated mere exposure effect in which patients become more attached to those they are interacting with whilst under psychedelics. There might also be aspects of the clinical setting conferring discomfort or distrust of clinicians, making future care more difficult. Of course, this could simply mean that any aspect of the experience that is out of step with day-to-day life could be overweighted, and thus garner outsized influence following the acute phase – consistent with the neutral amplifier of set and setting discussed above. Out of the psychedelic context, insights and beliefs arrived at may no longer carry their adaptive zeal. This amplifies the importance of an epistemically congruent (i.e., with the goals of the patients who ingests the psychedelic) set and setting – given that beliefs become more malleable during the acute phase, contextual factors in the post-acute phases (in which ‘integration’ therapy occurs) need to optimally encourage and support positive and adaptive insights.

We also note there may exist theoretical shortcomings to the REBUS model, which can paint an incomplete picture of belief change, and thus constrain our FIBUS model given we derive our predictions partly from the REBUS model. Some psychedelic studies find larger effect sizes outside of associative cortex, including in sensory areas with lower 5-HT 2A distribution 102 , 104 , 149 , 150 . Another outstanding question on REBUS effects is the presence of hallucinations during the acute phase. People often report reliving scenes from one’s past and immersive hallucinations of beings or ‘entities’, often considered high level-hallucinations. If the sense of self collapses under psychedelics, then it is unclear how one could have hallucinations that assume a sense of self (e.g., I am having a memory of something I have experienced before). Given our FIBUS model partially derives from and assumes REBUS effects, these shortcomings should be noted.

Another consideration with respect to our FIBUS model is the suggestion that hallucinations are due to overly strong priors - such as in acute episodes of psychosis 151 . If complex hallucinations are occasioned by strong priors, then we might expect less complex hallucinations at higher doses. Indeed, complex hallucinations are typically only occasioned with high doses and sensory deprivation (for example, in the case of Charles Bonnet syndrome 151 , 152 ). One explanatory model for hallucinations from dissociative hallucinogens (i.e., NMDA antagonists such as ketamine), which share some subjective, clinical, and neural effects as psychedelics, proposes the opposite of REBUS 153 . That is, hallucinations come from an overweighting of priors (e.g., “I am seeing my mother”) and an underweighting of sensory information (e.g., external input that would otherwise lead one to reject the idea that their mother is present). Indeed, the intensity and complexity of the hallucination (e.g., whether it is just geometric shapes, or reliving complex past experiences) could be highly dependent on dose, as well as effects on other cortical regions.

Recent work suggests that the degree of belief changes rests on how strong the prior initially was 154 , 155 . As such, weaker priors may be further weakened (e.g., psychedelics shifting a slightly differing opinion closer to the social norm 156 ), and stronger priors may become stronger due to the sociocultural and local environment tipping cognitive systems toward one belief (e.g., political liberalism) or another (e.g., political conservatism) during and after the acute phase 157 . Indeed, the current evidence seems to suggest that higher-level beliefs may be susceptible to change, although environmental noise (e.g., local, and broader sociocultural setting) may act as mediators of belief change as well 115 , 155 , 157 . Whilst these concerns do not preclude the FIBUS proposal here, future work should aim to further investigate the relationship between hallucinations, mechanisms pertaining to REBUS, and belief alterations.

Post-acute effects

The dopaminergic surge accompanying insights, combined with the memory alterations, may result in an undue sense of confidence for insights accrued. Insights gleaned during psychedelic experiences may therefore bear increased weighting in model selection (i.e., the set of beliefs that make up the world model following the acute phase). For example, mystical experiences encountered during the acute phase, and insights they incur may be primary mediators of beneficial belief updates 4 , 9 , 32 , 158 , 159 , 160 . However, it must also be noted that the subjective feeling of insight is not the same thing as a genuine breakthrough, as even mundane ideas engendered by the experience can seem more meaningful than what they really are 99 , 139 , 161 , 162 . For example, Mason et al 139 . found that higher decreases in functional connectivity within the default mode network predicted increased feelings of insightfulness, but decreases in objective originality. The key point is that consistent with our FIBUS proposal, insights seem to be exaggerated both in quality (subjectively defined) and in quantity during psychedelics, and they can deeply impact belief updating (perhaps even personality change 163 ), and thus subsequent model selection.

A second-order consequence is that these insights and subsequent beliefs may be more difficult to revise, particularly when ascribed the (memory systems modulated) noetic feelings accompanying them. A potential by-product of the precision (and associated noetic feelings modulated by memory systems 148 ) afforded to insights is the resulting belief updates may be less amenable to change 5 , 28 . With psychedelic occasioned insights, participants report a non-specific feeling of truth associated with insights 164 . If it is not clear why an insight feels true, it can be difficult to revise since it is not clear what information could contradict it. For example, if I have the insight that I am possessed by a negative entity, the prospect of which is central to some shamanic traditions, it may be extremely difficult to revise because the very foundation for the idea is simply the feeling. These insights seem to have lasting behavioural effects, as discussed, such as reductions in depression symptoms 10 , 41 , cessation of smoking and substance use 165 , 166 . This is important in a clinical context if the goal is to increase psychological flexibility such that more adaptive beliefs might be considered and adopted.

A recently developed framework aimed at addressing issues of psychedelic-induced false insights proposed the fostering of a ‘gentle touch’ for revelations occurring during psychedelic therapy sessions. In this framework (deemed ‘psychedelic apprenticeship’), the relational processes (e.g., therapeutic interventions performed by a therapist) occurring before, during, and after the psychedelic session could serve as a scaffold for users’ to hold novel insights lightly 11 . These relational processes could be seen as a form of ‘thinking through other minds’ or ‘cultural affordances’ 167 , whereby an experienced facilitator or therapist can aid in the modulation of the users’ precision weighting of newly acquired insights or beliefs during psychedelic therapy. With respect to our FIBUS model, the gentle touch framework, alongside a rubric for making sense of the adaptiveness, veracity, and falsifiability of psychedelically derived insights (Fig.  3 ), could offer clinicians a framework for integrating psychedelic insights in a clinically useful way. Below, we divide discussion of our FIBUS model into research and clinical implications, offering novel hypotheses and considerations for clinicians involved in integrating psychedelic insights.

The colored circles are illustrative examples only, and all these quantities and qualities may vary from person to person. Each example insight in the box has a number which corresponds to a particular insight under the ‘Insights’ title on the right-hand side. The green axis refers to the direction which may be the overall ideal therapeutic direction (i.e., veridical, adaptive, and, where possible, falsifiable beliefs) although there are likely exceptions. Each of these dimensions is explained in the box below with reference to the examples shown. Notably, some insights may confer a sense of wellbeing (i.e., adaptiveness), but are inherently difficult to verify. As such, the challenge for a clinician may be to unpack each insight along the falsifiability-veridicality-adaptiveness axis, making sure that insights are optimally leveraged to facilitate clinical improvements.

Future directions, insights, and epistemic hygiene

Our FIBUS model lends itself to novel empirical predictions. First, FIBUS predicts that subjective veracity and perceived number of insights under psychedelics should linearly increase alongside dopaminergic release during the acute psychedelic phase. This could be tested by leveraging tools such as Positron Emission Tomography (PET) 168 or blood sampling in the acute psychedelic phase. In the post-acute phase, researchers could ask how frequently and strongly participants experienced these insight moments during the acute phase. A second prediction here is that psychedelics should induce more false insights than during ordinary cognition, as psychedelics can increase the perceived (but not necessarily the objective) novelty or accuracy of new ideas 4 , 139 , 169 . To test this, future researchers could have participants perform tasks whilst under varied doses of psychedelics and see whether the number insights preceding a solution increase in number, and whether they are less accurate than during ordinary cognition. To test for strength of belief accuracy, researchers could administer, for example, a Brown Assessment of Beliefs Scale 170 post acutely. Of course, testing for the number and subjective veracity in these insight moments should themselves predict the strength of beliefs, without having to test for dopamine. The implication is that these beliefs should be stronger but less accurate when there was higher dopamine, and more false insights (both true and false), arising during the psychedelic experience.

A clinically relevant issue requiring elaboration is the relationship between truth and adaptiveness. As we have alluded to throughout this paper, determining what counts as a “true” belief or insight is challenging in complex domains beyond problem solving. We are also not making the claim that true beliefs and adaptive beliefs are synonymous in all circumstances. A related notion here is that of falsifiability. Indeed, many metaphysical beliefs that one may adopt following psychedelics do not easily lend themselves to testing, at least not at the individual level. For example, if one adopts panpsychist views (i.e., “everything in the universe is conscious”) following the acute phase, this is not a belief system that carries a clear criterion on which it may be empirically proven or disproven. As such, the challenge for the clinician may be to determine whether the newly found panpsychist views are supportive or refutative of the persons psychopathologies. Put differently, if one does adopt views that are not easy to amend or dispute, the clinical challenge becomes whether the belief betters or worsens the patients’ clinical symptoms. It could well be that when objectivity and falsifiability are not able to be established, adaptiveness could become a more important locus for care. As such, the relative weighing of these factors may depend on clinical judgement and the specific clinical goals of the patient.

Optimal integration of psychedelic insights in the clinical setting will be an important aspect of psychedelic-assisted interventions. However, this is no easy task given that any one insight can vary along several dimensions including subjective intensity, emotional valence, as well as veridicality. Moreover, as touched upon above, the contents of insights may vary along dimensions including veridicality (i.e., how likely or unlikely an insight or belief is to be objectively true), adaptiveness (i.e., whether an insight conducive to better or worse clinical outcomes), and falsifiability (i.e., whether the insights and consequent beliefs easily are updatable based on sampling more sensory data). In Fig.  3 , we provide a tool for thinking about this potential space of psychedelic insights, which may assist in identifying relatively more and relatively less desirable insights. This relates to the notion of ‘epistemic hygiene’ 171 , in essence a directive of ensuring healthy, appropriate, and (where possible) rigorous evaluation of claims arising from psychedelic insights. To establish epistemic hygiene, then, is to imbue thoughtful methods or frameworks for 1) demarcating insights and beliefs according to the norms and values of a specific social or cultural context and 2) developing techniques for promoting desired insights. We therefore offer a candidate tool or rubric for thinking through the issue of epistemic hygiene during psychedelic therapy, which may also be highly relevant for any practices or interventions that increase the incidence of insights. Of course, it is worth noting that where each insight ultimately falls is debatable, but the point here is that this veridicality-adaptiveness-falsifiability axis could offer a framework from which psychedelic induced insights can be optimally integrated in clinical settings. We invite future empirical work to shed light on these questions, particularly given forthcoming legalisation (for clinical purposes) of psychedelics in several jurisdictions.

Summary and conclusion

Psychedelics are increasingly considered a viable and effective treatment option for several psychiatric ailments. As such, understanding the mechanisms by which psychedelics confer insight and new beliefs is essential as they become increasingly integrated into clinical settings. However, extant research and theorising has not sufficiently considered the fact that psychedelics, and the feeling of insight they engender, do not necessarily prefer accuracy, and are not necessarily adaptive from a clinical perspective. This leaves open the possibility that rather than purely offering amelioration, psychedelics may also act as an amplifier for beliefs that enhance existing pathologies or even create new ones. To this end, we have offered the first cohesive account of how psychedelics may confer false beliefs through insight from a neurocomputational perspective – a process we have coined as FIBUS. While we remain optimistic about the future of psychedelic-assisted-therapy, in the interest of averting unfortunate surprises as psychedelic use increases it is important not to overlook the potential for epistemic harm. We also hope that our paper encourages future research on the effects of set, setting, and therapeutic interventions on facilitating valuable insights and adaptive beliefs.

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McGovern, H.T., Grimmer, H.J., Doss, M.K. et al. An Integrated theory of false insights and beliefs under psychedelics. Commun Psychol 2 , 69 (2024). https://doi.org/10.1038/s44271-024-00120-6

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Understanding Developmental Psychology

Physical, Cognitive, Emotional, and Social Development Through the Lifespan

• What is Developmental Psychology

When to See a Developmental Psychologist

Frequently asked questions.

Change is inevitable. As humans, we constantly grow throughout our lifespans, from conception to death (or 'womb to tomb'). The field of developmental psychology explores the physical, cognitive, emotional, and social changes that happen as people age.

Psychologists strive to understand and explain how and why people change throughout life. While many of these changes are normal and expected, they can still pose challenges that people sometimes need extra assistance to manage.

The principles of normative development with specific milestones help professionals spot potential problems and provide early intervention for better outcomes. Developmental psychologists can work with people of all ages to address roadblocks and support growth. However, some choose to specialize in a specific age group, such as childhood, adulthood, or old age.

Lifespan Development: How We Grow and Change Over the Years

Developmental psychology is the branch of psychology that focuses on how people grow and change over the course of a lifetime.

Those who specialize in this field are not just concerned with the physical changes that occur as people grow; they also look at the cognitive, emotional, and social development that occurs throughout life.

Some of the many issues developmental psychologists assist with include:

• Cognitive development during childhood and throughout life
• Developmental challenges and learning disabilities
• Emotional development
• Language acquisition
• Moral reasoning
• Motor skill development
• Personality development
• Self-awareness and self-concept
• Social and cultural influences on child development

These professionals spend a great deal of time investigating and observing how these processes occur under normal circumstances. Still, they are also interested in learning about things that can disrupt developmental processes.

By better understanding how and why people change and grow, developmental psychologists help people live up to their full potential. Understanding the course of normal human development and recognizing potential problems early on can prevent difficulties with depression, low self-esteem, frustration, and poor achievement in school or work.

Developmental Psychology Theories

Developmental psychologists often consider a wide array of theories to consider different aspects of human development. A few examples are listed below:

• Cognitive development. A psychologist assessing intellectual growth in a child might consider Jean Piaget's theory of cognitive development , which outlines the key stages children go through as they grow and learn.
• Attachment . A psychologist working with a child might also want to consider how the child's relationships with caregivers influence their behaviors, so they might turn to John Bowlby's theory of attachment .
• Personality . Sigmund Freud's psychosexual theory of personality development is another influential theory that explains the importance of childhood experiences on personality development and how maladaptive coping styles and defense mechanisms emerge.
• Social and emotional growth. Psychologists are also interested in looking at how social relationships influence children's and adults' development. Erik Erikson's theory of psychosocial development and Lev Vygotsky's theory of sociocultural development are two popular theoretical frameworks that address the social influences on the developmental process.

Each approach tends to stress different aspects of development, such as mental, parental, social, or environmental influences on children's growth and progress .

Developmental Psychology Stages

As you might imagine, developmental psychologists often break down development according to various phases of life. Each of these periods of development represents a time when different milestones are typically achieved.

People may face particular challenges at each point, and developmental psychologists can often help people who might be struggling with problems to get back on track.

Prenatal Development

Developmental psychologists are interested in the prenatal period , seeking to understand how the earliest influences on development can impact later growth during childhood. They may examine how primary reflexes emerge before birth, how fetuses respond to stimuli in the womb, and the sensations and perceptions that fetuses are capable of detecting prior to birth.

Developmental psychologists may also look at potential problems such as Down syndrome, maternal drug use, and inherited diseases that might have an impact on the course of future development.

Early Childhood Development

The period from infancy through early childhood is a time of remarkable growth and change. Developmental psychologists examine the physical, cognitive , and socio-emotional growth during this critical development period.

In addition to providing interventions for potential developmental problems at this point, psychologists are also focused on helping kids achieve their full potential. Parents and healthcare experts are often on the lookout to ensure that kids are growing properly, receiving adequate nutrition, and achieving cognitive milestones appropriate for their age.

Middle Childhood Development

This period of development is marked by both physical maturation and the increased importance of social influences as children make their way through elementary school.

Kids begin to make their mark on the world as they build their unique sense of self , form friendships , grasp principles of logic , and gain competency through schoolwork and personal interests. Parents may seek the assistance of a developmental psychologist to help kids deal with potential problems that might arise at this age, including academic, social, emotional, and mental health issues.

Adolescent Development

The teenage years are often the subject of considerable interest as children experience the psychological turmoil and transition that often accompanies this period of development. Psychologists such as Erik Erikson were especially interested in looking at how navigating this period leads to identity formation .

At this age, kids often test limits and explore new identities as they question who they are and who they want to be. Developmental psychologists can help support teens as they deal with some of the challenging issues unique to the adolescent period, including puberty, emotional turmoil, and social pressure.

Early Adult Development

This period of life is often marked by forming and maintaining relationships. Critical milestones during early adulthood may include forming bonds, intimacy, close friendships, and starting a family and career.

Those who can build and sustain such relationships tend to experience connectedness and social support, while those who struggle with such relationships may feel alienated and lonely .

People facing such issues might seek the assistance of a developmental psychologist to build healthier relationships and combat emotional difficulties.

Middle Adult Development

This stage of life tends to center on developing a sense of purpose and contributing to society. Erikson described this as the conflict between generativity and stagnation .

Those who engage in the world, contribute things that will outlast them, and leave a mark on the next generation emerge with a sense of purpose. Activities such as careers, families, group memberships, and community involvement are all things that can contribute to this feeling of generativity.

Older Adult Development

The senior years are often viewed as a period of poor health, yet many older adults can remain active and busy well into their 80s and 90s. Increased health concerns mark this period of development, and some individuals may experience mental declines related to dementia.

Theorist Erik Erikson also viewed the elder years as a time of reflecting back on life . Those who can look back and see a life well-lived emerge with a sense of wisdom and readiness to face the end of their lives, while those who look back with regret may be left with feelings of bitterness and despair.

Developmental psychologists may work with elderly patients to help them cope with issues related to the aging process.

While development tends to follow a fairly predictable pattern, there are times when things might go off course. Parents often focus on what are known as developmental milestones, which represent abilities that most children tend to display by a certain point in development. These typically focus on each of four main areas:

• Physical milestones
• Cognitive milestones, including language development
• Emotional milestones
• Social milestones

For example, walking is one physical milestone most children achieve between 9 and 15 months. If a child is not walking or attempting to walk by 16 to 18 months, parents might consider consulting with their family physician to determine if a developmental issue might be present.

While all children develop at different rates, when a child fails to meet certain milestones by a certain age, there may be cause for concern.

By being aware of these milestones, parents can seek assistance, and healthcare professionals can offer interventions to help kids overcome developmental delays.

These professionals often evaluate children to determine if a developmental delay might be present, or they might work with elderly patients who are facing health concerns associated with old age, such as cognitive declines, physical struggles, emotional difficulties, or degenerative brain disorders.

Developmental psychologists can support individuals at all stages of life who may be facing developmental issues or problems related to aging.

Diagnosing Developmental Issues

To determine if a developmental problem is present, a psychologist or other highly trained professional may administer a developmental screening or evaluation.

For children, such an evaluation typically involves interviews with parents and other caregivers to learn about behaviors they may have observed, a review of a child's medical history, and standardized testing to measure functioning in terms of physical and motor development, cognitive skills, language development and communication skills, and social/emotional skills.

If a problem is found, the patient may be referred to a specialist, such as a speech-language pathologist, physical therapist, or occupational therapist.

Coping With a Developmental Diagnosis

Receiving a diagnosis of a developmental issue can often feel both confusing and frightening, particularly when you, your own child, or an elderly parent is affected. Once you or your loved one has received a diagnosis of a developmental issue, spend some time learning as much as you can about the diagnosis and available treatments.

Prepare a list of questions and concerns you may have and discuss these issues with your doctor, developmental psychologist, and other healthcare professionals who may be part of the treatment team. By taking an active role in the process, you will feel better informed and equipped to tackle the next steps in the treatment process.

The four major developmental psychology issues are focused on physical, cognitive, emotional, and social development.

The Eight major stages of development are:

• Prenatal development
• Infant development
• Early childhood development
• Middle childhood development
• Adolescent development
• Early adult development
• Middle adult development
• Older adult development

The principles of developmental psychology outlined by Paul Baltes suggest that development is (1) lifelong, (2) multidimensional, (3) multidirectional, (4) involves gains and losses, (5) plastic (malleable and adaptive), and (6) multidisciplinary. 

Four developmental issues that psychologists explore are focused on the relative contributions of:

• Nature vs. nurture : Is development primarily influenced by genetics or environmental factors?
• Early vs. later experience : Do early childhood events matter more than events that happen later in life?
• Continuity vs discontinuity : Is developmental change a gradual process, or do changes happen suddenly and follow a specific course?
• Abnormal behavior vs. individual differences : What represents abnormal development, and what can be considered individual variations in development?

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