Thinking, Problem-Solving, Judgments, and Decision-Making - AP Psychology

SECTION 1

Historical Context & Motivation

For much of psychology's early history, the dominant paradigm of behaviorism treated the mind as a "black box," focusing exclusively on observable stimuli and responses while dismissing internal mental processes as unscientific. This approach left fundamental questions unanswered: How do people form concepts, solve novel problems, and choose between competing options? The cognitive revolution of the mid-twentieth century challenged behaviorism by arguing that mental representations and information-processing operations are not only real but essential to explaining human behavior. Researchers drew on insights from linguistics, computer science, and neuroscience to develop models of how the mind organizes knowledge, navigates uncertainty, and arrives at decisions—often in surprisingly systematic yet error-prone ways.

1956 • The Cognitive Revolution Begins

George Miller publishes "The Magical Number Seven," and Noam Chomsky critiques behaviorist accounts of language, catalyzing the shift toward studying internal mental processes.

1972 • Heuristics and Biases Program

Amos Tversky and Daniel Kahneman publish pioneering research on systematic judgment errors, demonstrating that humans rely on cognitive shortcuts that can lead to predictable biases.

1979 • Prospect Theory

Kahneman and Tversky propose prospect theory, showing that people evaluate losses more heavily than equivalent gains—a phenomenon called loss aversion—overturning classical economic assumptions of rationality.

1983 • Mental Models Theory

Philip Johnson-Laird formalizes the theory that reasoning involves constructing and manipulating internal mental models rather than applying formal logical rules.

2002 • Nobel Prize for Behavioral Economics

Daniel Kahneman receives the Nobel Memorial Prize in Economic Sciences, cementing the scientific legitimacy of psychological research on judgment and decision-making.

The central question driving this field is deceptively simple: If humans are intelligent, adaptable organisms, why do we so often reason poorly, fall prey to biases, and make decisions that undermine our own goals? Understanding the cognitive architecture behind thinking, problem-solving, and judgment reveals that many of our errors are not random failures but predictable consequences of the very mental shortcuts that usually serve us well. This tension between efficiency and accuracy is the thread running through every topic in this lesson.

SECTION 2

Core Principles & Definitions

Before diving into specific strategies and biases, it is essential to establish the foundational building blocks of cognition. Thinking is not a monolithic process; it encompasses several interrelated operations—forming concepts, using mental images, applying problem-solving strategies, and making judgments under conditions of uncertainty. The following grid introduces the core principles that organize these cognitive operations.

Concepts & Prototypes

A concept is a mental category that groups similar objects, events, or ideas. We often judge category membership by comparing new instances to a prototype—the most typical example of a category—rather than by checking a rigid list of features.

Algorithms vs. Heuristics

An algorithm is a step-by-step procedure guaranteed to produce a correct solution, but it can be slow. A heuristic is a mental shortcut that is faster but may lead to errors—a trade-off between speed and accuracy.

Cognitive Biases

Systematic deviations from rational judgment, such as confirmation bias (seeking information that supports existing beliefs) and overconfidence (excessive certainty in one's judgments), arise from the use of heuristics in complex situations.

Framing Effects

The way a question or choice is framed—whether in terms of gains or losses—can dramatically alter decisions, even when the underlying options are objectively identical. This principle is central to Kahneman and Tversky's prospect theory.

Dual-Process Theory

Cognitive psychologists distinguish between System 1 (fast, automatic, intuitive thinking) and System 2 (slow, deliberate, analytical thinking). Most heuristics and biases arise from System 1 processing.

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KEY TAKEAWAY

Think of heuristics as the cognitive equivalent of a GPS shortcut through side streets: most of the time, these routes save you time and get you where you need to go, but occasionally they lead you into a dead end or a one-way street going the wrong direction. The errors (biases) are not signs of stupidity—they are predictable side effects of a system optimized for speed in a complex world.

SECTION 3

Visual Explanation — Problem-Solving Strategies

Problem-solving can be understood as navigating a problem space—the set of all possible states between an initial state and a goal state. The diagram below contrasts the major strategies people use to traverse this space: algorithms, heuristics (including trial and error, means-end analysis, and working backward), and insight. Notice how each strategy trades off completeness for efficiency, and how barriers such as fixation can block progress along any path.

This diagram illustrates the four primary problem-solving strategies. Algorithms (top, cyan) check every possible state systematically. Means-end analysis (violet) breaks the problem into subgoals. Working backward (amber) begins at the goal and traces steps in reverse. Insight (pink) involves a sudden cognitive leap. The red dashed border at the bottom represents common barriers that can impede any strategy.

Several key ideas emerge from this visual comparison. First, algorithms are exhaustive but slow—trying every combination of a four-digit lock (10,000 possibilities) guarantees success but takes considerable time. Second, heuristic strategies like means-end analysis sacrifice completeness for speed by focusing only on moves that reduce the gap between the current state and the goal. Third, insight does not follow a linear path at all; instead, the solver suddenly reconceptualizes the problem in a way that makes the solution apparent. Wolfgang Köhler's famous experiments with chimpanzees stacking boxes to reach bananas illustrate this phenomenon. Finally, notice that barriers—mental set (the tendency to use strategies that worked in the past) and functional fixedness (the inability to see an object's potential new use)—can block progress regardless of which strategy is being employed.

SECTION 4

How Heuristics and Biases Work

Although this topic does not center on mathematical formulas in the way physics or statistics might, understanding the mechanisms behind key heuristics requires grasping the logical structure of the errors they produce. Tversky and Kahneman identified three major heuristics—representativeness, availability, and anchoring and adjustment—each of which operates through a distinct cognitive mechanism and produces characteristic biases.

Representativeness Heuristic

When people judge the probability that an object or event belongs to a particular category, they often rely on how well it matches their mental prototype of that category—its representativeness—rather than on base-rate statistical information. Consider the classic "Linda problem": participants are told that Linda is 31, single, outspoken, and deeply concerned with social justice, then asked whether she is more likely to be (a) a bank teller or (b) a bank teller who is active in the feminist movement. Most choose (b), even though the conjunction of two events is always less probable than either event alone. This is the conjunction fallacy, and it arises because the description is more representative of a feminist activist than of a generic bank teller.

Availability Heuristic

The availability heuristic leads us to judge the frequency or likelihood of events based on how easily examples come to mind. Events that are vivid, recent, or emotionally charged are more cognitively available, so we overestimate their probability. For instance, after extensive media coverage of plane crashes, people tend to overestimate the danger of flying relative to driving, even though car accidents are statistically far more common. The availability heuristic also explains why people fear rare but dramatic events—shark attacks, terrorist attacks—more than mundane but far deadlier risks like heart disease or diabetes.

Anchoring and Adjustment

The anchoring heuristic occurs when an initial piece of information—the "anchor"—disproportionately influences subsequent estimates. In a classic demonstration, Tversky and Kahneman spun a rigged wheel of fortune that landed on either 10 or 65, then asked participants to estimate the percentage of African countries in the United Nations. Those who saw the number 65 gave significantly higher estimates than those who saw 10, even though the wheel was obviously arbitrary. The mechanism is that people begin at the anchor and adjust insufficiently, leading final estimates to remain biased toward the starting value.

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AP EXAM TIP

Free-response questions often present a scenario and ask you to identify which heuristic or bias is at work. The key is to match the mechanism: if the error involves resemblance to a category → representativeness; ease of recall → availability; influence of an initial value → anchoring. Always name the heuristic, define it, and then explain how it applies to the specific scenario.

SECTION 5

Decision-Making & Framing Effects

Decision-making research reveals that human choices are shaped not only by the objective features of options but by the psychological context in which those options are presented. Framing effects demonstrate this powerfully: people tend to be risk-averse when a problem is framed in terms of gains and risk-seeking when the identical problem is framed in terms of losses. This asymmetry is a cornerstone of prospect theory, which replaced the classical expected-utility model by showing that the subjective value of losses looms larger than the subjective value of equivalent gains—a phenomenon known as loss aversion.

The prospect theory value function is S-shaped: concave for gains (above the horizontal axis) and convex for losses (below). Critically, the loss curve is steeper than the gain curve, reflecting the fact that a $100 loss feels approximately twice as painful as a $100 gain feels pleasurable. Decisions are evaluated relative to a reference point (typically the status quo), which is why framing a choice as a loss or a gain shifts behavior.

Consider Tversky and Kahneman's classic "Asian Disease" problem. Participants were told that 600 people were at risk and asked to choose between two programs. In the gain frame, Program A saves 200 people for certain, while Program B gives a one-third probability of saving all 600 and a two-thirds probability of saving no one. Most people chose the sure thing—Program A—demonstrating risk aversion in the domain of gains. When the identical options were reframed in terms of deaths (Program C: 400 will die for certain; Program D: one-third probability that nobody dies, two-thirds probability that all 600 die), the majority switched to the risky option, Program D. The objective outcomes are identical in both frames, but the psychological experience of potential loss triggers risk-seeking behavior.

Bias / Effect	Definition	Example
Confirmation Bias	Tendency to search for, interpret, and recall information in a way that confirms pre-existing beliefs.	A student who believes a study technique works only reads testimonials supporting it, ignoring research showing it is ineffective.
Belief Perseverance	Clinging to one's initial belief even after the basis for the belief has been discredited.	After learning that a news story was fabricated, a person continues to believe the claim.
Overconfidence	Excessive certainty in one's own answers, judgments, or predictions.	Students estimate they scored 90% on an exam but actually scored 72%.
Hindsight Bias	The "I knew it all along" effect—believing after learning an outcome that one would have predicted it.	After an election result, people claim they always expected that candidate to win.
Sunk Cost Fallacy	Continuing an endeavor because of previously invested resources (time, money, effort), even when it is no longer rational.	Staying in a bad movie because you already paid for the ticket.

Common Cognitive Biases in Judgment and Decision-Making

SECTION 6

Worked Example — Identifying Heuristics and Biases in a Scenario

AP Psychology free-response questions frequently present a real-world scenario and ask students to identify cognitive phenomena at work. The worked example below walks through a multi-part scenario systematically, demonstrating the kind of analysis expected on the exam.

Scenario: A Medical Decision

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Step 1 — Read the Scenario

Dr. Patel is deciding whether to recommend a new treatment. She reads that the treatment has a 90% survival rate. Separately, she recalls a recent patient who had a severe adverse reaction to a similar drug—an event covered extensively in the hospital newsletter.
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Step 2 — Identify the Framing Effect

The treatment is described in terms of a 90% survival rate (gain frame). If it had been described as a 10% mortality rate (loss frame), Dr. Patel might feel differently about the recommendation, even though the statistical information is identical. This illustrates how framing effects influence medical decision-making.

Framing effect: Gain frame promotes risk aversion and a positive attitude toward the treatment.
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Step 3 — Identify the Availability Heuristic

Dr. Patel's memory of the recent adverse reaction—made vivid by the newsletter coverage—causes her to overestimate the likelihood of similar reactions in the future. Because the event is cognitively available (recent, vivid, emotionally salient), she may weight it more heavily than base-rate data warrant.

Availability heuristic: The vividness and recency of the adverse event inflates its perceived probability.
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Step 4 — Consider Confirmation Bias

If Dr. Patel already suspects the new treatment is risky, she may selectively attend to case reports of adverse events while overlooking large-scale clinical trial data showing the drug's safety. This pattern of selectively seeking or interpreting evidence to confirm a pre-existing belief is confirmation bias.

Confirmation bias: Selective attention to evidence consistent with pre-existing skepticism.
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Step 5 — Synthesize and Apply

On an AP free-response question, you would name the heuristic or bias, define it in general terms, and then apply it to the specific details of the scenario. Notice that multiple cognitive phenomena can operate simultaneously, and a strong answer acknowledges their interaction—for example, the availability heuristic makes adverse events salient, which then fuels confirmation bias as Dr. Patel seeks supporting evidence for her growing concern.

Key exam strategy: Name → Define → Apply to the scenario. Multiple phenomena may overlap.

SECTION 7

Strengths & Limitations of Heuristic Thinking

It is tempting to view heuristics purely as sources of error, but this perspective is incomplete. The ecological rationality perspective, championed by Gerd Gigerenzer and colleagues, argues that heuristics are adaptive tools that perform remarkably well in the environments for which they evolved. The table below provides a balanced view of when heuristic thinking serves us well and when it leads us astray.

Feature	Strengths	Limitations
Speed	Heuristics enable rapid decisions when time is limited—critical in emergencies, sports, and social interactions.	Speed can lead to premature closure, causing decision-makers to commit to the first plausible option without evaluating alternatives.
Cognitive Efficiency	By reducing complex problems to simpler judgments, heuristics conserve limited working memory and attentional resources.	Oversimplification can ignore critical base-rate information or relevant variables, leading to systematic errors.
Ecological Fit	In natural environments with statistical regularities (e.g., more available events often are more frequent), heuristics yield accurate estimates.	Modern environments (mass media, advertising) can distort the cues heuristics rely on, amplifying biases.
Universality	Heuristics appear across cultures and age groups, suggesting they are fundamental features of human cognition.	Cultural universality does not imply infallibility; identical biases can produce serious errors in legal, medical, and financial contexts.

Strengths and Limitations of Heuristic Thinking

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KEY TAKEAWAY

Heuristics are like a surgeon's experienced intuition: in familiar contexts with reliable cues, intuitive judgments can rival or outperform deliberate analysis. But when the operating conditions change—unusual anatomy, deceptive symptoms—relying on intuition alone can lead to catastrophic errors. The art of good judgment lies in knowing when to trust System 1 and when to engage System 2.

SECTION 8

Connections to Advanced Theory & Other Domains

The study of thinking, problem-solving, and decision-making does not exist in isolation within psychology; it connects to virtually every other subfield and has profound implications for economics, law, medicine, and public policy. Kahneman's synthesis of these ideas in his dual-process framework—published accessibly in Thinking, Fast and Slow (2011)—has become one of the most influential models in all of behavioral science. The table below maps how the cognitive concepts from this lesson relate to more advanced areas you may encounter in later coursework or interdisciplinary study.

Concept from This Lesson	Advanced / Cross-Domain Connection
Representativeness Heuristic	Bayesian reasoning: Normative probability theory shows how base rates should update beliefs; representativeness ignores priors.
Availability Heuristic	Risk perception research: Public policy must account for availability-driven fear (e.g., terrorism vs. heart disease) when allocating resources.
Framing & Prospect Theory	Behavioral economics: Nudge theory (Thaler & Sunstein) uses framing to "architect" choices that improve outcomes without restricting freedom.
Functional Fixedness	Creativity research: Overcoming fixedness is central to divergent thinking, studied via tasks like Guilford's Alternative Uses Test.
Dual-Process Theory (System 1 / 2)	Neuroscience: System 1 maps roughly onto subcortical and medial prefrontal circuits; System 2 engages dorsolateral prefrontal cortex and anterior cingulate cortex.

From AP Psychology to Advanced Theory

Looking forward, recent research challenges the neat dichotomy of System 1 and System 2, proposing that cognition operates along a continuum of controlled versus automatic processing rather than in two discrete modes. Additionally, work in artificial intelligence has drawn on models of human heuristics to build "fast and frugal" algorithms that mimic human judgment under uncertainty—demonstrating that our cognitive shortcuts, far from being mere bugs, can be features worth emulating in computational systems. Understanding the material in this lesson prepares you not only for the AP exam but for a sophisticated engagement with these evolving interdisciplinary conversations.

SECTION 9

Practice Problems

Question 1: CONCEPTUAL

A person is asked to estimate how many words in a passage begin with the letter "K" versus how many words have "K" as the third letter. They estimate that more words begin with "K," even though the reverse is true in English. Which cognitive shortcut best explains this error?

A. Representativeness heuristic
B. Availability heuristic
C. Anchoring and adjustment heuristic
D. Confirmation bias

Show answer (B)

B is correct. The availability heuristic leads people to judge frequency based on how easily examples come to mind. Words beginning with "K" (e.g., king, kite) are easier to generate than words with "K" as the third letter (e.g., lake, ask), so people overestimate the former. A (representativeness) is incorrect because the error is not about resemblance to a category prototype. C (anchoring) is incorrect because no initial numerical anchor is influencing the estimate. D (confirmation bias) is incorrect because the person is not selectively seeking evidence to support a pre-existing belief—they are making a frequency judgment based on ease of mental retrieval.

Question 2: BASIC CALCULATION

Steve is described as shy, helpful, and detail-oriented with a passion for order. When asked whether Steve is more likely to be a librarian or a farmer, most people say librarian—despite the fact that farmers vastly outnumber librarians in the population. Which heuristic and which specific error are demonstrated?

A. Availability heuristic; hindsight bias
B. Anchoring heuristic; insufficient adjustment
C. Representativeness heuristic; base-rate neglect
D. Representativeness heuristic; framing effect

Show answer (C)

C is correct. People judge Steve's likely occupation by how well his description matches the prototype of a librarian (representativeness heuristic), while ignoring the base rate—the far greater number of farmers in the population. This specific error is called base-rate neglect. A is wrong because the error involves resemblance, not ease of recall, and hindsight bias involves after-the-fact judgment of predictability. B is wrong because no numerical anchor is involved. D correctly identifies the representativeness heuristic but pairs it with the framing effect, which concerns how options are presented (gain vs. loss frame), not category judgment.

Question 3: INTERMEDIATE

A public health campaign tells residents: "If you get the vaccination, 95 out of 100 people remain completely healthy." Another version of the same campaign states: "If you do not get the vaccination, 5 out of 100 people will become seriously ill." Both statements convey the same statistical information. Which psychological phenomenon explains why the first version is likely to produce higher vaccination rates?

A. Functional fixedness
B. Framing effect
C. Belief perseverance
D. Availability heuristic

Show answer (B)

B is correct. The framing effect demonstrates that identical information elicits different responses depending on whether it is presented as a gain ("95 remain healthy") or a loss ("5 become ill"). Gain framing promotes risk aversion and compliance, leading to higher vaccination rates. A (functional fixedness) refers to the inability to see novel uses for objects and is unrelated to how information is presented. C (belief perseverance) involves clinging to discredited beliefs, not responding to framing. D (availability heuristic) involves ease of recall influencing probability estimates—while vivid disease imagery could separately invoke availability, the scenario specifically describes identical statistics presented differently, which is the hallmark of framing.

Question 4: APPLIED

4 pts

Read the following scenario and respond to the prompts below. Maria is a high school senior preparing for college applications. She is choosing between two schools: State University and Lakewood Private College. Her two close friends recently visited State University and told her the campus felt "too big and impersonal" and that "nobody knew their names." Maria found their stories compelling and now views State University negatively. However, published data from both institutions tell a different story. The table below summarizes key statistics: Table: Comparison of Key College Metrics | Metric | State University | Lakewood Private College | |-------------------------------------|------------------|--------------------------| | Average class size | 23 students | 31 students | | Student satisfaction (out of 5) | 4.3 | 3.8 | | Student-to-faculty ratio | 14:1 | 20:1 | | Four-year graduation rate | 78% | 71% | Despite these data, Maria prefers Lakewood Private College. She has already spent $500 on a test-prep course for Lakewood's required entrance exam. After receiving a score well below the competitive range, she decides to pay for an additional prep course and retake the exam rather than redirect her efforts toward applying to State University, which does not require a separate entrance exam. Maria also spends her research time reading Lakewood's promotional materials, following Lakewood student accounts on social media, and asking a friend who attends Lakewood about her positive experiences—while skipping State University's virtual open house and ignoring a favorable article about State University's honors program that her guidance counselor shared with her. (a) Identify the heuristic or bias that best explains why Maria trusts her friends' anecdotal accounts over the published statistical data shown in the table. Explain how this heuristic or bias applies to Maria's thinking. (b) Identify the bias that best explains why Maria continues to invest money and time in the Lakewood entrance exam despite her low score. Explain how this bias applies to her decision. (c) Using the concept of confirmation bias, explain how Maria's information-seeking behavior further reinforces her preference for Lakewood Private College. (d) Suggest one specific debiasing strategy Maria could use to make a more rational college decision. Explain how this strategy would counteract one of the biases identified above.

Show answer

Point 1 (Part a): Availability heuristic. Maria relies on her friends' vivid, personal anecdotes about State University being impersonal because these stories are more cognitively available—they are recent, emotionally engaging, and come from trusted sources. This ease of recall leads her to overweight anecdotal evidence and underweight the more reliable but less emotionally salient published statistics showing favorable class sizes and satisfaction scores. Point 2 (Part b): Sunk cost fallacy. Maria has already invested $500 in the test-prep course, and this prior expenditure—a sunk cost that cannot be recovered—irrationally influences her decision to continue investing time and money in retaking the exam. Rational decision-making dictates evaluating future costs and benefits independently of past expenditures, but the sunk cost fallacy causes people to 'throw good money after bad.' Point 3 (Part c): Confirmation bias reinforcement. Because Maria already prefers the private college, she is likely to selectively seek out information that supports this preference—reading positive reviews, following the college on social media, and asking friends who attend there about their experience—while ignoring or dismissing favorable information about State University. This selective information processing strengthens her existing belief and makes it resistant to change. Point 4 (Part d): Debiasing strategy. Maria could use the 'consider the opposite' technique: deliberately listing reasons why State University might be a good fit and reasons why the private college might not be ideal. Research by Charles Lord and colleagues shows that actively generating counter-arguments reduces confirmation bias. Alternatively, she could consult a neutral party (guidance counselor) who has no stake in the outcome and can present objective data, helping Maria engage System 2 analytical thinking rather than relying on System 1 intuitions.

Question 5: CRITICAL THINKING

4 pts

Some researchers, notably Gerd Gigerenzer, have argued that Kahneman and Tversky's heuristics-and-biases program overstates human irrationality by testing people with problems that are deliberately designed to produce errors. Gigerenzer contends that when information is presented in natural frequency formats rather than probabilities, many so-called biases disappear. Construct an argument that evaluates the debate between the heuristics-and-biases view and the ecological rationality view. Your response should: (a) Explain the core claim of the heuristics-and-biases program regarding human judgment. (b) Explain the core claim of the ecological rationality perspective. (c) Provide one piece of evidence supporting each perspective. (d) Take a position on which perspective provides a more complete account of human cognition and justify your reasoning.

Show answer

Point 1 (Part a): Heuristics-and-biases claim. Kahneman and Tversky's program argues that human judgment systematically deviates from normative standards of probability and logic. Because people rely on cognitive shortcuts (heuristics) such as representativeness and availability, they make predictable errors (biases) like base-rate neglect, the conjunction fallacy, and overconfidence. These errors are not random but systematic, occurring across diverse populations and contexts. Point 2 (Part b): Ecological rationality claim. Gigerenzer's ecological rationality perspective argues that heuristics are not inherently flawed but are adaptive tools that evolved to perform well in the information environments humans naturally encounter. When the structure of the environment matches the heuristic's assumptions—what Gigerenzer calls a 'fit' between mind and environment—heuristics can be fast, frugal, and accurate. Apparent biases often result from artificial laboratory tasks that present information in formats (e.g., single-event probabilities) that humans did not evolve to process. Point 3 (Part c): Evidence for each view. Supporting heuristics-and-biases: The conjunction fallacy (Linda problem) has been replicated across cultures and even among statistically trained individuals, suggesting a robust cognitive tendency to prioritize resemblance over probability. Supporting ecological rationality: When Bayesian reasoning problems are presented using natural frequencies (e.g., '10 out of 1,000 women tested have breast cancer') rather than conditional probabilities, the percentage of correct responses among non-experts jumps from approximately 15% to over 50%, suggesting that the 'bias' is partly an artifact of an unnatural problem format. Point 4 (Part d): Position and justification. A strong response argues that the two perspectives are complementary rather than contradictory. The heuristics-and-biases program correctly identifies the mechanisms of error and their real-world consequences (e.g., in medicine, law, and finance), while the ecological rationality perspective provides essential context about when and why those errors arise. A complete account of human cognition must explain both why we err systematically in some contexts and why our intuitive judgments are remarkably accurate in others. Alternatively, a student may argue for one perspective but must provide clear justification and acknowledge the other view's contributions.

SUMMARY

Lesson Summary

Summary

Human cognition relies on concepts and prototypes to organize the world into manageable categories, and on a toolkit of problem-solving strategies—including algorithms (exhaustive but slow) and heuristics (fast but error-prone)—to navigate from initial states to goal states. Barriers such as mental set, functional fixedness, and confirmation bias can impede effective problem-solving, while insight allows sudden breakthroughs through cognitive restructuring.

Judgment under uncertainty is shaped by three major heuristics identified by Tversky and Kahneman: the representativeness heuristic (judging by resemblance to prototypes, leading to base-rate neglect), the availability heuristic (judging by ease of recall), and anchoring and adjustment (insufficient movement from an initial value). Decision-making is further influenced by framing effects and loss aversion, as described by prospect theory. Kahneman's dual-process theory integrates these findings, distinguishing between fast, intuitive System 1 thinking and slow, deliberate System 2 thinking—a framework essential to understanding both everyday cognition and the systematic errors that appear on the AP Psychology exam.