PSY 324 Topic: Reasoning Dr. Ellen Campana Arizona State University Memory and Cognition PSY 324 Topic: Reasoning Dr. Ellen Campana Arizona State University

Reasoning Reasoning is Process of drawing conclusions Cognitive process by which people start with information and come to conclusions that go beyond that information Using those definitions we’ve already seen many examples of reasoning – can you think of some?

Reasoning Focus today on two specific types of reasoning Deductive reasoning (Aristotle) Sequences of statements (syllogisms) What can logically be concluded? Definite conclusions Inductive reasoning Based on evidence What is probably true in the world? Probable conclusions Both types tell us about cognition in general

Deductive Reasoning

Deductive Reasoning Deductive reasoning is all about logic This is logic as mathematicians, philosophers and computer scientists define it As we’ll see, it doesn’t always match up with common sense Note for tests: When you encounter a logic problem, be careful. It’s asking for very specific things. I’ll give you a strategy later.

Deductive Reasoning Basic form of deductive reasoning is syllogism Includes 2 or more statements (premises), followed by a conclusion Categorical syllogisms are a type of syllogism Premises and conclusion describe the relationship between categories Words like all, no, and some are used

Categorical Syllogisms This is an example of a categorical syllogism: Premise 1: All birds are animals Premise 2: All animals eat food Conclusion: (Therefore) all birds eat food In “perfect form” this can be written as Premise 1: All A are B Premise 2: All B do C Conclusion: (Therefore) all A do C

Validity and Deductive Reasoning Syllogisms can be valid or not valid Here valid means something very specific – be careful! Valid syllogisms The conclusions follow logically from the premises If all premises are true, then the conclusion is true What if one premise is not true? Syllogism can still be valid if the form is valid Truth and validity are NOT the same This is difficult for people!

Validity and Deductive Reasoning Let’s practice…. Valid or not? Premise 1: All red cars are sports cars Premise 2: All sports cars are fast Conclusion: All red cars are fast VALID!

Validity and Deductive Reasoning Let’s practice…. Valid or not? Premise 1: All red cars are sports cars Premise 2: All red cars are fast Conclusion: All sports cars are fast NOT VALID!

Validity and Deductive Reasoning Let’s practice…. Valid or not? Premise 1: All of the students are tired Premise 2: Some tired people are irritable Conclusion: Some of the students are irritable NOT VALID!

Validity and Deductive Reasoning Let’s practice…. Valid or not? Premise 1: All of the men are tired Premise 2: Some tired people are women Conclusion: Some men are women NOT VALID!

Validity and Deductive Reasoning Let’s practice…. Valid or not? Premise 1: All lava lamps are lamps Premise 2: All lamps are furniture Conclusion: All lava lamps are furniture VALID!

Validity and Deductive Reasoning This practice was intended to illustrate that truth and validity are not the same Truth depends on consistency with the world Validity depends on the form of the statements Does it follow a logical progression? Do people think logically? Philosophers say yes, errors are due to carelessness Cognitive Psychologists say no, errors tell us how people really think in everyday situations

Studying Deductive Reasoning Two methods have been used Evaluation method: present premises and conclusion, ask people to judge validity Production method: present premises, ask what can logically be concluded, if anything Most research has used the evaluation method People make lots of errors (70-80%) Error rate determined by many factors, including whether problem is abstract or concrete

Problem Statement A categorical syllogism that’s concrete: Premise 1: All robins are birds Premise 2: All birds are animals Conclusion: (Therefore) all robins are animals A categorical syllogism that’s abstract: Premise 1: All A are B Premise 2: All B are C Conclusion: (Therefore) all A are C Lower error rates for abstract statements Here’s my tip: convert to abstract for the test

Errors and Concrete Problems When logic problems are stated in concrete terms, error rate goes up Part of this effect is due to biases that people bring to the task of reasoning Atmosphere effect Belief bias Biases are another example of heuristics Much faster, often right Sometimes cause errors

Atmosphere Effect Atmosphere effect: the words all, some, no in the premises create an overall “mood” or “atmosphere” that can influence judgement Alls in premise suggest all in conclusion Nos in premise suggest no in conclusion Somes in premise suggest some in conclusion This is sometimes, but not always, correct Be careful of this bias in the test!

Belief Bias If a conclusion is true (meaning it matches the world) or consistent with a person’s belief, the whole syllogism will often be judged valid It’s as if people skip the logic and move right to evaluating the conclusion

Validity and Deductive Reasoning Let’s practice…. Valid or not? Premise 1: No police dogs are vicious Premise 2: Some highly trained dogs are vicious Conclusion: Some police dogs are not highly trained NOT VALID!

Validity and Deductive Reasoning Let’s practice…. Valid or not? Premise 1: No addictive drugs are inexpensive Premise 2: Some cigarettes are inexpensive Conclusion: Some addictive drugs are not cigarettes NOT VALID!

Thinking Conditionally Much of the research on deductive reasoning has focused on conditional syllogisms Conditional syllogisms are like categorical syllogisms, except the first premise is if… then.. Common in everyday life Let’s say you lent your friend Steve $20, but he has never paid you back. Knowing Steve, you might say to yourself that you knew this would happen.

Thinking Conditionally Much of the research on deductive reasoning has focused on conditional syllogisms Conditional syllogisms are like categorical syllogisms, except the first premise is if… then.. Common in everyday life Premise 1: If I lend Steve $20, then I won’t get it back Premise 2: I lent Steve $20 Conclusion: I won’t get my $20 back

Thinking Conditionally If …… p then …… q antecedent consequent The second premise can either affirm or deny either the antecedent or the consequent This relationship is what determines validity As before, validity is NOT the same as truth in the world

Valid Conditional Syllogisms Affirming the antecedent is valid Affirming the antecedent is when the second premise asserts that the antecedent of the first premise is true Premise 1: If p, then q. Premise 2: p. Conclusion: (Therefore), q.

Valid Conditional Syllogisms Affirming the antecedent is valid Affirming the antecedent is when the second premise asserts that the antecedent of the first premise is true Premise 1: If I study, then I’ll get a good grade. Premise 2: I studied. Conclusion: (Therefore), I’ll get a good grade.

Valid Conditional Syllogisms Denying the consequent is valid Denying the consequent is when the second premise asserts that the consequent of the first premise is NOT true Premise 1: If p, then q. Premise 2: not q. Conclusion: (Therefore), not p.

Valid Conditional Syllogisms Denying the consequent is valid Denying the consequent is when the second premise asserts that the consequent of the first premise is NOT true Premise 1: If I study, then I’ll get a good grade. Premise 2: I didn’t get a good grade. Conclusion: (Therefore), I didn’t study.

Invalid Conditional Syllogisms Affirming the consequent is not valid Affirming the consequent is when the second premise asserts that the consequent of the first premise is true Premise 1: If p, then q. Premise 2: q. Conclusion: (Therefore), p.

Invalid Conditional Syllogisms Affirming the consequent is not valid Affirming the consequent is when the second premise asserts that the consequent of the first premise is true Premise 1: If I study, then I’ll get a good grade. Premise 2: I got a good grade. Conclusion: (Therefore), I studied.

Invalid Conditional Syllogisms Denying the antecedent is not valid Denying the antecedent is when the second premise asserts that the antecedent of the first premise is NOT true Premise 1: If p, then q. Premise 2: not p. Conclusion: (Therefore), not q.

Invalid Conditional Syllogisms Denying the antecedent is not valid Denying the antecedent is when the second premise asserts that the antecedent of the first premise is NOT true Premise 1: If I study, then I’ll get a good grade. Premise 2: I didn’t study. Conclusion: (Therefore), I didn’t get a good grade.

Conditional Syllogisms Let’s practice…. Type? Valid or not? Premise 1: If it’s a robin, then it’s a bird Premise 2: It’s not a robin. Conclusion: (Therefore) it’s not a bird. NOT VALID! Denying the Antecedent

Conditional Syllogisms Let’s practice…. Type? Valid or not? Premise 1: If it’s a robin, then it’s a bird Premise 2: It’s not a bird. Conclusion: (Therefore) it’s not a robin. VALID! Denying the Consequent

Conditional Syllogisms Let’s practice…. Type? Valid or not? Premise 1: If it’s a robin, then it’s a bird Premise 2: It’s a bird. Conclusion: (Therefore) it’s a robin. NOT VALID! Affirming the Consequent

Conditional Syllogisms Let’s practice…. Type? Valid or not? Premise 1: If it’s a robin, then it’s a bird Premise 2: It’s a robin. Conclusion: (Therefore) it’s a bird. VALID! Affirming the Antecedent

Errors in Reasoning If people used logical rules in reasoning there would be no effect of the problem statement As we’ll see, there is an effect of problem statement Errors can tell us how people represent the problem and reason about it One task that has been used to study errors in reasoning is the Wason four-card problem Different versions lead to different patterns

Coglab Note If you have not done this week’s Coglab, the Wason Selection Task, do it now!

The Wason Four-Card Problem Four cards Number on one side Letter on the other side Test the following rule If there’s a vowel on one side, then there’s an even number on the other side The cache: Flip the minimum number of cards

The Wason Four-Card Problem K 4 7 Rule: If vowel, then even number Which card would you flip first? Are there any other cards you would need to flip in order to be completely sure?

The Wason Four-Card Problem K 4 7 Rule: If vowel, then even number Flipping the E directly tests the rule Even number consistent with the rule Odd number violates the rule In this version 53% picked E (correct)

The Wason Four-Card Problem K 4 7 Rule: If vowel, then even number What about flipping the 4? Vowel consistent with the rule Consonant does not violate the rule In this version 46% picked 4 (incorrect/suboptimal)

The Wason Four-Card Problem K 4 7 Rule: If vowel, then even number What about flipping the 7? Consonant consistent with the rule Vowel violates the rule In this version 4% picked 4 (correct)

The Wason Four-Card Problem The correct answer to the problem is that in order to be 100% certain that the rule is correct you would need to flip a minimum of 2 cards E (affirmation of antecedent) 7 (denial of the consequent) Key to solution is the falsification principle Falsification principle: to test a rule it is necessary to look for situations that falsify (violate) that rule This version is called the abstract version

The Wason Four-Card Problem Flip… Result is… Then this __ the rule E Even Confirms Odd Falsifies K Is irrelevant to 4 Vowel Consonant 7

Real-world Four-card Problems If we change the way the problem is stated, people change their behavior Griggs and Cox did a version with drinking age Imagine you are a police officer applying the following rule: if a person is drinking beer, then he or she must be over 19 years old Each card has an age on one side and a drink on the other

Griggs and Cox (1982) Beer Soda 24 16 Rule: If drinking beer, must be over 19 years old Which card would you flip first? Are there any other cards you would need to flip in order to be completely sure?

Griggs and Cox (1982) Beer Soda 24 16 Rule: If drinking beer, must be over 19 years old Correct answers are Beer and 16 (no others) This is much easier for people than the abstract version of the task (73% correct vs. 0% correct)

Real-world Four-card Problems The beer version of the problem was easier for people because it was more concrete Or could it have been experience with the situation described by the problem? There’s another version of the problem that was used to investigate this possibility Problem described in terms of postal regulations Great Britain has different regulations than the US

Johnson-Laird and Coworkers (1972) Rule: If a letter is sealed, must have 5d stamp This rule was familiar to people in Great Britain Original experiment done in Great Britain Easier for British people than the abstract version of the task (81% correct vs. 15% correct) Griggs and Cox (1982) tested Americans, found that this was just as difficult as abstract

Postal Four-Card Problem What’s the point of the postal version of the four-card problem? Demonstrates that problem statement affects accuracy Effect goes beyond concrete vs. abstract wording Related to experience with the situation, too Next, two more aspects that affect performance Permission schemas Social-exchange theory

Permission Schemas Cheng & Holyoak (1985) proposed that people solve the real-world versions of the task using pragmatic reasoning schemas Pragmatic reasoning schemas: ways of thinking about cause & effect that we learn in daily life Permission schemas are one type If a person satisfies condition A, they have permission to carry out action B Activating a permission schema can improve performance in a four-card task

Permission Schemas In the abstract problem, participants were not encouraged to activate permission schemas indicated whether an abstract statement was true In the drinking problem, people were encouraged to activate permission schemas Indicated whether people drinking beer had permission to do so In the postal problem, British people but not Americans were encouraged to activate permission schemas (based on experience)

Cheng and Holyoak (1985) Previous results were consistent with the hypothesis, but it needed a direct test Another version of the problem, related to immigration Two descriptions, same cards, same solution Some participants were encouraged to activate permission schemas Other participants were not encouraged to activate permission schemas

Cheng and Holyoak (1985) Entering Transient Cholera Typhoid Hepatitis Typhoid Hepatitis You are an immigration officer at the International Airport in Manila, capital of the Philippines. Among the documents you have to check is a sheet called Form H. One side of the form indicates the whether the passenger is entering the country or in transit and the other side of the form lists names of tropical diseases. You have to make sure that if the form says “entering” on one side, that the other side includes cholera among the list of diseases. Which of the following forms would you have to turn over to check? Indicate only those that you need to check to be sure.

Cheng and Holyoak (1985) Entering Transient Cholera Typhoid Hepatitis Typhoid Hepatitis You are an immigration officer at the International Airport in Manila, capital of the Philippines. Among the documents you have to check is a sheet called Form H. One side of the form indicates the whether the passenger is entering the country or in transit and the other side of the form lists inoculations the travelers had received in the past 6 months. You have to make sure that if the form says “entering” on one side, that the other side includes cholera among the list of diseases. This is to ensure that the entering passengers are protected against the disease. Which of the following forms would you have to turn over to check? Indicate only those that you need to check to be sure.

Cheng and Holyoak (1985) first version = abstract task Entering Transient Cholera Typhoid Hepatitis Typhoid Hepatitis first version = abstract task second version = checking to see if the travelers had the correct inoculations to gain permission to enter the country This difference had an important effect No permission / abstract: 56% correct Permission: 91% correct

Cheng and Holyoak (1985) It seems likely that people use permission schemas to reason in four-card problems Analogy may play a role, because participants had never been immigration officers in the Philippines More on this point a little later There is another explanation (as always) This one comes from the evolutionary approach It is based on social-exchange theory and the idea of cheating

The Evolutionary Approach The evolutionary perspective on cognition: we can trace many properties of our minds to the evolutionary principles of natural selection Characteristics that help an individual survive will, over time, become basic characteristics of humans This is applied to cognition as well as physical traits One such adaptive characteristic relates to social-exchange theory

Social-Exchange Theory An important aspect of human behavior is the ability for two people to cooperate in a way that is beneficial to both people Morg lends Eng a carving tool in exchange for food Works well when each person receives benefits, but problems arise when someone cheats Morg lends Eng a carving too but gets no food According to Evolutionary Approach, detecting cheating is adaptive due to Social-Exchange Those who can detect and avoid cheating have had a better chance of survival, so it has become part of us

Card Task: Evolutionary Explanation In the abstract task, no reference to cheating In the beer task, people detect cheating Find underage drinkers In the postage task, British people but not Americans, detect cheating Find letters without proper postage In the immigration task, people detect cheating in the permission version Find people entering without proper vaccinations

Cosmides and Tooby (1992) Goal: separate permission and cheating Used unfamiliar situations to do this Story involved a fake culture called the Kulwane If a man eats cassava root, then he must have a tatoo on his face (violators would be cheating) People performed well in this task, compared to similar unfamiliar stories that didn’t involve cheating Interpretation: observed effects are due to ability to detect cheating (not permissions)

Permission vs. Cheating Others have gone on to investigate the permissions account with unfamiliar situations “If you clean up spilt blood you must wear gloves” Permissions improve accuracy Evidence for and against both accounts Situations where improvements are not observed Others have proposed other explanations Evidence to support and refute these, too

Take-home Message We still don’t know exactly how people do conditional reasoning in the four-card problem Familiarity sometimes (not always) affects it Permissions and Cheating sometimes (not always) may affect it Example of how different researchers come to different conclusions about the same data The workings of the mind must be observed from behavioral observations Just like Donders’ experiment in the first chapter

Inductive Reasoning

Deductive vs. Inductive Reasoning Deductive reasoning Premises are facts (general) Reason about specifics Definite conclusions Validity is important Inductive reasoning Based on observation of specifics Reason about general conclusion Conclusions are suggestive (not definite) Strength of argument is important

Inductive Reasoning Observation: All the crows I’ve seen in Pittsburgh are black. When I visited my brother in Washington D.C. the crows I saw there were black, too. Conclusion: I think it’s a pretty good bet that all crows are black.

Inductive Reasoning Observation: Here in Nashville, the sun has risen every morning. Conclusion: The sun is going to rise in Nashville tomorrow morning. The second argument is more convincing We say it is a “stronger argument”

Strength of Inductive Argument Strong arguments are more likely to result in conclusions that are true than weak ones Many factors contribute to argument strength Representativeness of observations Number of observations Quality of evidence Scientific evidence contributes to quality

Inductive Reasoning in Science Inductive reasoning is the basic procedure used for scientific discoveries Goal: discover something new Method: collect specific observations Representative sample As many observations as possible High quality through experimental design Inductive Reasoning: use this data to make inferences about the general case with theory

Inductive Reasoning in Science Example: determining attitudes by polling If you poll only a few people, it’s less strong If you poll only this class, results may not generalize to the whole U.S. population (less strong) The postal version of the 4-card problem was first tested with British people, did not generalize to Americans) If the poll is not well-designed it will be difficult to see how the results relate to other findings or predict behavior in the general case (less strong)

Inductive Reasoning in Science Inductive reasoning can be used to reach conclusions about the data These conclusions can also be seen as general predictions about future experiments Can then be combined with deductive reasoning to set forth the rationale for the next experiment If the rationale is logically valid, then if the conclusion is not true it tells us something about the truth of the individual premises

Reasoning in Science Reasoning in action: Immigration 4-card problem Inductive inference Wason, Griggs & Cox, and Johnson-Laird collected observations and wrote about them Cheng and Holyoak looked at those observations and made an inductive inference based on theory Observation: Performance on the 4-card task improves when the problem is stated in terms of permission Conclusion: In general, people use permission schemas to reason about problems

Reasoning in Science Reasoning in action: Immigration 4-card problem Inductive inference Cheng and Holyoak could have stopped there, but they wanted/needed to strengthen the argument To strengthen argument there are 3 things you can do Increase representativeness of sample of observations Increase number of observations Increase quality of evidence Adding another experiment accomplished all three Deductive Inference was used to lay out the rationale for their next experiment

Reasoning in Science Reasoning in action: Immigration 4-card problem Deductive (Conditional) Inference Premise 1: If a permission schema is activated, then performance on the Wason task should improve Premise 2: In this experiment, one of the groups will read a sentence that will activate a permissions schema Conclusion: Therefore, performance of this group will improve This is a valid conditional syllogism If the conclusion is not true in the experiment, then we know that at least one of the premises is untrue

Inductive Reasoning in Daily Life We use inductive reasoning for determining choices we make in daily life Based on observations about previous tests in this class you predict what the next test will be like You use this prediction to make choices about how to study and what notes to prepare Often not even aware that we’re reasoning Predict that a chair will be stable, so just sit on it (without testing it to see if it’s sturdy)

Inductive Reasoning in Daily Life Every time we make a prediction about what will happen based on what has happened in the past, we are using inductive reasoning It would be very time-consuming if every situation was experienced as if it was the first Every time you chose your route to school Every problem-solving situation It would be especially time-consuming if you also had to reach definite (100%) conclusions

Inductive Reasoning in Daily Life When people use past experience to guide current behavior they often use short-cuts to help them reach a decision quickly These short-cuts are called heuristics We’ve seen heuristics before (vision, language, etc.) Rapid and often correct, but sometimes cause errors We’ll talk about two of these heuristics Availability heuristic Representativeness heuristic

Availability Heuristic Availability heuristic: Events that are more easily remembered are judged as being more probable Which is more probable? Words that begin with r or words that have r in the third position? It’s actually those that have r in the third position, but people guess the other option r at the start is easier to remember (coded phonologically)

Lichtenstein and Coworkers, 1978 Which cause of death is more likely? Homicide or Appendicitis Auto-train Collision or Drowning Measles or Smallpox Botulism or Asthma Asthma or Tornado Appendicitis or Pregnancy

Lichtenstein and Coworkers, 1978 Which cause of death is more likely? Homicide or Appendicitis (20x) Auto-train Collision or Drowning (5x) Measles or Smallpox (infinite) Botulism or Asthma (920x) Asthma or Tornado (20x) Appendicitis or Pregnancy (2x)

Lichtenstein and Coworkers, 1978 Which cause of death is more likely? Homicide or Appendicitis (20x) Auto-train Collision or Drowning (5x) Measles or Smallpox (infinite) Botulism or Asthma (920x) Asthma or Tornado (20x) Appendicitis or Pregnancy (2x) Does not match actual probabilities 9% error 30% error 40% error 41% error 58% error 80% error

Lichtenstein and Coworkers, 1978 Which cause of death is more likely? Homicide or Appendicitis (20x) Auto-train Collision or Drowning (5x) Measles or Smallpox (infinite) Botulism or Asthma (920x) Asthma or Tornado (20x) Appendicitis or Pregnancy (2x) Does not match actual probabilities 9% error 30% error 40% error 41% error 58% error 80% error

Lichtenstein and Coworkers, 1978 What does account for the error patterns? Those causes of death that are talked about most (in media, etc) are more available in memory Because they’re more available, they’re assumed to be more probable This is the availability heuristic at work

McKelvie (1997) Direct test of the availability heuristic hypothesis People saw lists of names and had to estimate whether there were more males or females Some saw famous women, non-famous men Some saw famous men, non-famous women Whichever was famous actually had fewer names Results Famous women condition: 77% error (“more women”) Famous men condition: 81% error (“more men”) Famous = more available

Availability Heuristic 911 Example After 911 there were many images of planes smashing into buildings in the media Air travel dropped – people drove instead Fatality rate of driving is 500x greater than air travel on commercial planes, even after 911 Correlations between events When it smells a certain way in the morning it rains later that day – I should bring my umbrella

Illusory Correlations Being able to detect correlations is useful, but because of the availability heuristic we can observe correlations where they don’t really exist Stereotypes: oversimplified generalizations about a group of people that often focuses on negatives Examples that are consistent are more available because we focus attention on aspects that are consistent with stereotypes Related to confirmation bias (later)

Representativeness Heuristic Representativeness Heuristic: probability that event A comes from class B can be determined by how well A resembles properties of class B We randomly pick one male from the population of the US. That male, Robert, wears glasses, speaks quietly, and reads a lot. Is it more likely that Robert is a librarian or a farmer?

Representativeness Heuristic We randomly pick one male from the population of the US. That male, Robert, wears glasses, speaks quietly, and reads a lot. Is it more likely that Robert is a librarian or a farmer? Most people think he’s a librarian because the facts about him are more consistent with stereotypes of typical librarians Ignore the base rate – there are more farmers in US

Representativeness Heuristic In a group of 100 people, there are 70 lawyers and 30 engineers. What is the chance that if we pick on person from the group at random that person will be an engineer? People get this problem right – 30% chances (more likely it’s a lawyer)

Representativeness Heuristic In a group of 100 people, there are 70 lawyers and 30 engineers. Say we pick a person from that group at random. The person is Jack. Jack is a 45-year-old man. He is married and has 4 children. He shows no interest in political and social issues and spends most of his free time on his many hobbies, which include home carpentry, sailing, and mathematical problems. What is the chance that Jack is an engineer? Still 30% but people get it wrong (disregard base rate)

Representativeness Heuristic Perhaps a stronger demonstration of the heuristic comes from cases where people ignore the conjuction rule Conjunction rule: the probability of the conjunction of two events (A and B) cannot be higher than the probability of the single constituents (A alone or B alone) When A is a subset of B it is easy to see why this is the case

Conjunctions Probability of femist bank tellers cannot be higher than probability of bank tellers Bank Tellers Feminist Bank Tellers

Representativeness Heuristic Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations. Which of the following alternatives is more probable? 1. Linda is a bank teller 2. Linda is a bank teller and is active in the feminist movement

Representativeness Heuristic Correct answer to the bank teller problem is that it’s more probable that Linda is a bank teller This is due to the conjunction rule When Tversky and Kaneman (1983) asked people to do this rating, 85% got it wrong The details about Linda were more representative of bank tellers who are active in the feminist movement Representativeness guided choice, instead of relationships between probabilities

Representativeness Heuristic Another demonstration of the heuristic is when people ignore the law of large numbers Law of large numbers: the larger the number of individuals that are randomly drawn from a population, the more representative the resulting group will be of the population Male and Female births example is a good demonstration of how people ignore this rule

About the same (within 5%) Ignoring Sample Size A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day and in the smaller hospital about 15 babies are born each day. As you know, about 50% of babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50% and sometimes it may be lower. For a period of 1 year, each hospital recorded the number of days in which more than 50% were boys. Which hospital recorded more such days? The larger hospital The smaller hospital About the same (within 5%)

Ignoring Sample Size Correct answer is the smaller hospital Because of the law of large numbers the larger hospital is more representative of the population probability It’s easy to see this if we pretend that the smaller hospital only had 1 birth a day Probabilities would always be 100% or 0% In the study (Tversky & Kahneman, 1974) 22% smaller, 22% larger, 56% said no diff 56% demonstrated representativeness heuristic

Errors in Reasoning We’ve talked about 2 heuristics that people use in reasoning Availability heuristic Representativeness heuristic I also promised we’d come back to confirmation bias Confirmation bias: our tendency to selectively look for information that conforms to our hypotheses and to overlook information that argues against it

Confirmation Bias Wason (1960) Asked people to try and guess a rule he was using to generate lists of numbers At each step, they produced a set of numbers and their rationale for choosing them Feedback on whether numbers fit experimentor’s rule No feedback on rationale Very few participants found Wason’s rule because they focused only on finding support for theirs

Confirmation Bias The key to finding Wason’s solution was to create sequences that disconfirmed their own current hypotheses Like in the 4-card task, key is falsification Not only do people seek out confirming information, rather than disconfirming information, they also disregard falsifying information when it is presented to them Lord and Coworkers (1979) – Capital punishment

Lord and Coworkers (1979) Questionairre used to separate people into groups in favor of or against capital punishment Groups were presented with articles about research on capital punishment Evidence that CP deters murder Evidence that CP has no effect on murder Results – reactions consistent with questionairre ratings (no main effect of article) Confirmation bias is like blinders that we wear!

Decision-Making

Decision-Making Decision-making: choosing among alternatives What college to attend Who to marry What job to choose What to eat or wear Decision-making uses both inductive and deductive reasoning Affected by biases and heuristics we’ve discussed

Decision-Making In discussing reasoning, the focus was on how people make judgments of probability or validity In discussing decision-making we’ll focus on how people make judgments that involve different courses of action Decisions involve costs and benefits

Utility Approach to Decisions Early theory on decision-making was influenced by economic utility theory People are basically rational If they have all relevant information, they will decide based on maximum expected utility Utility: outcomes are in the person’s best interest Maximum monetary payoff in economics

Utility Approach to Decisions Advantage of Utility Approach is that it clearly specifies procedures that will allow people to determine which choice will result in highest payoff Given the odds of winning at slots over time it is possible to determine that playing slots has low utility It is unwise to play the slots But people do play the slots, even though they know this

Limits of Utility Theory It could be that people play slots (and do other things that don’t maximize payoff) because they’re irrational Not likely to be the case Maybe they just value other things besides money Benefit of winning may outweigh monetary cost Maybe they represent the decision differently

Limits of Utility Theory Tickets to the ball game, $60. Hot dogs, $10. Your team’s baseball cap, $20. Seeing the game with your son or daughter, “priceless.” Costs and benefits cannot always be determined or compared objectively Often in the mind of the person making the decision

Mental Simulations People create mental simulations and use them to make decisions To decide between college A and college B, one could imagine what it might be like to be at each one Simulations used to estimate costs and benefits Sometimes simulations can be inaccurate People only imagine positive outcomes of winning the lottery There are negative outcomes too

Decisions and Emotions Many decisions involve estimating the emotions you will have with each possible outcome Having a baby will make me happy People are bad at estimating how things will make them feel One factor responsible is the focusing illusion, in which focusing attention changes estimates of emotion

Strack and Coworkers (1988) Students were asked two questions: How happy are you? How many dates did you have last month? The order of the questions affected the answers Happy first, correlation 0.12 Happy second, correlation 0.66 Dating question first caused people to focus on dating as a determinant of happiness

Schkade and Kahneman (1998) Asked students two questions How would you rate your life satisfaction? Are people who live in California or the Midwest more satisfied? Results For self ratings, no difference between locations For other ratings, everyone said California For other vs. self, people focused on different determinants of satisfaction

Presentation Form Decisions can also depend on how the decision is presented Just like for reasoning Different problem statements Opt-in vs. opt-out 85% of people support organ donation, but only 28% of Americans opt-in to the program In countries with the opt-out version, 99% stay in (support rate is about the same)

Justification In Decision Making Participants heard a story about taking an exam and then being offered a vacation package Some knew they’d passed in the story Some knew they’d failed in the story Some knew results were not yet available People who “knew” about a pass OR fail were more likely to say they’d buy the package Like to attach a “reason” to a risky decision

Physiology of Thinking

Prefrontal Cortex (PFC) Neuropsychology PFC damage impaired meal planning PFC damage leads to perseveration, less flexibility PFC damage leads to poor problem solving Patients with PFC damage have difficulty arranging people by height when given statements like “Paul is taller than Sarah”. Imaging PFC activation during problem-solving

Final Example: Personal Health Imagine that there will be a deadly flu going around your area next winter. Your doctor says that you have a 10 percent chance (10 in 100) of dying from this flu. A new flu vaccine has been developed and tested. If administered, the vaccine will prevent you from catching the deadly flu. However, there is one serious risk involved: The vaccine is made from a somewhat weaker type of the flu virus, so there is a 5 percent risk (5 out of 100) that the vaccine could kill you. Considering this information, what would you do? I will not take the vaccine and I accept the 10 percent chance of dying from this flu I will take the vaccine and I accept the 5 percent chance of dying from the weaker flu in the vaccine

Flu Vaccine Example People choose the higher risk because of the omission bias Do nothing to avoid making a decision that could be seen as causing harm If deciding for others, more likely to go with the lower risk option (getting the shot) If anything bad happens (either way) they may be held responsible REMINDER: Get your flu shot this year (with H1N1). And in case you are wondering I actually did get mine….

This is the end of the last chapter! Good luck on exams!