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How we know what isn’t so – cognitive factors in false beliefs

How we know what isn’t so – cognitive factors in false beliefs. Dr. Jeff Saunders Dept of Psychology Hong Kong University. Topic: cognitive factors in false beliefs How we acquire false beliefs? What cognitive tendencies lead us acquire mistaken beliefs about the world?

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How we know what isn’t so – cognitive factors in false beliefs

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  1. How we know what isn’t so – cognitive factors in false beliefs Dr. Jeff Saunders Dept of Psychology Hong Kong University

  2. Topic: cognitive factors in false beliefs • How we acquire false beliefs? • What cognitive tendencies lead us acquire mistaken beliefs about the world? • Why do false beliefs persist? • What tendencies inhibit us from learning from experience and correcting false beliefs False beliefs

  3. Demo: try to outwit the professor in a card guessing game

  4. ‘Strategy’: choices were entirely random! But might have perceived that that there was some causal strategy behind choices No way that opponent could have done better or worse than chance But might have perceived that there was some strategy that would have increased the likelihood of winning Card game strategy?

  5. One factor in acquiring false beliefs: tendency to see structure in randomness We are very good at seeing meaningful patterns in the world Useful and impressive ability! But so pervasive and automatic... can sometimes lead us astray Seeing structure in randomness

  6. Distribution of bombs in London during WWII Was Regent’s Park targeted? Clustering illusion No, just random

  7. These are randomly generated points Our minds readily see structure in random Clustering illusion

  8. Your friend’s remedy for common cold: eat large amounts of radishes One day when you are really sick, the friend brings you a bunch of radishes to eat You think: oh well, might as well try it Next day: you are feeling a lot better! Wow, do radishes really work? Or … could this just be coincidence? Home remedy: radishes! (sounds unlikely to you)

  9. Health varies over time, semi-randomly, and most illnesses get better on their own Seek treatment when health at low points Likely to get better regardless of treatment Misinterpretation of spontaneous recovery could lead to false beliefs about remedies Spontaneous recovery

  10. We are very good at seeing patterns in the world and generating causal hypotheses • But this tendency can lead us to mistakenly see causal relations in randomness • Clustering illusion • Misinterpretation of streaks or clusters that occur naturally by chance • Regression fallacy • Misinterpretation of regression to the mean Summary - misperceived causation

  11. Suppose we have an false belief • Due to clustering, regression, coincidence ... • In principle, exposure to counterevidence might allow one to correct the error • However, some cognitive factors interfere with correction of false beliefs… Why do erroneous beliefs persist?

  12. Neglect missing information…

  13. A manager at a company has unusual method for choosing who to hire Top 8 candidates compete in chess tournament, winner gets the job Example: selection criteria Manager: “Everyone I’ve hired with this method has been excellent!”

  14. Hiring method: chess tournament If previous employees performed well – does that mean that hiring method is good? Or are we missing information? Example: selection criteria Manager: “Everyone I’ve hired with this method has been excellent!”

  15. Problem: missing data about how rejected candidates would have performed if hired Only get feedback about the performance of candidates that were hired If strong candidate pool, even flawed system would select good performers Some rejected candidates might have been even better, but no way of knowing Example: selection criteria

  16. Your friend convinced you that radishes are a good treatment for colds, which is false Will you learn from experience that radishes do not really help? Problem: missing information If you belief in treatment, then every time you are sick you will eat radishes Never get to see how quickly you would recover without radishes Home remedy: radishes! (again)

  17. Confirmatory evidence more noticeable…

  18. Example: belief that your roommate never remembers to wash their dishes Lots of relevant evidence – every meal at home, either does or does not wash dishes But which cases will be noticed? What evidence is noticed?

  19. “Roommate never washes dishes” Sunday Monday Tues Again??! I need a new roommate! Weds Thurs Friday

  20. Example: belief that your roommate never remembers to wash their dishes Pos / neg evidence not equally noticeable Salient evidence would only reinforce belief What evidence is noticed? Reinforce belief (even if rare) Going smoothly is non-event, not salient Unpleasant event, highly salient!

  21. Using my psychic powers, I predict: On the first day of Lunar New Year in 2013, … there will be a major earthquake in China Example: prophecy

  22. Prophesy: On the first day of Lunar New Year in 2013, major earthquake in China Specific, falsifiable prediction But - what outcome would you notice? Example: prophecy

  23. Prophesy: On the first day of Lunar New Year in 2013, major earthquake in China Example: prophecy Non-event, unlikely to remember prophesy New Year No earthquake WOW! He really is psychic! Earthquake! New Year

  24. Prophesy: On the first day of Lunar New Year in 2013, major earthquake in China Example: prophecy Noticeable outcome would confirm psychic powers, not disconfirm New Year No earthquake WOW! He really is psychic! Earthquake! New Year

  25. http://xkcd.com/628/

  26. Bias in seeking information…

  27. Goal: to figure out an unknown rule for sequences of three numbers • Some sequences of numbers satisfy the rule, some sequences do not • Here is one sequence of numbers that satisfied the rule: 2-4-6 • Now you can suggest numbers for testing • I will tell you “yes” or “no” Exercise: rule discovery

  28. Demo: test cases to discover rule • Initial example chosen so that you would likely have some guess about the rule • Tendency: test additional examples that would also satisfy rule • Problem: did not get opportunity to learn that your guess was wrong • Restricted test cases could only reinforce mistaken belief about rule Exercise: rule discovery

  29. How might confirmation bias lead to misdiagnosis by doctors? Application: medical diagnosis

  30. Goal: scale for measuring extroversion • Items are self-reflective statements • “I often feel that …” “I generally do not …” • Each item is rated on scale agree/disagree • 1 – ‘strongly agree’ • 2 – ‘agree’ • 3 – ‘neither agree not disagree’ • 4 – ‘disagree’ • 5 – ‘strongly disagree’ • Exercise: everyone write down a possible item for an extroversion scale Example: test for extroversion

  31. In principle, items could test for either presence or absence of extroversion • Presence: “I am often the life of the party” • Extroverts would “agree” • Absence: “I often keep to myself at parties” • Extroverts would “disagree” • In your sample items, would extroverts be expected to agree or disagree? • Expected result: mostly “agree” items Example: test for extroversion

  32. Extroversion test example: tended to seek information that confirms not disconfirms • Look for presence of a trait not absence • Look for +extroversion not -introversion • If asked to make a test for introversion, would have chosen different statements • … even though these are assumed to be opposites along the same continuum Seeking confirmation

  33. Hypothesis: “Cards with an odd number on one side have a circle on the other side” Which cards need to be flipped to evaluate this hypothesis? Wason selection task

  34. To evaluate hypothesis “if odd, then circle” Typical answer: (a) and (c) Correct answer: (a) and (d) Wason selection task

  35. To evaluate hypothesis “if odd, then circle” Wason selection task • If odd, refutes • hypothesis • Does not matter • If odd, supports hypothesis • If not circle, would refute hypothesis • If even, does not refute!

  36. To evaluate hypothesis “if odd, then circle” Wason selection task • If odd, refutes • hypothesis • Does not matter • If not circle, would refute hypothesis • Could only confirm, never refute

  37. To evaluate hypothesis “if odd, then circle” Wason selection task Incorrectly treated as strong evidence Relevant but neglected Obviously relevant Obviously irrelevant

  38. Hypothesis: “If drinking beer, then over 18” Which people need to be checked? Logically, exactly same as previous, but people tend to get this version correct “Permission” schema seems to help Wason selection task

  39. Hypothesis: “If odd, then circle” Logically, need to check: odd, square But typically drawn to check circle card, which could confirm but never disconfirm Confirmation bias in seeking information Wason selection task

  40. When evaluating a belief, we tend to seek and focus on confirmatory information • As a result, less likely to be exposed to evidence that might refute a false belief • Example demonstrations: • Rule discovery exercise • Look for stereotypical extroverted traits • Wason selection task Summary - Confirmation bias

  41. Expectations influence interpretation of evidence…

  42. Example: knowledge -> perception Once you know what it is, looks different! By R. C. James

  43. Bias due to expectations can allow us to interpret highly ambiguous information Perceptual example: experience allows us to see Dalmatian from degraded image Adaptive use of knowledge and experience Is there a downside to this? Potential problem: re-enforce false beliefs Bias due to expectations

  44. Referees in sports often have to interpret ambiguous information to evaluate penalties Susceptible to bias from expectations Example: Referees’ judgments High tackle or not? Depends: are you an All-Blacks fan??

  45. Frank & Gilovich (1988) –effect of black uniforms on referee judgments Referees evaluated possible penalties from videos Identical situations except varied uniform color Finding: more penalties for players wearing black Explanation: stereotype of black as “bad guy” Example: Referees’ judgments

  46. Lack of source memory…

  47. Answer trivia quiz Trivia quiz

  48. Compute the number of “true” responses for even numbered statements Actual: equal true/false Even numbered statements were repeated from previous trivia quiz (if you took it) Prediction: more “true” for statements that were repeated Trivia quiz

  49. Illusion of truth effect Data from my HKU class: For identical statements, more “true” judgments on second test Percent judged “true” Change due to just one prior exposure

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