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Introduction to Psychology Suzy Scherf Lecture 10: How Do We Know? Higher-Order Cognition

Introduction to Psychology Suzy Scherf Lecture 10: How Do We Know? Higher-Order Cognition. Cognitive Modules. “Cognitive modules are designed not for cool rationality, but for hot cognition, to respond to crucial events related to survival and reproduction.”

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Introduction to Psychology Suzy Scherf Lecture 10: How Do We Know? Higher-Order Cognition

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  1. Introduction to Psychology Suzy Scherf Lecture 10: How Do We Know? Higher-Order Cognition

  2. Cognitive Modules “Cognitive modules are designed not for cool rationality, but for hot cognition, to respond to crucial events related to survival and reproduction.” - Douglas Kendrick (Kendrick et al., 1998)

  3. Cognitive Modules • Memory • Language • Categorization • Recognition • Object knowledge • Thinking about Minds • Learning • Reading • Problem Solving • Cognitive Heuristics • Mathematics

  4. Categorization • Prevents us from having to - • Process of -

  5. Categorization

  6. Categorization Three dimensions: 1. 2. 3. Members =

  7. No No Yes No No No No Yes No Yes Categorization

  8. Categorization • _________ categories vs. ___________ categories • ___________ categories exist in real-world and have an internal structure organized around ___________ • Natural categories - • Non-natural categories -

  9. Categorization • Natural categories - • Easier to identify and remember central members than non-central members - • Typical members of a category - • Atypical members -

  10. Why do We Categorize this Way? • Typicality - • Typicality-based categories -

  11. Rips (1975) study: • You are on an island with robins, sparrows, hawks, eagles, ducks, geese, ostriches, and bats - some of which are infected with a highly contagious disease Why do We Categorize this Way? • Allows us to - • What is likelihood that other critters will become infected if it is the sparrows who have the disease?

  12. Why do We Categorize this Way? • Allows us to use our experiences to anticipate properties of living things and make predictions • Rips (1975) study: • Predictions showed a strong influence of typicality

  13. Thinking about Minds • Minds and beliefs are - • Beliefs can be totally false - • Beliefs not concrete - • Reasoning about minds -

  14. Thinking about Minds • False belief task -

  15. Thinking about Minds • False belief task -

  16. Thinking about Minds • Deception Tasks -

  17. Thinking about Minds • Cues to what’s in a mind - intentions: • Useful to -

  18. Thinking about Minds • Some evidence that _____________ and ___________individuals’ theory of mind skills deficient • Also, people with ______________ and __________ lesions can have changes in their theory of mind skills

  19. Are we Designed to Think Logically? You have been hired as a bouncer at a bar and you must enforce the following rule: “If a person is drinking beer, then s/he must be over 21.” Coke Beer 25 yrs. 16 yrs. Which of the card(s) do you have to turn over to make sure that no one is breaking the law?

  20. D F 3 7 Are we Designed to Think Logically? You have been hired as a clerk. Your job is to make sure that a set of documents is marked correctly, according the the following rule: “If the document has a D rating, then it must be marked code 3.” Which of the card(s) do you have to turn over to check for errors?

  21. Cognitive Heuristics for Solving Problems • Which problem was easier? • The problems are - • The first problem involves -

  22. Cognitive Heuristics for Solving Problems • Our evolved tendencies toward - • The kinds of reasoning errors - • Heuristics are -

  23. The Availability Heuristic • A heuristic that - • It leads us to -

  24. The Representativeness Heuristic • A heuristic that - • It leads us to -

  25. Cognitive Heuristics Solve What Kind of Problems? • Problems that our ancestors in the EEA likely encountered. • Are we stupid since we cannot solve probability problems well?

  26. Cognitive Heuristics Solve What Kind of Problems? • Did our ancestors succeed by solving probability problems? • Probability -

  27. Probability Problem The probability of breast cancer is 1% for a woman of age 40 who has a routine mammogram exam. If such a woman has breast cancer, the probability is 80% that her mamm exam will be positive. If a woman does not have breast cancer, the probability is 9.6% that her mamm exam will be positive. A 40-yr-old woman has just received a positive mamm exam. What is the probability that she actually has breast cancer?

  28. Frequency Problem As a physician, you examine many women for breast cancer. Most of them do not have it. Of the women who you’ve treated with breast cancer, 8 of them had a positive mammogram. Of those women who did not have breast cancer, 95 had positive mammograms. You just got back a positive mammogram result for a new client. What are the chances that she has breast cancer?

  29. Why are we better intuitive statisticians on frequency problems as opposed to probability problems?

  30. The Gambler’s Fallacy: Another Clue About our Designed Cognitive Heuristics

  31. The Gambler’s Fallacy: When Does it Work? ------------------------------------------------------------------------ 24 Hour Summary Time EST (UTC) Temperature F (C) Dew Point F (C) Pressure Inches (hPa) Wind MPH Weather Latest 4 PM (21) Oct 17 59 (15) 57 (14) 30.07 (1018) NNE 5 light rain; mist 3 PM (20) Oct 17 57.9 (14.4) 57.0 (13.9) 30.08 (1018) Calm light rain; mist 2 PM (19) Oct 17 57.9 (14.4) 57.0 (13.9) 30.09 (1018) Variable 3 light rain; mist 1 PM (18) Oct 17 57 (14) 57 (14) 30.1 (1019) Calm light rain; mist Noon (17) Oct 17 57 (14) 55 (13) 30.12 (1019) NE 3 light rain; mist 11 AM (16) Oct 17 57 (14) 55 (13) 30.13 (1020) NE 3 mist 10 AM (15) Oct 17 55 (13) 55 (13) 30.14 (1020) ESE 5 light rain; mist 9 AM (14) Oct 17 55.0 (12.8) 53.1 (11.7) 30.14 (1020) ENE 3 light rain; mist 8 AM (13) Oct 17 55.0 (12.8) 53.1 (11.7) 30.14 (1020) Calm light rain; mist 7 AM (12) Oct 17 55 (13) 51 (11) 30.14 (1020) Calm light rain; mist 6 AM (11) Oct 17 54.0 (12.2) 51.1 (10.6) 30.12 (1019) NE 3 mist 5 AM (10) Oct 17 53.1 (11.7) 51.1 (10.6) 30.12 (1019) NE 4 mist 4 AM (9) Oct 17 54.0 (12.2) 52.0 (11.1) 30.11 (1019) N 6 mist 3 AM (8) Oct 17 53.1 (11.7) 51.1 (10.6) 30.1 (1019) Calm 2 AM (7) Oct 17 53.1 (11.7) 52.0 (11.1) 30.12 (1019) E 3 1 AM (6) Oct 17 55.0 (12.8) 52.0 (11.1) 30.13 (1020) NNW 5 Midnight (5) Oct 17 54.0 (12.2) 51.1 (10.6) 30.14 (1020) NNW 3 11 PM (4) Oct 16 55.9 (13.3) 52.0 (11.1) 30.14 (1020) Calm 10 PM (3) Oct 16 55.0 (12.8) 52.0 (11.1) 30.15 (1020) Calm 9 PM (2) Oct 16 57.9 (14.4) 53.1 (11.7) 30.14 (1020) Calm 8 PM (1) Oct 16 57.9 (14.4) 53.1 (11.7) 30.14 (1020) Calm 7 PM (0) Oct 16 59.0 (15.0) 53.1 (11.7) 30.14 (1020) Calm 6 PM (23) Oct 16 61.0 (16.1) 54.0 (12.2) 30.13 (1020) Calm

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