Introduction to PsychologySuzy 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.”

- Douglas Kendrick (Kendrick et al., 1998)

Cognitive Modules

• Memory• Language• Categorization• Recognition• Object knowledge• Thinking about Minds

• Learning• Reading• Problem Solving• Cognitive Heuristics• Mathematics

Categorization

• Prevents us from having to -

• Process of -

CategorizationItem Member?

s20 yes

10h no

rs no

10k no

p20 no

m20 yes

20y no

d10 yes

j10 no

1020 no

CategorizationItem Member?

s20 yes

10h no

rs no

10k no

p20 no

m20 yes

20y no

d10 yes

j10 no

1020 no

Three dimensions:

1.

2.

3.

Members =

Categorization

No

No

No No

NoNoNo

Yes

Yes

Yes

Categorization

• _________ categories vs. ___________ categories

• ___________ categories exist in real-world and have an internal structure organized around ___________

• Natural categories -

• Non-natural categories -

Categorization

• Natural categories -

• Easier to identify and remember central members than non-central members -

• Typical members of a category -

• Atypical members -

Why do We Categorize this Way?

• Typicality -

•

• Typicality-based categories -

Why do We Categorize this Way?

• Allows us to -

• 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

• Rips (1975) study:

• What is likelihood that other critters will become infected if it is the sparrows who have the disease?

Why do We Categorize this Way?

• Allows us to use our experiences to anticipate properties of living things and make predictions

• Predictions showed a strong influence of typicality

• Rips (1975) study:

Thinking about Minds

• Minds and beliefs are -

• Reasoning about minds -

• Beliefs can be totally false -

• Beliefs not concrete -

Thinking about Minds

• False belief task -

Thinking about Minds

• False belief task -

Thinking about Minds

• Deception Tasks -

0

10

20

30

40

50

60

70

80

90

100

3 years 4 years 5 years

% of children

conceal reveal

Thinking about Minds

• Cues to what’s in a mind - intentions:

•

•

•

•

• Useful to -

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

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?

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.”

D F 3 7

Which of the card(s) do you have to turn over to check for errors?

Are we Designed to Think Logically?

Cognitive Heuristics for Solving Problems

• Which problem was easier?

• The problems are -

• The first problem involves -

Cognitive Heuristics for Solving Problems

• Our evolved tendencies toward -

• The kinds of reasoning errors -

• Heuristics are -

The Availability Heuristic

• A heuristic that -

• It leads us to -

• •

The Representativeness Heuristic

• A heuristic that -

• It leads us to -

• •

Cognitive HeuristicsSolve What Kind of Problems?

• Problems that our ancestors in the EEA likely encountered.

• Are we stupid since we cannot solve probability problems well?

Cognitive HeuristicsSolve What Kind of Problems?

• Did our ancestors succeed by solving probability problems?

• Probability -

•

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?

Probability 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?

Frequency Problem

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

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

Heuristics

The Gambler’s Fallacy: When Does it Work?

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24 Hour Summary

TimeEST (UTC) TemperatureF (C) Dew PointF (C) PressureInches (hPa) WindMPH WeatherLatest 4 PM (21) Oct 17 59 (15) 57 (14) 30.07 (1018) NNE 5 light rain; mist3 PM (20) Oct 17 57.9 (14.4) 57.0 (13.9) 30.08 (1018) Calm light rain; mist2 PM (19) Oct 17 57.9 (14.4) 57.0 (13.9) 30.09 (1018) Variable 3 light rain; mist1 PM (18) Oct 17 57 (14) 57 (14) 30.1 (1019) Calm light rain; mistNoon (17) Oct 17 57 (14) 55 (13) 30.12 (1019) NE 3 light rain; mist11 AM (16) Oct 17 57 (14) 55 (13) 30.13 (1020) NE 3 mist10 AM (15) Oct 17 55 (13) 55 (13) 30.14 (1020) ESE 5 light rain; mist9 AM (14) Oct 17 55.0 (12.8) 53.1 (11.7) 30.14 (1020) ENE 3 light rain; mist8 AM (13) Oct 17 55.0 (12.8) 53.1 (11.7) 30.14 (1020) Calm light rain; mist7 AM (12) Oct 17 55 (13) 51 (11) 30.14 (1020) Calm light rain; mist6 AM (11) Oct 17 54.0 (12.2) 51.1 (10.6) 30.12 (1019) NE 3 mist5 AM (10) Oct 17 53.1 (11.7) 51.1 (10.6) 30.12 (1019) NE 4 mist4 AM (9) Oct 17 54.0 (12.2) 52.0 (11.1) 30.11 (1019) N 6 mist3 AM (8) Oct 17 53.1 (11.7) 51.1 (10.6) 30.1 (1019) Calm2 AM (7) Oct 17 53.1 (11.7) 52.0 (11.1) 30.12 (1019) E 31 AM (6) Oct 17 55.0 (12.8) 52.0 (11.1) 30.13 (1020) NNW 5Midnight (5) Oct 17 54.0 (12.2) 51.1 (10.6) 30.14 (1020) NNW 311 PM (4) Oct 16 55.9 (13.3) 52.0 (11.1) 30.14 (1020) Calm10 PM (3) Oct 16 55.0 (12.8) 52.0 (11.1) 30.15 (1020) Calm9 PM (2) Oct 16 57.9 (14.4) 53.1 (11.7) 30.14 (1020) Calm8 PM (1) Oct 16 57.9 (14.4) 53.1 (11.7) 30.14 (1020) Calm7 PM (0) Oct 16 59.0 (15.0) 53.1 (11.7) 30.14 (1020) Calm6 PM (23) Oct 16 61.0 (16.1) 54.0 (12.2) 30.13 (1020) Calm