Psychology 100 Chapter 8 Part III Thinking & Intelligence

Outline Cognition, C’ont Intelligence Study Question: Inductive reasoning Limits in reasoning Intelligence Study Question: • What is the availability heuristic? Give an example of a reasoning error that might be attributed to availability. 2

Cognition Inductive Reasoning Algorithms and Heuristics Reasoning under uncertainty: Inductive reasoning Algorithms versus heuristics Kahneman and Tversky’s work Behavioural decision work Ups and downs of heuristics Cf. Visual illusions

Cognition Inductive Reasoning Algorithms and Heuristics The representiveness heuristic E.g., Flip a coin 6 times, which is more likely HHHHHH or HHTHTT Which lottery ticket is most likely to win the next 6-49? 04-11-19-29-33-39 or 01-02-03-04-05-06 The representativeness heuristic - samples are like the populations that they are pulled from. The representativeness heuristic leads to a number of decision biases

Cognition Inductive Reasoning The representiveness heuristic The law of small numbers Who is more likely to have days where more than 60% of the births are male? St. Martha’s or the IWK? Ignoring base rates Cancer Screening example 1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive results. 9.6% of women without breast cancer will also get positive results. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer? The Gambler’s fallacy The hot hand in basketball

Cognition Inductive Reasoning The Availability Heuristic Our estimates of how often things occurs or are influenced by the ease with which relevant examples can be remembered This leads to a number of biases 1) Which is a more likely cause of death in the United States: being killed by falling airplane parts or being killed by a shark? Airplane parts! 30 X more likely than shark attacks. 2) Do more Americans die from a) homicide and car accidents, or b) diabetes and stomach cancer? Diabetes and stomach cancer by a ratio of nearly 2:1. 3) Which claims more lives in the US: lightning or tornadoes? Lightning

Cognition Inductive reasoning The Availability Heuristic Important factors Vividness and Saliency E.g., the full moon Repetition effects Anything that makes recollection easier Role of the media

Cognition Inductive reasoning Government cutbacks are about take a hit on students. It is expected that 600 people will lose their bursaries. The student union has proposed two alternative programs to fight the cutbacks: If Program A is adopted, 200 students will have their bursaries saved. If Program B is adopted (a legal option), there is a one-third probability that 600 students will have their bursaries saved, and a two-thirds probability that no students will have their bursaries saved. Which program would you favour?

Cognition Inductive Reasoning The framing effect (Kahneman & Tversky) The wording of question in conjunction with the background context can influence the decision. Both of the previous plans were rejected by the N.S. federation of students, who are now consideing the following: If Plan C is adopted, 400 students will lose their bursaries. If Plan D is adopted (another legal option), there is one-third probability that nobody will lose their bursaries, and a two-thirds probability that 600 students will lose their bursaries. Kahneman & Tversky’s results Plan A 1/3 Saved Plan B P=1/3 Saved Plan C 2/3 Die Plan D P=2/3 Die 72% 28 % 22% 78 %

Cognition Inductive reasoning The framing effect (Kahneman & Tversky) Risk seeking and avoidance When questions are framed in terms of gains we avoid risk (Prefer A over B) When framed in terms of losses we are risk-seekers (Prefer D over C) Other findings relating to the Framing Effect It is unrelated to statistical sophistication It is not eliminated when the contradiction is pointed out

Cognition Limitations in reasoning Limited domain knowledge Our cognitive representation of the situation (AKA mental model) often has incomplete information. Thermostats do not work like water faucets Hitting the elevator button 5 times is not faster than hitting it once 20° C is not twice as warm as 10 °C Quasi-magical behaviour

Cognition Limitations in reasoning

Cognition Limitations in reasoning

Cognition Limitations in reasoning Naïve Physics and Mental Models (McCloskey et al.)

Cognition Limitations in reasoning Results (A & B)

Cognition Limitations in reasoning Results (C)

Cognition Limitations in reasoning Domain of knowledge Our domain of knowledge concerning physics is poor. Impetus theory: a pre-Newtonian and incorrect concept concerning “curvature momentum” Linda is 31 years old, single outspoken, and very bright. She majored in philosophy. As a student she was deeply concerned with the issues of discrimination and social justice, and also participated in anti-globalization demonstrations. Rank the following in terms of their likelihood of describing Linda Linda is a teacher at a local elementary school Linda is a bank teller and is active in the feminist movement Linda is an insurance agent Linda is psychiatric social worker Linda is a bank teller

Problem Solving Cognition Limitations in reasoning Conjunction fallacy: Judging the probability of a conjunction to be greater than the probability of a constituent event. Very Unlikely 6 4 Very Likely Likelihood ratio 5 3 Statiscally Naive Intermediate Statistically Sophisticated

Intelligence Alfred Binet (1857-1911) Role of environment Higher-order abilities Focus on children Binet/ simon test Stanford-Binet scale: Mental age revision IQ (intelligence Quotient) = (Mental/Chronological)X100 e.g, 15/12 X 100 = 125 Weschler Adult Intelligence Scale-Rev. (WAIS-R) Verbal and Performance components 3

Intelligence Intelligence Decisions Francis Galton (1822-1911) Role of heredity Speed of processing Correlational statistics Charles Spearman (1863-1945) Intelligence: solo entity or many abilities? Invented factor analysis to look at multiple correlation Example: Six tests Vocabulary Picture completion Reading comprehension Object assembly (puzzle) General information Block design Charles Spearman 3

Intelligence Hypothetical Correlations among scores Tests 0.32 0.60 Picture Completion Reading Comprehension Object Assembly General Information Block Designs Tests Vocabulary 0.32 0.60 0.39 0.58 0.44 Picture Completion 0.40 0.54 0.38 0.64 Reading Comprehension 0.29 0.64 0.31 Object Assembly 0.33 0.60 General Information 0.37

Intelligence g Spearman’s theory All the tests are positively correlated “g”: General ability, which effects all tests The tests are not perfectly correlated “s”: Specific abilities, which effect each test g S1 S2 S3 S4 S5 S6 Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 3

Intelligence Hypothetical Correlations among scores Tests 0.32 0.60 Picture Completion Reading Comprehension Object Assembly General Information Block Designs Tests Vocabulary 0.32 0.60 0.39 0.58 0.44 Picture Completion 0.40 0.54 0.38 0.64 Reading Comprehension 0.29 0.64 0.31 Object Assembly 0.33 0.60 General Information 0.37

Intelligence Raymond Cattell (1905-1998) Discovered two underlying g’s Fluid Intelligence Raw ability to manipulate information Crystallized Intelligence Ability acquired through experience Test 1 Test 3 Test 5 Test 2 Test 4 Test 6 Fluid Crystal 3