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Chapter 4: Local integration 1: Reasoning & evolutionary psychology. Overview. • Introduce experimental data from psychology of reasoning • Outline how these data have been interpreted by evolutionary psychologists • Draw out some implications for thinking about the integration challenge.
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Chapter 4:Local integration 1: Reasoning & evolutionary psychology
Overview • Introduce experimental data from psychology of reasoning • Outline how these data have been interpreted by evolutionary psychologists • Draw out some implications for thinking about the integration challenge
Psychology of reasoning • Psychologists have produced evidence that subjects regularly contravene basic principles of deductive logic probability theory when engaged in • conditional reasoning • judgments of likelihood
Conditional reasoning rules Modus ponens Modus tollens If p then q If p then q p not-q Therefore q Therefore not-p Affirming the consequent Denying antecedent If p then q If p then q q not-p Therefore p Therefore not-q
Wason selection task What cards need to be turned over to evaluate: If a card has a vowel on one side then it has an odd number on the other side
Cassava root studies (Cosmides and Tooby) Background about (imaginary) Pacific island: • Only married men have facial tattoos • Cassava roots are a highly prized delicacy and aphrodisiac • Molo nuts are bitter and not valued in the community
Social exchange Two versions of cassava root story Descriptive: married men live on the side of the island where cassava roots grow, while unmarried men live on the side where the molo nuts grow Social exchange: only married men have the right to eat cassava roots.
Test and results If a man is eating cassava root, he must have a tattooed face EATS CASSAVA ROOT EATS MOLO NUTS TATTOO NO TATTOO Descriptive version Poor performance (21%) Social exchange version Better performance (75%)
Cosmides and Tooby analysis EATS CASSAVA ROOT EATS MOLO NUTS TATTOO NO TATTOO Cost paid Cost not paid Benefit No benefit Social exchange version has following structure If BENEFIT then COST Cheater = BENEFIT without COST [i.e. p & ~ q]
Local integration 1 Solution of adaptive problems Explains Emergence of dedicated cheater detection system Explains Patterns of error in logical reasoning tasks
The structure of the argument!! If CONDITIONAL REASONING EXPLOITS A CHEATER DETECTION MODULE (p) then PERFORMANCE WILL BE BETTER ON THE SOCIAL EXCHANGE VERSION (q) PERFORMANCE IS BETTER ON THE SOCIAL EXCHANGE VERSION (q) Therefore, CONDITIONAL REASONING EXPLOITS A CHEATER DETECTION MODULE (p)
Switched selection task Standard social exchange selection task • If BENEFIT (p) then COST (q) • violation = p and not-q Switched social exchange selection task • If COST (p) then BENEFIT (q) • violation = q and not-p • Subjects typically give the logically correct answer on the standard version, but not on the switched version • Detecting a violation of the switched version is not the same as detecting a counter-example to the conditional
Cosmides and Tooby analysis EATS CASSAVA ROOT EATS MOLO NUTS TATTOO NO TATTOO Cost paid Cost not paid Benefit No benefit Switched social exchange version has following structure If COST then BENEFIT Logically correct answers are cards 2 and 3 Cheater detection answers remain 1 and 4
Evolutionary psychology and conditional reasoning evolutionary psychologists reject the idea of domain-general reasoning skills either mental logic or mental models suggest that we employ context-dependent inference rules – in particular, rules for detecting cheaters in social exchanges integrate these experimental data with a model of how the mind is organized and how it evolved
Massive modularity thesis Gives a picture of the overall organization of the mind • mind composed of highly specialized cognitive modules (Darwinian modules) • each module evolved to solve a particular adaptive problem • each module exploits specialized rules that are domain-specific • No domain-general “central cognition” orabstract reasoning mechanism
Cheater detection module The Cosmides/Tooby experiments seem to show specialized skills for cheater detection • not simply specialized skills for conditional reasoning involving social exchanges These experimental results are integrated with the massive modularity hypothesis via an evolutionary explanation of why there needs to be a cheater detection module • evolutionary explanation itself grounded in an account of the evolution of altruism
The puzzle of altruistic behavior Cooperative behavior widespread in animal kingdom • even in lower animals ants, termites, bees etc (individuals fed by others etc) • not restricted to kin Cooperative behavior presumably has a genetic basis But how did the genes coding for cooperative behavior ever get established in the gene pool? • natural selection seems to favor “selfish” behavior - free riders can always exploit altruists
Modeling the evolution of cooperation The prisoner’s dilemma is a very useful tool for modeling the problem • we can assume that participants are purely selfish • set up so that cooperation is not the dominant strategy for • can easily be extrapolated to multi-person interactions (tragedy of the commons)
One-shot PD Player A COOP DEFECT Player COOP 5, 5 10, 0 B DEFECT 0, 10 2, 2 Illustrates basic structure of interactions where being a free rider is advantageous
Decision-making in a one-shot PD • Work backwards from what the other agent might do • Look at your options if the other agent cooperates – it is best for you to defect • Look at your options if the other agent defects – it is best for you to defect The dominant strategy for each play is DEFECT But mutual defection is sub-optimal
Iterated PDs • A backwards induction argument shows that DEFECT is dominant when the number of plays is known • But for modeling the evolution of cooperation the interesting case is the indefinitely iterated PD • opens up possibility of strategies that “punish” other player for defecting • and “rewarding” for cooperating
Axelrod’s computer tournament Invited game theorists to submit strategies for iterated PD tournament • played strategies against each other for around 200 iterations Highest average score came from TIT-FOR-TAT • Start by cooperating • Then do what the opponent did on the previous round
TIT-FOR-TAT • Shows how cooperative behavior might emerge in very simple organisms • and be maintained since, in the right conditions, it is an evolutionarily stable strategy • Some evidence that TIT-FOR-TAT is followed in the animal kingdom (3-spined sticklebacks) • Has been used to model complicated human interactions (e.g. voting patterns in US Senate)
Back to cheater detection • TIT-FOR-TAT (or some similar strategy, such as TIT-FOR-TWO-TATS) can only work if there is a reliable mechanism for detecting cheaters. . . • Evolutionary pressure for selection of cheater detection module • According to Cosmides and Tooby, this module explains the pattern of choices made in conditional reasoning tasks
Local integration 1 Solution of adaptive problems Explains Emergence of dedicated cheater detection system Explains Patterns of error in logical reasoning tasks