Outline for PSYCH 315 at UMass
Outline for PSYCH 315 at UMass
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This 2 page Class Notes was uploaded by an elite notetaker on Friday February 6, 2015. The Class Notes belongs to a course at University of Massachusetts taught by a professor in Fall. Since its upload, it has received 16 views.
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Date Created: 02/06/15
Psych 315 2005 Decision Making and Reasoning Chapter 12 December 6 8 l Deductive and inductive reasoning decision making a Deduction from general to specific b induction from speci c to general c Decision making similar to induction start with partial data make most beneficial outcome or decide what is most likely to be true 2 Inductive reasoning a Wason 2 4 6 task b confirmation bias can39t ever prove inductive inference could disprove it c illusory covariation again 3 Deductive reasoning Mental logic vs mental models a Is there a language of thought b Examples of deductive reasoning i propositional reasoning conditional syllogisms l combine statements propositions and logical connectives with and or not if then deduce consequences 2 Valid and invalid rules ii syllogistic reasoning categorical syllogisms reasoning with quantifiers all some no common format 1 Allsomeno AB are not BA 2 Allsomeno BC are not CB 3 therefore A C 4 which ones valid Not all have any valid conclusion c mental logic Could it possibly be learned i if we want to accept that we reason according to general rules of mental logic we have to explain errors ii Belief bias iii Inconsistent or improper use oflogical terms like OR IF SOME iv Also having a NO or NOT makes problems harder SOME is harder than ALL d mental models alternative to rules of logic claim Don39t have to reason just represent situation and see what is true of it i linear order problems distance effect also end term effect ii Euler circles as mental models iii Johnson Laird syllogistic reasoning with mental models set up tableaus representing terms 1 but why are some problems hard Multiple possible mental models The more models to consider the harder the problem iv naive physics as mental models gone wrong e Importance of domain speci c schemata i Wason card problem ii Concreteness familiarity permission schema Darwinian algorithms Decisionmaking Reasoning under uncertainty judgments of likelihood potential success etc Kahnemann amp Tversky a See how reason by looking where it goes wrong i realize that things work really well most of the time b Normative model Expected utility probability subjective value i Expected value of a bet payoff odds ii Subjective value not a linear mction of objective value iii Framing effects and nonlinear subjective values gain vs loss 1 Frame in terms of gain riskaversion 2 Frame in terms of loss risktaking c Availability heuristic How likelyfrequentcommon is a given event or class of events i heuristic the easier instances or occurrences are brought to mind the more likely the eventclass 1 But possible errors 2 illusory correlation again d Representativeness heuristic judge about the relationship between 2 events eg likelihood that instance A is a member of category B i heuristic if A resemblesis representative of B to a greater degree the probability that A is B is higher ii Entails reasoning quotom the population to an instance e Failure to take relevant data into account especially base rate insensitivity i eg Sam writes poetry and paints in watercolors in his spare time is shy and has a wispy beard Is he a psychologist or a Chinese studies scholar Grossly overestimate latter ii Examples from medical diagnosis iii Cab problem insensitivity to base rate 1 but sensitivity to causal schemata f Interesting data learning psychology or social science makes people more sensitive to statistics more rational although taking logic courses doesn39t help Maybe
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