Effects of Temporal and Causal Schemas on Probability Problem Solving

S. Sonia GuggaColumbia University
James E. CorterTeachers College, Columbia University

Abstract

Causal beliefs have been shown to affect performance in a wide variety of reasoning and problem solving tasks. One type of judgment bias that can result from implicit causal models is causal asymmetry - the tendency to judge predictive inferences as more plausible than comparable diagnostic inferences. The directionality of implicit causal models may also affect the application of formal methods. Pairs of conditional probability (CP) problems were written depicting events E1 and E2, such that E1 either occurs before E2 or causes E2. Probability problems were defined with respect to the order of events expressed in CPs, so that P(E2|E1) represents the CP in schema-consistent, intact order by considering the occurrence of E1 before E2; while P(E1|E2) represents CP in schema-inconsistent, inverted order. Participants had greater difficulty encoding CP for events in schema-inconsistent order than CP of events in the conventional deterministic order.

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Effects of Temporal and Causal Schemas on Probability Problem Solving (119 KB)



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