Effects of Temporal and Causal Schemas on Probability Problem Solving

Causal beliefs have been shown to affect performance in a wide variety of reasoning and problem solving. 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. In the present study we investigate if the directionality of implicit causal models can also affect application of formal methods, specifically the solution of conditional probability word problems. The study examined temporal and causal schemas, in which the convention is that events are considered in forward direction. Pairs of conditional probability (CP) problems were written depicting events E1 and E2, such that E1 either occurs before E2 or causes E2. 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 schemainconsistent, inverted order. Participants had greater difficulty encoding CP for events in schema-inconsistent order than CP of events in the conventional deterministic order.