Category: Reasoning

The purpose of this dissertation is to examine aspects of the representational and computational influences on Bayesian reasoning as they relate to reference dependence. Across three studies, I explored how dependence on the initial problem structure influences the ability to solve Bayesian reasoning tasks. Congruence between the problem and question of interest, response errors, and individual differences in numerical abilities was assessed. The most consistent and surprising finding in all three experiments was that people were much more likely to utilize the superordinate value as part of their solution rather than the anticipated reference class values. This resulted in a weakened effect of congruence, with relatively low accuracy even in congruent conditions, as well as a different pattern of response errors than what was anticipated. There was consistent and strong evidence of a value selection bias in that incorrect responses almost always conformed to values that were provided in the problem rather than errors related to computation. The one notable exception occurred when no organizing information was available in the problem, other than the instruction to consider a sample of the same size as that in the problem. In that case, participants were most apt to sum all of the subsets of the sample to yield the size of the original sample (N). In all three experiments, higher numerical skills were generally associated with higher accuracy, whether calculations were required or not.