ABSTRACT

This chapter proposes an associative account of how probability judgments arise and demonstrates the influence of category structure, or the alignment of a decision environment, on probability judgments. Associative models of probability judgment are usually applied to situations where people experience sequentially presented events. While this measure usually provides a good proxy for probability judgments, there are situations where it can lead to probabilistic incoherence. Central to this approach is the idea that probability judgments are best understood in the context of the learning that precedes them. The chapter introduces an alternative correspondence model, one where people attune to statistical relations between events. It also argues that the way in which people learn about the statistical structure of their environment determines how they arrive at probability judgments, whether accurate or biased. The chapter finally shows how associative models might underpin mental simulation, and be extended to larger-scale problems.