Causal Learning: Association Versus Computation

Abstract

Causal learning enables humans and other animals not only to predict important events or outcomes, but also to control their occurrence in the service of needs and desires. Computational theories assume that causal judgments are based on an estimate of the contingency between a causal cue and an outcome. However, human causal learning exhibits many of the characteristics of the associative learning processes thought to underlie animal conditioning. One problem for associative theory arises from the finding that judgments of the causal power of a cue can be revalued retrospectively after learning episodes when that cue is not present. However, if retrieved representations of cues can support learning, retrospective revaluation is anticipated by modified versions of standard associative theories.