Causal learning: Association versus computation

Causal learning enables humans and other animals not only to predict import ant 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 out come. However, human causal learning exhibits many of the characteristics of the associative learning processes thought to underlie animal conditioni ng. One problem for associative theory arises from the finding that judgmen ts of the causal power of a cue can be revalued retrospectively after learn ing episodes when that cue is not present. However, if retrieved representa tions of cues can support learning, retrospective revaluation is anticipate d by modified versions of standard associative theories.