title: Cue interaction effects in causal judgement: an interpretation in terms of the evidential evaluation model.
abstract: In judging the extent to which a cue causes an outcome, judgement can be affected by information about other cues that are correlated with the one being judged. These cue interaction effects have usually been interpreted in terms of associative learning processes. I propose that a different model of causal judgement, the evidential evaluation model, offers a viable alternative interpretation of cue interaction phenomena. Under the evidential evaluation model, instances of contingency information are interpreted as evidence, which is confirmatory, disconfirmatory, or irrelevant for the cue being judged. When two cues co-occur in a set of instances the evidential value of the instances for one of them is determined by three factors: the proportion of confirming instances in the set; disambiguation value, which concerns the relation between the set of information and prior beliefs about the co-occurring cue; and confirmation value, which concerns the relation between the set of information and prior beliefs about the cue being judged. Any previous judgement of the cue is then modified in the light of these. It is shown that this model can account for all the cue interaction phenomena that have been investigated in studies of human causal judgement. The model also generates novel predictions, and the results of three experiments give support to these predictions. It is also shown that several other current models of causal judgement fail to predict a key result from Experiment 3.
authors.affiliation: School of Psychology, Cardiff University, Cardiff, Wales, UK. whitepa@cardiff.ac.uk
authors.forename: Peter A
authors.initials: PA
authors.lastname: White
mesh_terms: D000704:Analysis of Variance; D001245:Association Learning; D001288:Attention; D001499:Bayes Theorem; D015984:Causality; D003463:Cues; D006801:Humans; D007600:Judgment; D008960:Models, Psychological; D015233:Models, Statistical; D011337:Probability Learning; D011340:Problem Solving