title: Causal inferences as perceptual judgements.
abstract: We analyze how subjects make causal judgements based on contingency information in two paradigms. In the discrete paradigm, subjects are given specific information about the frequency a, with which a purported cause occurs with the effect; the frequency b, with which it occurs without the effect; the frequency c, with which the effect occurs when the cause is absent; and the frequency d, with which both cause and effect are absent. Subjects respond to P1 = a/(a + b) and P2 = c/(c + d). Some subjects' ratings are just a function of P1 while others are a function of delta P = P1-P2. Subjects' post-experiment reports are accurate reflections of which model they use. Combining these two types of subjects results in data well fit by the weighted delta P model (Allan, 1993). In the continuous paradigm, subjects control the purported causes (by clicking a mouse) and observe whether an effect occurs. Because cause and effects occur continuously in time, it is not possible to explicitly pair causes and effects. Rather, subjects report that they are responding to the rate at which the effects occur when they click versus when they do not click. Their ratings are a function of rates and not probabilities. In general, we argue that subjects' causal ratings are judgments of the magnitude of perceptually salient variables in the experiment.
authors.affiliation: Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213 USA.
authors.forename: J R
authors.initials: JR
authors.lastname: Anderson
authors.forename: C F
authors.initials: CF
authors.lastname: Sheu
mesh_terms: D000328:Adult; D001245:Association Learning; D001499:Bayes Theorem; D003210:Concept Formation; D064420:Drug-Related Side Effects and Adverse Reactions; D005260:Female; D006801:Humans; D008297:Male; D011939:Mental Recall; D015233:Models, Statistical; D011337:Probability Learning; D011340:Problem Solving