title: Accounting for occurrences: a new view of the use of contingency information in causal judgment.
abstract: When people make causal judgments from contingency information, a principal aim is to account for occurrences of the outcome. When 2 causes are under consideration, the capacity of either to account for occurrences is judged from how likely the cause is to be present when the outcome occurs and from the rate at which the outcome occurs when that cause alone is present, which gives an estimate of the strength of the cause. These propositions are formalized in a weighted averaging model, which successfully predicted several judgmental phenomena not predicted by other models of causal judgment. These include a tendency for judgment of one cause (A) to be reduced as the number of occurrences of when only the other one (B) increases and a tendency for A to receive higher judgments than B if A is better able to account for occurrences than B is even if B has a higher contingency with the outcome than A does. Overshadowing, a tendency for judgments of B to be depressed if A has a higher contingency, is weak or absent when B is better able to account for occurrences than A. Results of several experiments support these and related predictions derived from the accounting for occurrences hypothesis.
authors.affiliation: School of Psychology, Cardiff University, Cardiff, Wales, UK, CF10 3AT. whitepa@cardiff.ac.uk
authors.forename: Peter A
authors.initials: PA
authors.lastname: White
mesh_terms: D001245:Association Learning; D015984:Causality; D006801:Humans; D007600:Judgment; D016013:Likelihood Functions; D011337:Probability Learning; D011340:Problem Solving