Four experiments examined trial sequencing effects in human contingency judgment. In Experiments 1-3, ratings of contingency between a target cue and outcome were affected by the presentation order of a series of trials distributed in 2 distinct blocks and showed a recency bias. Experiment 4 replicated this effect when the trials were partly intermixed. These recency effects are predicted by an associative learning model that computes associative strengths trial by trial and incorporates configural coding of cues but are problematic for probabilistic contrast accounts, which currently have no provision in the contingency computation for the differential weighting of trials as a function of their order of presentation.

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According to the comparator process hypothesis (Matute, Arcediano, & Miller, 1996), cue competition in the learning of between-events relationships arises if the judgement required involves a comparison between the probability of the outcome given the target cue and the probability of the outcome given the competing cue. Alternatively, other associative accounts (the Rescorla-Wagner model: Rescorla & Wagner, 1972) conceive cue competition as a learning deficit affecting the target cue-outcome association. Consequently, the comparator process hypothesis predicts that cue competition occurs in inference judgements but not in contiguity ones, for only the first type of judgement implicitly involves such a comparison. On the other hand, the Rescorla-Wagner model predicts cue competition in both inference and contiguity judgements, because it establishes no relevant role for the type of judgement in producing cue competition. In Experiments 1 and 2 we manipulated the relative validity of cues and the type of question (inference vs. contiguity) in a predictive learning task. In both experiments we found a cue competition effect, but no interaction between the relative validity of cues and the type of question, suggesting that the Rescorla-Wagner theory suffices to explain cue competition.

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Three experiments show that understanding of biases in probability judgment can be improved by extending the application of the associative-learning framework. In Experiment 1, the authors used M. A. Gluck and G. H. Bower's (1988a) diagnostic-learning task to replicate apparent base-rate neglect and to induce the conjunction fallacy in a later judgment phase as a by-product of the conversion bias. In Experiment 2, the authors found stronger evidence of the conversion bias with the same learning task. In Experiment 3, the authors changed the diagnostic-learning task to induce some conjunction fallacies that were not based on the conversion bias. The authors show that the conjunction fallacies obtained in Experiment 3 can be explained by adding an averaging component to M. A. Gluck and G. H. Bower's model.