Within compound Associations Mediate the Retrospective Revaluation of Causality Judgements

The role of within-compound associations in the retrospective revaluation of causality judgements was investigated in a two-stage procedure in which the subjects were asked to learn whether or not different food stimuli caused an allergic reaction in hypothetical patients. In the compound-cue stage a number of compound cues, each consisting of a competing stimulus and a target stimulus, were associated with the reaction across a series of trials, whereas in the single-cue stage the subjects had the opportunity to learn which of the competing cues, when presented alone, caused the reaction. Each target stimulus was presented with the same competing cue across all compound trials in the consistent condition, but with a different competing cue on each trial in the varied condition. In a forward procedure, in which the single-cue stage preceded compound cue training, judgements of the causal effectiveness of the target stimuli were reduced or blocked by training them in compound with a competing cue that had been previously paired with the reaction. Moreover, the magnitude of this reduction was comparable in the consistent and varied conditions. This was not true, however, when the single- and compound-cue stages were reversed in the backward procedure. Judgements for target cues compounded with competing cues that were subsequently paired with the reaction were reduced only in the consistent condition. If it is assumed that stronger associations were formed between the competing and target stimuli during the compound-cue stage in the consistent condition than in the varied condition, this pattern suggests that the retrospective revaluation of causality judgements can be mediated by the formation of within-compound associations.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.