Stable Individual Differences in Occasion Setting

Abstract. In the current investigation, we classified participants as inhibitors or non-inhibitors depending on the extent to which they showed conditioned inhibition in a context that had been used for extinction of a conditioned response. This classification enabled us to predict participant responses in a second experiment which used a different design and a different experimental task. In the second experiment a feature-negative discrimination survived reversal training of the feature to a greater extent in the non-inhibitors than in the inhibitors and this result was supported by Bayesian analyses. We propose that the fundamental distinction between inhibitors and non-inhibitors is based on a tendency to utilize first-order (direct associations) or second-order (occasion-setting) strategies when faced with ambiguous information and that this classification is a stable individual differences attribute.

Reduced Summation with Common Features in Causal Judgments

In three experiments human participants received training in a causal judgment task. After learning which patterns were associated with an outcome, participants rated the likelihood of the outcome in the presence of a novel combination of the patterns. The first two experiments used two conditions in which two visual patterns were associated with the outcome. In one condition these patterns shared a common feature. The third experiment only used the common feature condition. According to an elemental theory ( Rescorla & Wagner, 1972 ) the response to the novel test pattern should have exceeded that made to the individual training patterns, a summation effect, and this effect should have been reduced by the addition of a common feature. Summation was observed but since the common feature condition abolished, rather than merely reduced, summation the results were not consistent with the Rescorla-Wagner Model (RWM) nor with a configural alternative ( Pearce, 1994 ). Instead, it is necessary to consider models which allow the possibility of both elemental and configural strategies in causal learning. The Replaced Elements Model ( Wagner, 2003 ) is a development of the RWM which can best predict the patterns of summation and summation failure in these experiments.

Multimodal discrimination learning in humans: evidence for configural theory

Human contingency learning was used to compare the predictions of configural and elemental theories. In three experiments, participants were required to learn which indicators were associated with an increase in core temperature of a fictitious nuclear plant. Experiments 1 and 2 investigated the rate at which a triple-element stimulus (ABC) could be discriminated from either single-element stimuli (A, B, and C) or double-element stimuli (AB, BC, and AC). Experiment 1 used visual stimuli, whilst Experiment 2 used visual, auditory, and tactile stimuli. In both experiments the participants took longer to discriminate the triple-element stimulus from the more similar double-element stimuli than from the less similar single-element stimuli. Experiment 3 tested for summation with stimuli from either a single or multiple modalities, and summation was found only in the latter case. Thus, the pattern of results seen in Experiments 1 and 2 was not dependent on whether the stimuli were single modal or multimodal, nor was it dependent on whether the stimuli could elicit summation. This pattern of results is consistent with predictions derived from Pearce's (1987, 1994) configural theory.

Evaluation and development of a connectionist theory of configural learning

A configural theory of associative learning is described that is based on the assumption that conditioning results in associations between the unconditioned stimulus and a representation of the entire pattern of stimulation that was present prior to its delivery. Configural theory was formulated originally to account for generalization and discrimination in Pavlovian conditioning. The first part of the article demonstrates how this theory can be used to explain results from studies of overshadowing, blocking, summation, and discrimination learning. The second part of the article shows how the theory can be developed to explain a broader range of phenomena, including mediated conditioning, reinforcer devaluation effects, the differential outcomes effect, acquired equivalence, sensory preconditioning, and structural discriminations.