Studies of extinction of a target stimulus in compound with another excitor have produced evidence of both deepened and attenuated extinction. The present experiments attempted to resolve this discrepancy by assessing the effect of the extinction context-US association on compound extinction. In an ABC renewal situation, Experiment 1 replicated the observation of enhanced extinction as a result of nonreinforcement with a concurrent excitor relative to elemental extinction. Experiment 2 demonstrated that inflating the extinction context-US association through unsignaled, intertrial US presentations results in extinction with a concurrent excitor being less effective than elemental extinction. Experiment 3 compared compound extinction and second-order conditioning with respect to context inflation and produced results incompatible with the view that second-order conditioning caused the response increase due to compound extinction observed in Experiment 2. In Experiment 4, the concurrent excitor was shown to enhance extinction in ABC renewal and attenuate extinction in AAC renewal. The present results suggest that the effect of a concurrent excitor during extinction depends on the strength of the extinction context-US association, thereby confirming predictions of the sometimes competing retrieval model of associative acquisition and extinction. Computational modeling in Part 1 suggests that the present results are best explained by a model of associative learning that emphasizes the role of within-compound associations in Pavlovian cue interactions. Part 2 extends this view to retrospective revaluation situations by comparing simulations of a model that emphasizes within-compound associations to a model that asserts that within-compound associations are not important for retrospective revaluation.
|Advisor:||Miller, Ralph R.|
|Commitee:||Balsam, Peter D., Di Lorenzo, Patricia M., Kurtz, Kenneth J.|
|School:||State University of New York at Binghamton|
|School Location:||United States -- New York|
|Source:||DAI-B 71/10, Dissertation Abstracts International|
|Keywords:||Associative learning, Computational modeling, Context, Error reduction, Fear, Fear conditioning, Pavlovian conditioning|
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