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An elemental model of retrospective revaluation without within-compound associations
Patrick C Connor1, Vincent M Lolordo, Thomas P Trappenberg
1Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada, patrick.connor@dal.ca.
Abstract:
When retrospective revaluation phenomena (e.g., unovershadowing: AB+, then A-, then test B) were discovered, simple elemental models were at a disadvantage because they could not explain such phenomena. Extensions of these models and novel models appealed to within-compound associations to accommodate these new data. Here, we present an elemental, neural network model of conditioning that explains retrospective revaluation apart from within-compound associations. In the model, previously paired stimuli (say, A and B, after AB+) come to activate similar ensembles of neurons, so that revaluation of one stimulus (A-) has the opposite effect on the other stimulus (B) through changes (decreases) in the strength of the inhibitory connections between neurons activated by B. The ventral striatum is discussed as a possible home for the structure and function of the present model.
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