Neural-network simulations of two context-dependence phenomena.

This paper describes simulations of two context-dependence phenomena in Pavlovian conditioning, using a neural-network model that draws on knowledge from neuroscience and makes no distinction between operant and respondent learning mechanisms. One phenomenon is context specificity or the context-shift effect, the decrease of conditioned responding (CR) when the conditioned stimulus (CS) is tested in a context different from the one in which it had been paired with the unconditioned stimulus (US). The other effect is renewal, the recovery of CR in the training context after extinction in another context. For specificity (simulation 1), two neural networks were first given 200 CS-US pairings in a context. Then, the CS was tested either in the training context or a new context. Output activations in the new context were substantially lower. For renewal (simulation 2), two networks were first given 200 CS-US pairings in a context, then 100 extinction trials in either the same context or a new one, and then tested back in the training context. Output activations during the test phase were substantially higher after extinction in a new context. The results are interpreted in terms of the dynamics of activations and weights.