Including weight decay in the Rescorla-Wagner model avoids an unlikely prediction

Weight decay is a common technique for improving generalization in the field of neural networks. If weight decay is added to the Rescorla-Wagner model, a desirable result emerges in that the effects of prior learning are not expected to be permanent. This revised model accommodates an experimental finding called unblocking that was claimed to refute the Rescorla-Wagner model.