Amnesia and the declarative/nondeclarative distinction: a recurrent network model of classification, recognition, and repetition priming.

A key claim of current theoretical analyses of the memory impairments associated with amnesia is that certain distinct forms of learning and memory are spared. Supporting this claim, B. J. Knowlton and L. R. Squire found that amnesic patients and controls were indistinguishable in their ability to learn about and classify strings of letters generated from a finite-state grammar, but that the amnesics were impaired at recognizing the training strings. We show, first, that this pattern of results is predicted by a single-system connectionist model of artificial grammar learning (AGL) in which amnesia is simulated by a reduced learning rate. We then show in two experiments that a counterintuitive assumption of this model, that classification and recognition are functionally identical in AGL, is correct. In three further simulation studies, we demonstrate that the model also reproduces another type of dissociation, namely between recognition memory and repetition priming. We conclude that the performance of amnesic patients in memory tasks is better understood in terms of a nonselective, rather than a selective, memory deficit.