In recognition paradigms, increasing the number of occurrences or presentation time in a study list of some words improves performance on these words (the item strength effect), but does not affect the performance on other words {null list strength effect). In contrast, adding new items results in a deterioration of performance on the other words {list length effect). Taken together these results place strong constraints on models of recognition memory. To explain these data an account based on optimisation to the environment is presented. A summary is given of environmental analyses which suggest that (1) the likelihood of recurrence of a word within a context increases as the number of occurrences increases; (2) the repetition rates of other words in a context has no significant effect on the recurrence probability of a word; and (3) the recurrence probability of a word drops as a function of the number of words since the last occurrence of that word. A training set which reflected these constraints was constructed and presented to an optimising connectionist network which was designed to extract recurrence statistics (the Hebbian Recurrent Network). The resultant model is able to model all three of the effects outlined above.

We develop an interactive model of human mem- ory called the Hebbian Recurrent Network ( HRN ) which integrates work in the mathematical modeling of memory with that in error correcting connection- ist networks. It incorporates the Matrix Model (Pike, 1984) into the Simple Recurrent Network (SRN, El- man, 1989). The result is an architecture which has the desirable memory characteristics of the matrix model such as low interference and massive general- ization, but which is able to learn appropriate en- codings for items, decision criteria and the control functions of memory which have traditionally been chosen a priori in the mathematical memory litera- ture. Simulations demonstrate that the HRN is well suited to a recognition task inspired by typical mem- ory peiradigms. In comparison to the SRN , the HRN is able to learn longer lists, and is not degraded sig- nificantly by increasing the vocabulary size.