A theory for the storage and retrieval of item and associative information.

Abstract

A theory for the storage and retrieval of item and associative information is presented. In the theory, items or events are represented as random vectors. Convolution is used as the storage operation, and correlation is used as the retrieval operation. A distributed-memory system is assumed; all information is stored in a common memory vector. The theory applies to both recognition and recall and covers both accuracy and latency. Noise in the decision stage neces-sitates a two-criterion decision system, and over time the criteria converge until a decision is reached. Performance is predicted from the moments (expectation and variance) of the similarity distributions, and these can be derived from the theory. Several alternative models with varying degrees of distributed memory are considered, and expressions for signal-to-noise ratio and relative efficiency are derived. The nature of associations is a classic prob-lem in the area of human learning and mem-ory. According to the traditional view, ideas (events, items) exist separately in memory but are somehow connected or related (see, e.g., Anderson & Bower, 1973). The labeled paths of current network models are an elab-oration of these simple connections, but the essential idea is the same. The two items en-tering into an association are stored sepa-rately, and the association is the link between them. This work was supported by Natural Sciences and Engineering Research Council of Canada Grant APA 146. It was begun while I was on sabbatical leave in the laboratory of W. K. Estes at Harvard, and I am grateful to Bill Estes in particular and the department in general for their generous hospitality and stimulating environ-ment. I would also like to thank Ian Spence for several very helpful discussions in the early stages of this work, James Alexander for a very careful reading of an earlier version of this paper, and Mary Lamon for many helpful comments on this paper. Requests for reprints should be sent to Bennet B.

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