| Abstract: | Describes a theory in which 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 necessitates a 2-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. (40 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved) |
