Search of associative memory.

TLDR

Search of associative memory (SAM) is a general retrieval theory that blends associative network and random search models, positing cue‑dependent probabilistic sampling from a retrieval‑structured network. A quantitative computer simulation of SAM was applied to the part‑list cuing paradigm. When free recall was cued by a random subset of words, recall probability for remaining words was lower than with no cues, a pattern SAM predicted by leveraging interword associations. The paper cites 55 references.

Abstract

Describes search of associative memory (SAM), a general theory of retrieval from long-term memory that combines features of associative network models and random search models. It posits cue-dependent probabilistic sampling and recovery from an associative network, but the network is specified as a retrieval structure rather than a storage structure. A quantitative computer simulation of SAM was developed and applied to the part-list cuing paradigm. When free recall of a list of words was cued by a random subset of words from that list, the probability of recalling one of the remaining words was less than if no cues were provided at all. SAM predicted this effect in all its variations by making extensive use of interword associations in retrieval, a process that previous theorizing has dismissed. (55 ref)