A theory of memory retrieval.

TLDR

Memory retrieval is conceptualized as a resonance process, allowing neural network and semantic memory models to interface with the theory. The authors develop a theory of memory retrieval applicable across multiple experimental paradigms. The model posits that a probe item activates a search set via relatedness, with evidence accumulated in parallel through continuous random walks, and decisions self‑terminate on matches or exhaustively on mismatches. The model accurately predicts accuracy, reaction times, error latency, and distributions, and its application to four recognition paradigms and speed‑accuracy trade‑offs provides a comparative framework.

Abstract

A theory of memory retrieval is developed and is shown to apply over a range of experimental paradigms. Access to memory traces is viewed in terms of a resonance metaphor. The probe item evokes the search set on the basis of probe-memory item relatedness, just as a ringing tuning fork evokes sympathetic vibrations in other tuning forks. Evidence is accumulated in parallel from each probe-memory item comparison, and each comparison is modeled by a continuous random walk process. In item recognition, the decision process is self-terminating on matching comparisons and exhaustive on nonmatching comparisons. The mathematical model produces predictions about accuracy, mean reaction time, error latency, and reaction time distributions that are in good accord with experimental data. The theory is applied to four item recognition paradigms (Sternberg, prememorized list, study-test, and continuous) and to speed-accuracy paradigms; results are found to provide a basis for comparison of these paradigms. It is noted that neural network models can be interfaced to the retrieval theory with little difficulty and that semantic memory models may benefit from such a retrieval scheme.