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Likelihood ratio sequential sampling models of recognition memory

Citation

Osth, AF and Dennis, S and Heathcote, A, Likelihood ratio sequential sampling models of recognition memory, Cognitive Psychology, 92 pp. 101-126. ISSN 0010-0285 (2017) [Refereed Article]

Copyright Statement

Copyright 2016 Elsevier Inc.

DOI: doi:10.1016/j.cogpsych.2016.11.007

Abstract

The mirror effect – a phenomenon whereby a manipulation produces opposite effects on hit and false alarm rates – is benchmark regularity of recognition memory. A likelihood ratio decision process, basing recognition on the relative likelihood that a stimulus is a target or a lure, naturally predicts the mirror effect, and so has been widely adopted in quantitative models of recognition memory. Glanzer, Hilford, and Maloney (2009) demonstrated that likelihood ratio models, assuming Gaussian memory strength, are also capable of explaining regularities observed in receiver-operating characteristics (ROCs), such as greater target than lure variance. Despite its central place in theorising about recognition memory, however, this class of models has not been tested using response time (RT) distributions. In this article, we develop a linear approximation to the likelihood ratio transformation, which we show predicts the same regularities as the exact transformation. This development enabled us to develop a tractable model of recognition-memory RT based on the diffusion decision model (DDM), with inputs (drift rates) provided by an approximate likelihood ratio transformation. We compared this ‘‘LR-DDM" to a standard DDM where all targets and lures receive their own drift rate parameters. Both were implemented as hierarchical Bayesian models and applied to four datasets. Model selection taking into account parsimony favored the LR-DDM, which requires fewer parameters than the standard DDM but still fits the data well. These results support log-likelihood based models as providing an elegant explanation of the regularities of recognition memory, not only in terms of choices made but also in terms of the times it takes to make them.

Item Details

Item Type:Refereed Article
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Computer Perception, Memory and Attention
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Psychology and Cognitive Sciences
Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:113329
Year Published:2017
Deposited By:Psychology
Deposited On:2016-12-21
Last Modified:2017-03-28
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