A Bayesian Race Model for Recognition Memory

Sungmin Kim, Kevin Potter, Peter F. Craigmile, Mario Peruggia and Trisha Van Zandt

Journal of the American Statistical Association, 2017, vol. 112, issue 517, 77-91

Abstract: Many psychological models use the idea of a trace, which represents a change in a person’s cognitive state that arises as a result of processing a given stimulus. These models assume that a trace is always laid down when a stimulus is processed. In addition, some of these models explain how response times (RTs) and response accuracies arise from a process in which the different traces race against each other. In this article, we present a Bayesian hierarchical model of RT and accuracy in a difficult recognition memory experiment. The model includes a stochastic component that probabilistically determines whether a trace is laid down. The RTs and accuracies are modeled using a minimum gamma race model, with extra model components that allow for the effects of stimulus, sequential dependencies, and trend. Subject-specific effects, as well as ancillary effects due to processes such as perceptual encoding and guessing, are also captured in the hierarchy. Predictive checks show that our model fits the data well. Marginal likelihood evaluations show better predictive performance of our model compared to an approximate Weibull model. Supplementary materials for this article are available online.

Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1080/01621459.2016.1194844

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