A Bayesian Race Model for Recognition Memory

• 成果类型: Article
• 署名作者: Kim, Sungmin; Potter, Kevin; Craigmile, Peter F.; Peruggia, Mario; Van Zandt, Trisha
• 署名单位: University System of Ohio; Ohio State University; University System of Ohio; Ohio State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1194844
• 发表日期: 2017
• 页码: 77-91
• 关键词: signal-detection; instance theory; reaction-time; connectionist; distributions; AUTOMATICITY; frequency

摘要

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.