A Bayesian Race Model for Recognition Memory
识别记忆的贝叶斯竞赛模型AI 解读 ↓
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识别记忆的贝叶斯竞赛模型
- 研究问题
- 在困难的识别记忆实验中，若刺激处理未必形成记忆痕迹，如何同时解释反应时间、回答准确性及个体差异？
- 方法与思路
- 作者建立贝叶斯层级模型，以随机成分决定痕迹是否形成，用最小伽马竞赛模型描述反应时间与准确性，并纳入辅助效应。
- 材料陈述的贡献
- 摘要称预测检查显示模型拟合良好，边际似然评估显示其预测表现优于近似 Weibull 模型，并能容纳多类实验效应。
- 阅读关注点
- 应核对痕迹形成概率与竞赛参数的可识别性、编码及猜测成分的设定，以及预测检查和边际似然比较的具体实施。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；未给出实验规模、先验敏感性或比较幅度，不能将特定模型比较推广为普遍优越或推断刊本变化。
原文摘要
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.