Psychology Department, Tufts University Medford, MA, USA.
Front Psychol. 2014 Jun 27;5:641. doi: 10.3389/fpsyg.2014.00641. eCollection 2014.
In the area of memory research there have been two rival approaches for memory measurement-signal detection theory (SDT) and multinomial processing trees (MPT). Both approaches provide measures for the quality of the memory representation, and both approaches provide for corrections for response bias. In recent years there has been a strong case advanced for the MPT approach because of the finding of stochastic mixtures on both target-present and target-absent tests. In this paper a case is made that perceptual detection, like memory recognition, involves a mixture of processes that are readily represented as a MPT model. The Chechile (2004) 6P memory measurement model is modified in order to apply to the case of perceptual detection. This new MPT model is called the Perceptual Detection (PD) model. The properties of the PD model are developed, and the model is applied to some existing data of a radiologist examining CT scans. The PD model brings out novel features that were absent from a standard SDT analysis. Also the topic of optimal parameter estimation on an individual-observer basis is explored with Monte Carlo simulations. These simulations reveal that the mean of the Bayesian posterior distribution is a more accurate estimator than the corresponding maximum likelihood estimator (MLE). Monte Carlo simulations also indicate that model estimates based on only the data from an individual observer can be improved upon (in the sense of being more accurate) by an adjustment that takes into account the parameter estimate based on the data pooled across all the observers. The adjustment of the estimate for an individual is discussed as an analogous statistical effect to the improvement over the individual MLE demonstrated by the James-Stein shrinkage estimator in the case of the multiple-group normal model.
在记忆研究领域，有两种相互竞争的记忆测量方法——信号检测理论（SDT）和多项处理树（MPT）。这两种方法都为记忆表现质量提供了衡量标准，并且都为响应偏差提供了校正。近年来，由于在目标存在和目标不存在测试中都发现了随机混合物，MPT 方法得到了强有力的支持。本文提出了一种观点，即感知检测与记忆识别一样，涉及到一系列易于用 MPT 模型表示的过程。为了将 Chechile（2004）的 6P 记忆测量模型应用于感知检测，对其进行了修改。这个新的 MPT 模型被称为感知检测（PD）模型。开发了 PD 模型的性质，并将其应用于放射科医生检查 CT 扫描的一些现有数据。PD 模型揭示了标准 SDT 分析中不存在的新特征。还通过蒙特卡罗模拟探讨了个体观察者基础上的最佳参数估计问题。这些模拟表明，贝叶斯后验分布的均值是比相应的最大似然估计（MLE）更准确的估计器。蒙特卡罗模拟还表明，仅基于单个观察者数据的模型估计可以通过考虑基于所有观察者数据的参数估计进行改进（在更准确的意义上）。讨论了对个体估计的调整，这与在多组正态模型中 James-Stein 收缩估计器对个体 MLE 的改进类似，是一种统计效应。
