Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.
Cogn Psychol. 2012 Dec;65(4):486-518. doi: 10.1016/j.cogpsych.2012.07.002. Epub 2012 Aug 11.
We describe a computational model to explain a variety of results in both standard and false recognition. A key attribute of the model is that it uses plausible semantic representations for words, built through exposure to a linguistic corpus. A study list is encoded in the model as a gist trace, similar to the proposal of fuzzy trace theory (Brainerd & Reyna, 2002), but based on realistically structured semantic representations of the component words. The model uses a decision process based on the principles of neural synchronization and information accumulation. The decision process operates by synchronizing a probe with the gist trace of a study context, allowing information to be accumulated about whether the word did or did not occur on the study list, and the efficiency of synchronization determines recognition. We demonstrate that the model is capable of accounting for standard recognition results that are challenging for classic global memory models, and can also explain a wide variety of false recognition effects and make item-specific predictions for critical lures. The model demonstrates that both standard and false recognition results may be explained within a single formal framework by integrating realistic representation assumptions with a simple processing mechanism.
我们描述了一个计算模型，用以解释标准和错误识别中的各种结果。该模型的一个关键属性是，它使用合理的语义表示来表示单词，这些语义表示是通过对语言语料库的接触而构建的。研究列表在模型中被编码为概要痕迹，类似于模糊痕迹理论（Brainerd & Reyna，2002）的提议，但基于组件单词的真实结构的语义表示。该模型使用基于神经同步和信息积累原理的决策过程。决策过程通过将探针与研究上下文的概要痕迹同步来运行，从而可以积累有关单词是否出现在研究列表上的信息，并且同步的效率决定了识别。我们证明，该模型能够解释经典全局记忆模型难以解释的标准识别结果，也可以解释各种错误识别效应，并对关键诱饵进行特定项目的预测。该模型表明，通过将现实的表示假设与简单的处理机制相结合，可以在单个正式框架内解释标准和错误识别结果。
