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利用扩散模型理解临床疾病。

Using diffusion models to understand clinical disorders.

作者信息

White Corey N, Ratcliff Roger, Vasey Michael W, McKoon Gail

机构信息

The Ohio State University.

出版信息

J Math Psychol. 2010 Feb 1;54(1):39-52. doi: 10.1016/j.jmp.2010.01.004.

Abstract

Sequential sampling models provide an alternative to traditional analyses of reaction times and accuracy in two-choice tasks. These models are reviewed with particular focus on the diffusion model (Ratcliff, 1978) and how its application can aide research on clinical disorders. The advantages of a diffusion model analysis over traditional comparisons are shown through simulations and a simple lexical decision experiment. Application of the diffusion model to a clinically-relevant topic is demonstrated through an analysis of data from nonclinical participants with high- and low-trait anxiety in a recognition memory task. The model showed that after committing an error, participants with high trait anxiety responded more cautiously by increasing their boundary separation, whereas participants with low trait anxiety did not. The article concludes with suggestions for ways to improve and broaden the application of these models to studies of clinical disorders.

摘要

序贯抽样模型为二选一任务中反应时间和准确性的传统分析提供了一种替代方法。本文对这些模型进行了综述,特别关注扩散模型(拉特克利夫,1978年)及其应用如何有助于临床疾病的研究。通过模拟和一个简单的词汇判断实验,展示了扩散模型分析相对于传统比较方法的优势。通过对识别记忆任务中具有高特质焦虑和低特质焦虑的非临床参与者的数据进行分析,证明了扩散模型在临床相关主题上的应用。该模型表明,在犯错误后,高特质焦虑的参与者通过增加边界分离做出更谨慎的反应,而低特质焦虑的参与者则没有。文章最后提出了改进和拓宽这些模型在临床疾病研究中应用的方法建议。

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