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基于认知实验的速度-准确性权衡层次模型

A Speed-Accuracy Tradeoff Hierarchical Model Based on Cognitive Experiment.

作者信息

Guo Xiaojun, Luo Zhaosheng, Yu Xiaofeng

机构信息

School of Psychology, Jiangxi Normal University, Nanchang, China.

出版信息

Front Psychol. 2020 Jan 8;10:2910. doi: 10.3389/fpsyg.2019.02910. eCollection 2019.

Abstract

Most tests are administered within an allocated time. Due to the time limit, examinees might have different trade-offs on different items. In educational testing, the traditional hierarchical model cannot adequately account for the tradeoffs between response time and accuracy. Because of this, some joint models were developed as an extension of the traditional hierarchical model based on covariance. However, they cannot directly reflect the dynamic relationship between response time and accuracy. In contrast, response moderation models took the residual response time as the independent variable of the response model. Nevertheless, the models enlarge the time effect. Alternatively, the speed-accuracy tradeoff (SAT) model is superior to other experimental models in the SAT experiment. Therefore, this paper incorporates the SAT model with the traditional hierarchical model to establish a SAT hierarchical model. The results demonstrated that the Bayesian Markov chain Monte Carlo (MCMC) algorithm performed well in the SAT hierarchical model of parameters by using simulation. Finally, the deviance information criterion (DIC) more preferred the SAT hierarchical model than other models in empirical data. This means that it is indispensable to add the effect of response time on accuracy, but likewise should limit the effect on the empirical data.

摘要

大多数测试是在规定时间内进行的。由于时间限制,考生在不同题目上可能会有不同的权衡。在教育测试中,传统的分层模型无法充分解释答题时间和准确性之间的权衡。因此,一些联合模型作为基于协方差的传统分层模型的扩展而被开发出来。然而,它们不能直接反映答题时间和准确性之间的动态关系。相比之下,反应调节模型将剩余答题时间作为反应模型的自变量。尽管如此,这些模型扩大了时间效应。另外,速度 - 准确性权衡(SAT)模型在SAT实验中优于其他实验模型。因此,本文将SAT模型与传统分层模型相结合,建立了一个SAT分层模型。结果表明,通过模拟,贝叶斯马尔可夫链蒙特卡罗(MCMC)算法在SAT分层模型的参数估计中表现良好。最后,在实证数据中,离差信息准则(DIC)更倾向于SAT分层模型而不是其他模型。这意味着增加答题时间对准确性的影响是必不可少的,但同样也应该限制其对实证数据的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49d9/6960267/bc059a0a4a4b/fpsyg-10-02910-g001.jpg

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