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使用认知模型对复杂数据集的协方差结构进行建模:在个体差异和认知能力遗传性方面的应用。

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability.

作者信息

Evans Nathan J, Steyvers Mark, Brown Scott D

机构信息

Department of Psychology, University of Amsterdam.

Department of Cognitive Sciences, University of California, Irvine.

出版信息

Cogn Sci. 2018 Jun 5. doi: 10.1111/cogs.12627.

Abstract

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision-making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies-that the heritability of cognitive ability is based on cognitive processing speed-may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.

摘要

理解认知表现中的个体差异是理解潜在认知过程的变化如何导致任务表现变化的重要组成部分。然而,在以往的研究中,对决策过程组成部分(如认知处理速度、反应谨慎程度和运动执行速度)的个体差异探索一直有限。在此,我们评估决策过程组成部分的遗传力,遗传力一直是认知其他领域个体差异研究的一个共同方面。重要的是,以往关于认知遗传力研究的一个局限性是潜在假设,即反应时间的变异性仅反映认知处理速度的变异性。由于谨慎程度和运动执行速度对观察到的反应时间的混杂影响,这一假设在其他领域一直存在问题。我们扩展了一个决策认知模型,以在双生子研究范式中考虑相关性结构。这种方法可以分别量化对反应时间遗传力的不同贡献。利用来自人类连接组计划的数据,我们发现了反应谨慎程度具有遗传力的有力证据,而认知处理速度和运动执行速度具有遗传力的证据则更为模糊。我们的研究表明,以往研究中的假设——认知能力的遗传力基于认知处理速度——可能是不正确的。更一般地说,我们的方法为未来复杂数据研究提供了一条有用的途径,这类研究旨在分析来自不同相关数据源的认知特征,无论这种关系是人与人之间、任务之间、实验阶段之间还是测量方法之间的关系。

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