On the Need to Improve the Way Individual Differences in Cognitive Function Are Measured With Reaction Time Tasks
On the Need to Improve the Way Individual Differences in Cognitive Function Are Measured With Reaction Time Tasks
复制标题
关于改进反应时任务测量认知功能个体差异的必要性
DOI：
10.1177/09637214221077060
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发表时间：
2022-05
影响因子：
7.2
通讯作者：
C. White;Kiah N. Kitchen
中科院分区：
文献类型：
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作者：
C. White;Kiah N. Kitchen
几十年来，测量个体在特定认知功能上的差异一直是一个重要的研究领域。这类研究的目标通常是确定是否存在与特定人群、心理障碍、健康状况或年龄组相关的认知缺陷或增强。然而，内在的困难在于，大多数认知功能是不能直接观察到的，所以研究人员依赖间接测量来推断个人的功能。最常见的方法之一是使用旨在利用特定功能的任务，并使用行为测量，如反应时间(RTS)，来评估该任务的性能。虽然这种方法很普遍，但不幸的是，它存在着反向推理的问题：特定认知功能的差异可以表现为认知功能的差异，但这并不能保证认知功能的差异就意味着认知功能的差异。我们用一项关于老化和词汇加工的研究的数据来说明这个推理问题，强调了RTS如何导致关于加工的错误结论。然后，我们讨论了如何使用选择RT模型来分析数据可以改进推理，并突出了改进模型并将其纳入分析管道的实用方法。
The measurement of individual differences in specific cognitive functions has been an important area of study for decades. Often the goal of such studies is to determine whether there are cognitive deficits or enhancements associated with, for example, a specific population, psychological disorder, health status, or age group. The inherent difficulty, however, is that most cognitive functions are not directly observable, so researchers rely on indirect measures to infer an individual’s functioning. One of the most common approaches is to use a task that is designed to tap into a specific function and to use behavioral measures, such as reaction times (RTs), to assess performance on that task. Although this approach is widespread, it unfortunately is subject to a problem of reverse inference: Differences in a given cognitive function can be manifest as differences in RTs, but that does not guarantee that differences in RTs imply differences in that cognitive function. We illustrate this inference problem with data from a study on aging and lexical processing, highlighting how RTs can lead to erroneous conclusions about processing. Then we discuss how employing choice-RT models to analyze data can improve inference and highlight practical approaches to improving the models and incorporating them into one’s analysis pipeline.