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.

中文翻译：

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关于改进用反应时间任务测量认知功能个体差异的方法的必要性

几十年来，测量特定认知功能的个体差异一直是一个重要的研究领域。此类研究的目标通常是确定是否存在与特定人群、心理障碍、健康状况或年龄组等相关的认知缺陷或增强。然而，固有的困难在于大多数认知功能无法直接观察到，因此研究人员依靠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反应时间 (RT) 等行为测量来评估该任务的绩效。尽管这种方法很普遍，但不幸的是它存在反向推理的问题：给定认知功能的差异可以表现为 RT 的差异，但这并不能保证 RT 的差异意味着该认知功能的差异。我们用来自老化和词汇处理研究的数据来说明这个推理问题，强调 RTs 如何导致关于处理的错误结论。然后，我们讨论如何使用选择 RT 模型来分析数据可以改进推理，并强调改进模型并将它们纳入分析管道的实用方法。