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Can the Implicit Association Test Serve as a Valid Measure of Automatic Cognition? A Response to Schimmack (2021)

机译:隐性关联测试是否可以作为自动认知的有效测量? 对施马克的回应(2021)

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Much of human thought, feeling, and behavior unfolds automatically. Indirect measures of cognition capture such processes by observing responding under corresponding conditions (e.g., lack of intention or control). The Implicit Association Test (IAT) is one such measure. The IAT indexes the strength of association between categories such as “planes” and “trains” and attributes such as “fast” and “slow” by comparing response latencies across two sorting tasks (planes–fast/trains–slow vs. trains–fast/planes–slow). Relying on a reanalysis of multitrait–multimethod (MTMM) studies, Schimmack (this issue, p. 396) argues that the IAT and direct measures of cognition, for example, Likert scales, can serve as indicators of the same latent construct, thereby purportedly undermining the validity of the IAT as a measure of individual differences in automatic cognition. Here we note the compatibility of Schimmack’s empirical findings with a range of existing theoretical perspectives and the importance of considering evidence beyond MTMM approaches to establishing construct validity. Depending on the nature of the study, different standards of validity may apply to each use of the IAT; however, the evidence presented by Schimmack is easily reconcilable with the potential of the IAT to serve as a valid measure of automatic processes in human cognition, including in individual-difference contexts.
机译:人类的许多思想、情感和行为都是自动展开的。认知的间接测量通过观察相应条件下的反应(例如,缺乏意图或控制)来捕捉这些过程。内隐联想测验(IAT)就是这样一种测量方法。IAT通过比较两个排序任务(平面–快速/列车–慢速vs.列车–快速/飞机–慢速)的响应延迟,对“飞机”和“列车”等类别与“快速”和“慢速”等属性之间的关联强度进行索引。希马克(Schimmack,本期,第396页)通过对多训练-多方法(MTMM)研究的再分析,认为IAT和认知的直接测量,例如利克特量表,可以作为相同潜在结构的指标,从而据称破坏了IAT作为自动认知个体差异测量的有效性。在这里,我们注意到Schimmack的实证研究结果与一系列现有理论观点的兼容性,以及在MTMM方法之外考虑证据以建立结构效度的重要性。根据研究的性质,每次使用IAT可能适用不同的有效性标准;然而,Schimmack提出的证据很容易与IAT的潜力相协调,IAT可以作为人类认知中自动过程的有效衡量标准,包括在个体差异语境中。

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