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面部特异性机制——通过中央处理的事件相关电位区分面部认知中的速度和准确性。

Mechanisms of face specificity - Differentiating speed and accuracy in face cognition by event-related potentials of central processing.

作者信息

Meyer Kristina, Nowparast Rostami Hadiseh, Ouyang Guang, Debener Stefan, Sommer Werner, Hildebrandt Andrea

机构信息

Carl von Ossietzky Universität Oldenburg, Department of Psychology, Germany.

Humboldt-Universität zu Berlin, Institut für Psychologie, Germany.

出版信息

Cortex. 2021 Jan;134:114-133. doi: 10.1016/j.cortex.2020.10.016. Epub 2020 Nov 9.

Abstract

Given the crucial role of face recognition in social life, it is hardly surprising that cognitive processes specific for faces have been identified. In previous individual differences studies, the speed (measured in easy tasks) and accuracy (difficult tasks) of face cognition (FC, involving perception and recognition of faces) have been shown to form distinct abilities, going along with divergent factorial structures. This result has been replicated, but remained unexplained. To fill this gap, we first parameterized the sub-processes underlying speed vs. accuracy in easy and difficult memory tasks for faces and houses in a large sample. Then, we analyzed event-related potentials (ERPs) extracted from the EEG by using residue iteration decomposition (RIDE), yielding a central (C) component that is comparable to a purified P300. Structural equation modeling (SEM) was applied to estimate face specificity of C component latencies and amplitudes. If performance in easy tasks relies on purely general processes that are insensitive to stimulus content, there should be no specificity of individual differences in the latency recorded in easy tasks. However, in difficult tasks specificity was expected. Results indicated that, contrary to our predictions, specificity occurred in the C component latency of both speed-based and accuracy-based measures, but was stronger in accuracy. Further analyses suggested specific relationships between the face-related C latency and FC ability. Finally, we detected specificity in RTs of easy tasks when single tasks were modeled, but not when multiple tasks were jointly modeled. This suggests that the mechanisms leading to face specificity in performance speed are distinct across tasks.

摘要

鉴于人脸识别在社会生活中的关键作用,已识别出特定于面孔的认知过程也就不足为奇了。在以往的个体差异研究中,面孔认知(FC,涉及面孔的感知和识别)的速度(在简单任务中测量)和准确性(在困难任务中测量)已被证明形成了不同的能力,伴随着不同的因子结构。这一结果已得到重复,但仍未得到解释。为了填补这一空白,我们首先在一个大样本中对面孔和房屋的简单和困难记忆任务中速度与准确性背后的子过程进行了参数化。然后,我们使用残差迭代分解(RIDE)分析了从脑电图中提取的事件相关电位(ERP),产生了一个与纯化的P300相当的中央(C)成分。应用结构方程模型(SEM)来估计C成分潜伏期和振幅的面孔特异性。如果简单任务中的表现仅依赖于对刺激内容不敏感的纯粹一般过程,那么在简单任务中记录的潜伏期的个体差异就不应具有特异性。然而,在困难任务中预期会有特异性。结果表明,与我们的预测相反,基于速度和基于准确性的测量的C成分潜伏期都出现了特异性,但在准确性方面更强。进一步的分析表明,与面孔相关的C潜伏期和FC能力之间存在特定关系。最后,当对单个任务进行建模时,我们在简单任务的反应时中检测到了特异性,但在对多个任务进行联合建模时则没有。这表明导致表现速度方面面孔特异性的机制在不同任务中是不同的。

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