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Mechanisms of Face Specificity – Differentiating Speed and Accuracy in Face Cognition by Event-Related Potentials of Central Processing
Cortex ( IF 3.6 ) Pub Date : 2020-11-09 , DOI: 10.1016/j.cortex.2020.10.016
Kristina Meyer 1 , Hadiseh Nowparast Rostami 2 , Guang Ouyang 3 , Stefan Debener 4 , Werner Sommer 2 , Andrea Hildebrandt 4
Affiliation  

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.



中文翻译:

人脸专一性机制-通过中央处理的事件相关电位区分人脸认知的速度和准确性

鉴于人脸识别在社交生活中的关键作用,已经发现针对人脸的认知过程不足为奇。在以前的个体差异研究中,面部识别(FC,涉及面部的感知和识别)的速度(在简单任务中衡量)和准确性(困难任务)已显示形成不同的能力,以及不同的阶乘结构。此结果已被复制,但无法解释。为了填补这一空白,我们首先对大型样本中的人脸和房屋的容易和困难的存储任务中的速度与准确性之间的子过程进行了参数设置。然后,我们通过使用残基迭代分解(RIDE)分析了从脑电图中提取的事件相关电位(ERP),产生了与纯化P300相当的中心(C)成分。应用结构方程模型(SEM)来评估C分量延迟和幅度的面部特异性。如果轻松任务的性能依赖于对刺激内容不敏感的纯粹通用过程,则在轻松任务中记录的延迟中不应存在个体差异的特异性。但是,在艰巨的任务中,期望有特异性。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方法的C成分潜伏期均发生了特异性,但准确性更高。进一步的分析表明面部相关的C潜伏期和FC能力之间的特定关系。最后,当对单个任务进行建模时,但在对多个任务进行联合建模时,我们没有检测到简单任务在RT中的特异性。

更新日期:2020-12-01
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