Impact of face outline, parafoveal feature number and feature type on early face perception in a gaze-contingent paradigm: A mass-univariate re-analysis of ERP data

Q4 Neuroscience
Seth B. Winward, James Siklos-Whillans, Roxane J. Itier
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引用次数: 1

Abstract

Recent ERP research using a gaze-contingent paradigm suggests the face-sensitive N170 component is modulated by the presence of a face outline, the number of parafoveal facial features, and the type of feature in parafovea (Parkington and Itier, 2019). The present study re-analyzed these data using robust mass univariate statistics available through the LIMO toolbox, allowing the examination of the ERP signal across all electrodes and time points. We replicated the finding that the presence of a face outline significantly reduced ERP latencies and amplitudes, suggesting it is an important cue to the prototypical face template. However, we found that this effect began around 114 ms, and was maximal during the P1-N170 and N170-P2 intervals. The number of features present in parafovea also impacted the entire waveform, with systematic reductions in amplitude and latency as the number of features increased. This effect was maximal around 120 ms during the P1-N170 interval and around 170 ms between the N170 and P2. The ERP response was also modulated by feature type; contrary to previous findings this effect was maximal around 200 ms and the P2 peak. Although we provide partial replication of the previous results on the N170, the effects were more temporally distributed in the present analysis. These effects were generally maximal before and after the N170 and were the weakest at the N170 peak itself. This re-analysis demonstrates that classical ERP analysis can obscure important aspects of face processing beyond the N170 peak, and that tools like mass univariate statistics are needed to shed light on the whole time-course of face processing.

面部轮廓、旁中央凹特征数量和特征类型对注视条件下早期面部感知的影响:ERP数据的大规模单变量再分析
最近的ERP研究使用了凝视随机范式,表明面部敏感的N170成分受到面部轮廓、旁中央凹面部特征数量和旁中央凹特征类型的调节(Parkington和Itier, 2019)。本研究使用LIMO工具箱提供的强大的单变量统计数据重新分析了这些数据,允许检查所有电极和时间点的ERP信号。我们重复了这一发现,即面部轮廓的存在显著降低了ERP的潜伏期和振幅,这表明它是对原型面部模板的重要提示。然而,我们发现这种效应在114 ms左右开始,并在P1-N170和N170-P2区间达到最大。副波峰中出现的特征数量也会影响整个波形,随着特征数量的增加,振幅和延迟会系统性地减少。这种效应在P1-N170间隔的120毫秒左右达到最大值,在N170和P2间隔的170毫秒左右达到最大值。特征类型对ERP反应也有调节作用;与先前的发现相反,这种效应在200 ms和P2峰时达到最大。虽然我们在N170上提供了先前结果的部分复制,但在本分析中,效果更具有时间分布性。这些效应一般在N170前后最大,在N170峰本身最弱。这一重新分析表明,经典的ERP分析可能会掩盖N170峰值以外的人脸处理的重要方面,并且需要大量单变量统计等工具来阐明人脸处理的整个时间过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuroimage. Reports
Neuroimage. Reports Neuroscience (General)
CiteScore
1.90
自引率
0.00%
发文量
0
审稿时长
87 days
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