Winward Seth B, Siklos-Whillans James, Itier Roxane J
Cognitive Neuroscience Area, Psychology Department, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada.
Neuroimage Rep. 2022 Dec 5;2(4):100148. doi: 10.1016/j.ynirp.2022.100148. eCollection 2022 Dec.
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.
最近使用注视依赖范式的事件相关电位(ERP)研究表明,对面部敏感的N170成分受到面部轮廓的存在、中央凹周围面部特征的数量以及中央凹周围特征类型的调节(帕金顿和伊蒂尔,2019年)。本研究使用通过LIMO工具箱提供的稳健的大规模单变量统计方法对这些数据进行了重新分析,从而能够检查所有电极和时间点上的ERP信号。我们重复了这一发现,即面部轮廓的存在显著缩短了ERP的潜伏期和波幅,这表明它是原型面部模板的一个重要线索。然而,我们发现这种效应在大约114毫秒时开始出现,并且在P1-N170和N170-P2区间达到最大值。中央凹周围存在的特征数量也对面部整体波形产生了影响,随着特征数量的增加,波幅和潜伏期会系统性地降低。这种效应在P1-N170区间约120毫秒时以及N170和P2之间约170毫秒时达到最大值。ERP反应也受到特征类型的调节;与之前的研究结果相反,这种效应在大约200毫秒和P2波峰时达到最大值。尽管我们部分重复了之前关于N170的研究结果,但在本分析中,这些效应在时间上的分布更为分散。这些效应通常在N170之前和之后最为明显,而在N170波峰本身则最为微弱。这种重新分析表明,传统的ERP分析可能会掩盖N170波峰之外面部加工的重要方面,并且需要像大规模单变量统计这样的工具来揭示面部加工的整个时间进程。