The Influence of Face Outline, Number of Features, and Feature Saliency on Face Processing: A Mass Univariate Re-Analysis of ERP Data

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 & Itier, 2019). The present study re-analyzed these data using robust mass univariate statistics available through the LIMO toolbox (Pernet et al., 2011), 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 latency, suggesting it is an important part of the canonical face template. However, we found that this effect began around 130ms, and was maximal between the P1-N170 and N170-P2 intervals rather than on the N170 peak itself. We observed a main effect of the number of features present in parafovea, with amplitude and latency increasing as the number of features decreases. This effect was maximal around 180ms, between the N170 and P2. The ERP response was also modulated by feature type; contrary to previous findings this effect was maximal around 200ms and the P2 peak. Although we provide partial replication of previous results, the effects are more temporally distributed than previously observed. The effect of the face outline occurred prior to the N170, the effect of number of features was maximal between N170 and P2, and the effect of parafoveal feature type was most pronounced after the N170, around and after the P2 component. For all observed effects, relatively little modulation of amplitude or latency was observed on the N170 peak itself. This re-analysis demonstrates that classical ERP analysis can obscure important featural 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.