Cross-validating the electrophysiological markers of early face categorization

Abstract

Human face categorization has been widely studied using electroencephalogram (EEG) and event related potentials (ERP). Within this context, the N170 ERP component has emerged as the earliest and most robust neural marker of face categorization, documented in thousands of studies conducted over nearly three decades. However, in the last decade there has been a surge in research using the fast periodic visual stimulation (FPVS) methodology to investigate face categorization. FPVS studies have consistently reported robust bilateral face categorization responses over the occipitotemporal cortex with a right hemispheric dominance, closely mirroring the N170 scalp topography. Yet, the question remains whether the neural response elicited in FPVS can be considered a proxy for the N170 or if it might be driven by different components. To address this issue, we recorded the electrophysiological signals of human observers who viewed natural images of faces and non-face objects during FPVS and three different ERP paradigms. We quantified the FPVS response in the frequency domain and extracted ERP visual components, including the P1, N170 and P2 in response to face stimuli, from both the ERP paradigms as well as the time domain of the FPVS response. Our results revealed little relationship between any single ERP component and the FPVS frequency response. Across methodologies, only the peak-to-peak differences between N170 and P2 components significantly and consistently explained the FPVS frequency response. Our data show that the FPVS frequency response is not singularly contingent on any isolated ERP component, such as the N170, but rather reflects a later complex neural integration. These findings raise crucial methodological and theoretical considerations on the relationship between FPVS and ERP responses, urging caution when interpreting the neurofunctional role of both electrophysiological signals. While both markers are indicative of human face categorization, they appear to capture different stages in this cognitive process.