Lisa Stacchi¹, Fazilet Zeynep Yildirim-Keles¹, Roberto Caldara¹; ¹University of Fribourg, Switzerland

The N170 event-related potential (ERP) is the most widely investigated neurofunctional marker of early face categorization. In the past ten years, there has been a surge in research using the fast periodic visual stimulation (FPVS) methodology to delve into 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 topography. Yet, the precise neurofunctional correspondence between these two electrophysiological markers of face categorization remains elusive. To address this issue, we recorded the electrophysiological signals of human observers who viewed natural images of faces and objects using both ERP and FPVS paradigms. In the FPVS paradigm, participants were exposed to rapid presentations of objects with periodically intervening face stimuli, while in the ERP paradigm faces and objects were presented in isolation. 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 paradigm as well as the time domain of the FPVS response. Only the N170-P2 peak-to-peak amplitude recorded in the ERP paradigm significantly explained the amplitude of the FPVS frequency response. The N170-P2 peak-to-peak amplitude was also significantly associated with the respective component in the FPVS time domain, while the N170 was not. Altogether, 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. Both markers relate to distinct neural computations and dynamics of how the human brain processes faces.

Acknowledgements: This work was supported with funding from the Swiss National Science Foundation awarded to RC (10001C_201145).