Decoding the temporal structures and interactions of multiple face dimensions using optically pumped magnetometer magnetoencephalography (OPM-MEG)

Humans possess a remarkable ability to rapidly access diverse information from others’ faces with just a brief glance, which is crucial for intricate social interactions. While previous studies using event-related potentials/fields have explored various face dimensions during this process, the interplay between these dimensions remains unclear. Here, by applying multivariate decoding analysis to neural signals recorded with optically pumped magnetometer magnetoencephalography (OPM-MEG), we systematically investigated the temporal interactions between invariant and variable aspects of face stimuli, including race, gender, age and expression. First, our analysis revealed unique temporal structures for each face dimension with high test-retest reliability. Notably, expression and race exhibited a dominant and stably maintained temporal structure according to temporal generalization analysis. Further exploration into the mutual interactions among face dimensions uncovered age effects on gender and race, as well as expression effects on race, during the early stage (around 200-300 ms post face presentation). Additionally, we observed a relatively late effect of race on gender representation, peaking around 350 ms after stimulus onset. Taken together, our findings provide novel insights into the neural dynamics underlying the multi-dimensional aspects of face perception and illuminate the promising future of utilizing OPM-MEG for exploring higher-level human cognition. Significance statement In everyday social activities, people can quickly interpret a wide range of information from others’ faces. Although converging evidence has shed light upon the neural substrates underpinning the perception of invariant and variable aspects of faces, such as race, gender, age and expression, it is still not fully understood how the information of one face dimension alters the perception of another. In this study, we utilized multivariate decoding analysis on neural activity captured through OPM-MEG during face perception. Our approach enabled a comprehensive exploration of the temporal interactions among different face dimensions, providing an improved understanding of the temporally structured neural dynamics that support the multi-dimensional face perception in the human brain.