The neural dynamics of face detection in the wild revealed by MVPA

Abstract

Previous magnetoencephalography/electroencephalography (M/EEG) studies have suggested that face processing is extremely rapid, indeed faster than any other object category. Most studies, however, have been performed using centered, cropped stimuli presented on a blank background resulting in artificially low interstimulus variability. In contrast, the aim of the present study was to assess the underlying temporal dynamics of face detection presented in complex natural scenes. We recorded EEG activity while participants performed a rapid go/no-go categorization task in which they had to detect the presence of a human face. Subjects performed at ceiling (94.8% accuracy), and traditional event-related potential analyses revealed only modest modulations of the two main components classically associated with face processing (P100 and N170). A multivariate pattern analysis conducted across all EEG channels revealed that face category could, however, be readout very early, under 100 ms poststimulus onset. Decoding was linked to reaction time as early as 125 ms. Decoding accuracy did not increase monotonically; we report an increase during an initial 95-140 ms period followed by a plateau ~140-185 ms-perhaps reflecting a transitory stabilization of the face information available-and a strong increase afterward. Further analyses conducted on individual images confirmed these phases, further suggesting that decoding accuracy may be initially driven by low-level stimulus properties. Such latencies appear to be surprisingly short given the complexity of the natural scenes and the large intraclass variability of the face stimuli used, suggesting that the visual system is highly optimized for the processing of natural scenes. © 2014 the authors.