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Francesco Gentile, Bruno Rossion; Temporal frequency tuning of the cortical face-sensitive network for individual face perception. Journal of Vision 2012;12(9):1171. doi: 10.1167/12.9.1171.
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© ARVO (1962-2015); The Authors (2016-present)
The human brain is particularly efficient in face discrimination, a function that appears to be supported by a set of cortical areas sensitive to individual face adaptation. However, the rate at which individual faces can be discriminated remains unknown. Here we investigated the temporal frequency tuning of face-sensitive areas with functional magnetic resonance imaging (fMRI). Four observes were tested (5runs each) with an adaptation paradigm where blocks of same or different faces were presented at 11 different frequencies (1, 2, 3, 4, 5, 6, 6.66, 7.5, 8.57, 10, 12 Hz; same face identity or different identities at each cycle). In most pre-localized face-sensitive area and in particular in the right middle fusiform gyrus (FFA) we observed that, on average, the adaptation effect - computed as the difference between same and different faces - was peaked at 6 Hz. The analysis of the same and different conditions (separately) revealed that such an effect was due to both the quick drop of the signal during the transition from 4 to 6 Hz in the same condition and to a larger response for different faces (different condition) which decreased for frequencies higher than 6 Hz. The present study suggests that individual face discrimination is optimal when a face is presented around a rate of 6Hz. Interestingly, at this rate, the complete cycle of the sinusoidal stimulation of a face lasts about 166 ms which is also the latency of the earliest face identity adaptation effect as found on the face-sensitive N170 ERP component after flash stimulation (Jacques et al., J Vis, 2007). Therefore, while the exact nature of the relation between the N170 and the phenomenon reported here is still to be determined, our results support the view that the human brain requires about 160 ms to process individual facial information efficiently.
Meeting abstract presented at VSS 2012
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