Avantage computationnel de l'information de basse fréquence spatiale dans la reconnaissance d'expressions faciales de peur par un reseau de neurones artificiels

International audienceVuilleumier, Armony, Driver & Dolan (2003) have shown that amygdala cells to fearful expressions of human faces seem to be more activated by intact or low spatial frequency (LSF) faces than high spatial frequency (HSF) faces. These fMRI results may suggest that LSF components might be processed by a subcortical pathway that is assumed to bypass the striate cortex in order to process LSF components faster than HSF components of visual stimuli. The purpose of the present paper is to test the usefulness of LSF information as compared to HSF information in a visual classification task performed by an artificial neural network and a statistical classifier. Our results show that visual information, conveyed by LSF faces, allows the connectionist model to better recognize or categorize fearful faces amongst neutral faces than HSF faces. These results suggest that high-speed connections from the magnocellular layers to the amygdala might be a fast and efficient way to perform classification of human faces with respect to their emotional expressions