Organization of face and object recognition in modular neural network models
Neural Networks - Special issue on organisation of computation in brain-like systems
EMPATH: A Neural Network that Categorizes Facial Expressions
Journal of Cognitive Neuroscience
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A recent brain imaging study (Vuilleumier, Armony, Driver and Dolan 2003, Nature Neuroscience, 6, 624-631) has shown that amygdala responses to fearful expressions are preferentially driven by intact or low spatial frequency (LSF) images of faces, rather than by high spatial frequency (HSF) images. These results suggest that LSF components processed rapidly via magnocellular pathways within the visual system might be very efficiently conveyed to the amygdala for the rapid recognition of fearful expressions, perhaps via a subcortical pathway that activates the pulvinar and superior colliculus, but which bypasses any finer visual analysis of HSF cues in the striate and temporal extrastriate cortex. The purpose of this paper is to analyse the statistical properties of LSF compared with HSF and intact faces. The statistical analysis shows that the LSF components in faces, which are typically extracted rapidly by the visual system, provide a better source of information than HSF components for the correct categorisation of fearful expressions in faces. These results support the idea that visual pathways from the magnocellular visual neurons might be optimal, at a computational level, for the rapid classification of fearful emotional expressions in human faces.