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Faces are a class of visual stimuli with unique significance, for a varietyof reasons. They are ubiquitous throughout the course of a person's life, andface recognition is crucial for daily social interaction. Faces are also unlikeany other stimulus class in terms of certain physical stimulus characteristics.Furthermore, faces have been empirically found to elicit certain characteristicbehavioral phenomena, which are widely held to be evidence of "holistic"processing of faces. However, little is known about the neural mechanismsunderlying such holistic face processing. In other words, for the processing offaces by the primate visual system, the input and output characteristics arerelatively well known, but the internal neural computations are not. The mainaim of this work is to further the fundamental understanding of what causes thevisual processing of faces to be different from that of objects. In thiscomputational modeling work, we show that a single factor - "neural tuningsize" - is able to account for three key phenomena that are characteristic offace processing, namely the Composite Face Effect (CFE), Face Inversion Effect(FIE) and Whole-Part Effect (WPE). Our computational proof-of-principleprovides specific neural tuning properties that correspond to thepoorly-understood notion of holistic face processing, and connects these neuralproperties to psychophysical behavior. Overall, our work provides a unified andparsimonious theoretical account for the disparate empirical data onface-specific processing, deepening the fundamental understanding of faceprocessing.

Neural tuning size is a key factor underlying holistic face processing