Physiologically inspired neural model for the encoding of face spaces

The neural principles of the encoding of face spaces in visual cortex are still unclear and multiple competing theories have been proposed. Based on new electrophysiological data from macaque area IT we test two models realizing example-based and norm-referenced encoding. Comparing the experimentally measured tuning properties with predictions from the two models we find a better agreement for the norm-referenced encoding model. This suggests that a majority of IT neurons might represent deviations from a norm face, which is determined by an average over the distribution of typically occurring faces.