Mutual information of image fragments predicts categorization in humans: Electrophysiological and behavioral evidence

Computational models suggest that features of intermediate complexity (IC) play a central role in object categorization [Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 5, 682-687.]. The critical aspect of these features is the amount of mutual information (MI) they deliver. We examined the relation between ML human categorization and an electrophysiological response to IC features. Categorization performance correlated with MI level as well as with the amplitude of a posterior temporal potential, peaking around 270 ms. Hence, an objective MI measure predicts human object categorization performance and its underlying neural activity. These results demonstrate that informative IC features serve as categorization features in human vision. (C) 2007 Elsevier Ltd. All rights reserved.