Adaptation to statistical properties of visual scenes biases rapid categorization

Abstract

The initial categorization of complex visual scenes is a very rapid process. Here we find no differences in performance for upright and inverted images arguing for a neural mechanism that can function without involving high-level image orientation dependent identification processes. Using an adaptation paradigm we are able to demonstrate that artificial images composed to mimic the orientation distribution of either natural or man-made scenes systematically shift the judgement of human observers. This suggests a highly efficient feedforward system that makes use of “low-level” image features yet supports the rapid extraction of essential information for the categorization of complex visual scenes.