CAREER: Computational mechanisms of rapid visual categorization: Models and psychophysics

Brown University, Providence RI

Investigators

Abstract

Primates can recognize objects embedded in complex natural visual scenes at a glance. Despite the ease with which we see, visual recognition -- one of the key issues addressed in computer vision -- is quite difficult for machines. Understanding which computations are performed by the visual cortex would give scientists a powerful tool to uncover key mechanisms of human perception and cognition as well as to create a new generation of 'seeing' machines. The PI's central research goal is to identify the perceptual principles and model the neural mechanisms underlying rapid visual categorization. By forcing processing to be fast, rapid visual categorization paradigms help isolate the very first pass of visual information before more complex visual routines take place. Hence, understanding 'vision at a glance' is arguably a necessary first step before studying natural everyday vision where eye movements and attentional shifts are known to play a key role. Specifically, this proposal will lead to the development of a computational neuroscience model of rapid visual recognition in the primate visual system, which is both consistent with physiological properties of cells in the visual cortex and able to predict behavioral responses (both correct and incorrect responses as well as reaction times) from human participants across a range of conditions. The proposed model will integrate recent developments in computational models of vision and decision making with large-scale machine learning techniques. New stimulus sets will be generated, which are optimally tailored for testing among alternative visual representations and computations against human psychophysics data. These experiments will, in turn, enable the refinement of computational models. The computational models developed as part of this proposal will be integrated in courses and disseminated broadly via a web graphical interface. Overall the interdisciplinary nature of the proposal will give students the opportunity to experience a research environment that crosses traditional boundaries across disciplines and departments. Increased undergraduate participation in computational neuroscience will help integrate this area into the mainstream computer science and neuroscience curricula.