Charles E. Connor, Ph.D.

Research

The brain's most computationally remarkable ability is visual object perception. Computers can beat us at math and chess, but machine vision has never come remotely close to the human capacity for identifying, categorizing, evaluating, and interacting with objects. The difficulty lies in the enormous complexity and high dimensionality of object information. Our research aims at understanding the neural algorithms that make object vision possible. We hope that our findings will not only explain the neural basis of visual experience but will someday contribute to designs for more powerful machine vision systems and brain-machine interfaces.

Ongoing lines of research: 1) How is complex 3D object structure represented? 2) How is large-scale 3D structure (buildings, landscapes) represented? 3) How is 4D object structure (shape-in-motion through time) represented? 4) How are these representations generated from retinal input signals? 5) How is object information stored, recalled, and used in decision-making? 6) How do neural representations determine visual aesthetics – what is special about the neural activity patterns evoked by beautiful sculptures or paintings?