People
Robert A. Jacobs
PhD,
University of Massachusetts, 1990
Professor, Brain & Cognitive Sciences, Computer Science, & the Center for Visual Science
Curriculum
Vitae
- Meliora 416
- Brain & Cognitive Sciences
- University of Rochester
- Rochester, NY 14627-0268
- (585) 275-0753 (office)
- (585) 442-9216 (fax)
- Office Hours: By appointment
Short Bio: For my undergraduate studies, I attended the University of Pennsylvania where I majored in Psychology. I spent the next two years working as a Research Assistant in a biomedical research laboratory at Rockefeller University. For graduate school, I attended the University of Massachusetts at Amherst where I earned a Ph.D. degree in Computer and Information Science (graduate advisor: Andrew Barto). I then served in two postdoc positions, one in the Department of Brain & Cognitive Sciences at the Massachusetts Institute of Technology (postdoc advisor: Michael Jordan), and the other in the Department of Psychology at Harvard University (postdoc advisor: Stephen Kosslyn). I'm currently a faculty member at the University of Rochester where my title is Professor of Brain & Cognitive Sciences, of Computer Science, and of the Center for Visual Science. I am also a member of the Center for Computation and the Brain.
The Computational Cognition and Perception Lab uses experimental and computational methodologies to study human learning and decision making in cognitive, perceptual, and motor domains. How is new information—whether its a friend's new phone number or a newly recognized visual feature that distinguishes the appearances of identical twins—acquired and how does it become established in memory? How is perceptual learning similar or different from cognitive learning and motor learning? How do people reason about environments with complex temporal dynamics? How do they choose actions in these environments so as to achieve a goal? To what extent are people's behaviors consistent with the optimal behaviors of computational models based on Bayesian statistics? Our research lab addresses these and other questions.
Current Graduate Students
We are currently seeking new graduate students and postdoctoral fellows to join our lab. If you're interested, please contact .
Manu Chhabra
Manu is interested in experimental and computational studies of dynamic decision making, especially in the setting of motor control. He uses techniques from the statistics and machine learning literatures to build "ideal actors" which are models of reasoning and decision making based on Bayesian statistics. He then studies human decision making by comparing human performances with the optimal performances of these ideal actors.
Melchi Michel
Melchi is interested in perceptual learning and sensory cue combination. In one project, he studied cue acquisition (how people learn that a sensory signal is a cue to a perceptual judgment) and found that people can only acquire new cues in certain conditions. He formalized a theory of cue acquisition based on Bayesian networks, and hypothesized that people's low-level perceptual systems are capable of parameter learning but not structure learning. In another project, he noted that people often combine information from known perceptual cues in a statistically optimal manner, and he asked whether there is something special about these cues or if people will also optimally combine information from arbitrary features. He found that people also optimally combined information from arbitrary features, indicating that known perceptual cues do not have a special status.