Robert A. Jacobs

Robert A. Jacobs is a Professor at the University of Rochester, holding joint appointments in the Department of Brain & Cognitive Sciences, the Department of Computer Science, and the Center for Visual Science. He is also affiliated with the Center for Computation and the Brain. His primary research focuses on computational models of human learning, memory, and decision-making in perceptual and cognitive domains, particularly through the Computational Cognition and Perception Lab.

Education: Jacobs earned a B.A. in Psychology from the University of Pennsylvania (1982), followed by an M.S. (1987) and Ph.D. in Computer and Information Science from the University of Massachusetts Amherst (1990). He completed postdoctoral training at MIT (1990-1991) with Michael Jordan and at Harvard University (1991-1992) under Stephen Kosslyn.

Research Interests: Jacobs' work spans cognitive and perceptual learning, neural computation, multisensory perception, visual short-term memory, and computational models like Bayesian ideal observers and actors. He explores how humans learn to integrate sensory cues optimally and how low-level perceptual data form high-level conceptual knowledge. Recent studies emphasize perceptual training methods, capacity limitations in memory, and the parallels between human cognition and machine learning algorithms.

Scientific Awards: Jacobs has been honored with the Best Paper Award at the 2006 Cognitive Science Society Conference for his work on motor synergies and two McDonnell-Pew Postdoctoral Fellowships (1990–1992). His research consistently addresses optimal behavior through computational models, bridging neuroscience and machine learning.

Advising & Grants: Jacobs has advised postdoctoral fellows and graduate students, including Ruoyang Hu and current collaborators such as C.R. Sims and J.S. German. His grants include NSF funding for perceptual learning and multisensory representations, as well as collaborative projects with geologists like J.A. Tarduno to study visual training in natural environments. He is a past editor of journals like Cognitive Science and Topics in Cognitive Science.

Labs & Collaborations: The Computational Cognition and Perception Lab employs experimental and computational methods to study learning and memory. Notably, the lab is no longer accepting new trainees but continues research on Bayesian models of perception and neural representations. Jacobs collaborates with interdisciplinary teams across neuroimaging, geology, and machine learning.