Year: 2008
Type: article
Abstract: The promise of cognitive science i rooted in combining computational and empirical methods. Shimon Ullman’s study of both artificial and biological vision demonstrates the power of this approach in studying the mind and brain. Among his many contributions, Shimon’s computational models have provided working systems that offer theoretical accounts of how humans recogn ize objects, perceive mo- tion, probe their visual world for task-relevant information, and create coherent representa tions of their environments. At the same time, consideration of the neural bases of pri- mate vision and human visual behavior has provided Shimon with inspiration for his computational models, lead- ing to solutions to difficult problems in artificial intelli- gence. By learning from biological systems, Shimon has created novel and influential artificial intelligence systems. Shimon is a fitting recipient of the David E. Rumelhart prize in that his research ad dresses the theoretical founda- tions of perception, and draws heavily on both mathematical and experimental investigations, as did the research of David Rumelhart.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2768453243