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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.
