Overview of Presentations

Introduction The promise of cognitive science is 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 recognize objects, perceive motion, probe their visual world for task-relevant information, and create coherent representations of their environments. At the same time, consideration of the neural bases of primate vision and human visual behavior has provided Shimon with inspiration for his computational models, leading to solutions to difficult problems in artificial intelligence. 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 addresses the theoretical foundations of perception, and draws heavily on both mathematical and experimental investigations, as did the research of David Rumelhart.