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research interests are in visual perception, especially perceptual organization and shape; and in categorization and concept learning. In both these general areas my focus is on mathematical and computational models of human mental function.
In vision, I am interested in what makes human perceptual interpretations "make sense." Given a visual image, there are an infinite number of different ways to organize it, to group elements together, and to aggregate information in order to optimally estimate the structure of the world. The visual system is able to select from among these just that interpretation that seems most likely to provide a useful and accurate model of the world outside our heads. This idea has led me to Bayesian and other mathematical models that emphasize the maximization of simplicity in the organization of image data.
In categorization and concept learning, I am similarly interested in how the mind organizes groups of objects into coherent collections and hierarchies. In experimental work, we have found that human learners, given a set of objects to be learned, tend to form categories that are simple as possible. This idea opens up an enormous set of research questions about what perceptual features form the basis for categorization, how these features are selected in order to reduce representational complexity, and how these goals relate to the structure of the natural world.
Jacob Feldman is a Professor in the Department of Psychology and the Center for Cognitive Science (RuCCS) at Rutgers University - New Brunswick, New Jersey, USA. He holds a Ph.D. from M.I.T. in Brain and Cognitive Sciences. He has received a CAREER award from the NSF, the 2002 George Miller award for best paper in general psychology from the APA, the 2005 Troland Award from the National Academy of Sciences, as well as grant funding from NSF and NIH. His research focuses on computational and probabilistic models of human perceptual organization and concept learning.
In vision, I am interested in what makes human perceptual interpretations "make sense." Given a visual image, there are an infinite number of different ways to organize it, to group elements together, and to aggregate information in order to optimally estimate the structure of the world. The visual system is able to select from among these just that interpretation that seems most likely to provide a useful and accurate model of the world outside our heads. This idea has led me to Bayesian and other mathematical models that emphasize the maximization of simplicity in the organization of image data.
In categorization and concept learning, I am similarly interested in how the mind organizes groups of objects into coherent collections and hierarchies. In experimental work, we have found that human learners, given a set of objects to be learned, tend to form categories that are simple as possible. This idea opens up an enormous set of research questions about what perceptual features form the basis for categorization, how these features are selected in order to reduce representational complexity, and how these goals relate to the structure of the natural world.
Jacob Feldman is a Professor in the Department of Psychology and the Center for Cognitive Science (RuCCS) at Rutgers University - New Brunswick, New Jersey, USA. He holds a Ph.D. from M.I.T. in Brain and Cognitive Sciences. He has received a CAREER award from the NSF, the 2002 George Miller award for best paper in general psychology from the APA, the 2005 Troland Award from the National Academy of Sciences, as well as grant funding from NSF and NIH. His research focuses on computational and probabilistic models of human perceptual organization and concept learning.
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