Statistical learning
The world is a highly structured place with a tremendous amount of statistical structure. Not all objects or events are equally likely to occur – in fact, most physically realizable objects and events almost never occur in our world. The brain uses knowledge of this statistical structure improve performance in a number of ways. It uses statistical knowledge to disambiguate uncertain and ambiguous sensory information, to efficiently code information in working memory and to better perform sensorimotor tasks. Recently, a number of researchers including ourselves have shown that the visual system, and sensory systems in general, do not use static knowledge of environmental statistics, but rather adapts its internal models as the statistics of one's local environment changes (e.g. as you go from your office to the woods). We study how the brain adapts its computations and representations to the changing statistics of visual scenes using both computational models and psychophysical experiments.
- Kwon, O. S. and Knill, D. C. (2013) The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning, Proceedings of the National Academy of Sciences, 110(11): E1064-73. PDF Supplement
- Sims, C. R., Jacobs, R. A. and Knill, D. C. (2012) An ideal observer analysis of visual working memory, Psychological Review, 119(4): 807-30. PDF
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Knill, D. C., Bondada, A. and Chhabra, Manu (2011) Flexible, task-dependent use of sensory feedback to control hand movements, Journal of Neuroscience, 31(4): 1219-1237.
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Sims, C. R., Jacobs, R. A. and Knill, D. C. (2011) Adaptive allocation of vision under competing task demands, The Journal of Neuroscience, 31(3): 928-943.
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Seydell, A., Knill, D. C. and Trommershauser, J. (2010) Adapting internal statistical models for interpreting visual cues to depth, Journal of Vision. 10 (4), Article 1.
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Seydell, A., McCann, B. C., Trommershauser, J. and Knill, D. C. (2008) Learning stochastic reward distributions in a speeded pointing task, Journal of Neuroscience, 28: 4356-4367.
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Knill, D. C. (2007) Learning Bayesian priors for depth perception, Journal of Vision, 7 (8), 1 – 20.
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Atkins, J. E., Jacobs, R. A. and Knill, D. C. (2003) Experience-Dependent Visual Cue Recalibration Based on Discrepancies Between Visual and Haptic Percepts, Vision Research., 43 (25): 2603-2613.
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