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Statement of Interests
One of the great mysteries of science is how the human brain
translates the rich dynamically changing retinal input into useful
actions. The perception of what is out there in the world is
accomplished continually, instantaneously and usually without
conscious thought. The very effortlessness of perception disguises
the underlying difficulty of the problem. One of the surprises in
early attempts to give robots sight was that useful information about
the world could not be obtained from simple measurements of image
intensities. Images were much more complicated functions of the
objects in the world than had been expected. Extracting information
about objects and scenes is theoretically hard. The complexity of
perception is also reflected in the neurobiology of vision. The human
visual system may be one of the most complex pattern recognition
devices known. Approximately ten million retinal measurements are
sent to the brain each second, where they are processed by some
billion cortical neurons, spread over several dozen visual brain
areas. In ways that are yet to be fully understood, the visual brain
arrives at simple and unambiguous interpretations of data from the
retinal image that are useful for the decisions and actions of
everyday life. My research treats visual perception as a process of
statistical inference that transforms high-dimensional, often
ambiguous image data, into reliable estimates of object properties,
such as size and shape (Kersten, Mamassian & Yuille, 2004). These
theories lead to empirical tests regarding human perception using
behavioral and brain imaging methods (Liu & Kersten, 2003; Murray,
Boyaci & Kersten, 2006).
Selected Publications
Murray, S. O., Boyaci, H., & Kersten, D. (2006). The representation of perceived angular size in human primary visual cortex. Nat Neurosci, 9(3), 429-434.
Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian Inference. Annual Review of Psychology, 55, 271-304.
Knill, D. C., & Kersten, D. (2004). Visuomotor sensitivity to visual information about surface orientation. J Neurophysiol, 91(3), 1350-1366.
Liu, Z., & Kersten, D. (2003). Three-dimensional symmetric shapes are discriminated more efficiently than asymmetric ones. J Opt Soc Am A, 20(7), 1331-1340.
Kersten, D., & Yuille, A. (2003). Bayesian models of object perception. Current Opinion in Neurobiology, 13(2), 1-9.
Murray, S. O., Kersten, D., Olshausen, B. A., Schrater, P., & Woods, D. L. (2002). Shape perception reduces activity in human primary visual cortex. Proceedings of the National Academy of Sciences USA, 99, 15164-15169.
Bloj, M. G., Kersten, D., & Hurlbert, A. C. (1999). Perception of three-dimensional shape influences colour perception through mutual illumination. Nature, 402(6764), 877-879.
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