University of Minnesota
Department of Psychology
psych@umn.edu
612-625-2818


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Daniel J Kersten

Daniel John Kersten

Daniel J Kersten

612/625-2589
Psychology N218 Elt H 75 E River Rd

Department Affiliations

Narrative

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 directly obtained from simple measurements of image intensities. Images were much more complicated functions of the objects in the world than had been expected. The complexity of perception is also reflected in the neurobiology of vision. 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 seeks to understand visual perception as a process of statistical inference that transforms high-dimensional, ambiguous image data, into reliable estimates of object properties, such as size and shape.


Specialties

  • brain imaging
  • cognitive science
  • computational vision
  • human visual system
  • neural networks
  • neuroscience

Educational Background

  • Ph.D.: Experimental Psychology, University of Minnesota, Minneapolis, MN, 1983.
  • M.S.: Mathematics, University of Minnesota, Minneapolis, MN, 1978.
  • S.B.: Mathematics, MIT, Cambridge, MN, 1976.

Publications

  • Hegdé, J., Thompson, S., Brady, M., & Kersten, D (2012). Object Recognition in Clutter: Cortical Responses Depend on the Type of Learning. Frontiers in Human Neuroscience, doi:10.3389/fnhum.2012.00170
  • Doerschner, K., Fleming, R. W., Yilmaz, O., Schrater, P. R., Hartung, B., & Kersten, D. (2011). Visual Motion and the Perception of Surface Material. Current Biology, 21(23), 2010–2016.
  • Battaglia P, Kersten D, Schrater PR (2011). How Haptic Size Sensations Improve Distance Perception. PLoS Comput Biol 7(6): e1002080. doi:10.1371/journal.pcbi.1002080
  • Green, C. S., Benson, C., Kersten, D., & Schrater, P. (2010). ). Alterations in choice behavior by manipulations of world model. Proceedings of the National Academy of Sciences, 107(37), 16401-16406.
  • Hegdé, J., & Kersten, D. (2010). A link between visual disambiguation and visual memory. J Neurosci, 30(45), 15124-15133.
  • Kersten, D & Mamassian, P (2009). Ideal Observer Theory. In: Squire LR (ed.) Encyclopedia of Neuroscience, volume 5, 89-95.
  • Fang, F., Boyaci, H., Kersten, D., & Murray, S. O. ( (2008). Attention-dependent representation of a size illusion in human V1. Curr Biol, 18(21), 1707-1712.
  • Hegdé, J., Bart, E., & Kersten, D. (2008). Fragment-Based Learning of Visual Object Categories.
  • Boyaci, H., Fang, F., Murray, S. O., & Kersten, D. (2007). Responses to lightness variations in early human visual cortex. Curr Biol, 17(11), 989-993.
  • Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: analysis by synthesis?. Trends Cogn Sci, 10(7), 301-308.

Research Activities

  • Bottom-up and top down processing in human and computer vision, ONR.
  • Designing Visually Accessible Spaces, NIH.
  • Object Perception: Mechanisms for Resolving Ambiguity, NIH.

Professional Activities

  • Senior Editor: Vision Research , 2004 - 2007
  • Editorial board: Journal of Vision , 2000 - 2007
  • Max Planck Institute Advisory board: 1999 - 2006
  • NSF Frontiers in Computer vision: Advisory panel , 2011
  • National Eye Institute Strategic Planning Committee: 2011

Awards

  • National Institute of Health Award

Courses Taught

  • Psy 5036: Computational Vision
  • Psy 5038: Introduction to Neural Networks
  • Psy 8036: Topics in Computational Vision
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