Paul Schrater, Ph.D.
My research interests include human and computer vision, planning and guiding reaches with and without visual information, and the integration of visual, haptic, and motor information during the perception-action cycle. My research approach treats problems in vision and motor control as problems of statistical inference, which has led to a concurrent interest in statistical methods that includes Bayesian (Belief) Networks, Dynamic Markov Decision Networks, Pattern Theory, Machine Learning, and other topics in statistics and pattern recognition.
(For a comprehensive list of recent publications, refer to PubMed, a service provided by the National Library of Medicine.)
- Blohm G, Kording KP, Schrater PR. A how-to-model guide for neuroscience. eNeuro. 2020 Feb 14;7(1). pii: ENEURO.0352-19.2019.
- Christopoulos V, Schrater PR. Dynamic integration of value information into a common probability currency as a theory for flexible decision making. PLoS Comput Biol. 2015 Sep 22;11(9):e1004402
- Fulvio JM, Maloney LT, Schrater PR. Revealing individual differences in strategy selection through visual motion extrapolation. Cogn Neurosci. 2015;6(4):169-79.
- Green CS, Kattner F, Siegel MH, Kersten D, Schrater PR. Differences in perceptual learning transfer as a function of training task. J Vis. 2015;15(10):5.
- Fulvio JM, Green CS, Schrater PR. Task-specific response strategy selection on the basis of recent training experience. PLoS Comput Biol. 2014;10(1):e1003425.
- Micheyl C, Schrater PR, Oxenham AJ. Auditory frequency and intensity discrimination explained using a cortical population rate code. PLoS Comput Biol. 2013;9(11):e1003336.