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.