Paul R. Schrater

Associate Professor

Psychology & Computer Science

N218 Elliott Hall
75 E. River Rd.
Minneapolis, MN 55455
Phone: (612) 626-8638
FAX: 612-626-2079
schrater AT umn DOT edu

 schrater pic



Comp. Perception & Action Lab

About me:  

My CV as of Nov, 2011: CV (2011)

I hold a joint faculty position at the University of Minnesota, in the departments of Psychology and Computer Science

My current research interests generally involve using probabilistic methods to study issues in perception and motor control. 

Statement of Purpose

 From a single cell to the complexity of man, the story of life is one of creating an internal environment that protects against the forces of dissolution, of storing information, and of adapting to increase abilities to control its external and internal environment. Humans are the most sophisticated adaptive control engines we know of.  More than just an analogy, I believe that behavioral adaptation is a manifestation of deep control principles that are written on the history of life. Our brains have remarkably sophisticated control over almost all aspects of their own function, including what information gets in, how much food to eat in response to brain activity, even how much to learn.  From cognition to motivation, from perception to action with memory in between, I believe that it is possible to construct an integrated theory of human behavior and brain function from a core principle – optimizing our ability to predict and control our internal, external and social environment.  My research is aimed at that goal,  incorporating methods and concepts from machine learning and artificial intelligence, including probabilistic inference, hierarchy, and structure learning.  I take a structured interdisciplinary approach, putting computational theory first - using theory to drive my empirical work.  I want to make precise and testable theories of the brain’s computations and experimentally test them using both behavioral measures and brain imaging.  

Recent Courses

PSY 5018H Mathematical Models of Human Behavior, Spring 2011
CSCI 5512  Artificial Intelligence II

Selected Publications by Topic

Human Perception, Decision Theory and Human Behavior

Decision Making

  • Acuna, D., and Schrater, P.  (2010) Structure Learning in Human Sequential Decision-making. PLOS Computational Biology PLoS Computional Biol 6(12): e1001003.doi:10.1371/journal.pcbi.1001003
  • Green, C.S., Benson, C., Kersten, D., and Schrater, P. (2010) Alterations in choice behavior by manipulations of world-model.  Proceedings of the National Academy of Sciences, 107(37) 16401-16406.
  • Acuña, D. & Schrater, P. (2008). Bayesian Modeling of Human Sequential Decision-Making on the Multi-Armed Bandit Problem. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Washington, DC: Cognitive Science Society. [Details]
  • Schrater, P. and Sundareswara, R. (2006) “Theory and Dynamics of Perceptual Bistability”, Proceedings NIPS*2006. PDF
  • Schrater, P., Sodomka, E., and Sloane, C. (2005). (POSTER) Decision Making with Monetary Value Uncertainty., Society for Judgment and Decision Making. (POSTER PDF)
  • Yuille, A. L., Fang, F., Schrater, P., & Kersten, D. (2004). Human and Ideal Observers for Detecting Image Curves. In S. Thrun, L. Saul & B. Schoelkopf (Eds.), NIPS*2004. (PDF)
  • Schrater, P.R. (2003). Bayesian data fusion and credit assignment in vision and fMRI data analysis. Proc. SPIE Electronic Imaging, 5016, 24-35. (PDF)
  • Schrater, P. R., and Kersten, D. (2000). How Optimal Depth Cue Integration Depends on the Task. International Journal of Computer Vision,  40(1): 71-89. PDF
  • Schrater, P. R., and Kersten, D. (2000). Vision, Psychophysics, and Bayes. In: (R. Rao, B. Olshausen, and M. Lewicki, Ed.) Statistical Theories of the Brain, MIT Press. (PDF)

Vision and Motor Control
  • Christopolous, V. and Schrater, P. (2011) An optimal feedback control framework for grasping objects with position uncertainty. Neural Computation.
  • Battaglia PW, Di Luca M, Ernst MO, Schrater PR, Machulla T, Kersten DJ (2010). Within- and cross-modal distance information disambiguate visual size-change perception, PLoS Computational Biology
  • V.N. Christopoulos, Paul R. Schrater (2009), “Grasping objects with environmentally induced position uncertainty”, PLoS Comput Biol 5(10): e1000538. 
  • Doerschner, K., Kersten, D., and Schrater, P.R. (2010) Rapid classification of specular and diffuse reflection from image velocities, Pattern Recognition, online 15 September 2010, DOI: 10.1016/j.patcog.2010.09.007.
  • Doerschner, K., Kersten, D., and Schrater, P. (2009), Rapid Classification of surface reflectance from image velocities. The 13th International Conference on Computer Analysis of Images and Patterns, 02/09-04/09, Accepted for publication in the Springer LNCS series.
  • Zang, D., Doerschner, K., Schrater, P. (2009), Rapid inference of object rigidity and reflectance using optic flow. The 13th International Conference on Computer Analysis of Images and Patterns, 02/09-04/09, Accepted for publication in the Springer LNCS series.
  • Battaglia, P.W.,  and Schrater, P. (2007)  Humans trade off viewing time and movement duration to minimize visuomotor variability in a fast reaching task.  Journal of Neuroscience LINK
  • Schlicht, E., Schrater, P. (2007). Impact of coordinate transformation uncertainty on human sensorimotor control. Journal of Neurophysiology.  LINK
  • Schlicht, E., Schrater, P. (2007). Reach-to-grasp trajectories adjust for uncertainty in the location of visual targets, Experimental Brain Research.  LINK
  • Schlicht, E.J., & Schrater, P.R.  Planning for uncertainty: Bayesian model for human reach and grasp.
  • Kallie, C., Schrater, P., Legge, G. (2006) Variability in stepping direction explains veering behavior of blind walkers.  JEP:HPP 33(1) 183–200. (PDF)
  • Battaglia, P. W., Schrater, P., & Kersten, D. (2005).  Auxiliary object knowledge influences visually-guided interception behavior.  Proc. 2nd Symp. APGV, pp. 145 - 152. (PDF)
  • Hartung, B., Schrater, P., Kersten, D., Bülthoff, H., Franz, V.H. (2005) Is prior knowledge of object geometry used in visually guided reaching? Journal of Vision,
Paul R. Schrater, Erik J. Schlicht  (2006) Internal models for object manipulation: Determining optimal contact locations.  Department of Computer Science, University of Minnesota, Tech Report 06-003.

Matoba, A. and Schrater, P. (2005) An Information-Theoretic Approach to Human Reach Path Analysis,  Computational Neural Systems 2005, Madison, WI.

  • Sundareswara, R. and Schrater, P. (2007)  Perceptual multistability predicted by search model for Bayesian decisions,  Journal of Vision
  • Schrater, P. and Sundareswara, R. (2006) “Theory and Dynamics of Perceptual Bistability”, Proceedings NIPS*2006.  PDF
Motion Perception
  • Carlson, T. A., Schrater, P., & He, S. (2006). Floating square illusion: Perceptual uncoupling of static and dynamic objects in motion.  Journal of Vision, 6(2), 132-144,
  • Schrater, P.R., Knill, D.C., and Simoncelli, E.P. (2001). Perceiving visual expansion without optic flow. Nature, April, 12., 410, 816-819. PDF
  • Schrater, P.R., Knill, D.C., and Simoncelli, E.P. (2000). Mechanisms of visual motion detection. Nature Neuroscience, 3(1), 64-68. PDF
  • Schrater P R, and Simoncelli E P (1998) Local velocity representation: evidence from motion adaptation., Vision Research, 38, 3899-3912. PDF

Machine Vision/Learning

Acuña, D. & Schrater, P. (2009). Improving Bayesian Reinforcement Learning using Transition Abstraction. ICML/UAI/CLT Workshop on Abstraction in Reinforcement Learning 2009.

Christopoulos, V. and Schrater, P. Handling shape and contact uncertainty in grasping planar objects. To appear: International Conference on Intelligent Robots and Systems (IROS'07). PDF preprint

  • Veeraraghavan, H., Schrater, P.,R., Papanikolopoulos, N. (2007) Learning Dynamic Event Descriptions in Image Sequences. IEEE Proceedings on Computer Vision and Pattern Recognition, June, 2007. (PDF preprint)
  • Veeraraghavan, Harini, Schrater, Paul and Papanikolopoulos, Nikos (2006). Robust Target Detection and Tracking through Integration of Motion, Color, and Geometry, Compuier Vision and Image Understanding, 103(2) 2006, 121-138. (PDF)
  • Veeraraghavan, H., Schrater, P.,R., Papanikolopoulos, N. (2006) Adaptive Geometric Templates for Feature Matching.  Proceedings 2006 IEEE International Conference on Robotics and Automation 2006 (ICRA-2006), May 15-19, 2006. pp. 3393-3398. (PDF)
  • Veeraraghavan, H., Schrater, P.,R., Papanikolopoulos, N. (2005) Switching Kalman Filter-Based Approach for Tracking and Event Detection at Traffic Intersections: Proceedings of the 2005 IEEE International Symposium on Intelligent Control, pp. 1167-72. (PDF)
  • Veeraraghavan, H., Atev, S., Bird, N., Schrater, P, Papanikolopoulos, N. (2005).  Driver Activity Monitoring through Supervised and Unsupervised Learning. Proceedings 18th IEEE International Conference on Intelligent Transportation Systems, Sept. 17-20, 2006, pp. 1340-45. (PDF)
  • Veeraraghavan, H., Papanikolopoulos, N., Schrater, P. (2006) Deterministic sampling-based switching kalman filtering for vehicle tracking.  Proceedings 2006 IEEE Conference on Intelligent Transportation Systems Sept. 17-20, 2006. pp. 1340-1345.  (PDF)

Camera Calibration/Placement
  • Sundareswara, R., and Schrater, P. (2010) Bayesian discounting of camera parameter uncertainty for optimal 3D reconstruction from images, Computer Vision and Image Understanding, In Press, Corrected Proof, Available online 26 August 2010, ISSN 1077-3142, DOI: 10.1016/j.cviu.2010.07.001.
  • Bodor, R., Drenner,  A., Janssen,  M., Schrater, P., and Papanikolopoulos, N. (2005), "Mobile Camera Positioning to Optimize the Observability of Human Activity Recognition Tasks",  2005 IEEE/RSJ International Conference on International Conference on Intelligent Robotics and Systems, 2-6 Aug. 2005, pp. 1564-1569. (PDF)
  • Bodor, R., Schrater, P., Papanikolopoulos, N.  (2005) Multi-Camera Positioning to Optimize Task Observability.  Proceedings, IEEE Conference on Advanced Video and Signal-Based Surveillance 2005, Sept. 15-16, 552-557. (PDF)
  • Sundareswara, R., and Schrater, P. R. (2005). Bayesian Modelling of Camera Calibration and Reconstruction. Fifth International Conference on 3-D Digital Imaging and Modeling, 2005, pp. 394- 401, Ottawa, Canada June 13 - June 16th 2005. (PDF)
Temporal sequence prediction/learning
  • W. Ketter, J. Collins, M. Gini, A. Gupta, and P.  Schrater (2008), “Detecting and Forecasting Economic Regimes in Automated Exchanges”, To Appear: Decision Support Systems. (TECH REPORT PREPRINT)
  • Ketter, W., Collins, J., Gini, M., Gupta, A., and Schrater, P. (2007). A Predictive Empirical Model for Pricing and Resource Allocation Decisions. In International Conference on Electronic Commerce, Minneapolis, MN, August 2007. (PDF)
  • Ketter, W., Collins, J., Gini, M., Gupta, A., and Schrater, P. (2006),  Strategic Sales Management Guided By Economic Regimes. In Eric van Heck et al., editors, Edited Volume of  the 2nd Small Business Network Initiative Discovery Event, Springer Verlag, 2006. (PDF)
  • Ketter, W., Collins, J., Gini, M., Gupta, A., and Schrater, P. (2005) A Computational Approach to Predicting Economic Regimes in Automated Exchanges,''  Fifteenth Annual Workshop on Information Technologies and Systems, pp. 147-152, Las Vegas, Nevada, USA, December 2005 (PDF)
  • Ketter, W., Collins, J., Gini, M., Gupta, A., and Schrater, P. (2005) Identifying and Forecasting Economic Regimes in TAC SCM, TADA-05, IJCAI-05 Workshop on Trading Agent Design and Analysis, pp. 53–60, Edinburgh, Scotland, Aug. 1, 2005. (PDF)
  • Steven Jensen, Daniel Boley, Maria Gini and Paul Schrater (2005), "Rapid on-line temporal sequence prediction by an adaptive agent", In Proceedings of the Fourth international Joint Conference on Autonomous Agents and Multiagent Systems (The Netherlands, July 25 - 29, 2005). AAMAS '05. ACM Press, New York, NY, 67-73. (PDF)
  • Steven Jensen, Daniel Boley, Maria Gini and Paul Schrater (2005),  "Non-stationary Policy Learning in 2-player Zero Sum Games" Non-stationary Policy Learning in 2-player Zero Sum Games. In Twentieth National Conf. on Artificial Intelligence, pp. 789–794, 2005. (PDF)

Brain Imaging

  • Olman, C. A., Ugurbil, K., Schrater, P., & Kersten, D. (2004). BOLD fMRI and psychophysical measurements of contrast response to broadband images. Vision Res, 44(7), 669-683. (PDF)
  • Murray, S., Schrater, P. and Kersten, D. (2004) Perceptual grouping and the interactions between visual cortical areas. Neural Networks, 17,  695-705. (PDF)
  • Carlson, Thomas A., Schrater, P.R., and He, Sheng (2002). Patterns of activation in the categorical representation of objects: A new perspective in functional imaging analysis.  Journal of Cognitive Neuroscience. 15(5): 704-717.  (LINK)
  • Murray, Scott, O., Kersten, D., Olshausen, B., Schrater, P., and Woods, D. (2002) Shape perception reduces activity in human primary visual cortex. Proc. Nat’l Acad. Sci., November 12, 2002, vol. 99 u no. 23, pp. 15164–15169. (LINK)
Tech Reports


Christopoulos, V.N., Lilja, D.J., Schrater, P.R., Georgopoulos, A.(2008) "Independent Component Analysis and Evolutionary Algorithms for Building Representative Benchmark Subsets," IEEE International Symposium on Performance Analysis of Systems and software, 2008. ISPASS 2008, pp.169-178, 20-22 April 2008.

Shekhar, S., Schrater, P.R., Vatsavai, R., Wu, W., and Chawla, S. (2002). Spatial Contextual Classification and Prediction Models for Mining Geospatial Data.  IEEE Transactions on Multimedia and Multimedia Database, 4(2),  174-188. (PDF)

Eeckhout, L., Sundareswara, R., Yiz, J., Lilja, D.J.  and Schrater, P. (2005). Accurate Statistical Approaches for Generating Representative Workload Compositions. Proceedings of the 2005 IEEE International Workload Characterization Symposium, pp. 56-66.  (PDF)

Sundareswara, R., and Schrater, P. R. (2003). Extensible Point Location Algorithm. Proceedings of 2003 International Conference on Geometrical Modeling and Graphics,  pp. 84-89, July 16-18, 2003 (PDF)


The Computational Perception and Action Laboratory is located in the basement of Elliott Hall.  The laboratory is equipped with a Visuo-Haptic workbench to test visuo-motor behavior with virtual objects, and an adjustable reach laboratory to study reaching behavior with actual objects.   We move objects in the reach lab using a robot arm, acquired through RobotWorx' industrial robot donation program :