Perception & Action Lab

Computational modeling

Computational modeling work focuses on constructing Bayesian theories of both the information in images that is, in theory, available to the brain (ideal observer models) for performing tasks and similar theories of how the brain uses the information to make perceptual inferences about objects in scenes (Bayesian observer models) and to guide motor actions (like reaching for objects). Using a common mathematical framework for modeling how a task can be performed in theory (theories of competence) and how humans actually perform the tasks (theories of performance) allows us to determine what aspects of human performance are determined by the structure of available visual information and of task demands and what are due to limitations in how the brain represents and does computations on visual information.

Kwon, O. S. and Knill, D. C. (2013) The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning, Proceedings of the National Academy of Sciences, 110(11): E1064-73. PDF Supplement
Sims, C. R., Jacobs, R. A. and Knill, D. C. (2012) An ideal observer analysis of visual working memory, Psychological Review, 119(4): 807-30. PDF
Landy, M. S., Banks, M. and Knill, D. C. (2011) Ideal-Observer Models of Cue Integration, in (Trommershauser, J. Kording, K., and Landy, M. S. eds.) Sensory Cue Integration, Osford Univ. Press, Oxford, England.
Seydell, A., Knill, D. C. and Trommershauser, J. (2011) Priors and Learning in Cue Integration, in (Trommershauser, J. Kording, K., and Landy, M. S. eds.) Sensory Cue Integration, Osford Univ. Press, Oxford, England.
Michel, M., Brouwer, A. Jacobs, R. and Knill, D. C. (2011) Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action, in (Trommershauser, J. Kording, K., and Landy, M. S. eds.) Sensory Cue Integration, Osford Univ. Press, Oxford, England.
Moreno-Bote, R., Knill, D. C. and Pouget, A. (2011) Bayesian sampling in visual perception, Proceedings of the National Academy of Sciences, 108(30):12491-6. PDF
Knill, D. C., Bondada, A. and Chhabra, Manu (2011) Flexible, task-dependent use of sensory feedback to control hand movements, Journal of Neuroscience, 31(4): 1219-1237. PDF
Sims, C. R., Jacobs, R. A. and Knill, D. C. (2011) Adaptive allocation of vision under competing task demands, The Journal of Neuroscience, 31(3): 928-943. PDF
Greenwald, H. and Knill, D. C. (2009) Orientation Disparity: A Cue for 3D Orientation, Neural Computation, Vol. 21, No. 9, Pages 2581-2604. PDF
Seydell, A., McCann, B. C., Trommershauser, J. and Knill, D. C. (2008) Learning stochastic reward distributions in a speeded pointing task, Journal of Neuroscience, 28: 4356-4367. PDF
Knill, D. C. (2007) Robust cue integration: a Bayesian model and evidence from psychophysical studies with stereoscopic and figure cues to slant, Journal of Vision, 7 (7), 1 – 24. PDF
Knill, D. C. (2007) Learning Bayesian priors for depth perception, Journal of Vision, 7 (8), 1 – 20. PDF
Knill, D. C. (2007) Bayesian models of sensory cue integration, in (Doya, K., Ishii, S., Pouget, A. and Rao, R., eds.) Bayesian Brain: Probabilistic Approaches to Neural Coding, MIT Press, Cambridge, MA. PDF
Greenwald, H., Knill, D. C. and Saunders, J. (2005) Integrating depth cues for visuomotor control: A matter of time, Vision Research, 45 (15), 1975 - 1989. PDF
Knill, D. C. and Pouget, A. (2004) The Bayesian brain: The role of uncertainty in neural coding and computation, 27 (12), 712 – 719, Trends in Neuroscience. PDF
Saunders, J and Knill, D. C. (2004) Visual feedback control of hand movements, J. of Neuroscience, 24 (13), 3223-3234. PDF

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Saunders, J. and Knill, D. C. (2001) Perception of 3D surface orientation from skew symmetry, Vision Research, 41 (24), 3163 - 3185. PDF
Knill, D. C. (2001) Contour into texture: The information content of surface contours and texture flow, Journal of the Optical Society A, 18 (1), 12-36. PDF
Schrater, P., Knill, D. C. and Simoncelli, E. (2000) Mechanisms of visual motion detection, Nature Neuroscience, 3 (1), 64 – 68. PDF
Liu Z., Kersten D., & Knill D. C. (1999). Dissociating stimulus information from internal representation --- a case study in object recognition, Vision Research, 39 (3), 603-613. PDF
Knill, D. C. (1998) Surface orientation from texture: Ideal observers, generic observers and the information content of texture cues, Vision Research, 38 (11), 1655-1682. PDF
Knill, D. C. (1998) Discriminating planar surface slant from texture: Human and ideal observers compared, Vision Research, 38 (11), 1683-1711. PDF
Knill, D. C. (1998) Ideal observer perturbation analysis reveals human strategies for inferring surface orientation from texture, Vision Research, 38 (17): 2635-2656. PDF
Knill, D. C., Mamassian, P. and Kersten, D. (1997) The geometry of shadows, Journal of the Optical Society of America, 14 (12), 3216-3232. PDF
Liu, Z., Knill, D. C., & Kersten, D. (1995), Object classification for human and ideal observers, Vision Research 35 (4), 549-569. PDF
Knill, D. C. (1992) The perception of surface contours and surface shape: from computation to psychophysics. Journal of the Optical Society of America A., 9 (9), 1449-1464. PDF
Knill, D. C. & Kersten, D. (1990) Learning a near-optimal estimator for surface shape from shading. Computer Vision, Graphics and Image Processing, 50, 75-100.
Knill, D. C. (1990) Estimating illuminant direction and degree of surface relief. Journal of the Optical Society of America A, 7 (4), 759-775. PDF
Kersten, D., O'toole, A. J., Sereno, M. E., Knill, D. C. & Anderson, J.A. (1987) Associative learning of scene parameters from images. Applied Optics, 26 (23), 4999-5006. PDF