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Estimating and Representing Uncertainty in Perception and Action

Dr. Laurence T. Maloney

Tuesday, February 04, 2014, 01:00pm - 02:00pm

New York University, Department of Psychology, Center for Neural Science

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The key difficulty in making decisions is the uncertainty about the outcomes
that could result from any particular decision. In classical decision making under risk the
uncertainty is explicitly given as probabilities: a 50% chance of $100 or else nothing. In a
wide range of biologically important tasks, though, the organism must estimate the
uncertainties associated with outcomes. The sources of uncertainty can be perceptual,
motor, or environmental. Estimation can be based on simple frequency counts or explicit
models – as when we assume that a coin is fair despite never having tossed it.
Surprisingly, human performance in such tasks is often close to optimal
(Trommershäuser et al, 2008; Warren et al, 2012) despite the added burden. I will
describe a series of studies that let us examine how we estimate uncertainty in a variety
of perceptual, motor and cognitive tasks.
There is considerable evidence that humans systematically distort estimates of
uncertainty in making decisions and any complete description of how we estimate
uncertainty must explain such errors. I’ll present a model that captures how we distort
frequency in decision tasks and experimental evidence testing it.
Supported by Grant EY019889 from the National Institutes of Health and the Humboldt
Foundation.


References
Fleming, S. Maloney, L. T. & Daw, N. D. (2013), The irrationality of categorical
     perception. Journal of Neuroscience, in press, September 24, 2013.
Juni, M., Gureckis, T. M. & Maloney, L. T. (2012), Effective integration of serially
     presented stochastic cues. Journal of Vision, 12(8):12, 1-16.
Schüür, F., Gerhard, H. E. & Maloney, L. T. (2014), Perceptual-Motor Forecasting Based
     on Bayesian Updating in a Rapid Visual-Manual Pointing Task. Under review.
Schüür, F., Tam, B. P., & Maloney, L. T. (2013), Learning patterns in noise:
     environmental statistics explain the sequential effect. In Knauff, M., Pauen, M.,
     Sebanz, N. & Wachsmuth, I. (Eds.) Proceedings of the 35th Annual Conference of
     the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Trommershäuser, J., Maloney, L. T. & Landy, M. S. (2008), Decision making, movement
     planning and statistical decision theory. Trends in Cognitive Science, 12(8), 291-
     297.
Warren, P. A., Graf, E. W., Champion, R. & Maloney, L. T. (2012), Extrapolation under
     risk: human observers estimate and compensate for exogeneous uncertainty.
     Proceedings of the Royal Society, Series B, 279(1736), 2171-2179.
Zhang, H., Daw, N. & Maloney, L. T. (2013), Testing whether humans have an accurate
     model of their own motor uncertainty in a speeded reaching task. PLoS
     Computational Biology, 9(5):e1003080, 1-11.
Zhang, H. & Maloney, L.T. (2012), Ubiquitous log odds: a common representation for
     probability and frequency distortion in perception, action and cognition. Frontiers in
     Neuroscience, 6(1), 1-14.
Zhang, H., Paily, J. T. & Maloney, L. T. (2014), Decisions from models. Under review.

Dr. Laurence T. Maloney