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Bayesian decision theory as a model of human visual perception: Testing Bayesian transfer

Published online by Cambridge University Press:  01 January 2009

LAURENCE T. MALONEY  and
PASCAL MAMASSIAN
LAURENCE T. MALONEY*
Affiliation:
Department of Psychology, New York University, New York, New York Center for Neural Science, New York University, New York, New York
PASCAL MAMASSIAN
Affiliation:
CNRS Laboratoire Psychologie de la Perception, Université Paris Descartes, Paris, France
*
*Address correspondence and reprint requests to: Laurence T. Maloney, Department of Psychology, New York University, 6 Washington Place, 2nd Floor, New York, NY 10003. E-mail: ltm1@nyu.edu
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Abstract

Bayesian decision theory (BDT) is a mathematical framework that allows the experimenter to model ideal performance in a wide variety of visuomotor tasks. The experimenter can use BDT to compute benchmarks for ideal performance in such tasks and compare human performance to ideal. Recently, researchers have asked whether BDT can also be treated as a process model of visuomotor processing. It is unclear what sorts of experiments are appropriate to testing such claims and whether such claims are even meaningful. Any such claim presupposes that observers’ performance is close to ideal, and typical experimental tests involve comparison of human performance to ideal. We argue that this experimental criterion, while necessary, is weak. We illustrate how to achieve near-optimal performance in combining perceptual cues with a process model bearing little resemblance to BDT. We then propose experimental criteria termed transfer criteria that constitute more powerful tests of BDT as a model of perception and action. We describe how recent work in motor control can be viewed as tests of transfer properties of BDT. The transfer properties discussed here comprise the beginning of an operationalization (Bridgman, 1927) of what it means to claim that perception is or is not Bayesian inference (Knill & Richards, 1996). They are particularly relevant to research concerning natural scenes since they assess the ability of the organism to rapidly adapt to novel tasks in familiar environments or carry out familiar tasks in novel environments without learning.

Keywords

PerceptionBayesian decision theoryStatistical modelsLoss functionBayesian transfer
Type
Natural Tasks and Plasticity
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 147 - 155
Copyright
Copyright © Cambridge University Press 2009

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