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Related Concept Videos

Color Vision01:24

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
Visual System01:26

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Gestalt Principles of Perception01:21

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A Tactile Automated Passive-Finger Stimulator (TAPS)
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Bayesian decision theory as a model of human visual perception: testing Bayesian transfer.

Laurence T Maloney1, Pascal Mamassian

  • 1Department of Psychology, New York University, New York, New York 10003, USA. ltm1@nyu.edu

Visual Neuroscience
|February 6, 2009
PubMed
Summary

Bayesian decision theory (BDT) provides performance benchmarks for visuomotor tasks. New transfer criteria offer stronger tests for BDT as a process model, assessing adaptation in perception and action.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Neuroscience

Background:

  • Bayesian decision theory (BDT) models ideal performance in visuomotor tasks.
  • Researchers question if BDT can model the actual processes of visuomotor control.
  • Current experimental tests compare human performance to BDT benchmarks.

Purpose of the Study:

  • To evaluate the validity of BDT as a process model for visuomotor control.
  • To propose more rigorous experimental criteria for testing BDT's role in perception and action.
  • To explore the implications of BDT in understanding adaptation and learning in natural scenes.

Main Methods:

  • Illustrating near-optimal cue combination with a non-BDT process model.
  • Introducing "transfer criteria" as enhanced experimental tests for BDT.
  • Reviewing motor control research as tests of BDT's transfer properties.

Main Results:

  • Human performance often approximates ideal, making simple comparisons a weak test for BDT as a process model.
  • Near-optimal performance can be achieved by models dissimilar to BDT.
  • Transfer criteria provide a more robust method for evaluating BDT's explanatory power.

Conclusions:

  • Standard comparisons of human to ideal performance are insufficient to validate BDT as a process model.
  • Transfer criteria offer a stronger framework for operationalizing Bayesian inference in perception and action.
  • These criteria are crucial for understanding rapid adaptation in dynamic environments without explicit learning.