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Related Experiment Videos

Testing the Bayesian model of perceived speed.

Felix Hürlimann1, Daniel C Kiper, Matteo Carandini

  • 1Institute of Neuroinformatics, University of Zurich and ETH Zurich, CH-8057, Switzerland.

Vision Research
|September 11, 2002
PubMed
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A Bayesian model of motion perception accurately predicts perceived speed based on contrast. However, it requires a nonlinear contrast representation for precise quantitative predictions, improving our understanding of visual processing.

Area of Science:

  • Visual neuroscience
  • Computational modeling
  • Psychophysics

Background:

  • A Bayesian model by Weiss, Simoncelli, and Adelson suggests motion perception is biased by a prior favoring slow speeds.
  • This model qualitatively explains phenomena like perceived speed's dependence on contrast.

Purpose of the Study:

  • To test the quantitative predictions of the Bayesian motion perception model.
  • To investigate the relationship between perceived speed and contrast using psychophysical measurements.
  • To determine if the model requires modifications for accurate quantitative predictions.

Main Methods:

  • Developed a quantitative prediction for perceived speed as a function of contrast: proportional to c(q)/(k(q) + c(q)).
  • Conducted psychophysical experiments where observers compared perceived speeds of drifting gratings at varying contrasts.

Related Experiment Videos

  • Determined the test grating speed that matched a standard grating speed for each test contrast.

    • Main Results:

    • The Bayesian model fits the experimental data on perceived speed versus contrast.
    • The model's fit is accurate only when the exponent 'q' is less than 2.
    • A value of q=2 would imply a linear internal representation of contrast, which appears not to be the case.

    Conclusions:

    • The Bayesian model for motion perception can generate quantitative predictions.
    • The model's accuracy is enhanced by incorporating a nonlinear internal representation of contrast.
    • Further research is needed to refine Bayesian models with more realistic sensory representations.