Subtractive and divisive adaptation in visual motion computations

Keith Langley*, Stephen J. Anderson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Models of visual motion processing that introduce priors for low speed through Bayesian computations are sometimes treated with scepticism by empirical researchers because of the convenient way in which parameters of the Bayesian priors have been chosen. Using the effects of motion adaptation on motion perception to illustrate, we show that the Bayesian prior, far from being convenient, may be estimated on-line and therefore represents a useful tool by which visual motion processes may be optimized in order to extract the motion signals commonly encountered in every day experience. The prescription for optimization, when combined with system constraints on the transmission of visual information, may lead to an exaggeration of perceptual bias through the process of adaptation. Our approach extends the Bayesian model of visual motion proposed byWeiss et al. [Weiss Y., Simoncelli, E., & Adelson, E. (2002). Motion illusions as optimal perception Nature Neuroscience, 5:598-604.], in suggesting that perceptual bias reflects a compromise taken by a rational system in the face of uncertain signals and system constraints. © 2007.

Original languageEnglish
Pages (from-to)673-686
Number of pages14
JournalVision Research
Issue number5
Publication statusPublished - Mar 2007


  • auto-correlation function
  • Bayes prior
  • Garotte function
  • Kalman filter
  • Leaky predictive coding


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