August 2014
Volume 14, Issue 10
Vision Sciences Society Annual Meeting Abstract  |   August 2014
What determines the adaptation rate in the visual motion aftereffect?
Author Affiliations
  • Loes van Dam
    Cognitive Neurosciences, Faculty of Biology/CITEC, Bielefeld University
  • Marc Ernst
    Cognitive Neurosciences, Faculty of Biology/CITEC, Bielefeld University
Journal of Vision August 2014, Vol.14, 1330. doi:
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      Loes van Dam, Marc Ernst; What determines the adaptation rate in the visual motion aftereffect?. Journal of Vision 2014;14(10):1330.

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      © ARVO (1962-2015); The Authors (2016-present)

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The visual Motion After-Effect (MAE) is mostly investigated from a neurophysiological point of view. Here we take a more ecological/functional perspective of the MAE representing a shift in the reference of zero motion. Surprisingly, little is known how this reference shifts over time and how the adaptation rate depends on the statistical properties of the stimuli. An optimal adaptor, like the Kalman Filter, would adapt more slowly with increased levels of noise in the adapting signal. Furthermore, the Kalman Filter predicts faster adaptation after exposure to high noise motion stimuli, because higher noise prior to adaptation would lead to a higher degree of uncertainty in the initial motion estimate. To test these hypotheses, participants watched sequences of alternating adaptation (3 sec) and test stimuli (0.5 sec). For the adaptation stimulus, randomly distributed dots could either move in different directions (Von Mises noise on direction), or at different speeds (Gaussian noise on speed). Three noise levels were tested for both types of noise: zero, medium, or high. Test stimuli consisted of stationary limited-life-time dots. Participants reproduced the illusory motion perceived for test stimuli on a graphics tablet, thus indicating both aftereffect direction and strength. Halfway in each sequence we changed both the noise level and the motion direction of the adapting stimulus in order to investigate how quickly the MAE would be consistent with the new motion stimulus. We found that noise within the adaptation stimulus slows down the adaptation rate. More interestingly, when switching between different levels of noise within the sequence, the noise before such a switch influenced adaptation rates after the switch. If the initial noise level was high, adaptation was faster after the switch and vice versa. These results indicate that, at a perceptual level, MAE adaptation rates behave in a manner consistent with an optimal adaptor.


Meeting abstract presented at VSS 2014


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