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Bayesian motion estimation accounts for a surprising bias in 3D vision

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84307

Welchman,  AE
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Welchman, A., Lam, J., & Bülthoff, H. (2008). Bayesian motion estimation accounts for a surprising bias in 3D vision. Proceedings of the National Academy of the United States of America, 105(33), 12087-12092. doi:10.1073/pnas.0804378105.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C787-E
Zusammenfassung
Determining the approach of a moving object is a vital survival skill that depends on the brain combining information about lateral translation and motion-in-depth. Given the importance of sensing motion for obstacle avoidance, it is surprising that humans make errors, reporting an object will miss them when it is on a collision course with their head. Here we provide evidence that biases observed when participants estimate movement in depth result from the brainamp;lsquo;s use of a “prior” favoring slow velocity. We formulate a Bayesian model for computing 3D motion using independently estimated parameters for the shape of the visual systemamp;lsquo;s slow velocity prior. We demonstrate the success of this model in accounting for human behavior in separate experiments that assess both sensitivity and bias in 3D motion estimation. Our results show that a surprising perceptual error in 3D motion perception reflects the importance of prior probabilities when estimating environm ental pr operties.