August 2012
Volume 12, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2012
Investigating the specificity of experimentally induced expectations in motion perception
Author Affiliations
  • Nikos Gekas
    Doctoral Training Center in Neuroinformatics, University of Edinburgh\nInstitute for Adaptive and Neural Computation, University of Edinburgh
  • Aaron Seitz
    Department of Psychology, UC Riverside
  • Peggy Seriès
    Institute for Adaptive and Neural Computation, University of Edinburgh
Journal of Vision August 2012, Vol.12, 1137. doi:10.1167/12.9.1137
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      Nikos Gekas, Aaron Seitz, Peggy Seriès; Investigating the specificity of experimentally induced expectations in motion perception. Journal of Vision 2012;12(9):1137. doi: 10.1167/12.9.1137.

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

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Abstract

Our perceptions are fundamentally altered by our expectations, a.k.a. "priors" about the world. In previous statistical learning experiments (Chalk et al, 2010), we investigated how such priors are formed by presenting subjects with low contrast moving dots or a blank screen, and asking them to report the direction of motion, and whether the stimulus was present. We manipulated subjects’ expectations by using a bimodal distribution of motion directions such that two directions were more frequently presented than the others. We found that human observers quickly, automatically, and implicitly developed expectations for the most frequently presented directions of motion. These expectations induced attractive biases towards the perceived motion direction as well as visual hallucinations in the absence of a stimulus.

Here, we examine the specificity of these expectations. Would exposure to green dots lead to particular expectations about the motion of red dots? Can one learn simultaneously to expect different motion directions for dots of different colors?

We interleaved moving dot displays of two different colors, either red or green, with different motion direction distributions. When one distribution was bimodal while the other was uniform, we found that subjects learned a single bimodal prior for the two stimuli. On the contrary, when both distributions were similarly structured, we found evidence for the formation of two distinct priors, which were not strong enough to alter estimation behavior, but influenced significantly the subjects’ behavior when no stimulus was present.

Our results can be modeled using a Bayesian framework and discussed in terms of a sub-optimality of the statistical learning process under some conditions. Understanding the limitations of statistical learning for complex stimuli may help understanding how expectations are learned at the neural level.

Meeting abstract presented at VSS 2012

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