September 2011
Volume 11, Issue 11
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2011
Disruption and Transfer of Perceptual Learning for Visual Hyperacuity
Author Affiliations
  • Peggy Series
    Institute for Adaptive and Neural Computation, University of Edinburgh, UK
  • Grigorios Sotiropoulos
    University of Edinburgh, UK
  • Aaron Seitz
    University of California at Riverside, USA
Journal of Vision September 2011, Vol.11, 10. doi:10.1167/11.11.10
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      Peggy Series, Grigorios Sotiropoulos, Aaron Seitz; Disruption and Transfer of Perceptual Learning for Visual Hyperacuity. Journal of Vision 2011;11(11):10. doi: 10.1167/11.11.10.

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

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Abstract

Improvements of visual hyperacuity are a key focus in research of Perceptual Learning. Of particular interest has been the specificity of visual hyperacuity learning to the particular features of the trained stimuli as well as disruption of learning that occurs in some cases when different stimulus features are trained together. The implications of these phenomena on the underlying learning mechanisms are still open to debate; however, there is a marked absence of computational models that explore these phenomena in a unified way. Here we present a computational learning model based on reweighting and extend it to enable direct comparison, by means of simulations, with a variety of psychophysical data. We find that this very simple model can account for several findings, such as disruption of learning of one task by practice on a similar task, as well as transfer of learning across both tasks and stimulus configurations under certain conditions. These simulations help explain existing results in the literature as well as provide important insights and predictions regarding the reliability of different hyperacuity tasks and stimuli.

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