September 2011
Volume 11, Issue 11
Vision Sciences Society Annual Meeting Abstract  |   September 2011
Symposium Summary
Author Affiliations
  • Alexander A. Petrov
    Department of Psychology, Ohio State University, USA
Journal of Vision September 2011, Vol.11, 6. doi:
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      Alexander A. Petrov; Symposium Summary. Journal of Vision 2011;11(11):6.

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

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Perceptual learning refers to improvements in perceptual abilities through training. It has been a topic of growing interest over the last two decades. Perceptual learning is a valuable tool for studying the organization of the visual system and the mechanisms of brain plasticity. It also has a great potential for practical applications such as training of visual experts and rehabilitation of persons with disabilities. These challenges are complex and require an integrated, multidisciplinary approach. There is a wealth of behavioral data documenting the occurrence, speed, specificity, and other properties of perceptual learning under various conditions. There is also a growing stream of human neuroimaging and animal neurophysiological data. What continues to elude the field, however, is an integrated theoretical understanding of these disparate findings. Computational and mathematical modeling is an important tool in this regard. Models help us formulate explicit and consistent principles and mechanisms, generate novel predictions, and bridge the explanatory gap between brain and behavior. A number of models of perceptual learning with increasing scope and sophistication have been developed in recent years.

This symposium brings together an international panel of experts in perceptual learning, with particular emphasis on computational and/or formal approaches. These speakers have made important contributions to the field of perceptual learning using a mixture of psychophysical, computational, and neuroscientific approaches. Here they will each present computational models of perceptual learning that advance our understanding of the underlying brain mechanisms. Zhong-Lin Lu will start with a broad overview of the functions and mechanisms. Alex Petrov will explore one particular mechanism – selective reweighting – in some detail. Joshua Gold will present a novel analytical model of population coding that allows us to quantify how various changes in neuronal firing rates can affect perceptual performance. Peggy Seriès will present a reweighting account for patterns of disruption and transfer of perceptual learning for visual hyperacuity. Finally, Dov Sagi will discuss some unexpected consequences of the hypothesis that perceptual learning involves statistical modeling of the task at hand.

The symposium is designed to serve both as a tutorial of established ideas and techniques and as a venue to introduce new advances at the cutting edge of this active research area. Perceptual learning is a field of investigation that impacts all aspects of vision and thus this symposium will interest VSS attendees across disciplines and at all levels, from students to experts. An earlier symposium on perceptual learning attracted an audience beyond room capacity at VSS 2006. The current proposal builds on this success by adding an emphasis on modeling and reporting the exciting new developments in the intervening years.


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