September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
The Effect of Cognitive Load on Visual Statistical Learning
Author Affiliations
  • Amir Tal
    The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
  • Shira Baror
    The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
  • Moshe Bar
    The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
Journal of Vision August 2017, Vol.17, 505. doi:10.1167/17.10.505
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      Amir Tal, Shira Baror, Moshe Bar; The Effect of Cognitive Load on Visual Statistical Learning. Journal of Vision 2017;17(10):505. doi: 10.1167/17.10.505.

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

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Abstract

In times of limited resources or stress, not only behavior changes, but modes of memory and learning change as well. Under cognitive load, learning is biased towards a habitual "model-free" mode, rather than a goal-directed and more flexible "model-based" one. However, it is not known how cognitive load affects the potency of model-free learning itself. Learning of statistical regularities embedded in the visual environment had been shown to act efficiently and automatically, in a model-free manner. In this study, the effect of cognitive load on visual statistical learning has been examined. Using a behavioral decision-making paradigm, subjects were presented with visual regularities either from experiment onset or from a later point in time, simulating learning in novel and familiar environments, respectively. Results indicate that cognitive load delays simple reinforcement learning of novel regularities, and diminishes the ability to learn new rules when familiar settings change. These indications suggest that albeit automatic and implicit, visual statistical learning depends on the availability of cognitive resources for its successful function.

Meeting abstract presented at VSS 2017

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