September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
An integrated reweighting theory accounts for the role of task precision in transfer of perceptual learning for similar orientation tasks
Author Affiliations
  • Jiajuan Liu
    Department of Cognitive Sciences, University of California, Irvine
  • Barbara Dosher
    Department of Cognitive Sciences, University of California, Irvine
  • Zhong-Lin Lu
    Department of Psychology, The Ohio State University
Journal of Vision September 2015, Vol.15, 34. doi:10.1167/15.12.34
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      Jiajuan Liu, Barbara Dosher, Zhong-Lin Lu; An integrated reweighting theory accounts for the role of task precision in transfer of perceptual learning for similar orientation tasks. Journal of Vision 2015;15(12):34. doi: 10.1167/15.12.34.

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

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Abstract

Specificity is one of the hallmark findings of perceptual learning. One of the factors influencing the extent of specificity is the difficulty of the training task1 or, alternatively, the precision of the transfer task2. For example, the specificity of perceptual learning is higher for more precise (±5°) than less precise (±12°) orientation discrimination transfer tasks at new reference angle and retinal locations, essentially independent of the precision of the training task2 (see also second-order3 or first-order4 motion direction tasks). Recently, an integrated reweighting theory (IRT)5 was developed to account for the degree of specificity over position. The IRT reweights evidence from both location independent and location specific representations to decision to account for transfer and specificity. Here we develop the predictions of the IRT for the effects of judgment precision on transfer2, using a visual front end of normalized spatial-frequency and orientation tuned channels and Hebbian reweighting to decision, augmented by feedback and criterion correction5. The exact details of the experiment are reprised to generate simulated model predictions. The IRT correctly predicts that the specificity depends upon the precision of the transfer task, relatively independent of the precision of the training task. In sum, when the training and the transfer tasks involve the same kinds of judgments but use stimuli that are rotationally symmetric, the degree of specificity is primarily driven by the precision of the transfer task. A more precise judgment in the transfer task is more demanding and so shows more specificity and less transfer. The IRT model can also be used to make predictions about a number of related phenomena in perceptual learning.

Meeting abstract presented at VSS 2015

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