August 2009
Volume 9, Issue 8
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2009
Modeling perceptual learning in external noise with Hebbian reweighting
Author Affiliations
  • Zhong-Lin Lu
    Laboratory of Brain Processes (LOBES), Dana and David Dornsife Cognitive Neuroscience Imaging Center, Departments of Psychology and Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1061
  • Jiajuan Liu
    Laboratory of Brain Processes (LOBES), Dana and David Dornsife Cognitive Neuroscience Imaging Center, Departments of Psychology and Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1061
  • Barbara Dosher
    Memory, Attention and Perception Laboratory (MAPL), Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA 92697-5100
Journal of Vision August 2009, Vol.9, 863. doi:10.1167/9.8.863
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      Zhong-Lin Lu, Jiajuan Liu, Barbara Dosher; Modeling perceptual learning in external noise with Hebbian reweighting. Journal of Vision 2009;9(8):863. doi: 10.1167/9.8.863.

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

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Abstract

Using the external noise plus training paradigm, we find evidence that two independent mechanisms, stimulus enhancement and external noise exclusion, support perceptual learning in a range of tasks. Here, we show that re-weighting of stable early sensory representations through Hebbian learning (Petrov, Dosher & Lu, 2005) provides an excellent account of a large range of data: (1) perceptual learning reduced contrast thresholds at all levels of external noise in peripheral orientation identification (Dosher & Lu, 1998, 1999), (2) significant learning only occurred in the high external noise conditions but not in zero or low external noise conditions in foveal orientation identification (Lu & Dosher, 2004), (3) in second-order letter identification and auditory modulation detection, the performance improvements predominantly occurred in low external noise conditions (Dosher & Lu, 2007, Kong, Lu, Dosher & Zeng, 2004), (4) training with low noise exemplars transferred to high noise performance, while training with exemplars embedded in high external noise did not transfer to low noise performance (Dosher & Lu, 2005), and (5) pre-training in high external noise only reduced subsequent learning in high external noise, whereas pre-training in zero external noise practically eliminated or left very little additional learning in all the external noise conditions (Lu, Chu & Dosher, 2006). In the re-weighting model, perceptual learning strengthens or maintains the connections between the most closely tuned visual channels and a learned categorization structure, while it prunes or reduces inputs from task-irrelevant channels. Reducing the weights on irrelevant channels reduces the contributions of external noise and additive internal noise. Manifestation of stimulus enhancement or external noise exclusion depends on the initial state of internal noise and connection weights in the beginning of a learning task. Both mechanisms reflect re-weighting of stable early sensory representations.

Lu, Z.-L. Liu, J. Dosher, B. (2009). Modeling perceptual learning in external noise with Hebbian reweighting [Abstract]. Journal of Vision, 9(8):863, 863a, http://journalofvision.org/9/8/863/, doi:10.1167/9.8.863. [CrossRef]
Footnotes
 This research was supported by the National Science Foundation, National Institute of Mental Health, and National Eye Institute.
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