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Joshua Gold, Ching-Ling Teng, Chi-Tat Law; A neural-coding theory of perceptual learning-related plasticity. Journal of Vision 2011;11(11):9. doi: https://doi.org/10.1167/11.11.9.
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© ARVO (1962-2015); The Authors (2016-present)
A striking feature of perceptual learning is the diversity of neural mechanisms that have been implicated in different studies. For example, some forms of perceptual learning appear to involve changes in how sensory information is represented in early sensory areas of the brain. In contrast, other forms appear to involve improved read-out of information from unchanged sensory representations. Little is known about the principles that govern when these different forms of plasticity occur. Here we propose and test the theory that these different forms of plasticity represent the most effective ways to optimize task performance under different conditions. We test this idea using a novel analytical model of population coding that allows us to quantify how various changes in properties of a sensory representation and its readout can affect perceptual performance. The results indicate that diverse neural mechanisms of perceptual learning can reflect common principles of task optimization.
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