Perceptual Learning Improves Contrast Sensitivity of V1 Neurons in Cats

  • Hua T
  • Bao P
  • Huang C
 et al. 
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Background: Perceptual learning has been documented in adult humans over a wide range of tasks. Although the often-observed specificity of learning is generally interpreted as evidence for training-induced plasticity in early cortical areas, physiological evidence for training-induced changes in early visual cortical areas is modest, despite reports of learning-induced changes of cortical activities in fMRI studies. To reveal the physiological bases of perceptual learning, we combined psychophysical measurements with extracellular single-unit recording under anesthetized preparations and examined the effects of training in grating orientation identification on both perceptual and neuronal contrast sensitivity functions of cats. Results: We have found that training significantly improved perceptual contrast sensitivity of the cats to gratings with spatial frequencies near the "trained" spatial frequency, with stronger effects in the trained eye. Consistent with behavioral assessments, the mean contrast sensitivity of neurons recorded from V1 of the trained cats was significantly higher than that of neurons recorded from the untrained cats. Furthermore, in the trained cats, the contrast sensitivity of V1 neurons responding preferentially to stimuli presented via the trained eyes was significantly greater than that of neurons responding preferentially to stimuli presented via the "untrained" eyes. The effect was confined to the trained spatial frequencies. In both trained and untrained cats, the neuronal contrast sensitivity functions derived from the contrast sensitivity of the individual neurons were highly correlated with behaviorally determined perceptual contrast sensitivity functions. Conclusions: We suggest that training-induced neuronal contrast gain in area V1 underlies behaviorally determined perceptual contrast sensitivity improvements. © 2010 Elsevier Ltd. All rights reserved.

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  • Tianmiao Hua

  • Pinglei Bao

  • Chang Bing Huang

  • Zhenhua Wang

  • Jinwang Xu

  • Yifeng Zhou

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