August 2010
Volume 10, Issue 7
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2010
External Feedback Networks and Perceptual Learning
Author Affiliations
  • Marcus Grueschow
    Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin, Berlin, Germany
    Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
    Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
  • Hans-Jochen Heinze
    Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
  • Oliver Speck
    Department of Biomedical Magnetic Resonance, Institute for Experimental Physics, Magdeburg, Germany
  • John-Dylan Haynes
    Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin, Berlin, Germany
    Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
Journal of Vision August 2010, Vol.10, 1128. doi:10.1167/10.7.1128
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Marcus Grueschow, Hans-Jochen Heinze, Oliver Speck, John-Dylan Haynes; External Feedback Networks and Perceptual Learning. Journal of Vision 2010;10(7):1128. doi: 10.1167/10.7.1128.

      Download citation file:


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

      ×
  • Supplements
Abstract

Perceptual Learning refers to the continuous improvement of performance that follows practice in a perceptual task. Valid external feedback has been shown to enhance perceptual learning (Herzog & Fahle 1997). Here we used functional magnetic resonance imaging to identify cortical and subcortical regions that are activated when negative or positive external feedback is given during perceptual learning. We measured brain activity before and after extensive training of an orientation discrimination task with valid trial-wise external feedback (EF), enabling us to compare post and pre-training activity in regions activated by positive and negative EF. The results reveal two distinct networks. Positive EF engages the posterior and anteriorcingulate cortex, superior frontal gyrus, ventral striatum, bilateral putamen (Globus pallidus), superior temporal gyrus, bilateral parahippocampal gyrus and posterior middle temporal gyrus (all p <0.05 FWE-corrected). Negative EF engages the anterior cingulate cortex, bilateral insula, dorsolateral prefrontal cortex, substantia nigra, anterior thalamus and the right inferior parietal lobe (all p <0.05 FDR-corrected). Examining pre- and post-training differences of positive EF, we find that activity in the fusiform gyrus and orbitofrontal cortex decreases with training (p <0.01 uncorrected), while for negative EF decreased activity is observed in the inferior and medial frontal gyrus (p <0.001 uncorrected). Importantly, for both types of EF, we find increasing activity with training in the parahippocampal gyrus, a region involved in memory formation and the precuneus, which is associated with visuospatial processing. We conclude that the cortical regions most affected by feedback-based perceptual training are not primary sensory regions but frontal and parietal networks.

Grueschow, M. Heinze, H.-J. Speck, O. Haynes, J.-D. (2010). External Feedback Networks and Perceptual Learning [Abstract]. Journal of Vision, 10(7):1128, 1128a, http://www.journalofvision.org/content/10/7/1128, doi:10.1167/10.7.1128. [CrossRef]
Footnotes
 SFB779-A3, BMBF.
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×