Distributed neural plasticity for shape learning in the human visual cortex.

Z. Kourtzi, L.R. Betts, P. Sarkheil, A.E. Welchman

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Expertise in recognizing objects in cluttered scenes is a critical skill for our interactions in complex environments and is thought to develop with learning. However, the neural implementation of object learning across stages of visual analysis in the human brain remains largely unknown. Using combined psychophysics and functional magnetic resonance imaging (fMRI), we show a link between shape-specific learning in cluttered scenes and distributed neuronal plasticity in the human visual cortex. We report stronger fMRI responses for trained than untrained shapes across early and higher visual areas when observers learned to detect low-salience shapes in noisy backgrounds. However, training with high-salience pop-out targets resulted in lower fMRI responses for trained than untrained shapes in higher occipitotemporal areas. These findings suggest that learning of camouflaged shapes is mediated by increasing neural sensitivity across visual areas to bolster target segmentation and feature integration. In contrast, learning of prominent pop-out shapes is mediated by associations at higher occipitotemporal areas that support sparser coding of the critical features for target recognition. We propose that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations that determine the distinctiveness of target shapes.
Original languageEnglish
Article numbere204
Pages (from-to)1317-1327
Number of pages11
JournalPlos Biology
Volume3
Issue number7
DOIs
Publication statusPublished - Jul 2005

Keywords

  • INFERIOR TEMPORAL CORTEX
  • OBJECT RECOGNITION
  • TEXTURE-DISCRIMINATION
  • 3-DIMENSIONAL OBJECTS
  • CONTEXTUAL INFLUENCES
  • FACE RECOGNITION
  • ORIENTATION
  • REPRESENTATION
  • INTEGRATION
  • MECHANISMS

Cite this