Perceptual learning modulates electrophysiological and psychophysical response to visual texture segmentation in humans

We investigated the mechanisms that allow, via perceptual learning, selective modulation of a visual line-texture figure saliency in accordance with task relevance. Learning-dependent saliency increase was inferred by increased accuracy in orientation discrimination with task repetition. As a result of learning, accuracy increase was more pronounced when local and global orientation of the texture figure conflicted, and reached ceiling in both conflict and conflict-free conditions. This psychophysical effect was associated with a decrease in amplitude of negative VEP components in the configurations where global and local orientation conflicted, and to a weak increase of VEP's earliest negative component in the conflict-free condition. The VEP result is a direct demonstration that learning, in addition to increasing response of relevant channels, also reduces the weight of channels whose receptive field size and orientation tuning conflict with the task.

[1]  Victor A. F. Lamme,et al.  Texture segregation is processed by primary visual cortex in man and monkey. Evidence from VEP experiments , 1992, Vision Research.

[2]  B. Dosher,et al.  Mechanisms of perceptual learning , 1999, Vision Research.

[3]  H. Nothdurft.  The role of features in preattentive vision: Comparison of orientation, motion and color cues , 1993, Vision Research.

[4]  G. Schweigart,et al.  Spatio–temporal plasticity of cortical receptive fields in response to repetitive visual stimulation in the adult cat , 2002, Neuroscience.

[5]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[6]  D. Sagi,et al.  Excitatory-inhibitory network in the visual cortex: psychophysical evidence. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Clara Casco,et al.  A visual evoked potential correlate of global figure-ground segmentation , 1999, Vision Research.

[8]  Gianluca Campana,et al.  Learning in combined-feature search: Specificity to orientation , 2003, Perception & psychophysics.

[9]  C Casco,et al.  Spatial interactions in simple and combined-feature visual search. , 1999, Spatial vision.

[10]  D. Navon.  Forest before trees: The precedence of global features in visual perception , 1977, Cognitive Psychology.

[11]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[12]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[13]  D Sagi,et al.  Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Charles D. Gilbert,et al.  Learning: Neuronal dynamics and perceptual learning , 1994, Current Biology.

[15]  M. Kilgard,et al.  Cortical map reorganization enabled by nucleus basalis activity. , 1998, Science.

[16]  Lynn Hasher,et al.  Determinants of Negative Priming , 1995 .

[17]  Y. Zhuo,et al.  On the Different Processing of Wholes and Parts: A Psychophysiological Analysis , 1997, Journal of Cognitive Neuroscience.

[18]  C. Gilbert,et al.  Improvement in visual sensitivity by changes in local context: Parallel studies in human observers and in V1 of alert monkeys , 1995, Neuron.