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Qinlin Yu, Peng Zhang, Fang Fang; Perceptual learning of contrast detection strengthens the response of the magnocellular layers of the human LGN. Journal of Vision 2016;16(12):553. doi: 10.1167/16.12.553.
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© 2017 Association for Research in Vision and Ophthalmology.
In this study, we carried out psychophysical and fMRI experiments to investigate whether perceptual learning of contrast detection can modify the response properties of the lateral geniculate nucleus (LGN). Nineteen subjects underwent thirty daily training sessions to detect the presence of a checkerboard with one eye. The checkerboard was presented briefly at a near-threshold contrast in one visual hemifield. A daily session consisted of 30 QUEST staircases of 40 trials. Before and after training, we measured subjects' contrast detection thresholds and contrast response functions of BOLD signal. Their behavioral improvement was specific to the trained eye and the trained hemifield. During scanning, flickering checkerboards at three contrast levels (6%, 24%, or 96%) were presented in either the left or the right visual hemifield. Subjects viewed the stimuli with one eye. Meanwhile, they needed to detect the color change of the central fixation point. Training led to an eye- and hemifiled-specific response increase of the LGN to the checkerboard, only at the low contrast level (6%). There was no learning-induced change in V1, V2, and V3. Furthermore, we used the method proposed by Zhang et al. (2015) to separate the Parvocellular (P) and Magnocellular (M) layers of the LGN. We found that, following training, only the response of the M layers increased to the low contrast checkerboard. The response increase in the M layers significantly correlated with the behavioral improvement. Our study demonstrated that even in the absence of top-down modulation (e.g. attention), perceptual learning of contrast detection could modify the functional properties of the M layers of LGN by increasing their response gain to low-contrast stimuli.
Meeting abstract presented at VSS 2016
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