Temporal dynamics of the neural representation of hue and luminance polarity

Katherine L. Hermann, Shridhar R. Singh, Isabelle A. Rosenthal, Dimitrios Pantazis and Bevil R. Conway ()
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Katherine L. Hermann: National Eye Institute
Shridhar R. Singh: National Eye Institute
Isabelle A. Rosenthal: National Eye Institute
Dimitrios Pantazis: Massachusetts Institute of Technology
Bevil R. Conway: National Eye Institute

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: Abstract Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.

Date: 2022
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DOI: 10.1038/s41467-022-28249-0

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