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Temporal dynamics of the Representation of Hue and Luminance Polarity

This page contains the data and analysis for Hermann et al (2020), https://www.biorxiv.org/content/10.1101/2020.06.17.155713v4  as described in the guide.

Abstract: Hue and luminance contrast are basic visual features, yet the timing of the neural computations that extract them, and whether they depend on common neural circuits, is not well established. Using multivariate analyses of magnetoencephalography data, 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 appear to 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 suggests that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.

HueLum.zip (1.2GB) - includes scripts for producing all the figures from the paper. There are guides in that directory with more information. Please see the PDF guide for details about what is contained in the zip file.

The decoding scripts used in the analysis are modified files from the Neural Decoding Toolbox that was graciously provided, with permission, by Ethan Meyers (Meyers, 2013). Anyone interested in using these files should contact Ethan Meyers directly, and cite his paper:

Meyers, E. (2013). The Neural Decoding Toolbox Frontiers in Neuroinformatics, 7:8
www.readout.info
https://github.com/emeyers/NeuroDecodeR