Hue and luminance contrast are the most basic visual features, emerging in early layers of convolutional neural networks trained to perform object categorization. In human vision, the timing of the neural computations that extract these features, and the extent to which they are determined by the same or separate neural circuits, is unknown. We addressed these questions using multivariate analyses of human brain responses measured with magnetoencephalography. We report four discoveries. First, it was possible to decode hue tolerant to changes in luminance contrast, and luminance contrast tolerant to changes in hue, consistent with the existence of separable neural mechanisms for these features. Second, the decoding time course for luminance contrast peaked 16-24 ms before hue and showed a more prominent secondary peak corresponding to decoding of stimulus cessation. These results are consistent with the idea that the brain uses luminance contrast as an updating signal to separate events within the constant stream of visual information. Third, neural representations of hue generalized to a greater extent across time, providing a neural correlate of the preeminence of hue over luminance contrast in perceptual grouping and memory. Finally, decoding of luminance contrast was more variable across participants for hues associated with daylight (orange and blue) than for anti-daylight (green and pink), suggesting that color-constancy mechanisms reflect individual differences in assumptions about natural lighting
