Color perception is based on the differential spectral responses of the L, M, and S-cones and subsequent subcortical and cortical computations and may include the influence of higher-order factors such as language. Although the early subcortical stages of color vision are well characterized, the organization of cortical representations of color remain elusive, despite numerous models based on discrimination thresholds, appearance, and categorization. An underexplored aspect of cortical color representations is their dynamic evolution. Here, we compare the evolution of three different color representations over time using magnetoencephalography. We measured neural responses to 14 hues at each of three achromatic offsets (increment, isoluminant, and decrement) while participants attended either to the exact color of the stimulus or its color category. We used a series of classification analyses, combined with multidimensional scaling and representational similarity analysis, to ask how cortical representations of color unfold over time from stimulus onset. We compared the performance of “higher order” models based on hue and color category with a model based simply on stimulus cone contrast and found that all models had significant correlations with the data. However, the unique variance accounted for by each model revealed a dynamic change in hue responses over time, which was consistent with a “coarse to fine” transition from a broad clustering into categorical groups to a finer within-category representation. Notably, these dynamics were replicated across data sets from both tasks, suggesting they reflect a robust reorganization of cortical hue responses over time.

Every day, we respond to the dynamic world around us by choosing actions to meet our goals. Flexible neural populations are thought to support this process by adapting to prioritize task-relevant information, driving coding in specialized brain regions toward stimuli and actions that are currently most important. Accordingly, human fMRI shows that activity patterns in frontoparietal cortex contain more information about visual features when they are task-relevant. However, if this preferential coding drives momentary focus, for example, to solve each part of a task in turn, it must reconfigure more quickly than we can observe with fMRI. Here, we used multivariate pattern analysis of magnetoencephalography data to test for rapid reconfiguration of stimulus information when a new feature becomes relevant within a trial. Participants saw two displays on each trial. They attended to the shape of a first target then the color of a second, or vice versa, and reported the attended features at a choice display. We found evidence of preferential coding for the relevant features in both trial phases, even as participants shifted attention mid-trial, commensurate with fast subtrial reconfiguration. However, we only found this pattern of results when the stimulus displays contained multiple objects and not in a simpler task with the same structure. The data suggest that adaptive coding in humans can operate on a fast, subtrial timescale, suitable for supporting periods of momentary focus when complex tasks are broken down into simpler ones, but may not always do so.

Attention can be deployed in different ways: When searching for a taxi in New York City, we can decide where to attend (e.g., to the street) and what to attend to (e.g., yellow cars). Although we use the same word to describe both processes, nonhuman primate data suggest that these produce distinct effects on neural tuning. This has been challenging to assess in humans, but here we used an opportunity afforded by multivariate decoding of MEG data. We found that attending to an object at a particular location and attending to a particular object feature produced effects that interacted multiplicatively. The two types of attention induced distinct patterns of enhancement in occipital cortex, with feature-selective attention producing relatively more enhancement of small feature differences and spatial attention producing relatively larger effects for larger feature differences. An information flow analysis further showed that stimulus representations in occipital cortex were Granger-caused by coding in frontal cortices earlier in time and that the timing of this feedback matched the onset of attention effects. The data suggest that spatial and feature-selective attention rely on distinct neural mechanisms that arise from frontal-occipital information exchange, interacting multiplicatively to selectively enhance task-relevant information.