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Computationally expensive data processing in neuroimaging research places demands on energy consumption—and the resulting carbon emissions contribute to the climate crisis. We measured the carbon footprint of the functional magnetic resonance imaging (fMRI) preprocessing tool fMRIPrep, testing the effect of varying parameters on estimated carbon emissions and preprocessing performance. Performance was quantified using (a) statistical individual‐level task activation in regions of interest and (b) mean smoothness of preprocessed data. Eight variants of fMRIPrep were run with 257 participants who had completed an fMRI stop signal task (the same data also used in the original validation of fMRIPrep). Some variants led to substantial reductions in carbon emissions without sacrificing data quality: for instance, disabling FreeSurfer surface reconstruction reduced carbon emissions by 48%. We provide six recommendations for minimising emissions without compromising performance. By varying parameters and computational resources, neuroimagers can substantially reduce the carbon footprint of their preprocessing. This is one aspect of our research carbon footprint over which neuroimagers have control and agency to act upon. [ABSTRACT FROM AUTHOR]

Color-biased regions have been found between face- and place-selective areas in the ventral visual pathway. To investigate the function of the color-biased regions in a pathway responsible for object recognition, we analyzed the natural scenes dataset (NSD), a large 7T fMRI dataset from 8 participants who each viewed up to 30,000 trials of images of colored natural scenes over more than 30 scanning sessions. In a whole-brain analysis, we correlated the average color saturation of the images with voxel responses, revealing color-biased regions that diverge into two streams, beginning in V4 and extending medially and laterally relative to the fusiform face area in both hemispheres. We drew regions of interest (ROIs) for the two streams and found that the images for each ROI that evoked the largest responses had certain characteristics: they contained food, circular objects, warmer hues, and had higher color saturation. Further analyses showed that food images were the strongest predictor of activity in these regions, implying the existence of medial and lateral ventral food streams (VFSs). We found that color also contributed independently to voxel responses, suggesting that the medial and lateral VFSs use both color and form to represent food. Our findings illustrate how high-resolution datasets such as the NSD can be used to disentangle the multifaceted contributions of many visual features to the neural representations of natural scenes. • Color-biased regions in the ventral visual pathway are food selective • Two ventral food streams begin in V4 and diverge medially and laterally of the FFA • Food-selective streams use both visual form and color to represent food What is the role of color-biased regions in the ventral visual pathway? Pennock et al. redefine our understanding of color-biased regions by showing that they respond to both food and color. Their findings suggest that color contributes to the visual representation of food. [ABSTRACT FROM AUTHOR]

Studying color preferences provides a means to discover how perceptual experiences map onto cognitive and affective judgments. A challenge is finding a parsimonious way to describe and predict patterns of color preferences, which are complex with rich individual differences. One approach has been to model color preferences using factors from metric color spaces to establish direct correspondences between dimensions of color and preference. Prior work established that substantial, but not all, variance in color preferences could be captured by weights on color space dimensions using multiple linear regression. The question we address here is whether model fits may be improved by using different color metric specifications. We therefore conducted a large-scale analysis of color space models, and focused in-depth analysis on models that differed in color space (cone-contrast vs. CIELAB), coordinate system within the color space (Cartesian vs. cylindrical), and factor degrees (1st degree only, or 1st and 2nd degree). We used k-fold cross validation to avoid over-fitting the data and to ensure fair comparisons across models. The best model was the 2nd-harmonic Lch model ("LabC Cyl2"). Specified in CIELAB space, it included 1st and 2nd harmonics of hue (capturing opponency in hue preferences and simultaneous liking/disliking of both hues on an opponent axis, respectively), lightness, and chroma. These modeling approaches can be used to characterize and compare patterns for group averages and individuals in future datasets on color preference, or other measures in which correspondences between color appearance and cognitive or affective judgments may exist. [ABSTRACT FROM AUTHOR]

Millions of Internet users around the world challenged science by asking why a certain photo of a dress led different observers to have surprisingly different judgments about the color of the dress. The reason this particular photo produces so diverse a variety of judgments presumably is that the photo allows a variety of interpretations about the illumination of the dress. The most obvious explanation from color science should be that observers have different implicit assumptions about the illumination in the photo. We show that the perceived color of the dress is negatively correlated with the assumed illumination along the daylight locus. Moreover, by manipulating the observers' assumptions prior to seeing the photo, we can steer how observers will see the colors of the dress. These findings confirm the idea that the perceived colors of the dress depend on the assumptions about the illumination. The phenomenon illustrates the power of unconscious inferences and implicit assumptions in perception.