Swiping colors in virtual reality: How stable are color category borders?

Human color perception involves a tradeoff between our ability to discriminate millions of continuous hues and our reliance on a few discrete linguistic categories. Although some theories suggest these category boundaries are fixed perceptual anchors, others propose that judgments adapt dynamically to the statistical distribution of recent stimuli, known as the range effect. To test the stability of these boundaries, we adapted a fast-paced "match-to-sample" paradigm from animal learning into an immersive VR videogame. Participants used colored sabers to strike incoming cubes, matching saber color to the cube's stripe. We tested both the blue-green boundary (aligned with low-level cone mechanisms) and the pink-purple boundary (off-axis), using hue sets equated for discriminability. After establishing baseline category borders using psychometric functions, we shifted the range of tested colors toward one category endpoint to determine if the internal border remained stable or shifted with the stimulus distribution. Across four experiments, results consistently revealed a partial shift. Rather than remaining invariant, category borders shifted systematically in the direction of the stimulus range shift. Further manipulations demonstrated that this partial shift was unaffected by the proportion of responses and occurred even when using hues which don't contain a category boundary. These findings indicate that under rapid decision-making conditions, observers' judgments are strongly influenced by the statistical structure of the immediate stimulus set, with stable categorical anchors playing a more limited role. This suggests a limited role for linguistic color categories in active, matching-based tasks, where observers likely prioritize automatic statistical adaptation over fixed categorical distinctions.