Perceptual learning improves discrimination while distorting appearance

Perceptual sensitivity often improves with training, a phenomenon known as perceptual learning. Another important perceptual dimension is appearance, the subjective sense of stimulus magnitude. Are training-induced improvements in sensitivity accompanied by more accurate appearance? Here, we examine this question by measuring both discrimination and estimation capabilities for near-horizontal motion perception, before and after training. Human observers who trained on either discrimination or estimation exhibited improvements in discrimination accuracy alongside increased biases in their estimates away from horizontal. To explain this counterintuitive finding, we developed a computational observer model in which perceptual learning arises from increases in the precision of underlying neural representations. For each observer, the fitted model accounted for both discrimination performance and the distribution of estimates, and their changes after training. Our empirical findings and modeling suggest that learning enhances distinctions between categories, a potentially important aspect of real-world perception and perceptual learning.