The effect of category learning on the representation of shape: Dimensions can be biased but not differentiated

Recent studies have suggested a profound influence of category learning on visual perception, resulting in independent processing of previously integral dimensions. The authors reinvestigate this issue for shape dimensions. They first extend previous findings that some shape dimensions (aspect ratio and curvature) are processed in a separable way, whereas others (radial frequency components) are not. They then show that a category-learning phase improved the discrimination of a relevant with respect to an irrelevant dimension, but only for separable dimensions. No similar effect was found on the relative sensitivity for integral shape dimensions. Thus, category learning is capable of biasing separable shape dimensions but does not alter the status of dimensions in the visual system as either separable or integral.