The speed and accuracy of material recognition in natural images

Abstract

We studied the time course of material categorization in natural images relative to superordinate and basic-level object categorization, using a backward-masking paradigm. We manipulated several low-level features of the images-including luminance, contrast, and color-to assess their potential contributions. The results showed that the speed of material categorization was roughly comparable to the speed of basic-level object categorization, but slower than that of superordinate object categorization. The performance seemed to be crucially mediated by low-level factors, with color leading to a solid increase in performance for material categorization. At longer presentation durations, material categorization was less accurate than both types of object categorization. Taken together, our results show that material categorization can be as fast as basic-level object categorization, but is less accurate.