| Last Updated:
Creating DOI. Please wait...
Humans can efficiently see through the complexity of scenes, faces, and objects in order to extract the underlying image structure, the style of an image. It remains unclear, however, whether semantic coding is necessary, and whether global information is sufficient, for people to recognize and discriminate complex visual categories. In two experiments, we reduce the resolution of hundreds of unique paintings, birds, and faces, and test people’s ability to discriminate and recognize them. We show that the global information retained at extremely low image resolutions is sufficient for detecting the style of an image, and for discriminating the style of visual categories, with above-chance accuracy across the three image domains. People were even able to reliably recognize and discriminate natural images reduced down to a single pixel where there is no meaningful categorical structure to rely on. We situate our findings in the context of contemporary computational accounts of visual recognition, and contend that explicit encoding of the local features in the image, or of the semantic category, is not necessary for recognizing and distinguishing among visual categories.