Reverse correlation techniques yield visual classification images by combining large numbers of noise images based on neural or behavioral responses. These responses are commonly collected while viewing a combination of noise and target. In the reported studies, we produced pure top-down processing classification images by using noise only images while participants engaged in detection of either one of many different possible faces or one of many different possible letters. Classification images based on pixel noise require many thousands of separate noise trials. Instead, we sought evidence of higher level more complex characteristics by creating noise images that combined randomly placed Gaussian's "blobs" at varying spatial scales. Both face and letter detection was achieved with exactly the same sequence of 480 noise images in separate experiments. This was done such that any differences would only reflect the nature of the detection task. The resultant face and letter classification images differed in spatial frequency, laterality, and spatial heterogeneity (i.e., number of distinct regions). Because the noise images were briefly presented with insufficient time for saccades, these laterality effects may relate to cortical hemisphere specialization. |