Visual segmentation of complex naturalistic structures in an infant eye-tracking search task

An infant's everyday visual environment is composed of a complex array of entities, some of which are well integrated into their surroundings. Although infants are already sensitive to some categories in their first year of life, it is not clear which visual information supports their detection of meaningful elements within naturalistic scenes. Here we investigated the impact of image characteristics on 8-month-olds' search performance using a gaze contingent eye-tracking search task. Infants had to detect a target patch on a background image. The stimuli consisted of images taken from three categories: vegetation, non-living natural elements (e.g., stones), and manmade artifacts, for which we also assessed target background differences in lower- and higher-level visual properties. Our results showed that larger target-background differences in the statistical properties scaling invariance and entropy, and also stimulus backgrounds including low pictorial depth, predicted better detection performance. Furthermore, category membership only affected search performance if supported by luminance contrast. Data from an adult comparison group also indicated that infants' search performance relied more on lower-order visual properties than adults. Taken together, these results suggest that infants use a combination of property- and category-related information to parse complex visual stimuli.