The resilience of object predictions: early recognition across viewpoints and exemplars

Recognition of everyday objects can be facilitated by top-down predictions. We have proposed that these predictions are derived from rudimentary image information, or gist, extracted rapidly from the low spatial frequencies (LSFs) (Bar Journal of Cognitive Neuroscience 15: 600–609, 2003). Because of the coarse nature of LSF representations, we hypothesized here that such predictions can accommodate changes in viewpoint as well as facilitate the recognition of visually similar objects. In a repetition-priming task, we indeed observed significant facilitation of target recognition that was primed by LSF objects across moderate viewpoint changes, as well as across visually similar exemplars. These results suggest that the LSF representations are specific enough to activate accurate predictions, yet flexible enough to overcome small changes in visual appearance. Such gist representations facilitate object recognition by accommodating changes in visual appearance due to viewing conditions, and help generalize from familiar to novel exemplars.