Visual perceptual learning in human object recognition areas: a repetition priming study using high-density electrical mapping.

Cognitive Neurophysiology Laboratory, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA.
NeuroImage (Impact Factor: 6.13). 03/2001; 13(2):305-13. DOI: 10.1006/nimg.2000.0684
Source: PubMed

ABSTRACT It is often the case that only partial or degraded views of an object are available to an observer, and yet in many of these cases, object recognition is accomplished with surprising ease. The perceptual filling-in or "closure" that makes this possible has been linked to a group of object recognition areas in the human brain, the lateral occipital (LO) complex, and has been shown to have a specific electrophysiological correlate, the N(cl) component of the event related potential. Perceptual closure presumably occurs because repeated and varied exposure to different classes of objects has caused the brain to undergo "perceptual learning," which promotes a robust mnemonic representation, accessible under partial information circumstances. The present study examined the impact of perceptual learning on closure-related brain processes. Fragmented pictures of common objects were presented, such that information content was incrementally increased until just enough information was present to permit closure and object recognition. Periodic repetition of a subset of these picture sequences was used to induce repetition priming due to perceptual learning. This priming has an electrophysiological signature that is putatively generated in the LO complex, but significantly precedes the electrophysiological correlate of closure. The temporal progression of priming- and closure-related activity in the LO complex supports the view that sensory processing entails multiple reentrant stages of activity within processing modules of the visual hierarchy. That the earliest priming-related activity occurs over LO complex, suggests that the sensory trace itself may reside in these object recognition areas.

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