title: The time course of segmentation and cue-selectivity in the human visual cortex.
abstract: Texture discontinuities are a fundamental cue by which the visual system segments objects from their background. The neural mechanisms supporting texture-based segmentation are therefore critical to visual perception and cognition. In the present experiment we employ an EEG source-imaging approach in order to study the time course of texture-based segmentation in the human brain. Visual Evoked Potentials were recorded to four types of stimuli in which periodic temporal modulation of a central 3° figure region could either support figure-ground segmentation, or have identical local texture modulations but not produce changes in global image segmentation. The image discontinuities were defined either by orientation or phase differences across image regions. Evoked responses to these four stimuli were analyzed both at the scalp and on the cortical surface in retinotopic and functional regions-of-interest (ROIs) defined separately using fMRI on a subject-by-subject basis. Texture segmentation (tsVEP: segmenting versus non-segmenting) and cue-specific (csVEP: orientation versus phase) responses exhibited distinctive patterns of activity. Alternations between uniform and segmented images produced highly asymmetric responses that were larger after transitions from the uniform to the segmented state. Texture modulations that signaled the appearance of a figure evoked a pattern of increased activity starting at ∼143 ms that was larger in V1 and LOC ROIs, relative to identical modulations that didn't signal figure-ground segmentation. This segmentation-related activity occurred after an initial response phase that did not depend on the global segmentation structure of the image. The two cue types evoked similar tsVEPs up to 230 ms when they differed in the V4 and LOC ROIs. The evolution of the response proceeded largely in the feed-forward direction, with only weak evidence for feedback-related activity.
authors.affiliation: Center for Cognitive Neuroscience, Duke University, Durham, North Carolina, United States of America. greg@duke.edu
authors.forename: Lawrence G
authors.initials: LG
authors.lastname: Appelbaum
authors.forename: Justin M
authors.initials: JM
authors.lastname: Ales
authors.forename: Anthony M
authors.initials: AM
authors.lastname: Norcia
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology; D003463:Cues; D004569:Electroencephalography / Q000379:methods; D005074:Evoked Potentials, Visual; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D015588:Observer Variation; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors; D014785:Vision, Ocular; D014793:Visual Cortex / Q000502:physiology*