Spatio-temporal templates of transient attention revealed by classification images
Authors: Megna N., Rocchi F., Baldassi S.
Autors Affiliation: Univ Florence, Dept Psychol, I-50135 Florence, Italy; Univ Florence, Dept Phys & Astron, I-50019 Florence, Italy; Natl Opt Inst, I-50125 Florence, Italy; Univ Nottingham, Sch Psychol, Visual Neurosci Grp, Nottingham NG7 2RD, England
Abstract: Visual attention is captured by transient signals in the periphery of the visual field, allowing enhanced perceptual representations in spatial tasks. However, it has been reported that the same cues impair performance in temporal tasks (e.g.. Yeshurun, 2004; Yeshurun & Levy, 2003). This findings suggest that transient attention enhances the activity of slow, high-resolution channels, like parvocellular neurons, and/or shuts off faster channels better sensitive to low spatial frequencies, such as the ones of the magnocellular system. To test this idea, we have measured the spatio-temporal perceptive fields for transiently cued signals at various eccentricities using the classification images (CI) technique. At near eccentricities transient attention caused the perceptual templates to be sharper in space and characterized by much stronger high spatial frequency components. At the same time, they show a consistently larger temporal integration window. These effects of attention on perceptual filters are strongly reduced at far eccentricities and disappear when using longer target-cue lags. These data provide evidence in support of the parvocellular model of transient, exogenous attention, showing that in the presence of a well timed spatial cue observers rely on noisy evidence lasting longer and with finer spatial configurations. (C) 2011 Elsevier Ltd. All rights reserved.
Journal/Review: VISION RESEARCH
Volume: 54 Pages from: 39 to: 48
More Information: This work has been supported by the ERC project STANIB.KeyWords: Classification images; Attention; Parvocellular system; Magnocellular system; DOI: 10.1016/j.visres.2011.11.012Citations: 10data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2021-01-17References taken from IsiWeb of Knowledge: (subscribers only)Connecting to view paper tab on IsiWeb: Click hereConnecting to view citations from IsiWeb: Click here