2002
DOI: 10.1038/nn886
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Spatiotemporal mechanisms for detecting and identifying image features in human vision

Abstract: Our visual system constantly selects salient features in the environment, so that only those features are attended and targeted by further processing efforts to identify them. Models of feature detection hypothesize that salient features are localized based on contrast energy (local variance in intensity) in the visual stimulus. This hypothesis, however, has not been tested directly. We used psychophysical reverse correlation to study how humans detect and identify basic image features (bars and short line seg… Show more

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Cited by 134 publications
(118 citation statements)
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“…The detection of salient targets in noisy one-dimensional movies has been investigated by Neri and Heeger (2002). Their observers were asked to foveally detect and identify a target in a short movie sequence (nine frames, total trial from approximately 500 msec to approximately 550 msec for 1º to 15º eccentricity (top graph, black; p .05 for a t test that compared ranges from 1º to 10º and from 11º to 20º).…”
Section: Discussionmentioning
confidence: 99%
“…The detection of salient targets in noisy one-dimensional movies has been investigated by Neri and Heeger (2002). Their observers were asked to foveally detect and identify a target in a short movie sequence (nine frames, total trial from approximately 500 msec to approximately 550 msec for 1º to 15º eccentricity (top graph, black; p .05 for a t test that compared ranges from 1º to 10º and from 11º to 20º).…”
Section: Discussionmentioning
confidence: 99%
“…However, our own stereoscopic system may have evolved to achieve a different goal: solving the correspondence problem not uniformly but preferentially for a location closer to the fovea or the centre of attention. In order to achieve timely behaviour, the limited capacity of the visual system does not permit detailed analyses of all inputs [49,50]. For a peripheral visual field, a simpler mechanism may be preferentially used to detect disparity without solving the correspondence problem, at the cost of falsely detecting disparity from illusory binocular inputs such as anti-correlated stimuli.…”
Section: (D) Functional Advantage Of Multiple Disparity Computationsmentioning
confidence: 99%
“…We used classification image analysis (Ahumada and Lovell, 1971;Gold et al, 2000;Eckstein and Ahumada, 2002;Neri and Heeger, 2002;Levi and Klein, 2003;Rajashekar et al, 2006), which allowed us to measure and compare the behaviorally defined, spatial receptive fields of the visual mechanisms responsible for saccadic and perceptual decisions. With this method, the entire brain is viewed as a single system in which input is the stimulus and output is either the saccadic or perceptual decision.…”
Section: Introductionmentioning
confidence: 99%