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Is Depth Perception of Stereo Plaids Predicted by Intersection of Constraints, Vector Average or Second-order Features?

By Louise Delicato and Ning Qian


Stereo plaid stimuli were created to investigate whether depth perception is determined by an intersection of constraints (IOC) or vector average (VA) operation on the Fourier components, or by the second-order (non-Fourier) feature in a pattern. We first created stereo plaid stimuli where IOC predicted vertical disparity, VA predicted positive diagonal disparity and the second-order feature predicted negative diagonal disparity. In a depth discrimination task, observers indicated whether they perceived the pattern as 'near' or 'far' relative to a zero-disparity aperture. Observers' perception was consistent with the disparity predicted by VA, indicating its dominance over IOC and the second-order feature in this condition. Additional stimuli in which VA predicted vertical disparity were created to investigate whether VA would dominate perception when it was a less reliable cue. In this case, observers' performance was consistent with disparity predicted by IOC or the second-order feature, not VA. Finally, in order to determine whether the second-order feature contributes to depth perception, stimuli were created where IOC and VA predicted positive horizontal disparity while the second-order feature predicted negative horizontal disparity. When the component gratings were oriented near horizontal (+/- 83 degrees from vertical), depth perception corresponded to that predicted by the second-order feature. However, as the components moved away from horizontal (+/- 75 degrees and +/- 65 degrees from vertical), depth perception was increasingly likely to be predicted by an IOC or VA operation. These experiments suggest that the visual system does not rely exclusively on a single method for computing pattern disparity. Instead, it favours the most reliable method for a given condition. (c) 2004 Elsevier Ltd. All rights reserved

Topics: sub_neuropsychology
Year: 2005
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