September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Change blindness for changes in 3D structure
Author Affiliations
  • Ellis Gootjes-Dreesbach
    School of Psychology and Clinical Language Sciences, University of Reading
  • Peter Scarfe
    School of Psychology and Clinical Language Sciences, University of Reading
  • Andrew Glennerster
    School of Psychology and Clinical Language Sciences, University of Reading
Journal of Vision August 2017, Vol.17, 1206. doi:10.1167/17.10.1206
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      Ellis Gootjes-Dreesbach, Peter Scarfe, Andrew Glennerster; Change blindness for changes in 3D structure. Journal of Vision 2017;17(10):1206. doi: 10.1167/17.10.1206.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Scarfe and Glennerster (VSS, 2016) reported low detection rates for the displacement of a single element in an otherwise stable scene. Here we report performance on the same stimuli when the target is known and compare it to the existing literature on disparity change thresholds. Unlike the detection of change when the entire scene expands (Glennerster et al, 2006), stereo algorithms using these input images would readily detect the target movement. Participants had to identify whether the target, a chequered sphere (viewing distance between 2.5 and 7.5m) presented in immersive virtual reality, moved between intervals (it did so on 50% of trials). When the target was displaced (+/- 2m or, on other runs, +/- 0.3m) it maintained its retinal size. Either 3 or 15 additional distractor spheres were presented at a similar range of viewing distances. In one condition, rods or 'dipoles' joined pairs of spheres and switched to link different pairs between intervals. This manipulation provides no additional information for feature-based stereo algorithms. We found that detection thresholds for movement overlapped with the range of thresholds reported previously (McKee, Levi & Bowne, 1990) when the target was known, but were much higher when the target was unknown. We interpret these results in terms of the number of 'channels' that the participant must monitor and hence analyse limitations on performance in relation to memory constraints rather than 3D reconstruction.

Meeting abstract presented at VSS 2017

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