July 2019
Volume 19, Issue 8
Open Access
OSA Fall Vision Meeting Abstract  |   July 2019
Using EEG to examine the timecourse of motion-in-depth perception
Author Affiliations
  • Alex Wade
    University of York
  • Federico Segala
    University of York
  • Miaomiao Yu
    University of York
Journal of Vision July 2019, Vol.19, 104. doi:https://doi.org/10.1167/19.8.104
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      Alex Wade, Federico Segala, Miaomiao Yu; Using EEG to examine the timecourse of motion-in-depth perception. Journal of Vision 2019;19(8):104. doi: https://doi.org/10.1167/19.8.104.

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

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Abstract

Stereoscopic motion in depth generates two independent cues generated by differencing signals from the two eyes. ‘changing disparity’ (CD) is the temporal derivative of the instantaneous binocular disparity while the ‘interocular velocity difference’ (IOVD) is the difference between two measures of retinal motion. Both of these cues are used by the visual system estimate three-dimensional velocity.

We used high-density EEG to ask when cues for different types of motion in depth (MID) arrive in the brain. We recorded visually-evoked potentials to random dot MID stimuli presented in a circular aperture (r=5deg) in 12 subjects. Stimuli were generated in Psychtoolbox and presented on a stereoscopic ViewPixx 3D display. MID events lasting 250ms (3s inter-stimulus interval, 120 per type) were cued using either CD or IOVD and could be either towards or away from the observer. Subjects indicated the direction of the MID stimulus on a keypad.

We used a support vector machine (libsvm) with bootstrapped statistics to decode motion type based on electrode-level scalp responses at sequential 5ms blocks after the motion onset. We asked whether we could classify combinations of the MID cue (CD vs IOVD) and motion direction (towards vs away). For each combination [CDt vs CDa], [IOVDt vs IOVDa], [CDt vs IOVDt] and [CDa vs IOVDa] chance performance was 50% at every time point.

We were able to achieve above-chance performance for all combinations beginning at different time points (corrected for multiple comparisons). We discuss our results with reference to the patterns of electrode response that underlie the classifications and potential cortical pathways supporting the two 3D motion mechanisms.

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