September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Theta-cyclic binding of visual features
Author Affiliations
  • Ryohei Nakayama
    Department of Life Sciences, The University of Tokyo
  • Isamu Motoyoshi
    Department of Life Sciences, The University of Tokyo
Journal of Vision September 2018, Vol.18, 964. doi:https://doi.org/10.1167/18.10.964
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      Ryohei Nakayama, Isamu Motoyoshi; Theta-cyclic binding of visual features. Journal of Vision 2018;18(10):964. https://doi.org/10.1167/18.10.964.

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

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Abstract

Recent psychophysical studies have demonstrated that attention periodically facilitates visual detection performance at around 5-7 Hz. The present study examined the periodicity of feature binding – another major function of attention (Treisman & Gelade, 1980) – by using a dynamic visual display with conjunctive feature dimensions (Holcombe & Cavanagh, 2001). Observers viewed a pair of stimuli that alternated in/out of phase with each other in brightness (light/dark) and orientation (clock/unclockwise) and reported which of the two attributes appeared more temporally synchronous. The temporal frequency of alternation was manipulated by an adaptive staircase to maintain overall performance at ~70.1%. Correct response rate was obtained across trials for various SOAs with respect to the observer's button press (50-810 msec). We found that the correct rate fluctuated with a periodicity of 6-7 Hz as a function of SOA by means of fitting a sinusoid to individual data. Statistical comparisons with the bootstrapped data and spectrum analyses also confirmed the periodicity. These results support the notion that attention operates periodically not only to facilitate visual processing but also to bind visual features within the theta-rhythm band, and that phase locking in theta-band neural oscillations by voluntary action (Popovych et al., 2016) may underlie the periodicity of visual oscillations.

Meeting abstract presented at VSS 2018

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