December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Decoding visual feature attention control from scalp topography of alpha oscillations
Author Affiliations & Notes
  • Sreenivasan Meyyappan
    Center for Mind and Brain, University of California Davis
  • Srivatsa S. Katta
    Center for Mind and Brain, University of California Davis
  • Mingzhou Ding
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida
  • George R. Mangun
    Center for Mind and Brain, University of California Davis
    Department of Psychology, University of California Davis
    Department of Neurology, University of California Davis
  • Footnotes
    Acknowledgements  NIMH MH117991
Journal of Vision December 2022, Vol.22, 4208. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Sreenivasan Meyyappan, Srivatsa S. Katta, Mingzhou Ding, George R. Mangun; Decoding visual feature attention control from scalp topography of alpha oscillations. Journal of Vision 2022;22(14):4208.

      Download citation file:

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

  • Supplements

Anticipatory attention can be deployed to visual stimuli in advance of their appearance based on their location (spatial attention) or non-spatial attributes such as motion or color (feature attention). The neural mechanisms underlying attentional control remain to be better understood. During visual spatial attention control, alpha oscillations (8 to 12 Hz), as an inverse index of cortical excitability, are more suppressed in the hemisphere contralateral to the attended visual hemifield. For feature attention, however, much less is known about the behavior of alpha oscillations. We addressed this problem by recording EEG from subjects performing a cued visual feature attention task. Auditory cues directed subjects to attend either a specific motion direction (up or down) or a specific color (blue or green). Following a delay period, two streams of dots moving in opposite directions (up and down) and with different colors (blue and green) appeared briefly. Subjects were instructed to report the size of the dots (large versus small) in the stream that either moved in the cued direction or had cued color while ignoring the other stream. Applying univariate and multivariate pattern analysis (MVPA) to the cue-evoked pre-stimulus EEG data, we observed that (1) univariate alpha power did not differ significantly across cueing conditions, (2) the topographical patterns of alpha activity predicted the attended feature domain (attend-color versus attend-motion) at significantly above chance level, and (3) more importantly, within each attended feature domain, the accuracy of decoding attend-blue versus attend-green and attend-up versus attend-down was also significantly above chance level. These results show that the scalp topography of alpha oscillations contains sufficiently nuanced information that can differentiate between different conditions of feature attention control and provides a neural marker for understanding attention control mechanisms that lead to enhancement of attended information and suppression of distraction.


This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.