September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Modeling the neural underpinnings of attentional suppression as constrained by EEG and behavioral data
Author Affiliations
  • Chloe Callahan-Flintoft
    Pennsylvania State University
  • Brad Wyble
    Pennsylvania State University
Journal of Vision September 2018, Vol.18, 527. doi:
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      Chloe Callahan-Flintoft, Brad Wyble; Modeling the neural underpinnings of attentional suppression as constrained by EEG and behavioral data. Journal of Vision 2018;18(10):527.

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

  • Supplements

A major challenge of perception is the decision of what to attend to. What makes information "important" is thought to be a combination of goal-defined top-down control (e.g. searching for a highway sign while driving), and stimulus driven bottom-up salience (e.g. a deer suddenly appearing in front of your car). Mediating between these two competing requirements is a major challenge that the brain meets by allowing stimuli to compete for attention, a competition that is ultimately resolved by enhancing some information while suppressing others. However there is mixed evidence for how this suppression occurs. Some studies show evidence for graded suppression in the immediate vicinity of a target (Mounts, 2000), while others show suppression specifically applied at the locations of salient distractors (Gaspelin et al., 2015). While these results may seem at odds with one another, they can be explained by a single underlying model. We present such a model that uses hierarchical neural circuits specifically adapted for rapid, parallel decision making about how to deploy attention across the visual field. Another crucial contribution of the model is elucidating the underlying causes of the neural correlates of attention such as the Pd. While the Pd is commonly thought to reflect distractor suppression, the model attributes its source to the suppression of locations in the visual field that did not win the competition for attention. Depending upon the rigidity of attentional control settings, the losing location could be that of the target or the distractor. Rigid control settings (feature search) more often result in the target winning whereas weaker settings (singleton search) frequently allow the distractor to win. Thus the model explains how task requirements determine behavioral and neural correlates of attentional capture. More generally, the model provides an intuition linking behavior and neural correlates to underlying mechanisms.

Meeting abstract presented at VSS 2018


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