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Updated: Sep 10, 2025

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
Published on: March 18, 2019
Hierarchical Organization of Human Visual Feature Attention Control
Sreenivasan Meyyappan1, Mingzhou Ding2, George R Mangun3,4
1Center for Mind and Brain, University of California, Davis, California 95618.
Abstract:
Attention can be deployed in advance of visual stimuli based on features such as color or direction of motion. This anticipatory feature-based attention involves top-down neural control signals from the frontoparietal network that bias visual cortex to enhance attended information and suppress distraction. For example, anticipatory attention control can enable effective selection based on stimulus color while ignoring distracting information about stimulus motion. Anticipatory attention can also be focused more narrowly, for example, to select specific colors or motion directions that define task-relevant aspects of the stimuli. One important question that remains open is whether anticipatory attention control first biases broad feature dimensions such as color versus motion before biasing the specific feature attributes (e.g., blue vs green). To investigate this, we recorded EEG activity during a task where human participants of either sex were cued to either attend to a motion direction (up or down) or a color (blue or green) on a trial-by-trial basis. Applying multivariate decoding approaches to the EEG alpha band activity (8-12 Hz) during attention control (cue-target interval), we observed significant decoding for both the attended dimensions (motion vs color) and specific feature attributes (up vs down; blue vs green). Importantly, the temporal onset of the dimension-level biasing (motion vs color) preceded that of the attribute-level biasing (up vs down and blue vs green). These findings demonstrate that the top-down control of feature-based attention proceeds in a hierarchical fashion, first biasing the broad feature dimension, and then narrowing to the specific feature attribute.
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