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Intrinsic fluctuations in sustained attention and distractor processing.

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  • 1Veterans Administration Boston Healthcare System, Neuroimaging Center for Veterans, Boston, Massachusetts 02130, Boston University School of Medicine, Department of Psychiatry, Boston, Massachusetts 02118, Yale University, Department of Psychology, New Haven, Connecticut 06520, and Veterans Administration Puget Sound Health Care System, Seattle, Washington 98108.

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Attention fluctuates between optimal and suboptimal states. Brain activity during attention instability correlates with task networks and default mode network, revealing efficient resource allocation during peak attention.

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Attention is crucial for daily functioning but fluctuates between optimal and suboptimal states.
  • Understanding these attentional fluctuations is key to characterizing human cognitive performance.

Purpose of the Study:

  • To investigate the relationship between behavioral fluctuations in attention, brain activity, and distractor processing.
  • To characterize the neural underpinnings of optimal versus suboptimal attentional states.

Main Methods:

  • Utilized a gradual-onset continuous performance task with background distractors.
  • Employed functional magnetic resonance imaging (fMRI) to measure brain activity.
  • Analyzed reaction time variability as a measure of attentional instability.

Main Results:

  • Reaction time variability positively correlated with task-positive network activity and negatively with default mode network activity.
  • Greater processing of distractor images occurred during stable, "in the zone" attentional epochs compared to suboptimal epochs.
  • Attentional instability was linked to altered brain network engagement.

Conclusions:

  • Optimal attention involves efficient recruitment of task-relevant resources.
  • This efficiency may free up cognitive resources, allowing for processing of irrelevant environmental features.
  • Brain activity patterns reflect the dynamic shifts between attentional states.