Reading Distraction Task

 

Subjects read paragraphs of text in the presence and absence of externally driven distractions. Features of the eye position and pupil dilation can then be extracted and used in a machine learning classifier to create an objective, continuous, and metacognition-task-free (if noisy) metric of the subject's sustained attention to the material. To verify the classifier, we will see if moments of greater sustained attention, as inferred from these metrics, result in improved performance on later comprehension questions.

We will then find the aspects of BOLD activation/connectivity that correlate with the output of the classifier, and we will compare them to the correlates of the exogenous distraction used for the classifier's training.

Current Project
  • National Institute of Health
  • National Institute of Mental Health
  • Department of Health and Human Services