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A Machine Learning Approach for Detecting Cognitive Interference Based on Eye-Tracking Data.

Antonio Rizzo1, Sara Ermini1, Dario Zanca1,2

  • 1Department of Social, Political and Cognitive Science, University of Siena, Siena, Italy.

Frontiers in Human Neuroscience
|May 16, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately identified cognitive interference during Stroop tasks by analyzing eye movement patterns. This reveals common subject behaviors detectable by algorithms, despite individual differences in visual attention.

Keywords:
Stroop testattention loadclassificationcognitive interferenceeye-trackingmachine learning

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Area of Science:

  • Cognitive psychology
  • Neuroscience
  • Computer science

Background:

  • The Stroop test is a standard measure for assessing cognitive interference.
  • Cognitive interference arises when processing one stimulus attribute impacts another.
  • Eye movements reflect attentional demands during cognitive tasks.

Purpose of the Study:

  • To investigate if eye movement patterns can differentiate cognitive interference conditions.
  • To apply machine learning to eye-tracking data for classifying task difficulty.
  • To identify commonalities in visual behavior during cognitive interference.

Main Methods:

  • Collected eye movement data from over 60 participants performing Stroop-like tasks.
  • Extracted features from eye-tracking data, including fixations, saccades, and gaze trajectories.
  • Trained and evaluated various machine learning models to classify task conditions (with/without interference).

Main Results:

  • Machine learning models demonstrated high accuracy in distinguishing between tasks with and without cognitive interference.
  • Identified characteristic eye movement patterns associated with cognitive interference.
  • Observed consistent patterns across subjects, despite individual variations in visual behavior.

Conclusions:

  • Eye movement analysis combined with machine learning can reliably detect cognitive interference.
  • Machine learning algorithms can capture common neural signatures of cognitive load from visual behavior.
  • This approach offers a potential objective measure for cognitive state assessment.