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COLET: A dataset for COgnitive workLoad estimation based on eye-tracking.

Emmanouil Ktistakis1, Vasileios Skaramagkas2, Dimitris Manousos3

  • 1Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), GR-700 13 Heraklion, Greece; Laboratory of Optics and Vision, School of Medicine, University of Crete, GR-710 03 Heraklion, Greece.

Computer Methods and Programs in Biomedicine
|July 23, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed the COLET dataset to estimate cognitive workload using eye-tracking. Machine learning models achieved up to 88% accuracy in distinguishing workload levels, enabling new performance psychology research.

Keywords:
Affective computingCognitive workloadEye movementsEye-trackingMachine learningWorkload classification

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Area of Science:

  • Cognitive psychology
  • Human-computer interaction
  • Ergonomics

Background:

  • Cognitive workload is crucial in performance psychology, ergonomics, and human factors.
  • Limited publicly available datasets hinder research and comparative studies in cognitive workload estimation.
  • The COLET (COgnitive workLoad estimation based on Eye-Tracking) dataset is introduced to address this gap.

Purpose of the Study:

  • To present the COLET dataset for cognitive workload estimation using eye-tracking.
  • To analyze the relationship between eye movement metrics and cognitive workload levels.
  • To evaluate machine learning models for predicting cognitive workload.

Main Methods:

  • Eye movements of 47 participants were recorded during visual search tasks of varying complexity and duration.
  • Cognitive workload was assessed using the NASA-TLX subjective test, serving as data annotation.
  • Eye and gaze features were derived from recorded metrics, and machine learning models were applied for workload estimation.

Main Results:

  • Activities induced four distinct cognitive workload levels, with multitasking and time pressure increasing workload.
  • Multitasking significantly affected 17 eye features, while time pressure impacted 7 eye features.
  • Machine learning models achieved up to 88% accuracy in predicting cognitive workload levels.

Conclusions:

  • Machine learning effectively discriminates cognitive workload levels using eye-tracking data.
  • The COLET dataset offers a larger sample size and broader range of eye metrics compared to existing datasets.
  • This dataset facilitates further examination of eye metrics in relation to diverse cognitive states.