ArticlePeerJ2022
Classifying the difficulty levels of working memory tasks by using pupillary response.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Residual gaze behaviour during navigation in blindness and low vision.Disability and rehabilitation. Assistive technology · 2026Review
- Associations Between Eye-Movement Patterns, Pupil Dynamics, and the Interpretation of a Single Mixed-Dentition Panoramic Radiograph Among Dental Students: An Exploratory Eye-Tracking Study.Vision (Basel, Switzerland) · 2026Article
- A Pilot Study on Video Game Training Effects on Visual Working Memory: Behavioral and Neural Insights.Brain sciences · 2025Article
- Multimodal decoding of error processing in a virtual reality flight simulation.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowing the difficulty of a given task is crucial for improving the learning outcomes. This paper studies the difficulty level classification of memorization tasks from pupillary response data. Developing a difficulty level classifier from pupil size features is challenging because of the inter-subject variability of pupil responses. Eye-tracking data used in this study was collected while students solved different memorization tasks divided as low-, medium-, and high-level. Statistical analysis shows that values of pupillometric features (as peak dilation, pupil diameter change, and suchlike) differ significantly for different difficulty levels. We used a wrapper method to select the pupillometric features that work the best for the most common classifiers; Support Vector Machine (SVM), Decision Tree (DT), Linear Discriminant Analysis (LDA), and Random Forest (RF). Despite the statistical difference, experiments showed that a random forest classifier trained with five features obtained the best F1-score (82%). This result is essential because it describes a method to evaluate the cognitive load of a subject performing a task using only pupil size features.
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Registered trials
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