Classifying the difficulty levels of working memory tasks by using pupillary response

• Published in: PeerJ (San Francisco, CA) Vol. 10; p. e12864
• Main Authors: Mitre-Hernandez, Hugo, Sanchez-Rodriguez, Jorge, Nava-Muñoz, Sergio, Lara-Alvarez, Carlos
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 29.03.2022; PeerJ, Inc; PeerJ Inc
• Subjects: Classifiers; Cognition & reasoning; Cognitive ability; Cognitive load; Consciousness; Education; Eye movements; Geospatial data; Human-Computer Interaction; Humans; Learning; Learning outcomes; Memorization; Memory; Memory, Short-Term - physiology; Methods; Neuroscience; Phonology; Physiology; Psychiatry and Psychology; Pupil - physiology; Pupil size; Science and Medical Education; Short term memory; Statistical analysis; Statistics; Students; Working memory; Classifiers; Cognitive load; Pupil size; Working memory
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.12864

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.