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

PeerJ

article

Hugo Mitre-Hernandez¹  Jorge Sanchez-Rodriguez¹  Sergio Nava-Muñoz¹  Carlos Lara-Alvarez¹ 

[1] Unidad Zacatecas

关键词: Working memory;    Pupil size;    Cognitive load;    Classifiers;   

DOI  :  10.7717/peerj.12864

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY