Assessing cognitive load using oculometrics to identify deceit during interviews

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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Assessing cognitive load using oculometrics to identify deceit during interviews

Cited by 2 publications

References 40 publications

Classifying the Deceptive and Truthful Responses Through Ocular Measures in an Interview Process

Classifying the Deceptive and Truthful Responses Through Ocular Measures in an Interview Process

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

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