A temporally quantized distribution of pupil diameters as a new feature for cognitive load classification

TL;DR

This paper introduces a novel temporally quantized distribution feature of pupil diameters for improved classification of cognitive load from eye tracking data, with potential applications in burnout prevention.

Contribution

It proposes a new temporal segmentation-based feature that enhances cognitive load classification accuracy over existing statistical methods.

Findings

Significant improvement in classification accuracy.

Effective in detecting varying levels of cognitive load.

Potential applications in burnout warning systems.

Abstract

In this paper, we present a new feature that can be used to classify cognitive load based on pupil information. The feature consists of a temporal segmentation of the eye tracking recordings. For each segment of the temporal partition, a probability distribution of pupil size is computed and stored. These probability distributions can then be used to classify the cognitive load. The presented feature significantly improves the classification accuracy of the cognitive load compared to other statistical values obtained from eye tracking data, which represent the state of the art in this field. The applications of determining Cognitive Load from pupil data are numerous and could lead, for example, to pre-warning systems for burnouts. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FCognitiveLoadFeature&mode=list