Auto Detecting Cognitive Events Using Machine Learning on Pupillary Data

TL;DR

This paper demonstrates that machine learning models, specifically CNNs, can effectively detect cognitive events from pupillary data, advancing real-time cognitive workload assessment and personalized neurocognitive management.

Contribution

It introduces a CNN-based approach for automatic detection of cognitive events using pupillary data, addressing generalization and structural variances across tasks.

Findings

Matthew's correlation coefficient ranged from 0.47 to 0.80

Models can detect stimulus onset in real-time

Trade-offs between generalization and task-specific accuracy

Abstract

Assessing cognitive workload is crucial for human performance as it affects information processing, decision making, and task execution. Pupil size is a valuable indicator of cognitive workload, reflecting changes in attention and arousal governed by the autonomic nervous system. Cognitive events are closely linked to cognitive workload as they activate mental processes and trigger cognitive responses. This study explores the potential of using machine learning to automatically detect cognitive events experienced using individuals. We framed the problem as a binary classification task, focusing on detecting stimulus onset across four cognitive tasks using CNN models and 1-second pupillary data. The results, measured by Matthew's correlation coefficient, ranged from 0.47 to 0.80, depending on the cognitive task. This paper discusses the trade-offs between generalization and specialization, model behavior when encountering unseen stimulus onset times, structural variances among cognitive tasks, factors influencing model predictions, and real-time simulation. These findings highlight the potential of machine learning techniques in detecting cognitive events based on pupil and eye movement responses, contributing to advancements in personalized learning and optimizing neurocognitive workload management.