Pupillometry and autonomic nervous system responses to cognitive load and false feedback - an unsupervised machine learning approach

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Pupillary dynamics provide a window into sympathetic and parasympathetic control during cognitive stress. This study tests whether changes in pupil size under increasing cognitive load combined with false feedback can predict individual behavior when integrated with heart rate variability and eye-movement measures. Using an unsupervised k-means clustering approach on pupillometry from 70 participants, two physiological-behavioral groups are identified and compared on timing, errors, self-esteem, and lifestyle factors, supporting data-driven assessment of cognitive load adaptation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Objectives

Methods

Clustering approach

Results

Conclusion

TYPE Original Research PUBLISHED 30 August 2024 DOI 10.3389/fnins.2024.1445697 OPEN ACCESS EDITED BY Chin-An Josh Wang, Taipei Medical University, Taiwan REVIEWED BY Stuart R. Steinhauer, United States Department of Veterans Affairs, United States Jeff Huang, Queen’s University, Canada *CORRESPONDENCE Evgeniia I. Alshanskaia [eisokolova@hse. ru](eisokolova@hse. ru) RECEIVED 07 June 2024 ACCEPTED 09 August 2024 PUBLISHED 30 August 2024 CITATION Alshanskaia EI, Portnova GV, Liaukovich K and Martynova OV (2024) Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach. Front. Neurosci. 18:1445697 . doi: 10.3389/fnins.2024.1445697 COPYRIGHT © 2024 Alshanskaia, Portnova, Liaukovichand Martynova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach Evgeniia I. Alshanskaia1*, Galina V. Portnova2 , Krystsina Liaukovich2 and Olga V. Martynova3 1 Faculty of Social Sciences, School of Psychology, National Research University Higher School of Economics, Moscow, Russia, 2 Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia, 3 Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia Objectives: Pupil dilation is controlled both by sympathetic and parasympathetic nervous system branches. We hypothesized that the dynamic of pupil size changes under cognitive load with additional false feedback can predict individual behavior along with heart rate variability (HRV) patterns and eye movements reﬂecting speciﬁc adaptability to cognitive stress. To test this, we employed an unsupervised machine learning approach to recognize groups of individuals distinguished by pupil dilation dynamics and then compared their autonomic nervous system (ANS) responses along with time, performance, and self-esteem indicators in cognitive tasks. Methods: Cohort of 70 participants were exposed to tasks with increasing cognitive load and deception, with measurements of pupillary dynamics, HRV, eye movements, and cognitive performance and behavioral data. Utilizing machine learning k-means clustering algorithm, pupillometry data were segmented to distinct responses to increasing cognitive load and deceit. Further analysis compared clusters, focusing on how physiological (HRV, eye movements) and cognitive metrics (time, mistakes, self-esteem) varied across two clusters of different pupillary response patterns, investigating the relationship between pupil dynamics and autonomic reactions. Results: Cluster analysis of pupillometry data identiﬁed two distinct groups with statistically signiﬁcant varying physiological and behavioral responses. Cluster 0 showed elevated HRV, alongside larger initial pupil sizes. Cluster 1 participants presented lower HRV but demonstrated increased and pronounced oculomotor activity. Behavioral differences included reporting more errors and lower selfesteem in Cluster 0, and faster response times with more precise reactions to deception demonstrated by Cluster 1 . Lifestyle variations such as smoking habits and differences in Epworth Sleepiness Scale scores were signiﬁcant between the clusters. Conclusion: The differentiation in pupillary dynamics and related metrics between the clusters underlines the complex interplay between autonomic regulatio

FAQs

What is the main goal of the study?

To determine whether pupil-size dynamics under cognitive load with false feedback can distinguish individual response patterns and relate them to autonomic nervous system activity, eye movements, and behavioral measures.

How was the data analyzed?

The study applied an unsupervised k-means clustering algorithm to segmented pupillometry responses, then compared clusters using HRV, eye-movement metrics, cognitive performance, and self-esteem.

What differences were found between the identified clusters?

One cluster showed higher HRV with larger initial pupil sizes but more errors and lower self-esteem, while the other showed lower HRV with stronger oculomotor activity and faster, more precise responses to deception.

Aug 5, 2026

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