Recurrence Quantification Analysis for Group Eye Tracking Data

Date

2026-01-01

Author

Tajaddini, Mani
Çakır, Murat Perit
Acartürk, Cengiz

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Traditional eye tracking methodologies have largely focused on single-user data. The study of multi-user dynamics and social interaction requires a novel analysis framework, partially addressed in current research. In this study, we introduce Group Eye Tracking (GET) as a framework for simultaneously collecting and analyzing eye movement data from multiple participants to reveal group-level patterns of visual dynamics. We use a custom application, which synchronously records eye movements from multiple users performing tasks on separate computers, and a custom R package implementing Recurrence Quantification Analysis (RQA) for examining time-series recurrences of visual dynamics. By quantifying how eye movement patterns recur and align among group members, we potentially provide indicators of cognitive states in collaborative decision-making, within real-time group interactions. The resulting measures can also provide information about the role of task parameters, interface layouts, and team performance. This approach demonstrates how GET can serve for developing next-generation augmented cognition systems by integrating advanced analytics and real-time adaptivity by the analysis of collective task outcomes.

Subject Keywords

Decision Making, Eye Tracking Visualization, Group Eye Tracking, Recurrence Quantification Analysis

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105028359206&origin=inward
https://hdl.handle.net/11511/118581

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Graduate School of Informatics, Conference / Seminar