Exploring Gaze Re-Inspection Dynamics with Running Recurrence Quantification Analysis (RRQA)

Gaze behavior provides key insights into cognitive processes such as attention, perception, and decision-making. Traditional eye-tracking metrics—such as fixation durations—capture meaningful patterns but overlook re-fixation dynamics, which signal shifts in gaze strategies. Recurrence Quantification Analysis (RQA) detects such patterns but aggregates data across entire tasks, obscuring temporal variations. Sliding Window RQA (SWRQA) tracks recurrence over time, yet fixed window sizes struggle with short fixation sequences and long-span recurrences, limiting suitability for gaze data. To address these limitations, we present Running RQA (RRQA). This proof-of-concept computes standard RQA metrics at each fixation, enabling fine-grained temporal tracking of Recurrence Rate (REC), Determinism (DET), Laminarity (LAM), and Center of Recurrence Mass (CORM). We introduce three visualization techniques to map changes in RRQA metrics for per-fixation, multi-participant comparisons. An open-source web application enables researchers to upload, analyze, and explore gaze data with RRQA visualizations interactively, facilitating detailed investigations of re-examination behavior: https://osf.io/ptfe7/.