Sure About That Line? Approaching Confidence-Based, Real-Time Line Assignment in Reading Gaze Data

TL;DR

This paper introduces CONF-LA, a real-time, confidence-based method for line assignment in eye-tracking reading data that improves accuracy and latency, especially in noisy, multi-line reading scenarios.

Contribution

It presents a novel online fixation-to-line assignment algorithm that leverages confidence scores and Gaussian likelihoods, outperforming prior approaches in accuracy and latency.

Findings

CONF-LA achieves 95% median accuracy on children data.

It reduces mean per-fixation latency to 0.348 ms.

It closes the online-offline performance gap to 1-2%.

Abstract

Remote and webcam-based eye tracking in multi-line reading suffers from various noise factors and layout ambiguity, precisely where real-time reading support needs reliable, per-fixation line assignment. Prior work largely addresses this challenge post hoc or by restricting behavior (e.g., disallowing re-reading), undermining interactive use. We propose CONF-LA (Confidence-score-based Online Fixation-to-Line Assignment), a principled, low-latency approach that integrates knowledge about reading behavior and Gaussian line likelihoods over fixations to compute a posterior-line-score and defers assignments when uncertainty is high. Evaluated on existing open-source data, CONF-LA demonstrates stable performance in post hoc analysis and closes the online-offline gap (1-2 %) with a mean per-fixation latency of 0.348 ms. Our approach exhibits particular invariance toward regressions, yielding significant improvement in ad hoc median accuracies on children data (approx. 95 %) over all tested algorithms. We encourage further research in this direction and discuss possibilities for future development.