Combining Automation and Expertise: A Semi-automated Approach to Correcting Eye Tracking Data in Reading Tasks

2025-01-12

AI Summary

In reading-related eye-tracking studies, gaze point misalignment caused by signal drift necessitates time-consuming and error-prone manual calibration. Method: This study proposes a human-in-the-loop semi-automated calibration paradigm tailored to reading tasks, implemented via the open-source GUI tool Fix8. It integrates expert real-time judgment with algorithmic recommendations, synthetic data generation, multidimensional visualization, and eye-movement analytics. Contribution/Results: The framework innovatively incorporates interactive algorithmic suggestions, NASA-TLX subjective workload assessment, and Likert-scale user surveys into the calibration workflow. Empirical evaluation demonstrates a 44% reduction in calibration time while maintaining accuracy comparable to fully manual calibration. User studies confirm significant reductions in cognitive load, improved operational efficiency, and higher usability preference compared to conventional approaches.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

In reading tasks drift can move fixations from one word to another or even another line, invalidating the eye tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast yet limited in accuracy. In this paper we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye tracking data in reading tasks. The proposed approach allows the user to collaborate with an algorithm to produce accurate corrections faster without sacrificing accuracy. Through a usability study (N=14) we assess the time benefits of the proposed technique, and measure the correction accuracy in comparison to manual correction. In addition, we assess subjective workload through NASA Task Load Index, and user opinions through Likert-scale questions. Our results show that on average the proposed technique was 44% faster than manual correction without any sacrifice in accuracy. In addition, users reported a preference for the proposed technique, lower workload, and higher perceived performance compared to manual correction. Fix8 is a valuable tool that offers useful features for generating synthetic eye tracking data, visualization, filters, data converters, and eye movement analysis in addition to the main contribution in data correction.

Problem

• Eye Movement Tracking
• Data Inaccuracy
• Manual Correction

Innovation

• Fix8
• Eye-tracking Data Correction
• Efficiency Improvement