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the claim

Computational models of the human visual field account for head, neck, and eye movements to calculate fixation likelihood

the verdict
SUPPORTED
the evidence backs this
Recorded sources
4 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Recent computational models of gaze and visual attention incorporate head, neck, and body movements alongside eye tracking to accurately predict fixation likelihood and gaze trajectories in dynamic environments.

The analysis

Multiple recent papers ([1], [3], [5], [7]) directly support the claim that computational models of the human visual field and gaze prediction integrate head, neck, and body movements alongside eye tracking to calculate fixation likelihood. The retrieved literature offers strong empirical and computational evidence confirming this multi-modal approach without any papers refuting the claim.

Evidence for · 4
Recorded source metadata

Zhiming Hu, Congyi Zhang, Sheng Li, Guoping Wang, Dinesh Manocha. SGaze: A Data-Driven Eye-Head Coordination Model for Realtime Gaze Prediction. 2019. https://doi.org/10.1109/TVCG.2019.2899187

SGaze develops a data-driven eye-head coordination model for realtime gaze prediction by evaluating linear correlations between gaze positions and head rotation angular velocities.

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More for · 3
Recorded source metadata

Zhiming Hu, Jiahui Xu, Syn Schmitt, Andreas Bulling. Pose2Gaze: Eye-Body Coordination During Daily Activities for Gaze Prediction From Full-Body Poses. 2023. https://doi.org/10.1109/TVCG.2024.3412190

Pose2Gaze presents an eye-body coordination model that extracts features from head direction and full-body poses to accurately predict human eye gaze.

Recorded source metadata

Lingling Chen, Yingxi Li, Xiaowei Bai, Xiaodong Wang, Yongqiang Hu, Mingwu Song, Liang Xie, Ye Yan, Erwei Yin. Real-time Gaze Tracking with Head-eye Coordination for Head-mounted Displays. 2022. https://doi.org/10.1109/ISMAR55827.2022.00022

HE-Tracker demonstrates a multi-modal network that fuses head-movement features with eye features to successfully regress gaze positions in AR head-mounted displays.

Recorded source metadata

Colin Rubow, Chia-Hsuan Tsai, Erica Brewer, Connor Mattson, Daniel S. Brown, Haohan Zhang. A dataset of paired head and eye movements during visual tasks in virtual environments. 2024. https://doi.org/10.1038/s41597-024-04184-1

This dataset pairs head and eye movements during visual tasks in virtual environments to enable predictive models of intended head movement conditioned on gaze.

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first checked01 Aug 2026
judged → SUPPORTED · 8701 Aug 2026
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