A review of gaze entropy as a measure of visual scanning efficiency
Abstract
While the concept of entropy has been applied to gaze analysis, it is unclear what aspects of visual scanning it measures. In this review, we first outline gaze control as a complex system of spatial prediction. Second, we provide a brief introduction to the concept of entropy within the context of information theory as the foundation for gaze entropy measures; with a specific focus on equations for Shannon's entropy and conditional entropy. The application of these equations to gaze data is described as stationary gaze entropy (SGE) and gaze transition entropy (GTE) respectively. Third, we present an updated model of gaze orientation and propose an adaptable definition of GTE as a measure of visual scanning efficiency that underlies overall gaze dispersion measured by SGE. Finally, we review studies that have utilised GTE and SGE to assess visual scanning and discuss their results in relation to our proposed definitions and associated hypotheses. Methodological limitations in gaze entropy measures are discussed and suggestions provided to improve interpretability and generalisability of future studies.
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Pupil diameter, eyelid closure, and gaze entropy are highly correlated with a person's cognitive load.
“Beyond user reliance, eye tracking and pupillometry have been used to gauge the attentional, cognitive, and emotional state of a user [12,18]; for instance, pupil diameter, eyelid closure, and gaze entropy [71] are highly correlated with a person's cognitive load [84].”
From Understanding User Reliance on AI in Assisted Decision-Making (2022) · cites this paper as [71]
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Understanding User Reliance on AI in Assisted Decision-Making
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- Authors
- Shiferaw, B., Downey, L., & Crewther, D.
- Affiliations
- Swinburne University of Technology (AU)
- Austin Hospital (AU)
- Corresponding author
- Shiferaw, Brook
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 96
- Pages
- 353-366
- ISSN
- 0149-7634 (ISSN-L)
- OpenAlex
- W2905459027
- Cited by
- 203
- References
- 138
- Citations by year
- 2026: 40 · 2025: 34 · 2024: 40 · 2023: 17 · 2022: 29 · 2021: 26 · 2020: 11 · 2019: 6
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion)
- PubMed (publishedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- NHMRC Career Development Fellowship
- Australian Government through a Research Training Program Stipend (RTPS) scholarship
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- gaze entropy, gaze control, Shannon entropy, conditional entropy, gaze dispersion, information theory, eye tracking
- MeSH terms
- Appetitive Behavior, Eye Movements, Humans, Information Theory, Models, Biological, Psychophysics, Visual Perception, Entropy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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