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Cited by 22Gaze Tracking and Assistive TechnologyVisual Attention and Saliency DetectionVisual perception and processing mechanisms

The fundamentals of eye tracking part 6: working with areas of interest

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Hooge, Ignace T. C., Nyström, Marcus, Niehorster, Diederick C., Andersson, Richard, Foulsham, Tom, Nuthmann, Antje, Hessels, Roy S.·2026·Behavior Research Methods
DOI 10.3758/s13428-025-02937-3PMC PMC12913287
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Abstract

Researchers use area of interest (AOI) analyses to interpret eye-tracking data. This article addresses four key aspects of AOI use: 1) how to report AOIs to support replicable analyses, 2) how to interpret AOI-related statistics, 3) methods for generating both static and dynamic AOIs, and 4) recent developments and future directions in AOI use. The article underscores the importance of aligning AOI design with the study’s conceptual and methodological foundations. It argues that critical decisions, such as the size, shape, and placement of AOIs, should be made early in the experimental design process and should involve eye-tracking data quality, the research question, participant tasks, and the nature of the visual stimulus. It also evaluates recent advances in AOI automation, outlining both their benefits and limitations. The article’s main message is that researchers should plan AOIs carefully and explain their choices openly so others can replicate the work.

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  • The AOI classification enabled attention to equivalent information functions to be compared despite differences in page layout and interface structure.

    “This classification enabled attention to equivalent information functions to be compared despite differences in page layout and interface structure (Hooge et al., 2026).”

    From Responsible use of generative AI in Chinese vocational education: eye-tracking evidence on source verification and immediate near-transfer across three information-seeking conditions (2026) · cites this paper as Hooge et al., 2026

    In ‘Static AOIs > Deciding on the AOI’ (page not provided), the paper describes defining AOIs by information category and discusses comparing the same objects across different images or displays. It does not establish that the citing authors’ classification enabled comparison of equivalent information functions despite differences in page layout and interface structure. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Hooge, I. T. C., Nyström, M., Niehorster, D. C., Andersson, R., Foulsham, T., Nuthmann, A., & Hessels, R. S.
Affiliations
  • Utrecht University (NL)
  • Lund University (SE)
  • Tobii (Sweden) (SE)
  • University of Essex (GB)
  • Christian-Albrechts-Universität zu Kiel (DE)
Corresponding author
Hooge, Ignace T. C.
ORCID
Venue
Behavior Research Methods
Publisher
Springer Science and Business Media LLC
Published
2026-02-17
Volume
58
Issue
3
Pages
65-65
ISSN
  • 1554-351X (ISSN-L)
  • 1554-3528
DOI
10.3758/s13428-025-02937-3
PMC
PMC12913287
OpenAlex
W7129678306
Cited by
22
References
4
Citations by year
2026: 22
FWCI
110.68
Type
journal article
Language
en
Open access
Licence
Locations
  • Behavior Research Methods (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • Lund University Publications (Lund University) (submittedVersion)
  • Multimedialen Archiv und Publikationsserver der Christian-Albrechts-Universität zu Kiel (Christian-Albrechts-Universität zu Kiel) (submittedVersion, cc-by)
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
eye tracking, areas of interest, replicability, experimental design
MeSH terms
Eye-Tracking Technology, Eye Movements, Humans, Research Design

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