ArticleBehavior research methods2016
The area-of-interest problem in eyetracking research: A noise-robust solution for face and sparse stimuli.
Article in Behavior research methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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Who cites it
92 citing papers in PubMed.
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- Social Attention Through a New Lens: Autistic and ADHD Traits and Eye Occlusion Affect Gaze During Conversation Watching.Quarterly journal of experimental psychology (2006) · 2026Article
- FaceTrack-AOI: An AI-driven tool for automated dynamic AOI placement and eye movement analysis in face perception studies.Behavior research methods · 2026Article
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- The fundamentals of eye tracking part 6: Working with areas of interest.Behavior research methods · 2026Article
- Gaze allocation and evaluations of dynamic smiles following angry versus neutral expressions.PloS one · 2026Article
- Analysis of human-oriented facial signals of the domestic dog using eye tracker technology.Frontiers in veterinary science · 2026Article
- Food and body-related attentional biases in children and adolescents with eating disorder symptoms, overweight and obesity: a systematic review.Journal of eating disorders · 2025Review
- Entropy as a Lens: Exploring Visual Behavior Patterns in Architects.Journal of eye movement research · 2025Article
- Algorithmic gaze annotation for mobile eye-tracking.Behavior research methods · 2025Article
- When is enough enough? Empirical guidelines to determine participant sample size for scene viewing studies.Behavior research methods · 2025Article
- Distinct contributions of foveal and extrafoveal visual information to emotion judgments and gaze behavior for faces.Journal of vision · 2025Article
- gazeMapper: A tool for automated world-based analysis of gaze data from one or multiple wearable eye trackers.Behavior research methods · 2025Article
- Altered interactive dynamics of gaze behavior during face-to-face interaction in autistic individuals: a dual eye-tracking study.Molecular autism · 2025Article
32 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
A problem in eyetracking research is choosing areas of interest (AOIs): Researchers in the same field often use widely varying AOIs for similar stimuli, making cross-study comparisons difficult or even impossible. Subjective choices while choosing AOIs cause differences in AOI shape, size, and location. On the other hand, not many guidelines for constructing AOIs, or comparisons between AOI-production methods, are available. In the present study, we addressed this gap by comparing AOI-production methods in face stimuli, using data collected with infants and adults (with autism spectrum disorder [ASD] and matched controls). Specifically, we report that the attention-attracting and attention-maintaining capacities of AOIs differ between AOI-production methods, and that this matters for statistical comparisons in one of three groups investigated (the ASD group). In addition, we investigated the relation between AOI size and an AOI's attention-attracting and attention-maintaining capacities, as well as the consequences for statistical analyses, and report that adopting large AOIs solves the problem of statistical differences between the AOI methods. Finally, we tested AOI-production methods for their robustness to noise, and report that large AOIs-using the Voronoi tessellation method or the limited-radius Voronoi tessellation method with large radii-are most robust to noise. We conclude that large AOIs are a noise-robust solution in face stimuli and, when implemented using the Voronoi method, are the most objective of the researcher-defined AOIs. Adopting Voronoi AOIs in face-scanning research should allow better between-group and cross-study comparisons.
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