ArticleNPJ science of learning2022
Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models.
Article in NPJ science of learning, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Explanation strategies in humans versus current explainable artificial intelligence: Insights from image classification.British journal of psychology (London, England : 1953) · 2026Article
- Ambivalence in decision making for outdoor mobility among adult child carers of people with dementia: a study protocol for a randomized vignette study with message framing and eye-tracking.BMC geriatrics · 2026Article
- The effect of mask use on cross-race face perception: a simultaneous EEG and eye-tracking study.Cognitive research: principles and implications · 2026Article
- Understanding the role of eye movement pattern and consistency during face recognition through EEG decoding.NPJ science of learning · 2025Article
- Eye tracking based clustering using the Korean version of the reading the mind in the eyes test.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Greater eyes-focused eye movement pattern during face recognition is associated with better performance in adults but not in children. We test the hypothesis that higher eye movement consistency across trials, instead of a greater eyes-focused pattern, predicts better performance in children since it reflects capacity in developing visual routines. We first simulated visual routine development through combining deep neural network and hidden Markov model that jointly learn perceptual representations and eye movement strategies for face recognition. The model accounted for the advantage of eyes-focused pattern in adults, and predicted that in children (partially trained models) consistency but not pattern of eye movements predicted recognition performance. This result was then verified with data from typically developing children. In addition, lower eye movement consistency in children was associated with autism diagnosis, particularly autistic traits in social skills. Thus, children's face recognition involves visual routine development through social exposure, indexed by eye movement consistency.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.