ArticleBehavior research methods2018
Scanpath modeling and classification with hidden Markov models.
Article in Behavior research methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Detection, Inspection, Return: An Object-Based Classification and Metric of Fixations in Complex Scenes.Open mind : discoveries in cognitive science · 2026Article
- Visual attention graph.Behavior research methods · 2025Article
- Modeling gaze behavior with continuous-time markov chains to investigate social attention dynamics in autism.Scientific reports · 2025Article
- Examining the Visual Search Behaviour of Experts When Screening for the Presence of Diabetic Retinopathy in Fundus Images.Journal of clinical medicine · 2025Article
- The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study.Behavior research methods · 2025Article
- Spatiotemporal Eye Movement Dynamics Reveal Altered Face Prioritization in Early Visual Processing Among Autistic Children.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Short-time AOIs-based representative scanpath identification and scanpath aggregation.Behavior research methods · 2024Article
- Idiosyncratic fixation patterns generalize across dynamic and static facial expression recognition.Scientific reports · 2024Article
- Article
- A review of machine learning in scanpath analysis for passive gaze-based interaction.Frontiers in artificial intelligence · 2024Review
- Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models.Journal of vision · 2023Article
- Eye tracking: empirical foundations for a minimal reporting guideline.Behavior research methods · 2023Review
- The use of gaze to study cognition: limitations, solutions, and applications to animal welfare.Frontiers in psychology · 2023Article
- Great Minds Think Alike? Spatial Search Processes Can Be More Idiosyncratic When Guided by More Accurate Information.Cognitive science · 2022Article
- Hidden Semi-Markov Models to Segment Reading Phases from Eye Movements.Journal of eye movement research · 2022Article
- [A Gaussian mixture-hidden Markov model of human visual behavior].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Gaze aversion in conversational settings: An investigation based on mock job interview.Journal of eye movement research · 2021Article
- Model-based estimation of the state of vehicle automation as derived from the driver's spontaneous visual strategies.Journal of eye movement research · 2021Article
- Quantifying the Predictability of Visual Scanpaths Using Active Information Storage.Entropy (Basel, Switzerland) · 2021Article
- How Do Art Skills Influence Visual Search? - Eye Movements Analyzed With Hidden Markov Models.Frontiers in psychology · 2021Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, can be used to infer observer-related (e.g., task at hand) and stimuli-related (e.g., image semantic category) information. However, eye movements are complex signals and many of these studies rely on limited gaze descriptors and bespoke datasets. Here, we provide a turnkey method for scanpath modeling and classification. This method relies on variational hidden Markov models (HMMs) and discriminant analysis (DA). HMMs encapsulate the dynamic and individualistic dimensions of gaze behavior, allowing DA to capture systematic patterns diagnostic of a given class of observers and/or stimuli. We test our approach on two very different datasets. Firstly, we use fixations recorded while viewing 800 static natural scene images, and infer an observer-related characteristic: the task at hand. We achieve an average of 55.9% correct classification rate (chance = 33%). We show that correct classification rates positively correlate with the number of salient regions present in the stimuli. Secondly, we use eye positions recorded while viewing 15 conversational videos, and infer a stimulus-related characteristic: the presence or absence of original soundtrack. We achieve an average 81.2% correct classification rate (chance = 50%). HMMs allow to integrate bottom-up, top-down, and oculomotor influences into a single model of gaze behavior. This synergistic approach between behavior and machine learning will open new avenues for simple quantification of gazing behavior. We release SMAC with HMM, a Matlab toolbox freely available to the community under an open-source license agreement.
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