Boundaries in the eyes: Measure event segmentation during naturalistic video watching using eye tracking

During naturalistic information processing, individuals spontaneously segment their continuous experiences into discrete events, a phenomenon known as event segmentation. Traditional methods for assessing this process, which include subjective reports and neuroimaging techniques, often disrupt real-...

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Vydáno v:Behavior research methods Ročník 57; číslo 9; s. 255
Hlavní autoři: Li, Jiashen, Chen, Zhengyue, Hao, Xin, Liu, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 12.08.2025
Springer Nature B.V
Témata:
Access
Adult
Adults
Behavior
Behavioral Science and Psychology
Boundaries
Cognitive Psychology
Correlation analysis
Distance learning
Eye movements
Eye Movements - physiology
Eye tracking
Eye-Tracking Technology
Female
Humans
Information processing
Investigations
Learning outcomes
Male
Marking and tracking techniques
Markov analysis
Markov Chains
Mathematical models
Mathematics
Medical imaging
Memory
Neuroimaging
Neurosciences
Original Manuscript
Psychology
Pupil - physiology
Science and technology
Segmentation
Students
Subjectivity
Tracking
Young Adult
Young adults
Inter-subject correlation analysis
Hidden Markov model
Event segmentation
Naturalistic information processing
Eye tracking
ISSN:1554-3528, 1554-3528
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Popis
Shrnutí:During naturalistic information processing, individuals spontaneously segment their continuous experiences into discrete events, a phenomenon known as event segmentation. Traditional methods for assessing this process, which include subjective reports and neuroimaging techniques, often disrupt real-time segmentation or are costly and time-intensive. Our study investigated the potential of measuring event segmentation by recording and analyzing eye movements while participants viewed naturalistic videos. We collected eye movement data from healthy young adults as they watched commercial films ( N  = 104), or online Science, Technology, Engineering, and Mathematics (STEM) educational courses ( N  = 44). We analyzed changes in pupil size and eye movement speed near event boundaries and employed inter-subject correlation analysis (ISC) and hidden Markov models (HMM) to identify patterns indicative of event segmentation. We observed that both the speed of eye movements and pupil size dynamically responded to event boundaries, exhibiting heightened sensitivity to high-strength boundaries. Our analyses further revealed that event boundaries synchronized eye movements across participants. These boundaries can be effectively identified by HMM, yielding higher within-event similarity values and aligned with human-annotated boundaries. Importantly, HMM-based event segmentation metrics responded to experimental manipulations and predicted learning outcomes. This study provided a comprehensive computational framework for measuring event segmentation using eye-tracking. With the widespread accessibility of low-cost eye-tracking devices, the ability to measure event segmentation from eye movement data promises to deepen our understanding of this process in diverse real-world settings.
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ISSN:1554-3528
1554-3528
DOI:10.3758/s13428-025-02790-4