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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Abstract	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.
AbstractList	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.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. 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. 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.
ArticleNumber	255
Author	Chen, Zhengyue Li, Jiashen Hao, Xin Liu, Wei
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Snippet	During naturalistic information processing, individuals spontaneously segment their continuous experiences into discrete events, a phenomenon known as event...
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SubjectTerms	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
Title	Boundaries in the eyes: Measure event segmentation during naturalistic video watching using eye tracking
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