Boundaries in the eyes: Measure event segmentation during naturalistic video watching using eye tracking
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View abstract on PubMed
Summary
This summary is machine-generated.Researchers developed a novel eye-tracking method to measure event segmentation, the brain's process of dividing continuous experiences into discrete events. This technique offers a cost-effective and non-disruptive way to study how we understand information in real-time.
Area Of Science
- Cognitive Science
- Neuroscience
- Human-Computer Interaction
Background
- Individuals naturally segment continuous experiences into discrete events, a process called event segmentation.
- Traditional methods like subjective reports and neuroimaging are often disruptive, costly, or time-intensive.
- There is a need for accessible, real-time methods to measure event segmentation.
Purpose Of The Study
- To investigate the feasibility of using eye movements to measure event segmentation during naturalistic video viewing.
- To develop and validate a computational framework for analyzing eye-tracking data for event segmentation.
Main Methods
- Collected eye movement data (pupil size, movement speed) from adults watching commercial films and STEM educational videos.
- Utilized inter-subject correlation (ISC) and hidden Markov models (HMM) to identify event boundaries.
- Analyzed pupil size and eye movement speed changes near event boundaries and assessed HMM performance against human annotations.
Main Results
- Eye movement speed and pupil size dynamically responded to event boundaries, with greater sensitivity to stronger boundaries.
- Event boundaries were found to synchronize eye movements across participants.
- HMM successfully identified event boundaries, showing higher within-event similarity and aligning with human annotations.
- HMM-based metrics reflected experimental changes and predicted learning outcomes.
Conclusions
- Eye-tracking provides a viable, non-disruptive method for measuring event segmentation.
- A computational framework using HMM can effectively identify event boundaries from eye movement data.
- This approach has the potential to advance understanding of real-time information processing in various settings.
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