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Comparing Eye Tracking with Electrooculography for Measuring Individual Sentence Comprehension Duration
Jana Annina Müller1, Dorothea Wendt2,3, Birger Kollmeier1
1Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany.
Plos One
|October 21, 2016
Summary
This study validates a simplified audio-visual paradigm for sentence comprehension. Reduced measurement time and electrooculography (EOG) with eye tracking show comparable results, enhancing practical application.
Area of Science:
- Psycholinguistics
- Cognitive Science
- Neuroscience
Background:
- The audio-visual paradigm by Wendt et al. (2015) is valuable for studying sentence comprehension but presents practical challenges.
- Existing methods require extensive measurement time and complex analysis procedures.
Purpose of the Study:
- To validate a modified audio-visual paradigm with reduced practical challenges.
- To assess the efficacy of shorter measurement sets and electrooculography (EOG) for estimating sentence processing duration.
- To compare bootstrap procedures with growth curve analysis for eye-tracking data.
Main Methods:
- Reduced measurement time by using a smaller dataset with fewer trials.
- Introduced electrooculography (EOG) as an alternative to eye tracking for recording eye movements.
- Employed growth curve analysis as an alternative to bootstrap procedures for analyzing eye-tracking data.
Main Results:
- A smaller measurement set yielded comparable results to the original full set.
- Eye tracking and EOG showed a high correlation (r = 0.97), indicating suitability for processing duration estimation.
- Both growth curve analysis and bootstrap procedures demonstrated similar findings regarding processing durations and sentence complexity effects.
Conclusions:
- The validated audio-visual paradigm is more practical for assessing sentence comprehension.
- Electrooculography (EOG) offers a viable alternative to eye tracking for this paradigm.
- Growth curve analysis provides results comparable to traditional bootstrap methods for eye-tracking data analysis.

