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Hidden Semi-Markov Models to Segment Reading Phases from Eye Movements.

Brice Olivier1, Anne Guérin-Dugué2, Jean-Baptiste Durand1

  • 1Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Inria Grenoble Rhone-Alpes, France.

Journal of Eye Movement Research
|June 28, 2023
PubMed
Summary

This study introduces a hidden semi-Markov model to analyze reading scanpaths, identifying distinct cognitive strategies like normal reading and information search. Results reveal significant individual differences in reading behaviors.

Keywords:
Eye movementeye trackinghidden semi-Markov chainsindividual differencesreadingscanpathsegmentation

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Human-Computer Interaction

Background:

  • Understanding reading processes is crucial for educational and technological applications.
  • Eye-tracking provides valuable insights into cognitive strategies during reading.
  • Existing models may not fully capture individual variability in reading behavior.

Purpose of the Study:

  • To analyze scanpaths during a reading task to answer a binary topic relevance question.
  • To develop a data-driven method for segmenting scanpaths into distinct cognitive phases.
  • To investigate individual differences in reading strategies.

Main Methods:

  • Utilized hidden semi-Markov chains (HSMCs) for scanpath segmentation.
  • Identified cognitive strategies including normal reading, fast reading, information search, and slow confirmation.
  • Validated model states using external covariates, including semantic text information.

Main Results:

  • The HSMC model successfully segmented scanpaths into interpretable cognitive phases.
  • Semantic information from texts served as a significant covariate for phase identification.
  • Significant individual preferences for specific reading strategies were observed.
  • Substantial inter-individual variability in eye-movement characteristics was quantified.

Conclusions:

  • Hidden semi-Markov models offer a robust framework for analyzing reading scanpaths and cognitive strategies.
  • Individual heterogeneity in reading behavior is a key factor to consider in reading models.
  • This approach can enhance our understanding of complex reading tasks and inform adaptive systems.