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Advancing Dynamic-Time Warp Techniques for Correcting Eye Tracking Data in Reading Source Code.

Naser Al Madi1

  • 1Department of Computer Science, Colby College, USA.

Journal of Eye Movement Research
|May 6, 2024
PubMed
Summary
This summary is machine-generated.

New hybrid algorithms improve eye tracking data correction by accurately handling regressions and distortions during code reading. These methods outperform existing algorithms, offering better analysis of non-linear eye movements.

Keywords:
CorrectionDriftEye TrackingEye movementGazeReadingSource Code

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Area of Science:

  • Cognitive Science
  • Computer Science
  • Human-Computer Interaction

Background:

  • Automated eye tracking data correction algorithms, like Dynamic-Time Warp, face a trade-off between correcting regressions and distortions.
  • Eye movement during code reading exhibits non-linearity and frequent regressions, posing challenges for existing correction methods.

Purpose of the Study:

  • To introduce a family of hybrid algorithms designed to accurately correct both regressions and distortions in eye tracking data.
  • To evaluate the performance of these novel algorithms against the established Warp algorithm.

Main Methods:

  • Simulations using synthetic data to replicate known eye movement phenomena.
  • Evaluation on two real-world datasets: one from source code reading and another from natural language text reading.
  • Comparison of proposed hybrid algorithms with the baseline Warp algorithm.

Main Results:

  • Most proposed hybrid algorithms matched or surpassed the baseline Warp algorithm in accuracy for both synthetic and real data.
  • Demonstrated the significant prevalence of regressions during source code reading.
  • Confirmed the generalizability of the algorithms for correcting eye tracking data from both code and natural language text.

Conclusions:

  • The developed hybrid algorithms represent an advancement over Dynamic-Time Warp for eye tracking data correction.
  • These algorithms effectively address the challenge of handling regressions, particularly relevant in code reading scenarios.
  • The findings support the utility of these algorithms for more accurate analysis of eye movement data in various reading tasks.