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Correction to: "Using machine learning to detect events in eye-tracking data".

Raimondas Zemblys1, Diederick C Niehorster2, Kenneth Holmqvist3,4,5,6

  • 1Siauliai University, Siauliai, Lithuania. r.zemblys@tf.su.lt.

Behavior Research Methods
|September 26, 2018
PubMed
Summary
This summary is machine-generated.

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This study corrects an error in a previous publication regarding the post-processing of eye-tracking data. The corrected description clarifies the machine learning methods used for event detection and labeling.

Area of Science:

  • Computer Science
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Accurate event detection in eye-tracking data is crucial for understanding human behavior and cognition.
  • Previous research utilized machine learning for analyzing eye-tracking data, but a methodological error was identified.

Purpose of the Study:

  • To provide a corrected description of the post-processing steps for labeling events in eye-tracking data.
  • To ensure the accurate representation of machine learning techniques applied to eye-tracking event detection.

Main Methods:

  • Identification and correction of an erroneous description in the 'Post-processing: Labeling final events' section.
  • Clarification of the specific machine learning algorithms and procedures used for event labeling.

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Main Results:

  • The erroneous description on page 167 of the original publication has been identified.
  • A precise account of the post-processing methodology for event labeling in eye-tracking data is now provided.

Conclusions:

  • Correcting methodological descriptions is vital for the reproducibility and advancement of eye-tracking research.
  • Accurate reporting of machine learning applications enhances the reliability of findings in cognitive and HCI studies.