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An algorithmic approach to determine expertise development using object-related gaze pattern sequences.

Felix S Wang1, Céline Gianduzzo2, Mirko Meboldt2

  • 1ETH Zurich, Leonhardstrasse 21, 8092, Zurich, Switzerland. wangfe@ethz.ch.

Behavior Research Methods
|July 14, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm using k-mer analysis to identify expert gaze patterns. The method effectively quantifies learning progress and cognitive processes through eye tracking (ET) data.

Keywords:
Expertise developmentEye-trackingGaze patternsNovice trainingSequence analysis

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Computational Biology

Background:

  • Eye tracking (ET) is used to study expertise development, but traditional metrics offer limited insights into expert gaze.
  • Existing methods struggle to identify and measure distinct gaze patterns, leading to inconclusive findings on expert visual behavior.

Purpose of the Study:

  • To introduce an algorithmic approach for extracting object-related gaze sequences.
  • To determine task-related expertise by analyzing the development of gaze sequence patterns.
  • To validate the algorithm's effectiveness in quantifying learning progress and cognitive processes.

Main Methods:

  • Developed an algorithm to extract object-related gaze sequences from eye movement data.
  • Transformed AOI (Area of Interest) sequences into string representations for k-mer analysis.
  • Applied the k-mer method, adapted from computational biology, to analyze gaze patterns in novice and expert participants over multiple trials.

Main Results:

  • The k-mer approach, particularly for k > 2, revealed expertise development more clearly than traditional ET metrics like fixation duration.
  • Novice participants showed a significant increase in expert k-mer patterns with increased on-task experience (p < 0.001).
  • The multi-trial k-mer analysis successfully quantified learning progress by capturing spatial and temporal gaze information.

Conclusions:

  • The proposed k-mer-based algorithm is effective for revealing specific cognitive processes during skill acquisition.
  • This method provides a valuable tool for assessing learning progress and differentiating expertise levels.
  • The approach offers potential applications in novice training and expert performance evaluation.