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Quantifying the Predictability of Visual Scanpaths Using Active Information Storage.

Patricia Wollstadt1, Martina Hasenjäger1, Christiane B Wiebel-Herboth1

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Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

We introduce Active Information Storage (AIS) to better measure human gaze predictability over longer time scales than traditional methods. This new approach helps identify cognitive states for improved human-machine interaction.

Keywords:
active information storageeye trackinginformation theoryscanpath

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Information Theory

Background:

  • Entropy-based measures, like gaze transition entropy (GTE), assess human gaze predictability.
  • GTE quantifies scanpath predictability based on transitions between fixations.
  • Current methods like GTE have limitations in capturing longer temporal dependencies in gaze behavior.

Purpose of the Study:

  • To propose Active Information Storage (AIS) as a more comprehensive measure of scanpath predictability.
  • To address the limitations of existing methods by accounting for extended temporal dependencies in gaze data.
  • To investigate the utility of AIS in distinguishing between different observer states.

Main Methods:

  • Calculating AIS as the mutual information between a process's past states and its next value.
  • Applying AIS to quantify scanpath predictability by considering multiple past fixations.
  • Analyzing gaze data to estimate AIS and differentiate induced observer states.

Main Results:

  • AIS effectively quantifies scanpath predictability over longer temporal horizons.
  • The study demonstrated the ability to distinguish between induced observer states using estimated AIS.
  • AIS provides a more nuanced understanding of gaze behavior compared to GTE.

Conclusions:

  • Active Information Storage (AIS) offers a superior method for measuring gaze predictability by incorporating longer temporal dependencies.
  • AIS shows promise for inferring user cognitive states, which can enhance human-machine interaction.
  • This research provides foundational evidence for utilizing AIS in behavioral experiments and gaze-based applications.