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Related Experiment Video

Updated: Jan 20, 2026

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
06:46

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

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Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention.

Tobias M Peters1, Kai Biermeier1, Ingrid Scharlau1

  • 1Department of Psychology, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany.

Frontiers in Psychology
|January 19, 2026
PubMed
Summary

Investigating human-AI interaction, this study explored appropriate trust and healthy distrust. Covert visual attention, not just trust and reliance, was examined as a key indicator. Misclassifications impacted attentional capacity but not necessarily judgment.

Keywords:
Bayesian cognitive modelTheory of Visual Attentionappropriate trusthealthy distrusthuman-AI interactionimage classificationvisual attention

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Related Experiment Videos

Last Updated: Jan 20, 2026

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Artificial Intelligence Ethics

Background:

  • Appropriate trust in artificial intelligence (AI) is crucial for effective human-AI collaboration.
  • Current methods assessing trust and reliance are insufficient for measuring appropriate trust and healthy distrust.
  • Covert visual attention offers a novel perspective for evaluating human responses to AI.

Purpose of the Study:

  • To introduce and test covert visual attention as an additional indicator of appropriate trust in AI.
  • To explore the relationship between visual attention and healthy distrust in human-AI interactions.
  • To assess the sufficiency of current trust and reliance metrics.

Main Methods:

  • Formalized visual attention using the Theory of Visual Attention.
  • Measured attentional capacity and weight toward AI classifications using temporal-order judgment.
  • Utilized an image classification task with mock-up AI classifications (correct and incorrect).

Main Results:

  • AI misclassifications reduced attentional capacity compared to correct classifications.
  • This reduction in attentional capacity did not improve subsequent classification judgments.
  • Attentional weighting was influenced by stimulus difficulty, not classification correctness.

Conclusions:

  • Covert visual attention shows potential as an indicator for appropriate trust and healthy distrust in human-AI interaction.
  • Visual attention metrics may offer deeper insights beyond traditional trust and reliance measures.
  • Further research is needed to fully understand the implications of visual attention in human-AI trust dynamics.