---
_id: '63611'
abstract:
- lang: eng
text: When humans interact with artificial intelligence (AI), one desideratum is
appropriate trust. Typically, appropriate trust encompasses that humans trust
AI except for instances in which they either explicitly notice AI errors or are
suspicious that errors could be present. So far, appropriate trust or related
notions have mainly been investigated by assessing trust and reliance. In this
contribution, we argue that these assessments are insufficient to measure the
complex aim of appropriate trust and the related notion of healthy distrust. We
introduce and test the perspective of covert visual attention as an additional
indicator for appropriate trust and draw conceptual connections to the notion
of healthy distrust. To test the validity of our conceptualization, we formalize
visual attention using the Theory of Visual Attention and measure its properties
that are potentially relevant to appropriate trust and healthy distrust in an
image classification task. Based on temporal-order judgment performance, we estimate
participants' attentional capacity and attentional weight toward correct and incorrect
mock-up AI classifications. We observe that misclassifications reduce attentional
capacity compared to correct classifications. However, our results do not indicate
that this reduction is beneficial for a subsequent judgment of the classifications.
The attentional weighting is not affected by the classifications' correctness
but by the difficulty of categorizing the stimuli themselves. We discuss these
results, their implications, and the limited potential for using visual attention
as an indicator of appropriate trust and healthy distrust.
article_number: '1694367'
article_type: original
author:
- first_name: Tobias Martin
full_name: Peters, Tobias Martin
id: '92810'
last_name: Peters
orcid: 0009-0008-5193-6243
- first_name: Kai
full_name: Biermeier, Kai
id: '55908'
last_name: Biermeier
orcid: 0000-0002-2879-2359
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
citation:
ama: 'Peters TM, Biermeier K, Scharlau I. Assessing healthy distrust in human-AI
interaction: interpreting changes in visual attention. Frontiers in Psychology.
2026;16. doi:10.3389/fpsyg.2025.1694367'
apa: 'Peters, T. M., Biermeier, K., & Scharlau, I. (2026). Assessing healthy
distrust in human-AI interaction: interpreting changes in visual attention. Frontiers
in Psychology, 16, Article 1694367. https://doi.org/10.3389/fpsyg.2025.1694367'
bibtex: '@article{Peters_Biermeier_Scharlau_2026, title={Assessing healthy distrust
in human-AI interaction: interpreting changes in visual attention}, volume={16},
DOI={10.3389/fpsyg.2025.1694367},
number={1694367}, journal={Frontiers in Psychology}, publisher={Frontiers Media
SA}, author={Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}, year={2026}
}'
chicago: 'Peters, Tobias Martin, Kai Biermeier, and Ingrid Scharlau. “Assessing
Healthy Distrust in Human-AI Interaction: Interpreting Changes in Visual Attention.”
Frontiers in Psychology 16 (2026). https://doi.org/10.3389/fpsyg.2025.1694367.'
ieee: 'T. M. Peters, K. Biermeier, and I. Scharlau, “Assessing healthy distrust
in human-AI interaction: interpreting changes in visual attention,” Frontiers
in Psychology, vol. 16, Art. no. 1694367, 2026, doi: 10.3389/fpsyg.2025.1694367.'
mla: 'Peters, Tobias Martin, et al. “Assessing Healthy Distrust in Human-AI Interaction:
Interpreting Changes in Visual Attention.” Frontiers in Psychology, vol.
16, 1694367, Frontiers Media SA, 2026, doi:10.3389/fpsyg.2025.1694367.'
short: T.M. Peters, K. Biermeier, I. Scharlau, Frontiers in Psychology 16 (2026).
date_created: 2026-01-14T14:21:59Z
date_updated: 2026-01-14T14:29:03Z
department:
- _id: '424'
- _id: '660'
doi: 10.3389/fpsyg.2025.1694367
intvolume: ' 16'
keyword:
- appropriate trust
- healthy distrust
- visual attention
- Theory of Visual Attention
- human-AI interaction
- Bayesian cognitive model
- image classification
language:
- iso: eng
project:
- _id: '124'
name: 'TRR 318 ; TP C01: Gesundes Misstrauen in Erklärungen'
publication: Frontiers in Psychology
publication_identifier:
issn:
- 1664-1078
publication_status: published
publisher: Frontiers Media SA
status: public
title: 'Assessing healthy distrust in human-AI interaction: interpreting changes in
visual attention'
type: journal_article
user_id: '92810'
volume: 16
year: '2026'
...
---
_id: '59755'
abstract:
- lang: eng
text: "Due to the application of Artificial Intelligence (AI) in high-risk domains
like law or medicine,\r\ntrustworthy AI and trust in AI are of increasing scientific
and public relevance. A typical conception,\r\nfor example in the context of medical
diagnosis, is that a knowledgeable user receives AIgenerated\r\nclassification
as advice. Research to improve such interactions often aims to foster the\r\nuser’s
trust, which in turn should improve the combined human-AI performance. Given that
AI\r\nmodels can err, we argue that the possibility to critically review, thus
to distrust, an AI decision is\r\nan equally interesting target of research.\r\nWe
created two image classification scenarios in which the participants received
mock-up\r\nAI advice. The quality of the advice decreases for a phase of the experiment.
We studied the\r\ntask performance, trust and distrust of the participants, and
tested whether an instruction to\r\nremain skeptical and review each piece of
advice led to a better performance compared to a\r\nneutral condition. Our results
indicate that this instruction does not improve but rather worsens\r\nthe participants’
performance. Repeated single-item self-report of trust and distrust shows an\r\nincrease
in trust and a decrease in distrust after the drop in the AI’s classification
quality, with no\r\ndifference between the two instructions. Furthermore, via
a Bayesian Signal Detection Theory\r\nanalysis, we provide a procedure to assess
appropriate reliance in detail, by quantifying whether\r\nthe problems of under-
and over-reliance have been mitigated. We discuss implications of our\r\nresults
for the usage of disclaimers before interacting with AI, as prominently used in
current\r\nLLM-based chatbots, and for trust and distrust research."
article_type: original
author:
- first_name: Tobias Martin
full_name: Peters, Tobias Martin
id: '92810'
last_name: Peters
orcid: 0009-0008-5193-6243
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
citation:
ama: 'Peters TM, Scharlau I. Interacting with fallible AI: Is distrust helpful when
receiving AI misclassifications? Frontiers in Psychology. 2025;16. doi:10.3389/fpsyg.2025.1574809'
apa: 'Peters, T. M., & Scharlau, I. (2025). Interacting with fallible AI: Is
distrust helpful when receiving AI misclassifications? Frontiers in Psychology,
16. https://doi.org/10.3389/fpsyg.2025.1574809'
bibtex: '@article{Peters_Scharlau_2025, title={Interacting with fallible AI: Is
distrust helpful when receiving AI misclassifications?}, volume={16}, DOI={10.3389/fpsyg.2025.1574809},
journal={Frontiers in Psychology}, author={Peters, Tobias Martin and Scharlau,
Ingrid}, year={2025} }'
chicago: 'Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible
AI: Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in
Psychology 16 (2025). https://doi.org/10.3389/fpsyg.2025.1574809.'
ieee: 'T. M. Peters and I. Scharlau, “Interacting with fallible AI: Is distrust
helpful when receiving AI misclassifications?,” Frontiers in Psychology,
vol. 16, 2025, doi: 10.3389/fpsyg.2025.1574809.'
mla: 'Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI:
Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in Psychology,
vol. 16, 2025, doi:10.3389/fpsyg.2025.1574809.'
short: T.M. Peters, I. Scharlau, Frontiers in Psychology 16 (2025).
date_created: 2025-05-02T09:22:39Z
date_updated: 2025-05-27T09:10:09Z
department:
- _id: '424'
- _id: '660'
doi: 10.3389/fpsyg.2025.1574809
intvolume: ' 16'
keyword:
- trust in AI
- trust
- distrust
- human-AI interaction
- Signal Detection Theory
- Bayesian parameter estimation
- image classification
language:
- iso: eng
project:
- _id: '124'
name: 'TRR 318 - C1: TRR 318 - Subproject C1 - Gesundes Misstrauen in Erklärungen'
publication: Frontiers in Psychology
publication_status: published
status: public
title: 'Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?'
type: journal_article
user_id: '92810'
volume: 16
year: '2025'
...