@article{63611,
  abstract     = {{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.}},
  author       = {{Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}},
  issn         = {{1664-1078}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{appropriate trust, healthy distrust, visual attention, Theory of Visual Attention, human-AI interaction, Bayesian cognitive model, image classification}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention}}},
  doi          = {{10.3389/fpsyg.2025.1694367}},
  volume       = {{16}},
  year         = {{2026}},
}

@article{59755,
  abstract     = {{Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine,
trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception,
for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated
classification as advice. Research to improve such interactions often aims to foster the
user’s trust, which in turn should improve the combined human-AI performance. Given that AI
models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is
an equally interesting target of research.
We created two image classification scenarios in which the participants received mock-up
AI advice. The quality of the advice decreases for a phase of the experiment. We studied the
task performance, trust and distrust of the participants, and tested whether an instruction to
remain skeptical and review each piece of advice led to a better performance compared to a
neutral condition. Our results indicate that this instruction does not improve but rather worsens
the participants’ performance. Repeated single-item self-report of trust and distrust shows an
increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no
difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory
analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether
the problems of under- and over-reliance have been mitigated. We discuss implications of our
results for the usage of disclaimers before interacting with AI, as prominently used in current
LLM-based chatbots, and for trust and distrust research.}},
  author       = {{Peters, Tobias Martin and Scharlau, Ingrid}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{trust in AI, trust, distrust, human-AI interaction, Signal Detection Theory, Bayesian parameter estimation, image classification}},
  title        = {{{Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}}},
  doi          = {{10.3389/fpsyg.2025.1574809}},
  volume       = {{16}},
  year         = {{2025}},
}