[{"type":"journal_article","publication":"Frontiers in Psychology","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."}],"status":"public","project":[{"_id":"124","name":"TRR 318 ; TP C01: Gesundes Misstrauen in Erklärungen"}],"_id":"63611","user_id":"92810","department":[{"_id":"424"},{"_id":"660"}],"article_type":"original","article_number":"1694367","keyword":["appropriate trust","healthy distrust","visual attention","Theory of Visual Attention","human-AI interaction","Bayesian cognitive model","image classification"],"language":[{"iso":"eng"}],"publication_status":"published","publication_identifier":{"issn":["1664-1078"]},"year":"2026","citation":{"apa":"Peters, T. M., Biermeier, K., &#38; Scharlau, I. (2026). Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention. <i>Frontiers in Psychology</i>, <i>16</i>, Article 1694367. <a href=\"https://doi.org/10.3389/fpsyg.2025.1694367\">https://doi.org/10.3389/fpsyg.2025.1694367</a>","mla":"Peters, Tobias Martin, et al. “Assessing Healthy Distrust in Human-AI Interaction: Interpreting Changes in Visual Attention.” <i>Frontiers in Psychology</i>, vol. 16, 1694367, Frontiers Media SA, 2026, doi:<a href=\"https://doi.org/10.3389/fpsyg.2025.1694367\">10.3389/fpsyg.2025.1694367</a>.","bibtex":"@article{Peters_Biermeier_Scharlau_2026, title={Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention}, volume={16}, DOI={<a href=\"https://doi.org/10.3389/fpsyg.2025.1694367\">10.3389/fpsyg.2025.1694367</a>}, number={1694367}, journal={Frontiers in Psychology}, publisher={Frontiers Media SA}, author={Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}, year={2026} }","short":"T.M. Peters, K. Biermeier, I. Scharlau, Frontiers in Psychology 16 (2026).","ama":"Peters TM, Biermeier K, Scharlau I. Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention. <i>Frontiers in Psychology</i>. 2026;16. doi:<a href=\"https://doi.org/10.3389/fpsyg.2025.1694367\">10.3389/fpsyg.2025.1694367</a>","ieee":"T. M. Peters, K. Biermeier, and I. Scharlau, “Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention,” <i>Frontiers in Psychology</i>, vol. 16, Art. no. 1694367, 2026, doi: <a href=\"https://doi.org/10.3389/fpsyg.2025.1694367\">10.3389/fpsyg.2025.1694367</a>.","chicago":"Peters, Tobias Martin, Kai Biermeier, and Ingrid Scharlau. “Assessing Healthy Distrust in Human-AI Interaction: Interpreting Changes in Visual Attention.” <i>Frontiers in Psychology</i> 16 (2026). <a href=\"https://doi.org/10.3389/fpsyg.2025.1694367\">https://doi.org/10.3389/fpsyg.2025.1694367</a>."},"intvolume":"        16","publisher":"Frontiers Media SA","date_updated":"2026-01-14T14:29:03Z","author":[{"last_name":"Peters","orcid":"0009-0008-5193-6243","full_name":"Peters, Tobias Martin","id":"92810","first_name":"Tobias Martin"},{"first_name":"Kai","id":"55908","full_name":"Biermeier, Kai","orcid":"0000-0002-2879-2359","last_name":"Biermeier"},{"full_name":"Scharlau, Ingrid","id":"451","orcid":"0000-0003-2364-9489","last_name":"Scharlau","first_name":"Ingrid"}],"date_created":"2026-01-14T14:21:59Z","volume":16,"title":"Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention","doi":"10.3389/fpsyg.2025.1694367"},{"doi":"10.3389/fpsyg.2025.1574809","title":"Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?","volume":16,"date_created":"2025-05-02T09:22:39Z","author":[{"last_name":"Peters","orcid":"0009-0008-5193-6243","full_name":"Peters, Tobias Martin","id":"92810","first_name":"Tobias Martin"},{"first_name":"Ingrid","id":"451","full_name":"Scharlau, Ingrid","orcid":"0000-0003-2364-9489","last_name":"Scharlau"}],"date_updated":"2025-05-27T09:10:09Z","intvolume":"        16","citation":{"ama":"Peters TM, Scharlau I. Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications? <i>Frontiers in Psychology</i>. 2025;16. doi:<a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>","ieee":"T. M. Peters and I. Scharlau, “Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?,” <i>Frontiers in Psychology</i>, vol. 16, 2025, doi: <a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>.","chicago":"Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI: Is Distrust Helpful When Receiving AI Misclassifications?” <i>Frontiers in Psychology</i> 16 (2025). <a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">https://doi.org/10.3389/fpsyg.2025.1574809</a>.","apa":"Peters, T. M., &#38; Scharlau, I. (2025). Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications? <i>Frontiers in Psychology</i>, <i>16</i>. <a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">https://doi.org/10.3389/fpsyg.2025.1574809</a>","short":"T.M. Peters, I. Scharlau, Frontiers in Psychology 16 (2025).","bibtex":"@article{Peters_Scharlau_2025, title={Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}, volume={16}, DOI={<a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>}, journal={Frontiers in Psychology}, author={Peters, Tobias Martin and Scharlau, Ingrid}, year={2025} }","mla":"Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI: Is Distrust Helpful When Receiving AI Misclassifications?” <i>Frontiers in Psychology</i>, vol. 16, 2025, doi:<a href=\"https://doi.org/10.3389/fpsyg.2025.1574809\">10.3389/fpsyg.2025.1574809</a>."},"year":"2025","publication_status":"published","language":[{"iso":"eng"}],"keyword":["trust in AI","trust","distrust","human-AI interaction","Signal Detection Theory","Bayesian parameter estimation","image classification"],"article_type":"original","department":[{"_id":"424"},{"_id":"660"}],"user_id":"92810","_id":"59755","project":[{"_id":"124","name":"TRR 318 - C1: TRR 318 - Subproject C1 - Gesundes Misstrauen in Erklärungen"}],"status":"public","abstract":[{"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.","lang":"eng"}],"publication":"Frontiers in Psychology","type":"journal_article"}]