[{"user_id":"92810","department":[{"_id":"424"},{"_id":"660"}],"project":[{"_id":"124","name":"TRR 318 ; TP C01: Gesundes Misstrauen in Erklärungen"}],"_id":"63611","language":[{"iso":"eng"}],"article_number":"1694367","article_type":"original","keyword":["appropriate trust","healthy distrust","visual attention","Theory of Visual Attention","human-AI interaction","Bayesian cognitive model","image classification"],"type":"journal_article","publication":"Frontiers in Psychology","status":"public","abstract":[{"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.","lang":"eng"}],"author":[{"first_name":"Tobias Martin","id":"92810","full_name":"Peters, Tobias Martin","orcid":"0009-0008-5193-6243","last_name":"Peters"},{"first_name":"Kai","full_name":"Biermeier, Kai","id":"55908","last_name":"Biermeier","orcid":"0000-0002-2879-2359"},{"first_name":"Ingrid","orcid":"0000-0003-2364-9489","last_name":"Scharlau","id":"451","full_name":"Scharlau, Ingrid"}],"date_created":"2026-01-14T14:21:59Z","volume":16,"date_updated":"2026-01-14T14:29:03Z","publisher":"Frontiers Media SA","doi":"10.3389/fpsyg.2025.1694367","title":"Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention","publication_status":"published","publication_identifier":{"issn":["1664-1078"]},"citation":{"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} }","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>.","short":"T.M. Peters, K. Biermeier, I. Scharlau, Frontiers in Psychology 16 (2026).","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>","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","year":"2026"},{"publication":"Cognitive Systems Research","type":"journal_article","status":"public","abstract":[{"lang":"eng","text":"A current concern in the field of Artificial Intelligence (AI) is to ensure the trustworthiness of AI systems. The development of explainability methods is one prominent way to address this, which has often resulted in the assumption that the use of explainability will lead to an increase in the trust of users and wider society. However, the dynamics between explainability and trust are not well established and empirical investigations of their relation remain mixed or inconclusive.\r\nIn this paper we provide a detailed description of the concepts of user trust and distrust in AI and their relation to appropriate reliance. For that we draw from the fields of machine learning, human–computer interaction, and the social sciences. Based on these insights, we have created a focused study of empirical literature of existing empirical studies that investigate the effects of AI systems and XAI methods on user (dis)trust, in order to substantiate our conceptualization of trust, distrust, and reliance. With respect to our conceptual understanding we identify gaps in existing empirical work. With clarifying the concepts and summarizing the empirical studies, we aim to provide researchers, who examine user trust in AI, with an improved starting point for developing user studies to measure and evaluate the user’s attitude towards and reliance on AI systems."}],"department":[{"_id":"424"},{"_id":"660"}],"user_id":"92810","_id":"59756","project":[{"_id":"124","name":"TRR 318 - C1: TRR 318 - Subproject C1 - Gesundes Misstrauen in Erklärungen"}],"language":[{"iso":"eng"}],"keyword":["XAI","Appropriate trust","Distrust","Reliance","Human-centric evaluation","Trustworthy AI"],"article_number":"101357","publication_identifier":{"issn":["1389-0417"]},"publication_status":"published","citation":{"apa":"Visser, R., Peters, T. M., Scharlau, I., &#38; Hammer, B. (2025). Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation. <i>Cognitive Systems Research</i>, Article 101357. <a href=\"https://doi.org/10.1016/j.cogsys.2025.101357\">https://doi.org/10.1016/j.cogsys.2025.101357</a>","short":"R. Visser, T.M. Peters, I. Scharlau, B. Hammer, Cognitive Systems Research (2025).","bibtex":"@article{Visser_Peters_Scharlau_Hammer_2025, title={Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation}, DOI={<a href=\"https://doi.org/10.1016/j.cogsys.2025.101357\">10.1016/j.cogsys.2025.101357</a>}, number={101357}, journal={Cognitive Systems Research}, publisher={Elsevier BV}, author={Visser, Roel and Peters, Tobias Martin and Scharlau, Ingrid and Hammer, Barbara}, year={2025} }","mla":"Visser, Roel, et al. “Trust, Distrust, and Appropriate Reliance in (X)AI: A Conceptual Clarification of User Trust and Survey of Its Empirical Evaluation.” <i>Cognitive Systems Research</i>, 101357, Elsevier BV, 2025, doi:<a href=\"https://doi.org/10.1016/j.cogsys.2025.101357\">10.1016/j.cogsys.2025.101357</a>.","ama":"Visser R, Peters TM, Scharlau I, Hammer B. Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation. <i>Cognitive Systems Research</i>. Published online 2025. doi:<a href=\"https://doi.org/10.1016/j.cogsys.2025.101357\">10.1016/j.cogsys.2025.101357</a>","ieee":"R. Visser, T. M. Peters, I. Scharlau, and B. Hammer, “Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation,” <i>Cognitive Systems Research</i>, Art. no. 101357, 2025, doi: <a href=\"https://doi.org/10.1016/j.cogsys.2025.101357\">10.1016/j.cogsys.2025.101357</a>.","chicago":"Visser, Roel, Tobias Martin Peters, Ingrid Scharlau, and Barbara Hammer. “Trust, Distrust, and Appropriate Reliance in (X)AI: A Conceptual Clarification of User Trust and Survey of Its Empirical Evaluation.” <i>Cognitive Systems Research</i>, 2025. <a href=\"https://doi.org/10.1016/j.cogsys.2025.101357\">https://doi.org/10.1016/j.cogsys.2025.101357</a>."},"year":"2025","author":[{"last_name":"Visser","full_name":"Visser, Roel","first_name":"Roel"},{"full_name":"Peters, Tobias Martin","id":"92810","orcid":"0009-0008-5193-6243","last_name":"Peters","first_name":"Tobias Martin"},{"first_name":"Ingrid","orcid":"0000-0003-2364-9489","last_name":"Scharlau","id":"451","full_name":"Scharlau, Ingrid"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"}],"date_created":"2025-05-02T09:26:15Z","publisher":"Elsevier BV","date_updated":"2025-05-15T11:16:27Z","doi":"10.1016/j.cogsys.2025.101357","title":"Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation"}]