@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}}, } @inbook{63696, abstract = {{Das Kapitel beleuchtet, wie digitale Medien von Jugendlichen genutzt werden und wie diese Nutzung die Entwicklung von Jugendlichen beeinflusst, indem Einblicke in Nutzungsmuster, Chancen und Risiken digitaler Medien sowie in den Stand digitaler Kompetenzen gegeben werden. Zwei zentrale Sozialisationsinstanzen (Familie und Peers) werden genauer beleuchtet. Ziel ist es, pädagogische und gesellschaftliche Strategien zur Förderung digitaler Mündigkeit aufzuzeigen.}}, author = {{Kurock, Ricarda and Jungkeim, Lisa and Kuhn, Nicole}}, booktitle = {{Handbuch Entwicklungs- und Erziehungspsychologie}}, editor = {{Kracke, Bärbel and Noack, Peter }}, keywords = {{Mediennutzung, Soziale Medien, Digitale Kompetenzen, Peers, Familie}}, publisher = {{Springer}}, title = {{{Umgang mit digitalen Medien im Jugendalter}}}, year = {{2026}}, } @inproceedings{64827, author = {{Porwol, Philip Fabian and Körber, Miriam and Kern, Friederike and Schulte, Carsten and Scharlau, Ingrid}}, booktitle = {{Proceedings of the 3rd TRR 318 Conference: Contextualizing Explanations}}, editor = {{Cimiano, Philip and Paaßen, Benjamin and Vollmer, Anna-Lisa}}, location = {{Bielefeld}}, publisher = {{Bielefeld University Press}}, title = {{{Framing what and how to think: Lay people’s metaphors for algorithms}}}, doi = {{10.64136/ubio9074}}, year = {{2026}}, } @inproceedings{64872, author = {{Buhl, Heike M. and Fisher, Josephine Beryl and Rohlfing, Katharina J.}}, booktitle = {{Proceedings of the 3rd TRR 318 Conference: Contextualizing Explanations}}, editor = {{Cimiano, Philipp and Paassen, Benjamin and Vollmer, Anna-Lisa}}, publisher = {{Bielefeld University Press}}, title = {{{Cognitive and Interactive Adaptivity to the Explainee in an Explanatory Dialogue: An Experimental Study}}}, doi = {{10.64136/gumb4700}}, year = {{2026}}, } @inbook{65084, author = {{Buhl, Heike M. and Vollmer, Anna-Lisa and Alami, Rachid and Booshehri, Meisam and Främling, Kary}}, booktitle = {{Social explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kisten}}, pages = {{269--295}}, publisher = {{Springer}}, title = {{{Models of the situation, the explanandum, and the interaction partner}}}, doi = {{https://doi.org/10.1007/978-981-96-5290-7_14}}, year = {{2026}}, } @inbook{65083, author = {{Buhl, Heike M. and Wrede, Britta and Fisher, Josephine Beryl and Matarese, Marco}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kirsten}}, pages = {{247--267}}, publisher = {{Springer}}, title = {{{Adaptation}}}, doi = {{https://doi.org/10.1007/978-981-96-5290-7_13}}, year = {{2026}}, } @techreport{65180, author = {{Terfloth, Lutz and Buhl, Heike M. and Lohmer, Vivien and Schaffer, Michael and Kern, Frederike and Schulte, Carsten}}, title = {{{Bridging the Dual Nature: How Integrated Explanations Enhance Understanding of Technical Artifacts}}}, year = {{2026}}, } @article{65265, abstract = {{Abstract Background Research on procrastination mostly focuses on person‐related antecedents and neglects situational and social factors, such as group work. Prior research indicates that conjunctive and additive group work may increase individual effort and performance as compared to individual work. Aims Based on these findings, we investigate whether conjunctive and additive group work may also help reduce procrastination as compared to individual work. Methods In a registered field experiment, N  = 218 students with high levels of trait procrastination worked on an academic task over the course of 10 days in one of three conditions (individual work vs. conjunctive group work vs. additive group work). Dependent variables comprised task procrastination, task performance, and positive and negative task‐related affect. Results Regarding conjunctive group work, results are mixed, with some evidence that conjunctive group work leads to lower procrastination as compared to individual work. Both types of group work resulted in higher negative task‐related affect when assessed prospectively. No other effects were found. Conclusions The findings contribute to the idea that targeted changes in the learning environment, such as the implementation of group work, may help reduce procrastination. }}, author = {{Koppenborg, Markus and Hüffmeier, Joachim and Klingsieck, Katrin B.}}, issn = {{0007-0998}}, journal = {{British Journal of Educational Psychology}}, publisher = {{Wiley}}, title = {{{Is procrastination among students lower in group work? Evidence from a registered field experiment}}}, doi = {{10.1111/bjep.70069}}, year = {{2026}}, } @inbook{59754, author = {{Scharlau, Ingrid and Seifert, Andreas}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen: Ein Lehrbuch mit Unterrichtsmaterialien}}, editor = {{Scharlau, Ingrid and Bender, Elena and Patrzek, Justine and Schreiber, Christine}}, isbn = {{978-3-662-69480-1}}, pages = {{339--365}}, publisher = {{Springer Nature}}, title = {{{Empirische Methoden der psychologiedidaktischen Forschung}}}, year = {{2025}}, } @inbook{59752, author = {{Scharlau, Ingrid and Patrzek, Justine and Schreiber, Christine}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen: Ein Lehrbuch mit Unterrichtsmaterialien}}, editor = {{Scharlau, Ingrid and Bender, Elena and Patrzek, Justine and Schreiber, Christine}}, isbn = {{978-3-662-69480-0}}, pages = {{89--118}}, publisher = {{Springer Nature}}, title = {{{Psychologiedidaktik durch Analyse von Kommunikation}}}, year = {{2025}}, } @inbook{59753, author = {{Scharlau, Ingrid and Christine, Schreiber}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen: Ein Lehrbuch mit Unterrichtsmaterialien}}, editor = {{Scharlau, Ingrid and Bender, Elena and Patrzek, Justine and Schreiber, Christine}}, isbn = {{978-3-662-69480-1}}, pages = {{271--300}}, publisher = {{Springer Nature}}, title = {{{Schreiben im Psychologieunterricht unterstützen}}}, year = {{2025}}, } @unpublished{59839, abstract = {{In many scientific approaches, especially in those that try to foster explainability of Artificial Intelligences, a narrow conception of explaining prevails. This narrow conception implies that explaining is a one-directional action in which knowledge is transferred from the explainer to an addressee. By studying the amount of agency in metaphors for explaining in scientific texts, we want to find out – or at least to contribute a partial answer to the question – why this narrow conception is so dominant. For our analysis, we use a linguistic conception of agency, transitivity. This concept allows to specify the degree of agency or effectiveness of the action in a verbalised event. It is defined by several component parts. We detail and discuss both the parameters of and global transitivity. Overall, transitivity of explaining metaphors has a rather common pattern across metaphors. Agency is not high and reduced in characteristic aspects: The metaphors imply that the object of explaining is static, i.e., is not changed within the explanation, and that explaining is the activity of one person only. This pattern may account for the narrow conception of explaining. It contrasts strongly with current co-constructive or sociotechnical approaches to explainability.}}, author = {{Scharlau, Ingrid and Rohlfing, Katharina J.}}, publisher = {{Center for Open Science}}, title = {{{Agency in metaphors of explaining: An analysis of scientific texts}}}, year = {{2025}}, } @article{59756, abstract = {{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. In 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.}}, author = {{Visser, Roel and Peters, Tobias Martin and Scharlau, Ingrid and Hammer, Barbara}}, issn = {{1389-0417}}, journal = {{Cognitive Systems Research}}, keywords = {{XAI, Appropriate trust, Distrust, Reliance, Human-centric evaluation, Trustworthy AI}}, publisher = {{Elsevier BV}}, title = {{{Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation}}}, doi = {{10.1016/j.cogsys.2025.101357}}, year = {{2025}}, } @misc{59922, author = {{Porwol, Philip and Scharlau, Ingrid}}, publisher = {{OSF}}, title = {{{An annotated corpus of elicited metaphors of explaining and understanding using MIPVU}}}, doi = {{10.17605/OSF.IO/Y6SMX}}, year = {{2025}}, } @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}}, } @article{60144, author = {{Depenbusch, Sarah}}, journal = {{Frontiers in Computer Science}}, number = {{1553441}}, title = {{{VR-based avatar videos as an effective tool for process training in the context of digitalization?}}}, doi = {{10.3389/fcomp.2025.1553441}}, volume = {{7}}, year = {{2025}}, } @article{60306, author = {{Schoenert, Kathrin and Sommer, Sabrina and Buhl, Heike M.}}, journal = {{Frontiers in Developmental Psychology}}, title = {{{Impact of felt obligation and perceived mutual reciprocity on support between mothers and their adult children}}}, doi = {{10.3389/fdpys.2025.1508469}}, volume = {{3}}, year = {{2025}}, } @article{59636, author = {{Bohndick, Carla and Breetzke, Jonas and Klingsieck, Katrin B. and Buhl, Heike M.}}, journal = {{Social Psychology of Education}}, title = {{{Students’ personality impacts sense of belonging of students in different ways}}}, doi = {{10.1007/s11218-025-10058-0}}, volume = {{28}}, year = {{2025}}, } @unpublished{61119, abstract = {{

The present article offers an assessment of intra-individual variability in visualattention using the Theory of Visual Attention, which provides a formal framework forquantifying attentional components. We specifically investigated overall attentionalcapacity – that is, the available processing speed – and its distribution, the relativeattentional weight.By reanalyzing a large existing dataset from Tünnermann and Scharlau (2021),we found that across multiple testing days, participants either remained stable within a20 Hz margin or showed consistent improvements in capacity – in some cases triplingtheir initial capacity. The weights in response to salient stimuli were remarkablyconsistent.To determine whether increases in capacity reflect pure test-retest effects or arefacilitated by consolidation between days, and to quantify within-day variability, weconducted a second study in which participants completed five self-administeredsessions within a single day. Capacities remained within the same magnitude and didnot show a consistent directional trend. The relative weights exhibited comparativelylittle variation in most participants, akin to the previously analyzed dataset. Further,estimation uncertainty increased with higher capacity values.These results suggest that capacity may be subject to training effects, but thatsuch improvements appear to depend on longer breaks between sessions. This hasimportant implications for individualized assessment: A personal prior could beestimated from a single session to accelerate future estimations, as long as subsequentsessions occur on the same day. Participants with higher capacities may require tailoredexperimentation methods when small to medium effects are of interest, due to increaseduncertainty.

}}, author = {{Banh, Ngoc Chi and Scharlau, Ingrid}}, publisher = {{Center for Open Science}}, title = {{{Intra-individual variability in TVA attentional capacity and weight distribution: A reanalysis across days and an experiment within-day}}}, year = {{2025}}, } @misc{59921, author = {{Scharlau, Ingrid and Miriam, Körber}}, publisher = {{OSF}}, title = {{{Metaphors in 24 WIRED Level 5 Videos (Data corpus)}}}, doi = {{10.17605/OSF.IO/94A2J}}, year = {{2025}}, }