@inbook{61323, author = {{Wrede, Britta and Buschmeier, Hendrik and Rohlfing, Katharina Justine and Booshehri, Meisam and Grimminger, Angela}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, pages = {{227--245}}, publisher = {{Springer}}, title = {{{Incremental communication}}}, doi = {{10.1007/978-981-96-5290-7_12}}, year = {{2026}}, } @inbook{61321, author = {{Grimminger, Angela and Buschmeier, Hendrik}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, pages = {{351--365}}, publisher = {{Springer}}, title = {{{Theoretical aspects of multimodal processing}}}, doi = {{10.1007/978-981-96-5290-7_18}}, year = {{2026}}, } @inbook{61322, author = {{Lazarov, Stefan Teodorov and Tchappi, Igor and Grimminger, Angela}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, pages = {{367--390}}, publisher = {{Springer}}, title = {{{Characteristics of nonverbal behavior}}}, doi = {{10.1007/978-981-96-5290-7_19}}, year = {{2026}}, } @inbook{61112, author = {{Rohlfing, Katharina J. and Vollmer, Anna-Lisa and Grimminger, Angela}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina and Främling, Kary and Thommes, Kirsten and Alpsancar, Suzana and Lim, Brian Y.}}, publisher = {{Springer}}, title = {{{Practices: How to establish an explaining practice}}}, doi = {{10.1007/978-981-96-5290-7_5}}, year = {{2026}}, } @inbook{65069, author = {{Främling, Kary and Alami, Rachid and Hulstijn, Joris and Tchappi, Igor and Grimminger, Angela and Wrede, Britta and Buschmeier, Hendrik and Kubler, Sylvain}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, isbn = {{9789819652891}}, pages = {{19--38}}, publisher = {{Springer}}, title = {{{Scenarios of Social Explainable AI in practice}}}, doi = {{10.1007/978-981-96-5290-7_2}}, year = {{2026}}, } @inbook{61325, author = {{Vollmer, Anna-Lisa and Buhl, Heike M. and Alami, Rachid and Främling, Kary and Grimminger, Angela and Booshehri, Meisam and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kirsten}}, pages = {{39--53}}, publisher = {{Springer}}, title = {{{Components of an explanation for co-constructive sXAI}}}, doi = {{10.1007/978-981-96-5290-7_3}}, year = {{2026}}, } @inproceedings{64914, abstract = {{We investigate how verbal and nonverbal linguistic features, exhibited by speakers and listeners in dialogue, can contribute to predicting the listener's state of understanding in explanatory interactions on a moment-by-moment basis. Specifically, we examine three linguistic cues related to cognitive load and hypothesised to correlate with listener understanding: the information value (operationalised with surprisal) and syntactic complexity of the speaker's utterances, and the variation in the listener's interactive gaze behaviour. Based on statistical analyses of the MUNDEX corpus of face-to-face dialogic board game explanations, we find that individual cues vary with the listener's level of understanding. Listener states (‘Understanding’, ‘Partial Understanding’, ‘Non-Understanding’ and ‘Misunderstanding’) were self-annotated by the listeners using a retrospective video-recall method. The results of a subsequent classification experiment, involving two off-the-shelf classifiers and a fine-tuned German BERT-based multimodal classifier, demonstrate that prediction of these four states of understanding is generally possible and improves when the three linguistic cues are considered alongside textual features.}}, author = {{Wang, Yu and Türk, Olcay and Grimminger, Angela and Buschmeier, Hendrik}}, booktitle = {{Proceedings of the 15th Biennial Language Resources and Evaluation Conference}}, location = {{Palma, Mallorca, Spain}}, publisher = {{ELRA}}, title = {{{Predicting states of understanding in explanatory interactions using cognitive load-related linguistic cues}}}, doi = {{10.48550/arXiv.2603.20079}}, year = {{2026}}, } @unpublished{61151, abstract = {{In this paper, we discuss the application of retrospective video recall for the assessment of cognitive processes in explanatory interactions, such as understanding and mental models. Our purpose is to reflect on the benefits and limitations of video recall compared to another self-report method, ‘thinking-aloud’. To do so, we reveal empirical results from the application of video recall in three interdisciplinary research projects that applied the method for the qualitative and quantitative assessment of cognitive and behavioral phenomena in everyday explanations. In all three projects, video recall was applied as a post-hoc procedure following the recording of dyadic face-to-face explanations of board games. The design of the video recall procedure differed between individual projects because they pursued different research objectives – that is the investigation of (1) an interlocutor's multimodal signals of understanding, (2) the change in assumptions about an interlocutor's dispositional and situational knowledge, and (3) the differentiated assessment of an interlocutor's developing understanding of domain knowledge aspects by distinguishing between mechanistic and functional explanatory stances. By discussing the benefits and the limitations of each procedure, this article provides critical reflections on video recall as a versatile research method applied for the analysis of human multimodal behavior in interaction and cognitive processing.}}, author = {{Lazarov, Stefan Teodorov and Schaffer, Michael and Gladow, Viviane and Buschmeier, Hendrik and Buhl, Heike M. and Grimminger, Angela}}, pages = {{29}}, title = {{{Retrospective video recall for analyzing cognitive processes in naturalistic explanations}}}, year = {{2026}}, } @inproceedings{61154, author = {{Türk, Olcay and Lazarov, Stefan Teodorov and Buschmeier, Hendrik and Wagner, Petra and Grimminger, Angela}}, booktitle = {{LingCologne 2025 – Book of Abstracts}}, location = {{Cologne, Germany}}, pages = {{36}}, title = {{{Acoustic detection of false positive backchannels of understanding in explanations}}}, year = {{2025}}, } @misc{61429, author = {{Buschmeier, Hendrik and Grimminger, Angela and Wagner, Petra and Lazarov, Stefan Teodorov and Türk, Olcay and Wang, Yu}}, publisher = {{LibreCat University}}, title = {{{MUNDEX Annotations}}}, doi = {{10.5281/ZENODO.17129817}}, year = {{2025}}, } @article{61156, abstract = {{Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of providing or seeking explanations is to achieve (better) ‘understanding’ on the part of the explainee. However, what it means to ‘understand’ is still not clearly defined, and the concept itself is rarely the subject of scientific investigation. This conceptual article aims to present a model of forms of understanding for XAI-explanations and beyond. From an interdisciplinary perspective bringing together computer science, linguistics, sociology, philosophy and psychology, a definition of understanding and its forms, assessment, and dynamics during the process of giving everyday explanations are explored. Two types of understanding are considered as possible outcomes of explanations, namely enabledness, ‘knowing how’ to do or decide something, and comprehension, ‘knowing that’ – both in different degrees (from shallow to deep). Explanations regularly start with shallow understanding in a specific domain and can lead to deep comprehension and enabledness of the explanandum, which we see as a prerequisite for human users to gain agency. In this process, the increase of comprehension and enabledness are highly interdependent. Against the background of this systematization, special challenges of understanding in XAI are discussed.}}, author = {{Buschmeier, Hendrik and Buhl, Heike M. and Kern, Friederike and Grimminger, Angela and Beierling, Helen and Fisher, Josephine Beryl and Groß, André and Horwath, Ilona and Klowait, Nils and Lazarov, Stefan Teodorov and Lenke, Michael and Lohmer, Vivien and Rohlfing, Katharina and Scharlau, Ingrid and Singh, Amit and Terfloth, Lutz and Vollmer, Anna-Lisa and Wang, Yu and Wilmes, Annedore and Wrede, Britta}}, journal = {{Cognitive Systems Research}}, keywords = {{understanding, explaining, explanations, explainable, AI, interdisciplinarity, comprehension, enabledness, agency}}, title = {{{Forms of Understanding for XAI-Explanations}}}, doi = {{10.1016/j.cogsys.2025.101419}}, volume = {{94}}, year = {{2025}}, } @inproceedings{57204, abstract = {{In this study on the use of gesture deixis during explanations, a sample of 24 videorecorded dyadic interactions of a board game explanation was analyzed. The relation between the use of gesture deixis by different explainers and their interpretation of explainees' understanding was investigated. In addition, we describe explainers' intra-individual variations related to their interactions with three different explainees consecutively. While we did not find a relation between interpretations of explainees' complete understanding and a decrease in explainers' use of gesture deixis, we demonstrated that the overall use of gesture deixis is related to the process of interactional monitoring and the attendance of a different explainee.}}, author = {{Lazarov, Stefan Teodorov and Grimminger, Angela}}, booktitle = {{Proceedings of the Annual Meeting of the Cognitive Science Society}}, keywords = {{explanation, gesture deixis, monitoring, understanding}}, location = {{Rotterdam}}, title = {{{Variations in explainers’ gesture deixis in explanations related to the monitoring of explainees’ understanding}}}, volume = {{46}}, year = {{2024}}, } @article{59888, abstract = {{Everyday explanations are interactive processes with the aim to provide a less knowledgeable person with reasonable information about other people, objects, or events. Because explanations are interactive communicative processes, the topical structure of an explanation may vary dynamically depending on the immediate feedback of the explainee. In this paper, we analyse topical transitions in medical explanations organised by different physicians (explainers) related to different forms of multimodal behaviour of caregivers (explainees) attending an explanation about the procedures of an upcoming surgery of a child. The analyses reveal that explainees’ multimodal behaviour with gaze shifts (and particularly gaze aversion) can predict a transition from an elaborated topic to a new one, whereas explainees’ forms of multimodal behaviour with static gaze cannot be related to changes of the topical structure.}}, author = {{Lazarov, Stefan Teodorov and Biermeier, Kai and Grimminger, Angela}}, issn = {{1572-0381}}, journal = {{Interaction Studies}}, keywords = {{explanations, multimodal behaviour, elaborations, conditional probabilities}}, number = {{3}}, pages = {{257 -- 280}}, publisher = {{John Benjamins}}, title = {{{Changes in the topical structure of explanations are related to explainees’ multimodal behaviour}}}, doi = {{10.1075/is.23033.laz}}, volume = {{25}}, year = {{2024}}, } @article{58708, abstract = {{Research about explanation processes is gaining relevance because of the increased popularity of artificial systems required to explain their function or outcome. Following an interactive approach, not only explainers, but also explainees contribute to successful interactions. However, little is known about how explainees actively guide explanation processes and how their involvement relates to learning. We explored the occurrence and type of explainees’ questions in 20 adult — adult explanation dialogues about unknown present and absent objects. Crucially, we related the question types to the explainees’ subsequent recall of the unknown object labels. We found that explainees asked different types of questions, especially about the object’s label and facts. Questions about the object’s function were asked more when objects were present. In addition, requests for labelling were linked to better recall. The results contribute to designing explainable AI that aims to provide relevant and adaptive explanations and to further experimental approaches to study explanations.}}, author = {{Fisher, Josephine Beryl and Rohlfing, Katharina J. and Donnellan, Ed and Grimminger, Angela and Gu, Yan and Vigliocco, Gabriella}}, journal = {{Interaction Studies}}, number = {{2}}, pages = {{244--255}}, publisher = {{John Benjamins}}, title = {{{ Explain with, rather than explain to: How explainees shape their own learning}}}, doi = {{doi.org/10.1075/is.23019.fis}}, volume = {{25}}, year = {{2024}}, } @inproceedings{55912, abstract = {{In explanatory interactions, explainees are expected to continuously provide feedback to explainers by signaling whether they understand an ongoing explanation. The study presented in this paper is based on the hypothesis that explainees use a set of multimodal cues, including vocalizations, facial expressions, and movements of the torso, head, and hands, to do so. We test this hypothesis by building a random forest classifier based on a multimodal corpus of dyadic explanations (21 explainers and explainees), in which windows of understanding or non-understanding were identified by participants in a retrospective video recall task. Results show that sequences of understanding can indeed be differentiated from those of non-understanding, and that a diverse set of predictors covering a wide range of modalities contributes to this classification. Due to data sparsity and a high degree of individual variation, the generalizability of our results is currently limited, but they support our hypothesis of the relevance of multimodal display in explanatory interactions.}}, author = {{Türk, Olcay and Lazarov, Stefan Teodorov and Wang, Yu and Buschmeier, Hendrik and Grimminger, Angela and Wagner, Petra}}, booktitle = {{Proceedings of the 26th ACM International Conference on Multimodal Interaction}}, location = {{San José, Costa Rica}}, pages = {{449--458}}, title = {{{Predictability of understanding in explanatory interactions based on multimodal cues}}}, doi = {{10.1145/3678957.3685741}}, year = {{2024}}, } @article{37074, abstract = {{Pointing is one of the first conventional means of communication and infants have various motives for engaging in it such as imperative, declarative, or informative. Little is known about the developmental paths of producing and understanding these different motives. In our longitudinal study (N = 58) during the second year of life, we experimentally elicited infants’ pointing production and comprehension in various settings and under pragmatically valid conditions. We followed two steps in our analyses and assessed the occurrence of canonical index-finger pointing for different motives and the engagement in an ongoing interaction in pursuit of a joint goal revealed by frequency and multimodal utterances. For understanding the developmental paths, we compared two groups: typically developing infants (TD) and infants who have been assessed as having delayed language development (LD). Results showed that the developmental paths differed according to the various motives. When comparing the two groups, for all motives, LD infants produced index-finger pointing 2 months later than TD infants. For the engagement, although the pattern was less consistent across settings, the frequency of pointing was comparable in both groups, but infants with LD used less canonical forms of pointing and made fewer multimodal contributions than TD children.}}, author = {{Rohlfing, Katharina J. and Lüke, Carina and Liszkowski, Ulf and Ritterfeld, Ute and Grimminger, Angela}}, issn = {{1660-4601}}, journal = {{International Journal of Environmental Research and Public Health}}, keywords = {{Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health}}, number = {{9}}, publisher = {{MDPI AG}}, title = {{{Developmental Paths of Pointing for Various Motives in Infants with and without Language Delay}}}, doi = {{10.3390/ijerph19094982}}, volume = {{19}}, year = {{2022}}, } @article{24456, abstract = {{One objective of current research in explainable intelligent systems is to implement social aspects in order to increase the relevance of explanations. In this paper, we argue that a novel conceptual framework is needed to overcome shortcomings of existing AI systems with little attention to processes of interaction and learning. Drawing from research in interaction and development, we first outline the novel conceptual framework that pushes the design of AI systems toward true interactivity with an emphasis on the role of the partner and social relevance. We propose that AI systems will be able to provide a meaningful and relevant explanation only if the process of explaining is extended to active contribution of both partners that brings about dynamics that is modulated by different levels of analysis. Accordingly, our conceptual framework comprises monitoring and scaffolding as key concepts and claims that the process of explaining is not only modulated by the interaction between explainee and explainer but is embedded into a larger social context in which conventionalized and routinized behaviors are established. We discuss our conceptual framework in relation to the established objectives of transparency and autonomy that are raised for the design of explainable AI systems currently.}}, author = {{Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and Horwath, Ilona and Hüllermeier, Eyke and Kern, Friederike and Kopp, Stefan and Thommes, Kirsten and Ngonga Ngomo, Axel-Cyrille and Schulte, Carsten and Wachsmuth, Henning and Wagner, Petra and Wrede, Britta}}, issn = {{2379-8920}}, journal = {{IEEE Transactions on Cognitive and Developmental Systems}}, keywords = {{Explainability, process ofexplaining andunderstanding, explainable artificial systems}}, number = {{3}}, pages = {{717--728}}, title = {{{Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}}}, doi = {{10.1109/tcds.2020.3044366}}, volume = {{13}}, year = {{2021}}, } @inbook{61111, author = {{Rohlfing, Katharina J. and Fischer, Silke and Viertel, Franziska E. and Grimminger, Angela}}, booktitle = {{Sprache in Therapie und neurokognitiver Forschung}}, editor = {{Müller, Horst M.}}, pages = {{21--41}}, publisher = {{Stauffenberg Verlag}}, title = {{{Spracherwerb: Warum ist die Situation des gemeinsamen Buchvorlesens für die Sprachentwicklung förderlich?}}}, year = {{2021}}, } @inbook{20210, author = {{Rohlfing, Katharina and Grimminger, Angela and Wrede, Britta}}, booktitle = {{International perspectives on digital media and early literacy: The impact of digital devices on learning, language acquisition and social interaction.}}, editor = {{Rohlfing, Katharina and Müller-Brauers, Claudia}}, title = {{{The role of caregivers in keeping a child-robot interaction going.}}}, year = {{2020}}, } @article{20198, author = {{Lüke, Carina and Ritterfeld, Ute and Grimminger, Angela and Rohlfing, Katharina and Liszkowski, Ulf}}, journal = {{Frontiers in Psychology 11}}, title = {{{Integrated Communication System: Gesture and language acquisition in typically developing children and children with LD and DLD}}}, doi = {{10.3389/fpsyg.2020.00118}}, year = {{2020}}, }