The study investigates two different ways of guiding the addressee of an explanation - an explainee, through action demonstration: contrastive and non-contrastive. Their effect was tested on attention to specific action elements (goal) as well as on event memory. In an eye-tracking experiment, participants were shown different motion videos that were either contrastive or non-contrastive with respect to the segments of movement presentation. Given that everyday action demonstration is often multimodal, the stimuli were created with re- spect to their visual and verbal presentation. For visual presentation, a video combined two movements in a contrastive (e.g., Up-motion following a Down-motion) or non-contrastive way (e.g., two Up-motions following each other). For verbal presentation, each video was combined with a sequence of instruction descriptions in the form of negative (i.e., contrastive) or assertive (i.e., non-contrastive) guidance. It was found that a) attention to the event goal increased for this condition in the later time window, and b) participants’ recall of the event was facilitated when a visually contrastive motion was combined with a verbal contrast.
}}, author = {{Singh, Amit and Rohlfing, Katharina J.}}, booktitle = {{Proceedings of the Annual Meeting of the Cognitive Science Society 45 (45)}}, keywords = {{Attention, negation, contrastive guidance, eye-movements, action understanding, event representation}}, location = {{Sydney}}, publisher = {{Cognitive Science Society}}, title = {{{Contrastiveness in the context of action demonstration: an eye-tracking study on its effects on action perception and action recall}}}, year = {{2023}}, } @inproceedings{17667, abstract = {{Resolving distributed attacks benefits from collaboration between networks. We present three approaches for the same multi-domain defensive action that can be applied in such an alliance: 1) Counteract Everywhere, 2) Minimize Countermeasures, and 3) Minimize Propagation. First, we provide a formula to compute efficiency of a defense; then we use this formula to compute the efficiency of the approaches under various circumstances. Finally, we discuss how task execution order and timing influence defense efficiency. Our results show that the Minimize Propagation approach is the most efficient method when defending against the chosen attack.}}, author = {{Koning, Ralph and Polevoy, Gleb and Meijer, Lydia and de Laat, Cees and Grosso, Paola}}, booktitle = {{2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom)}}, issn = {{null}}, keywords = {{computer network security, multinetwork environments, multidomain defensive action, task execution order, timing influence defense efficiency, distributed attacks, collaborative security defence approach, minimize propagation approach, minimize countermeasure approach, counteract everywhere approach, Conferences, Cloud computing, Computer crime, Edge computing, Security, Defense Approaches, Multi-Domain Defense, Collaborative Defense, Defense Algorithms, Computer Networks}}, pages = {{113--123}}, title = {{{Approaches for Collaborative Security Defences in Multi Network Environments}}}, doi = {{10.1109/CSCloud/EdgeCom.2019.000-9}}, year = {{2019}}, } @inproceedings{17655, author = {{Polevoy, Gleb and de Weerdt, M.M. and Jonker, C.M.}}, booktitle = {{Proceedings of the 2016 European Conference on Artificial Intelligence}}, keywords = {{agents, action, repeated reciprocation, fixed, floating, network, Nash equilibrium, social welfare, price of anarchy, price of stability, convex combination}}, pages = {{417--425}}, title = {{{The Game of Reciprocation Habits}}}, doi = {{10.3233/978-1-61499-672-9-417}}, volume = {{Volume 285: ECAI 2016}}, year = {{2016}}, } @article{17189, abstract = {{Alignment is a phenomenon observed in human conversation: Dialog partners' behavior converges in many respects. Such alignment has been proposed to be automatic and the basis for communicating successfully. Recent research on human-computer dialog promotes a mediated communicative design account of alignment according to which the extent of alignment is influenced by interlocutors' beliefs about each other. Our work aims at adding to these findings in two ways. (a) Our work investigates alignment of manual actions, instead of lexical choice. (b) Participants interact with the iCub humanoid robot, instead of an artificial computer dialog system. Our results confirm that alignment also takes place in the domain of actions. We were not able to replicate the results of the original study in general in this setting, but in accordance with its findings, participants with a high questionnaire score for emotional stability and participants who are familiar with robots align their actions more to a robot they believe to be basic than to one they believe to be advanced. Regarding alignment over the course of an interaction, the extent of alignment seems to remain constant, when participants believe the robot to be advanced, but it increases over time, when participants believe the robot to be a basic version.}}, author = {{Vollmer, Anna-Lisa and Rohlfing, Katharina and Wrede, Britta and Cangelosi, Angelo}}, issn = {{1875-4791}}, journal = {{International Journal of Social Robotics}}, keywords = {{learning, Human-robot interaction, Alignment, Robot social, Action understanding}}, number = {{2}}, pages = {{241--252}}, publisher = {{Springer-Verlag}}, title = {{{Alignment to the Actions of a Robot}}}, doi = {{10.1007/s12369-014-0252-0}}, volume = {{7}}, year = {{2015}}, } @article{4700, author = {{Becker, Jorg and Beverungen, Daniel and Knackstedt, Ralf and Matzner, Martin and Müller, Oliver and Pöppelbuss, Jens}}, isbn = {{0018-9391}}, issn = {{00189391}}, journal = {{IEEE Transactions on Engineering Management}}, keywords = {{Action research, boundary spanning, business process management (BPM), service blueprinting, service networks}}, number = {{3}}, pages = {{468----482}}, title = {{{Bridging the gap between manufacturing and service through IT-based boundary objects}}}, doi = {{10.1109/TEM.2012.2214770}}, year = {{2013}}, } @inproceedings{17244, abstract = {{Robots interacting with humans need to understand actions and make use of language in social interactions. Research on infant development has shown that language helps the learner to structure visual observations of action. This acoustic information typically in the form of narration overlaps with action sequences and provides infants with a bottom-up guide to find structure within them. This concept has been introduced as acoustic packaging by Hirsh-Pasek and Golinkoff. We developed and integrated a prominence detection module in our acoustic packaging system to detect semantically relevant information linguistically highlighted by the tutor. Evaluation results on speech data from adult-infant interactions show a significant agreement with human raters. Furthermore a first approach based on acoustic packages which uses the prominence detection results to generate acoustic feedback is presented. Index Terms: prominence, multimodal action segmentation, human robot interaction, feedback}}, author = {{Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede, Britta and Rohlfing, Katharina}}, booktitle = {{Interspeech 2011 (12th Annual Conference of the International Speech Communication Association)}}, keywords = {{Feedback, Human Robot Interaction, Prominence, Multimodal Action Segmentation}}, pages = {{3105--3108}}, title = {{{Using Prominence Detection to Generate Acoustic Feedback in Tutoring Scenarios}}}, year = {{2011}}, } @inproceedings{17245, author = {{Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede, Britta and Rohlfing, Katharina}}, issn = {{1662-5188}}, keywords = {{Prominence, Multimodal Action Segmentation, Feedback, Color Saliency, Human Robot Interaction}}, title = {{{Acoustic Packaging and the Learning of Words}}}, doi = {{10.3389/conf.fncom.2011.52.00020}}, year = {{2011}}, } @article{6067, abstract = {{Implicit change detection demonstrates how the visual system can benefit from stored information that is not immediately available to conscious awareness. We investigated the role of motor action in this context. In the first two experiments, using a one-shot implicit change detection paradigm, participants responded to unperceived changes either with an action (jabbing the screen at the guessed location of a change) or with words (verbal report), and sat either 60 cm or 300 cm (with a laser pointer) away from the display. Our observers guessed the locations of changes at a reachable distance better with an action than with a verbal judgment. At 300 cm, beyond reach, the motor advantage disappeared. In experiment 3, this advantage was also unavailable when participants sat at a reachable distance but responded with hand-held laser pointers near their bodies. We conclude that a motor system specialized for real-time visually guided behavior has access to additional visual information. }}, author = {{Tseng, Philip and Tuennermann, Jan and Roker-Knight, Nancy and Winter, Dorina and Scharlau, Ingrid and Bridgeman, Bruce}}, issn = {{0301-0066}}, journal = {{Perception}}, keywords = {{implicit change detection, action perception, visual system, perceptual judgment, verbal fluency, Analysis of Variance, Awareness, Female, Humans, Male, Psychomotor Performance, Random Allocation, Reaction Time, Visual Perception, Visual Memory, Visual Stimulation, Implicit Memory, Judgment, Perceptual Discrimination}}, number = {{10}}, pages = {{1311 -- 1321}}, title = {{{Enhancing implicit change detection through action.}}}, volume = {{39}}, year = {{2010}}, } @article{28955, abstract = {{Attention speeds up information processing. Although this finding has a long history in experimental psychology, it has found less regard in computational models of visual attention. In psychological research, two frameworks explain the function of attention.Selection for perception emphasizes that perception- or consciousness-related processing presupposes selection of relevant information, whereas selection for action emphasizes that action constraints make selection necessary. In the present study, we ask whether or how far attention, as measured by the speed-up of information processing, is based on selection for perception or selection for action. The accelerating effect was primarily based on selection for perception, but there was also a substantial effect of selection for action.}}, author = {{Weiß, Katharina and Scharlau, Ingrid}}, journal = {{KI 2009: Advances in Artificial Intelligence. Proceedings of the 32nd Annual Conference on Artificial Intelligence.}}, keywords = {{visuo-spatial attention, prior entry, selection for action, selection for perception}}, title = {{{Attention Speeds Up Visual Information Processing: Selection for Perception or Selection for Action?}}}, year = {{2009}}, } @inproceedings{17272, abstract = {{In developmental research, tutoring behavior has been identified as scaffolding infants' learning processes. It has been defined in terms of child-directed speech (Motherese), child-directed motion (Motionese), and contingency. In the field of developmental robotics, research often assumes that in human-robot interaction (HRI), robots are treated similar to infants, because their immature cognitive capabilities benefit from this behavior. However, according to our knowledge, it has barely been studied whether this is true and how exactly humans alter their behavior towards a robotic interaction partner. In this paper, we present results concerning the acceptance of a robotic agent in a social learning scenario obtained via comparison to adults and 8-11 months old infants in equal conditions. These results constitute an important empirical basis for making use of tutoring behavior in social robotics. In our study, we performed a detailed multimodal analysis of HRI in a tutoring situation using the example of a robot simulation equipped with a bottom-up saliency-based attention model. Our results reveal significant differences in hand movement velocity, motion pauses, range of motion, and eye gaze suggesting that for example adults decrease their hand movement velocity in an Adult-Child Interaction (ACI), opposed to an Adult-Adult Interaction (AAI) and this decrease is even higher in the Adult-Robot Interaction (ARI). We also found important differences between ACI and ARI in how the behavior is modified over time as the interaction unfolds. These findings indicate the necessity of integrating top-down feedback structures into a bottom-up system for robots to be fully accepted as interaction partners.}}, author = {{Vollmer, Anna-Lisa and Lohan, Katrin Solveig and Fischer, Kerstin and Nagai, Yukie and Pitsch, Karola and Fritsch, Jannik and Rohlfing, Katharina and Wrede, Britta}}, booktitle = {{Development and Learning, 2009. ICDL 2009. IEEE 8th International Conference on Development and Learning}}, keywords = {{robot simulation, hand movement velocity, robotic interaction partner, robotic agent, robot-directed interaction, multimodal analysis, Motionese, Motherese, intelligent tutoring systems, immature cognitive capability, human computer interaction, eye gaze, child-directed speech, child-directed motion, bottom-up system, bottom-up saliency-based attention model, adult-robot interaction, adult-child interaction, adult-adult interaction, human-robot interaction, action learning, social learning scenario, social robotics, software agents, top-down feedback structures, tutoring behavior}}, pages = {{1--6}}, publisher = {{IEEE}}, title = {{{People modify their tutoring behavior in robot-directed interaction for action learning}}}, doi = {{10.1109/DEVLRN.2009.5175516}}, year = {{2009}}, }