Volume 22, Issue 2
  • ISSN 1572-0373
  • E-ISSN: 1572-0381
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Research in social robotics is commonly focused on designing robots that imitate human behavior. While this might increase a user’s satisfaction and acceptance of robots at first glance, it does not automatically aid a non-expert user in naturally interacting with robots, and might hurt their ability to correctly anticipate a robot’s capabilities. We argue that a faulty mental model, that the user has of the robot, is one of the main sources of confusion. In this work, we investigate how communicating technical concepts of robotic systems to users affect their mental models, and how this can increase the quality of human-robot interaction. We conducted an online study and investigated possible ways of improving users’ mental models. Our results underline that communicating technical concepts can form an improved mental model. Consequently, we show the importance of consciously designing robots that express their capabilities and limitations.


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  1. Bangor, A., Kortum, P. T., & Miller, J. T.
    (2008) An empirical evaluation of the system usability scale. Intl. Journal of Human-Computer Interaction, 24 (6), 574–594. 10.1080/10447310802205776
    https://doi.org/10.1080/10447310802205776 [Google Scholar]
  2. Bartneck, C., Kulié, D., Croft, E., & Zoghbi, S.
    (2009) Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International journal of social robotics, 1 (1), 71–81. 10.1007/s12369‑008‑0001‑3
    https://doi.org/10.1007/s12369-008-0001-3 [Google Scholar]
  3. Beller, W. E., & Wang, Y. P.
    (1997) Bar code dataform scanning and labeling apparatus and method [US Patent 5,602,377].
    [Google Scholar]
  4. Brand, R. J., Baldwin, D. A., & Ashburn, L. A.
    (2002) Evidence for ‘motionese’: Modifications in mothers’ infant-directed action. Developmental Science, 5(1), 72–83. 10.1111/1467‑7687.00211
    https://doi.org/10.1111/1467-7687.00211 [Google Scholar]
  5. Breazeal, C., Dautenhahn, K., & Kanda, T.
    (2016) Social robotics. Springer handbook of robotics (pp.1935–1972). Springer. 10.1007/978‑3‑319‑32552‑1_72
    https://doi.org/10.1007/978-3-319-32552-1_72 [Google Scholar]
  6. Breazeal, C., Kidd, C., Thomaz, A., Hoffman, G., & Berlin, M.
    (2005) Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. IEEE/RSJ International Conference on Intelligent Robots and Systems, 708–713. doi:  10.1109/IROS.2005.1545011
    https://doi.org/10.1109/IROS.2005.1545011 [Google Scholar]
  7. Breslow, N.
    (1970) A generalized kruskal-wallis test for comparing k samples subject to unequal patterns of censorship. Biometrika, 57 (3), 579–594. 10.1093/biomet/57.3.579
    https://doi.org/10.1093/biomet/57.3.579 [Google Scholar]
  8. Bruner, J.
    (1985) Child’s talk: Learning to use language. Child Language Teaching and Therapy, 1 (1), 111–114. doi:  10.1177/026565908500100113
    https://doi.org/10.1177/026565908500100113 [Google Scholar]
  9. Cakmak, M., & Takayama, L.
    (2014) Teaching people how to teach robots: The effect of instructional materials and dialog design. Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, 431–438. 10.1145/2559636.2559675
    https://doi.org/10.1145/2559636.2559675 [Google Scholar]
  10. Clement, J.
    (2020) Most popular mobile messaging apps worldwide as of october 2019, based on number of monthly active users [Retrieved: 2020-06-09, fromhttps://www.statista.com/statistics/258749/most-popular-global-mobile-messenger-apps/\#statisticContainer].
  11. de Greeff, J., & Belpaeme, T.
    (2015) Why robots should be social: Enhancing machine learning through social human-robot interaction. PLOS ONE, 10 (9), 1–26. doi:  10.1371/journal.pone.0138061
    https://doi.org/10.1371/journal.pone.0138061 [Google Scholar]
  12. Duffy, B. R.
    (2006) Fundamental issues in social robotics. International Review of Information Ethics, 6 (12) 2006 10.29173/irie137
    https://doi.org/10.29173/irie137 [Google Scholar]
  13. Dunn, O. J.
    (1964) Multiple comparisons using rank sums. Technometrics, 6 (3), 241–252. 10.1080/00401706.1964.10490181
    https://doi.org/10.1080/00401706.1964.10490181 [Google Scholar]
  14. Franke, T., Attig, C., & Wessel, D.
    (2019a) A personal resource for technology interaction: Development and validation of the affinity for technology interaction (ati) scale. International Journal of Human-Computer Interaction, 35(6), 456–467. 10.1080/10447318.2018.1456150
    https://doi.org/10.1080/10447318.2018.1456150 [Google Scholar]
  15. (2019b) A personal resource for technology interaction: Development and validation of the affinity for technology interaction (ati) scale. International Journal of Human-Computer Interaction, 35 (6), 456–467. 10.1080/10447318.2018.1456150
    https://doi.org/10.1080/10447318.2018.1456150 [Google Scholar]
  16. Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F. J., & Marién-Jiménez, M. J.
    (2014) Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition, 47(6), 2280–2292. 10.1016/j.patcog.2014.01.005
    https://doi.org/10.1016/j.patcog.2014.01.005 [Google Scholar]
  17. Hamacher, A., Bianchi-Berthouze, N., Pipe, A. G., & Eder, K.
    (2016) Believing in bert: Using expressive communication to enhance trust and counteract operational error in physical human-robot interaction. 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 493–500. doi:  10.1109/ROMAN.2016.7745163
    https://doi.org/10.1109/ROMAN.2016.7745163 [Google Scholar]
  18. Hassenzahl, M., Borchers, J., Boll, S., Pütten, A. R.-V. D., & Wulf, V.
    (2020) Otherware: How to best interact with autonomous systems. Interactions, 28(1), 54–57. doi:  10.1145/3436942
    https://doi.org/10.1145/3436942 [Google Scholar]
  19. Hegel, F., Gieselmann, S., Peters, A., Holthaus, P., & Wrede, B.
    (2011) Towards a typology of meaningful signals and cues in social robotics. 2011 RO-MAN, 72–78. 10.1109/ROMAN.2011.6005246
    https://doi.org/10.1109/ROMAN.2011.6005246 [Google Scholar]
  20. Hindemith, L., Vollmer, A.-L., Wrede, B., & Joublin, F.
    (2019) Pragmatic frames as an approach to reduce misinterpretations in human-robot-interaction. Proc. Int. Conf. on Development and Learning (ICDL-EPIROB).
    [Google Scholar]
  21. Kaptein, F., Broekens, J., Hindriks, K., & Neerincx, M.
    (2017) Personalised self-explanation by robots: The role of goals versus beliefs in robot-action explanation for children and adults. 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 676–682. 10.1109/ROMAN.2017.8172376
    https://doi.org/10.1109/ROMAN.2017.8172376 [Google Scholar]
  22. Kwon, M., Huang, S. H., & Dragan, A. D.
    (2018) Expressing robot incapability. Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 87–95. doi:  10.1145/3171221.3171276
    https://doi.org/10.1145/3171221.3171276 [Google Scholar]
  23. Liu, S.
    (2020) Global market share held by operating systems for desktop pcs, from january 2013 to january 2020 [Retrieved: 2020-06-09, fromhttps://www.statista.com/statistics/218089/global-market-share-of-windows-7/].
  24. McCracken, D. D., & Reilly, E. D.
    (2003) Backus-naur form (bnf). Encyclopedia of computer science (pp.129–131). John Wiley; Sons Ltd.
    [Google Scholar]
  25. Nelson, D. G. K., Hirsh-Pasek, K., Jusczyk, P. W., & Cassidy, K. W.
    (1989) How the prosodic cues in motherese might assist language learning. Journal of child Language, 16 (1), 55–68. 10.1017/S030500090001343X
    https://doi.org/10.1017/S030500090001343X [Google Scholar]
  26. Otero, N., Alissandrakis, A., Dautenhahn, K., Nehaniv, C., Syrdal, D. S., & Koay, K. L.
    (2008) Human to robot demonstrations of routine home tasks: Exploring the role of the robot’s feedback. 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), 177–184. doi:  10.1145/1349822.1349846
    https://doi.org/10.1145/1349822.1349846 [Google Scholar]
  27. Pitsch, K., Vollmer, A.-L., Rohlfing, K. J., Fritsch, J., & Wrede, B.
    (2014) Tutoring in adult-child interaction: On the loop of the tutor’s action modification and the recipient’s gaze. Interaction Studies, 15(1), 55–98. 10.1075/is.15.1.03pit
    https://doi.org/10.1075/is.15.1.03pit [Google Scholar]
  28. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O.,
    (2019) Machine behaviour. Nature, 568(7753), 477–486. 10.1038/s41586‑019‑1138‑y
    https://doi.org/10.1038/s41586-019-1138-y [Google Scholar]
  29. Rohlfing, K. J., Wrede, B., Vollmer, A.-L., & Oudeyer, P.-Y.
    (2016) An alternative to mapping a word onto a concept in language acquisition: Pragmatic frames. Frontiers in psychology, 7, 470. 10.3389/fpsyg.2016.00470
    https://doi.org/10.3389/fpsyg.2016.00470 [Google Scholar]
  30. Saunders, J., Syrdal, D. S., Koay, K. L., Burke, N., & Dautenhahn, K.
    (2015) “teach me-show me” – end-user personalization of a smart home and companion robot. IEEE Transactions on Human-Machine Systems, 46 (1), 27–40. 10.1109/THMS.2015.2445105
    https://doi.org/10.1109/THMS.2015.2445105 [Google Scholar]
  31. Schillinger, P., Kohlbrecher, S., & von Stryk, O.
    (2016) Human-robot collaborative high-level control with application to rescue robotics. Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), 2796–2802. 10.1109/ICRA.2016.7487442
    https://doi.org/10.1109/ICRA.2016.7487442 [Google Scholar]
  32. Schulte, C., & Budde, L.
    (2018) A framework for computing education: Hybrid interaction system: The need for a bigger picture in computing education. Proceedings of the 18th Koli Calling International Conference on Computing Education Research, 1–10. 10.1145/3279720.3279733
    https://doi.org/10.1145/3279720.3279733 [Google Scholar]
  33. Shapiro, S. S., & Wilk, M. B.
    (1965) An analysis of variance test for normality (complete samples). Biometrika, 52(3–4), 591–611. doi:  10.1093/biomet/52.3‑4.591
    https://doi.org/10.1093/biomet/52.3-4.591 [Google Scholar]
  34. Soon, T. J.
    (2008) Qr code. Synthesis Journal, 2008, 59–78.
    [Google Scholar]
  35. Staggers, N., & Norcio, A. F.
    (1993) Mental models: Concepts for human-computer interaction research. International Journal of Man-machine studies, 38(4), 587–605. 10.1006/imms.1993.1028
    https://doi.org/10.1006/imms.1993.1028 [Google Scholar]
  36. Stanford Artificial Intelligence Laboratory et al.
    Stanford Artificial Intelligence Laboratory et al. (2014, July22). Robotic operating system (Version ROS Indigo Igloo). https://www.ros.org
    [Google Scholar]
  37. Sterelny, K.
    (1990) The representational theory of mind: An introduction. Basil Blackwell.
    [Google Scholar]
  38. Sweller, J., van Merriënboer, J. J., & Paas, F.
    (2019) Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 1–32. 10.1007/s10648‑019‑09465‑5
    https://doi.org/10.1007/s10648-019-09465-5 [Google Scholar]
  39. Thomaz, A. L., & Cakmak, M.
    (2009) Learning about objects with human teachers. 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 15–22. doi:  10.1145/1514095.1514101
    https://doi.org/10.1145/1514095.1514101 [Google Scholar]
  40. Vollmer, A.-L., Lohan, K. S., Fritsch, J., Wrede, B., & Rohlfing, K.
    (2009) Which motionese parameters change with children’s age?
    [Google Scholar]
  41. Vollmer, A.-L., Mühlig, M., Steil, J. J., Pitsch, K., Fritsch, J., Rohlfing, K. J., & Wrede, B.
    (2014) Robots show us how to teach them: Feedback from robots shapes tutoring behavior during action learning. PloS one, 9 (3). 10.1371/journal.pone.0091349
    https://doi.org/10.1371/journal.pone.0091349 [Google Scholar]
  42. Vollmer, A.-L., & Schillingmann, L.
    (2018) On studying human teaching behavior with robots: A review. Review of Philosophy and Psychology, 9 (4), 863–903. 10.1007/s13164‑017‑0353‑4
    https://doi.org/10.1007/s13164-017-0353-4 [Google Scholar]
  43. Vollmer, A.-L., Wrede, B., Rohlfing, K. J., & Oudeyer, P.-Y.
    (2016) Pragmatic frames for teaching and learning in human-robot interaction: Review and challenges. Frontiers in neurorobotics, 10, 10. 10.3389/fnbot.2016.00010
    https://doi.org/10.3389/fnbot.2016.00010 [Google Scholar]
  44. Wortham, R., Theodorou, A., & Bryson, J.
    (2017) Robot transparency: Improving understanding of intelligent behaviour for designers and users, 274–289. doi:  10.1007/978‑3‑319‑64107‑2_22
    https://doi.org/10.1007/978-3-319-64107-2_22 [Google Scholar]

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