Multimedia Tools and Applications

, Volume 78, Issue 10, pp 13435–13459 | Cite as

Learning communication from first- and third-person POVs: how perceptual differences influence the interpretation of conversations whilst waiting

  • Sutasinee ThovuttikulEmail author
  • Yoshimasa Ohmoto
  • Toyoaki Nishida


The difficulties and social anxiety associated with living in unfamiliar places are often caused by different patterns of thinking, points of view (POVs) and physical styles. Learning to communicate better will help us understand that differences are normal, and that life can be lived in harmony. We study herein how participants learn and understand different behavioural patterns during interactions using experiments on perceived communication differences in first- and third-person POVs for simulated crowds. In our experiment, participants interact with autonomous agents and experimenters via avatars in a shared virtual space. We ask the participants to obtain multiple tickets from two service counters in the system. The virtual service avatar provides a ticket upon request. One or more autonomous customer agents then navigate the system to obtain the ticket. If a service counter is already occupied, other customers must wait in accordance with the “first-come, first-serve” rule. The fairness-of-waiting behaviour is interpreted using two features to understand the perceptual differences of varying perspectives: waiting styles (i.e. line and group waiting) and fairness (i.e. fair and unfair services). Participants with differing perspectives focus on different features whilst waiting. An analysis of variance of reactions and reasoning demonstrates that the participants in the first-person group tend to focus on interaction and feelings whilst waiting, whereas participants in the third-person group emphasised fairness.


Communication learning system First- and third-person points of view (POVs) Perception of conversation Fairness of waiting Simulated crowd 



This research was supported by the RIKEN Center for Advanced Intelligence Project as part of the “Human–AI Communication project”.


  1. 1.
    Aylett R, Hall L, Tazzyman S, Endrass B, André E, Ritter C, Nazir A, Paiva A, Höfstede G J, Kappas A (2014) Werewolves, cheats and cultural sensitivity. In: Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), May 5–9, 2014, Paris, France, p 1085–1092Google Scholar
  2. 2.
    Bernstein DA, Nash WP (2007) Essentials of psychology, 4th edn. Houghton Mifflin company, Boston, MAGoogle Scholar
  3. 3.
    Black D (2017) Why can I see my avatar? Embodied visual engagement in the third-person video game. Games and Culture 12(2):179–199. CrossRefGoogle Scholar
  4. 4.
    Celentano A, Dubois E (2017) Interaction-in-the-large vs interaction-in-the-small in multi-device systems. In: Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter 19. ACM.
  5. 5.
    Degens N, Hofstede GJ, Beulens A, Krumhuber E, Kappas A (2016) Don't be a stranger: designing a digital intercultural sensitivity training tool that is culture general. IEEE Trans Learn Technol 9(2):120–132. CrossRefGoogle Scholar
  6. 6.
    Degens N, Endrass B, Hofstede GJ, Beulens A, André E (2017) What I see is not what you get?: why culture-specific behaviours for virtual characters should be user-tested across cultures. AI & Soc 32:37–49. CrossRefGoogle Scholar
  7. 7.
    Denisova A, Cairns P (2015) First person vs. third person perspective in digital games: do player preferences affect immersion? In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, Seoul, Republic of Korea, p 145–148.
  8. 8.
    Divesh L, Thovuttikul S, Nishida T (2011) Towards a virtual environment for capturing behavior in cultural crowds. Proc ICDIM 2011:310–315. Google Scholar
  9. 9.
    Dresser N (2011) Multicultural manners: essential rules of etiquette for the 21st century. Wiley, CanadaGoogle Scholar
  10. 10.
    Endrass B, André E, Rehm M, Lipi AA, Nakano Y (2011) Culture-related differences in aspects of behavior for virtual characters across Germany and Japan. In: Tumer, Yolum, Sonenberg and Stone (eds) Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2011), Taipei, p 441–448Google Scholar
  11. 11.
    Endrass B, Rehm M, André E (2011) Planning small talk behavior with cultural influences for multiagent systems. Comput Speech Lang 25(2):158–174. CrossRefGoogle Scholar
  12. 12.
    Endrass B, Degens N, Hofstede GJ, Andre E, Mascarenhas SF, Mehlmann G, Paiva A, Ritter C, Swiderska A (2011) Integration and evaluation of prototypical culture-related differences. Culturally motivated virtual characters. In: Proceeding of the 11th International Conference on Intelligent virtual Agents, Reykjavík, Iceland, Springer. p 1–9Google Scholar
  13. 13.
    Gary J, Alan MS (2005) Organizational behaviour: understanding and managing life at work. Pearson Prentice Hall, CanadaGoogle Scholar
  14. 14.
    Hall ET (1989) Beyond culture. Anchor, Garden City, NY.Google Scholar
  15. 15.
    Hall ET, Hall MR (1989) Understanding cultural differences. Intercultural Press, New YorkGoogle Scholar
  16. 16.
    Hall L, Tazzyman S, Hume C, Endrass B, Lim MY, Hofstede GJ, Paiva A, Andre E, Kappas A, Aylett R (2015) Learning to overcome cultural conflict through engaging with intelligent agents in synthetic cultures. Int J Artif Intell Educ 25(2):291–317. CrossRefGoogle Scholar
  17. 17.
    James LB, Anthony FB (1997) A primer on organizational behavior, 4th edn. Wiley, New YorkGoogle Scholar
  18. 18.
    Kistler F, Endrass B, Damian I, Dang C, André E (2012) Natural interaction with culturally adaptive virtual characters. Journal on Multimodal User Interfaces 6:39–47. CrossRefGoogle Scholar
  19. 19.
    Kullu K, Güdükbay U, Manocha D (2018) ACMICS: an agent communication model for interacting crowd simulation. In: Dastani M, Sukthankar G, André E, Koenig S (eds) Proc. of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, p 1177–1179Google Scholar
  20. 20.
    Maister DH (1984) The psychology of waiting lines. Harvard Business School, BostonGoogle Scholar
  21. 21.
    Mascarenhas SF, Silva A, Paiva A, Aylett R, Kistler F, André E, Degens N, Hofstede GJ, Kappas A (2013) Traveller: an intercultural training system with intelligent agents. In: Proceedings of 2013 Autonomous Agents and Multi-agent Systems, USA, p 1387–1388. Accessed 15 October 2017
  22. 22.
    Mascarenhas SF, Degens N, Paiva A, Prada R, Hofstede GJ, Beulens A, Aylett R (2016) Modelling culture in intelligent virtual agents. Auton Agent Multi-Agent Syst 30(5):931–962. CrossRefGoogle Scholar
  23. 23.
    Mullins LJ (2011) Essentials of organizational behaviour, 3rd edn. Pearson Education, ItalyGoogle Scholar
  24. 24.
    Nakai A, Pyae A, Luimula M, Hongo S, Vuola H, Smed J (2015) Investigating the effects of motion-based Kinect game system on user cognition. Journal of Multimodal User Interfaces 9(4):403–411. CrossRefGoogle Scholar
  25. 25.
    Nishida T et al (2015) Synthetic evidential study as augmented collective thought process: preliminary report. In: Nguyen N, Trawiński B, Kosala R (eds) Intelligent information and database systems. ACIIDS 2015, Lecture notes in computer science, vol 9011. Springer, Cham, pp 13–22. CrossRefGoogle Scholar
  26. 26.
    Nishida T, Nakazawa A, Ohmoto Y, Nitschke C, Mohammad Y, Thovuttikul S, Lala D, Abe M, Ookaki T (2015) Synthetic evidential study as primordial soup of conversation. In: Chu W, Kikuchi S, Bhalla S (eds) Databases in networked information systems. DNIS 2015, Lecture notes in computer science, vol 8999. Springer, Cham, pp 74–83. Google Scholar
  27. 27.
    Ookaki T, Abe M, Yoshino M, Ohmoto Y, Nishida T (2015) Synthetic evidential study for deepening inside their heart. In: Ali M, Kwon Y, Lee CH, Kim J, Kim Y (eds) Current approaches in applied artificial intelligence. IEA/AIE 2015, Lecture notes in computer science, vol 9101. Springer, Cham, pp 161–170. Google Scholar
  28. 28.
    Rafaeli A, Greg B, Keren H (2002) The effects of queue structure on attitudes. J Serv Res 5(2):125–139. CrossRefGoogle Scholar
  29. 29.
    Samovar LA (2009) Communication between cultures, 7th edn. Wadsworth, Belmont, CAGoogle Scholar
  30. 30.
    Thovuttikul S, Nishida T (2011) Handling greeting gesture in simulated crowd. Proc GrC 2011:659–664. Google Scholar
  31. 31.
    Thovuttikul S, Lala D, Ohashi H, Okada S, Ohmoto Y, Nishida T (2011) Simulated crowd: towards a synthetic culture for engaging a learner in culture dependent nonverbal interaction. 2nd Workshop on Eye Gaze in Intelligent Human Machine Interaction. February 13, 2011, Stanford University, Palo Alto, California, USAGoogle Scholar
  32. 32.
    Thovuttikul S, Lala D, Kleef NV, Ohmoto Y, Nishida T (2011) Comparing people’s preference on culture-dependent queuing behaviors in a simulated crowd. In: Sugawara K et al (eds) Proc. 11th IEEE Int. Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC 2012), p 153–162.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Intelligence Science and Technology, Graduate School of InformaticsKyoto UniversityKyotoJapan
  2. 2.RIKEN Center for Advanced Intelligence ProjectTokyoJapan

Personalised recommendations