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International Journal of Social Robotics

, Volume 7, Issue 4, pp 497–512 | Cite as

Why Should We Imitate Robots? Effect of Back Imitation on Judgment of Imitative Skill

  • Yasser MohammadEmail author
  • Toyoaki Nishida
Article

Abstract

How we perceive robots affects how we interact with them and vice versa. This leads us to hypothesize that imitating a robot (back imitation) would affect human’s perception of this robot. More specifically, we suggest that it would lead to the attribution to a higher imitative skill to the robot when it subsequently imitates the human. Given that one of the major challenges in learning from demonstration (imitation) in robotics is the limited number of training examples that the demonstrator is usually willing to provide, it would be beneficial to design the interaction context in such a way to increase human’s subjective evaluation of the robot’s imitative skills and back-imitation may be a way to achieve that. Three studies were conducted—involving 78 subjects and 150 HRI sessions—to evaluate the effect of back imitation on human’s perception of the robot along several dimensions including imitation skill, motion human likeness, interaction quality, humanness and likability. These studies show that people who imitated the robot for few minutes assigned it later higher imitative skill and motion human-likeness. Moreover, back imitation was shown to lead to higher intention of future interaction. The paper reports the results of these studies and discusses their implications for the design of imitation interactions.

Keywords

Learning through imitation Back imitation Perception of robots Social factors in imitation 

Notes

Acknowledgments

This study has been carried out with financial support from the Center of Innovation Program from JST, JSPS KAKENHI Grant Number 24240023, JSPS Grant-in-Aid for JSPS Postdoctoral Fellows P12046, and AFOSR/AOARD Grant No. FA2386-14-1-0005.

References

  1. 1.
    Argall B, Browning B, Veloso M (2007) Learning by demonstration with critique from a human teacher. In: Proceedings of the ACM/IEEE international conference on human–robot interaction. ACM, New York, NY, USA, pp 57–64. doi: 10.1145/1228716.1228725
  2. 2.
    Aronson E, Mills J (1959) The effect of severity of initiation on liking for a group. J Abnorm Social Psychol 59(2):177CrossRefGoogle Scholar
  3. 3.
    Bailenson JN, Yee N (2005) Digital chameleons automatic assimilation of nonverbal gestures in immersive virtual environments. Psychol Sci 16(10):814–819CrossRefGoogle Scholar
  4. 4.
    Chartrand TL, Bargh JA (1999) The chameleon effect: the perception–behavior link and social interaction. J Personal Social Psychol 76(6):893CrossRefGoogle Scholar
  5. 5.
    Cooper J (2007) Cognitive dissonance: 50 years of a classic theory. Sage, Thousand OaksGoogle Scholar
  6. 6.
    Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35(8):982–1003CrossRefGoogle Scholar
  7. 7.
    Goldman AI (2006) Simulating minds: the philosophy, psychology, and neuroscience of mindreading. Oxford University Press, OxfordCrossRefzbMATHGoogle Scholar
  8. 8.
    Grimm LG, Yarnold PR (1995) Reading and understanding multivariate statistics. American Psychological Association, WashingtonGoogle Scholar
  9. 9.
    Haslam N, Loughnan S, Kashima Y, Bain P (2008) Attributing and denying humanness to others. Eur Rev Social Psychol 19(1):55–85CrossRefGoogle Scholar
  10. 10.
    Hedges LV (1981) Distribution theory for glass’s estimator of effect size and related estimators. J Educ Behav Stat 6(2):107–128CrossRefGoogle Scholar
  11. 11.
    Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Social Robot 2(4):361–375CrossRefGoogle Scholar
  12. 12.
    Iacoboni M, Molnar-Szakacs I, Gallese V, Buccino G, Mazziotta JC, Rizzolatti G (2005) Grasping the intentions of others with one’s own mirror neuron system. PLoS Biol 3(3):e79CrossRefGoogle Scholar
  13. 13.
    Jones EE, Harris VA (1967) The attribution of attitudes. J Exp Social Psychol 3(1):1–24CrossRefGoogle Scholar
  14. 14.
    Jones SS (2006) Infants learn to imitate by being imitated. In: Proceedings of the international conference on development and learning (ICDL)Google Scholar
  15. 15.
    Jones SS (2009) The development of imitation in infancy. Philos Trans R Soc B Biol Sci 364(1528):2325–2335CrossRefGoogle Scholar
  16. 16.
    Jonsson CO, Clinton D (2006) What do mothers attune to during interactions with their infants? Infant Child Dev 15(4):387–402CrossRefGoogle Scholar
  17. 17.
    Lee C, Lesh N, Sidner CL, Morency LP, Kapoor A, Darrell T (2004) Nodding in conversations with a robot. In: CHI’04 extended abstracts on human factors in computing systems. ACM, pp 785–786Google Scholar
  18. 18.
    Mohammad Y, Nishida T (2013) Tackling the correspondence problem: closed-form solution for gesture imitation by a humanoid’s upper body. In: International conference on active media technology (AMT). WIC and IEEE TF-BI. Maebashi, Japan, pp 84–95Google Scholar
  19. 19.
    Oberman LM, McCleery JP, Ramachandran VS, Pineda JA (2007) Eeg evidence for mirror neuron activity during the observation of human and robot actions: toward an analysis of the human qualities of interactive robots. Neurocomputing 70(13):2194–2203CrossRefGoogle Scholar
  20. 20.
    Rice WR (1989) Analyzing tables of statistical tests. Evolution 43(1):223–225CrossRefGoogle Scholar
  21. 21.
    Riek LD, Paul PC, Robinson P (2010) When my robot smiles at me: enabling human–robot rapport via real-time head gesture mimicry. J Multimodal User Interfaces 3(1–2):99–108CrossRefGoogle Scholar
  22. 22.
    Rizzolatti G, Fadiga L, Gallese V, Fogassi L (1996) Premotor cortex and the recognition of motor actions. Cogn Brain Res 3(2):131–141CrossRefGoogle Scholar
  23. 23.
    Salem M, Eyssel F, Rohlfing K, Kopp S, Joublin F (2013) To err is human (-like): effects of robot gesture on perceived anthropomorphism and likability. Int J Social Robot 5:1–11CrossRefGoogle Scholar
  24. 24.
    Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 3:425–478Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Assiut UniversityAssiutEgypt

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