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International Journal of Social Robotics

, Volume 10, Issue 1, pp 33–42 | Cite as

Can Human–Robot Interaction Promote the Same Depth of Social Information Processing as Human–Human Interaction?

  • Mingyu Kim
  • Taesoo Kwon
  • Kwanguk Kim
Article

Abstract

Recent studies on human–robot interactions have suggested that humanoid robots have considerable potential in social cognition research. However, the authors are not aware of any studies regarding social information processing from human–robot interactions. To address this issue, we considered two types of social interaction tasks (initiating and responding joint attention tasks) and two types of interaction partners (robot and human partners). Distinguishing between these types of joint attention (JA) is important, because they are thought to reflect unique but common constellations of processes in human social cognition and social learning. Thirty-seven participants were recruited (Study 1: 20 participants, Study 2: 17 participants) for the current study, and they conducted a picture recognition social information processing task with either robot or human partners. The results of Study 1 suggested that participants who interacted with a humanoid robot achieved a better recognition memory performance in the initiating JA condition than in the responding JA condition. The results of Study 2 suggested that the human–human and human–robot interactions resulted in no quantifiable differences in recognition memory. We discuss the implications of our results for the utility of humanoid robots in social cognition studies and future research questions on human–robot interactions.

Keywords

Humanoid robot Human–human interaction Human–robot interaction Social cognition Joint attention 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A1A1005390 and NRF-2016R1E1A2020733).

References

  1. 1.
    Chaminade T, Cheng G (2009) Social cognitive neuroscience and humanoid robotics. J Physiol Paris 103(3):286–295CrossRefGoogle Scholar
  2. 2.
    Bluethmann W, Ambrose R, Diftler M, Askew S, Huber E, Goza M, Rehnmard F, Lovchik C, Magruder D (2003) Robonaut: a robot designed to work with humans in space. Auton Robot 14(2–3):179–197CrossRefzbMATHGoogle Scholar
  3. 3.
    Fasola J, Matarić MJ (2012) Using socially assistive human–robot interaction to motivate physical exercise for older adults. P IEEE 100(8):2512–2526CrossRefGoogle Scholar
  4. 4.
    Marti P, Bacigalupo M, Giusti L, Mennecozzi C, Shibata T (2006) Socially assistive robotics in the treatment of behavioural and psychological symptoms of dementia. In: The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob, pp 483–488Google Scholar
  5. 5.
    Tapus A, Mataric MJ, Scasselati B (2007) Socially assistive robotics [Grand challenges of robotics]. IEEE Robot Autom Mag 14(1):35–42CrossRefGoogle Scholar
  6. 6.
    Anzalone SM, Tilmont E, Boucenna S, Xavier J, Jouen AL, Bodeau N, Maharatna K, Cehtouani M, Cohen D (2014) How children with autism spectrum disorder behave and explore the 4-dimensional (spatial 3D+ time) environment during a joint attention induction task with a robot. Res Autism Spect Dis 8(7):814–826CrossRefGoogle Scholar
  7. 7.
    Fiske ST, Taylor SE (2013) Social cognition: from brains to culture. Sage, CaliforniaCrossRefGoogle Scholar
  8. 8.
    Horan WP, Kern RS, Green MF, Penn DL (2008) Social cognition training for individuals with schizophrenia: emerging evidence. Am J Psychiatr Rehabil 11(3):205–252CrossRefGoogle Scholar
  9. 9.
    Lewis M (2012) Social cognition and the acquisition of self. Springer, HeidelbergGoogle Scholar
  10. 10.
    Burns JK (2006) Psychosis: a costly by-product of social brain evolution in Homo sapiens. Prog Neuro Psychopharmacol 30(5):797–814CrossRefGoogle Scholar
  11. 11.
    Premack D, Woodruff G (1978) Does the chimpanzee have a theory of mind? Behav Brain Sci 1(4):515–526CrossRefGoogle Scholar
  12. 12.
    Robins B, Dautenhahn K, Te Boekhorst R, Billard A (2005) Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills? Univ Access Inf 4(2):105–120CrossRefGoogle Scholar
  13. 13.
    Chaminade T, Okka MM (2013) Comparing the effect of humanoid and human face for the spatial orientation of attention. Front Neurorobot 7(12):1–7Google Scholar
  14. 14.
    Pierno AC, Mari M, Lusher D, Castiello U (2008) Robotic movement elicits visuomotor priming in children with autism. Neuropsychologia 46(2):448–454CrossRefGoogle Scholar
  15. 15.
    Duquette A, Michaud F, Mercier H (2008) Exploring the use of a mobile robot as an imitation agent with children with low-functioning autism. Auton Robot 24(2):147–157CrossRefGoogle Scholar
  16. 16.
    Bisio A, Sciutti A, Nori F, Metta G, Fadiga L, Sandini G, Pozzo T (2014) Motor contagion during human–human and human–robot interaction. PLoS ONE 9(8):1–10CrossRefGoogle Scholar
  17. 17.
    Imai M, Ono T, Ishiguro H (2003) Physical relation and expression: joint attention for human–robot interaction. IEEE Trans Ind Electron 50(4):636–643CrossRefGoogle Scholar
  18. 18.
    Skantze H, Hjalmarsson A, Oertel C (2014) Turn-taking, feedback and joint attention in situated human–robot interaction. Speech Commun 65:50–66CrossRefGoogle Scholar
  19. 19.
    Johansson M, Skantze G, Gustafson J (2013) Head pose patterns in multiparty human-robot team-building interactions. In: International conference on social robotics, pp 351–360Google Scholar
  20. 20.
    Baldwin MW (1995) Relational schemas and cognition in close relationships. J Soc Pers Relat 12(4):547–552CrossRefGoogle Scholar
  21. 21.
    Mundy P, Sullivan L, Mastergeorge AM (2009) A parallel and distributed-processing model of joint attention, social cognition and autism. Autism Res 2(1):2–21CrossRefGoogle Scholar
  22. 22.
    Kasari C, Paparella T, Freeman S, Jahromi LB (2008) Language outcome in autism: randomized comparison of joint attention and play interventions. J Consult Clin Psychol 76(1):125–137CrossRefGoogle Scholar
  23. 23.
    Tomasello M, Carpenter M, Call J, Behne T, Moll H (2005a) Understanding and sharing intentions: the origins of cultural cognition. Behav Brain Sci 28(5):675–691Google Scholar
  24. 24.
    Tomasello M, Carpenter M, Hobson RP (2005b) The emergence of social cognition in three young chimpanzees. Monogr Soc Res Child 70(1):1–152CrossRefGoogle Scholar
  25. 25.
    Baron-Cohen S (1997) Mindblindness: an essay on autism and theory of mind. MIT Press, CambridgeGoogle Scholar
  26. 26.
    Seibert JM, Hogan AE, Mundy PC (1982) Assessing interactional competencies: The early social-communication scales. Infant Ment Health J 3(4):244–258CrossRefGoogle Scholar
  27. 27.
    Redcay E, Dodell-Feder D, Pearrow MJ, Mavros PL, Kleiner M, Gabrieli JD, Saxe R (2010) Live face-to-face interaction during fMRI: a new tool for social cognitive neuroscience. Neuroimage 50(4):1639–1647CrossRefGoogle Scholar
  28. 28.
    Schilbach L, Wilms M, Eickhoff SB, Romanzetti S, Tepest R, Bente G, Shah NJ, Fink GR, Vogeley K (2010) Minds made for sharing: initiating joint attention recruits reward-related neurocircuitry. J Cogn Neurosci 22(12):2702–2715CrossRefGoogle Scholar
  29. 29.
    Kim K, Mundy P (2012) Joint attention, social-cognition, and recognition memory in adults. Front Hum Neurosci 6(172):1–11Google Scholar
  30. 30.
    Ricks DJ, Colton MB (2010) Trends and considerations in robot-assisted autism therapy. In: 2010 IEEE international conference on robotics and automation (ICRA), pp 4354–4359Google Scholar
  31. 31.
    Scassellati B (2007) How social robots will help us to diagnose, treat, and understand autism. In: Robotics research, pp 552–563. Springer, BerlinGoogle Scholar
  32. 32.
    Warren ZE, Zheng Z, Swanson AR, Bekele E, Zhang L, Crittendon JA, Weitlauf AF, Sarkar N (2013) Can robotic interaction improve joint attention skills? J Autism Dev Disord 45(11):3726–34Google Scholar
  33. 33.
    Dautenhahn K (2003) Roles and functions of robots in human society: implications from research in autism therapy. Robotica 21(4):443–452CrossRefGoogle Scholar
  34. 34.
    Kaliouby RE (2005) Ming-reading machines: automated inference of complex mental States. University of CambridgeGoogle Scholar
  35. 35.
    Hadar U, Steiner TJ, Rose FC (1983) Head movement during listening turns in conversation. J Nonverbal Behav 9(4):214–228CrossRefGoogle Scholar
  36. 36.
    Wallbott HG (1998) Bodily expression of emotion. Eur J Soc Psychol 28(6):879–896CrossRefGoogle Scholar
  37. 37.
    Torta E, van Heumen J, Piunti F, Romeo L, Cuijpers R (2015) Evaluation of unimodal and multimodal communication cues for attracting attention in human–robot Interaction. Int J Soc Robot 7(1):89–96CrossRefGoogle Scholar
  38. 38.
    Zheng Z, Zhang L, Bekele E, Swanson A, Crittendon JA, Warren Z, Sarkar N (2013) Impact of robot-mediated interaction system on joint attention skills for children with autism. In: 2013 IEEE international conference on rehabilitation robotics (ICORR), pp 1–8Google Scholar
  39. 39.
    Hall ET (1966) The hidden dimension. Doubleday, New YorkGoogle Scholar
  40. 40.
    Schulz KP, Fan J, Magidina O, Marks DJ, Hahn B, Halperin JM (2007) Does the emotional go/no-go task really measure behavioral inhibition?: convergence with measures on a non-emotional analog. Arch Clin Neuropsychol 22(2):151–160CrossRefGoogle Scholar
  41. 41.
    Stanislaw H, Todorov N (1999) Calculation of signal detection theory measures. Behav Res Method Intrum C 31(1):137–149CrossRefGoogle Scholar
  42. 42.
    Sheslow D, Adams W (2003) Wide range assessment of memory and learning-revised (WRAML-2). Administration and Technical Manual. Wide Range. Inc., WilmingtonGoogle Scholar
  43. 43.
    Watson D, Friend R (1969) Measurement of social-evaluative anxiety. J Consult Clin Psych 33(4):448–457CrossRefGoogle Scholar
  44. 44.
    Bandura A (1977) Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 84(2):191–215CrossRefGoogle Scholar
  45. 45.
    Vygotsky L (1978) Interaction between learning and development. Read Dev Child 23(3):34–41Google Scholar
  46. 46.
    Brooks R, Meltzoff AN (2002) The importance of eyes: how infants interpret adult looking behavior. Dev Psychol 38(6):958CrossRefGoogle Scholar
  47. 47.
    Mundy P, Newell L (2007) Attention, joint attention, and social cognition. Curr Dir Psychol Sci 16(5):269–274CrossRefGoogle Scholar
  48. 48.
    Mundy P, Card J, Fox N (2000) EEG correlates of the development of infant joint attention skills. Dev Psychobiol 36(4):325CrossRefGoogle Scholar
  49. 49.
    Larsen RJ, Shackelford TK (1996) Gaze avoidance: personality and social judgments of people who avoid direct face-to-face contact. Pers Indiv Differ 21(6):907–917CrossRefGoogle Scholar
  50. 50.
    Won AS, Perone B, Friend M, Bailenson JN (2016) Identifying Anxiety Through Tracked Head Movements in a Virtual Classroom. Cyberpsychol Behav Soc Netw 19(6):380–387CrossRefGoogle Scholar
  51. 51.
    Eagly AH (1983) Gender and social influence: a social psychological analysis. Am Psychol 38(9):971–981CrossRefGoogle Scholar
  52. 52.
    Tay B, Jung Y, Park T (2014) When stereotypes meet robots: the double-edge sword of robot gender and personality in human–robot interaction. Comput Hum Behav 38:75–84CrossRefGoogle Scholar
  53. 53.
    Siegel M, Breazeal C, Norton MI (2009) Persuasive robotics: the influence of robot gender on human behavior. In: 2009 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2563–2568Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Human-Computer Interaction Lab., Department of Computer ScienceHanyang UniversitySeoulSouth Korea
  2. 2.Computer Graphics & Animation Lab., Department of Computer ScienceHanyang UniversitySeoulSouth Korea

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