Advertisement

AI & SOCIETY

pp 1–13 | Cite as

God-like robots: the semantic overlap between representation of divine and artificial entities

  • Nicolas SpatolaEmail author
  • Karolina Urbanska
Original Article
  • 78 Downloads

Abstract

Artificial intelligence and robots may progressively take a more and more prominent place in our daily environment. Interestingly, in the study of how humans perceive these artificial entities, science has mainly taken an anthropocentric perspective (i.e., how distant from humans are these agents). Considering people’s fears and expectations from robots and artificial intelligence, they tend to be simultaneously afraid and allured to them, much as they would be to the conceptualisations related to the divine entities (e.g., gods). In two experiments, we investigated the proximity of representation between artificial entities (i.e., artificial intelligence and robots), divine entities and natural entities (i.e., humans and other animals) at both an explicit (Study 1) and an implicit level (Study 2). In the first study, participants evaluated these entities explicitly on positive and negative attitudes. Hierarchical clustering analysis showed that participants’ representation of artificial intelligence, robots and divine entities were similar, while the representation of humans tended to be associated with that of animals. In the second study, participants carried out a word/non-word decision task including religious semantic-related words and neutral words after the presentation of a masked prime referring to divine entities, artificial entities and natural entities (or a control prime). Results showed that after divine and artificial entity primes, participants were faster to identify religious words as words compared to neutral words arguing for a semantic activation. We conclude that people make sense of the new entities by relying on already familiar entities and in the case of artificial intelligence and robots, people appear to draw parallels to divine entities.

Highlights

  • Artificial Intelligence and robots share common representations with divine entities (e.g., gods).

  • Artificial Intelligence and robots, similar to divine entities, are conceptualized as non-natural entities with high power over human life.

  • These common representations rely on conceptual semantic proximity at the explicit and implicit level.

Keywords

Artificial intelligence Robots Gods Semantic representation Perception of robots 

Notes

Acknowledgements

Authors thank Natalia Martinelli and Jordan Chambas for the assistance with data collection and Thia Sagherian-Dickey for helpful comments on the manuscript.

References

  1. Adee S (2018) Can’t hack it. New Sci 239(3190):36–39.  https://doi.org/10.1016/S0262-4079(18)31437-4 CrossRefGoogle Scholar
  2. Akhtar A, Gasser SM (2007) The nuclear envelope and transcriptional control. Nat Rev Genet 8:507–517.  https://doi.org/10.1038/nrg2122 CrossRefGoogle Scholar
  3. Appel M (2008) Fictional narratives cultivate just-world beliefs. J Commun 58(1):62–83.  https://doi.org/10.1111/j.1460-2466.2007.00374.x CrossRefGoogle Scholar
  4. Appel M, Mara M (2013) The persuasive influence of a fictional character’s trustworthiness. J Commun 63(5):912–932.  https://doi.org/10.1111/jcom.12053 CrossRefGoogle Scholar
  5. Bainbridge WA, Hart JW, Kim ES, Scassellati B (2011) The benefits of interactions with physically present robots over video-displayed agents. Int J Soc Robot 3(1):41–52.  https://doi.org/10.1007/s12369-010-0082-7 CrossRefGoogle Scholar
  6. Balota DA, Yap MJ, Cortese MJ (2006) Visual word recognition: the journey from features to meaning (a travel update). In: Handbook of psycholinguistics. Academic Press, pp 285–375.  https://doi.org/10.1016/B978-012369374-7/50010-9
  7. Bartneck C, Suzuki T, Kanda T, Nomura T (2007) The influence of people’s culture and prior experiences with Aibo on their attitude towards robots. AI Soc 21(1):217–230.  https://doi.org/10.1007/s00146-006-0052-7 CrossRefGoogle Scholar
  8. Bentin S, McCarthy G, Wood CC (1985) Event-related potentials, lexical decision and semantic priming. Electroencephalogr Clin Neurophysiol 60(4):343–355.  https://doi.org/10.1016/0013-4694(85)90008-2 CrossRefGoogle Scholar
  9. Bostrom N (2003) Ethical issues in advanced artificial intelligence. Science fiction and philosophy: from time travel to superintelligence, pp 277–284Google Scholar
  10. Breazeal C (2004) Social interactions in HRI: the robot view. IEEE Trans Syst Man Cybern Part C Appl Rev 34(2):181–186.  https://doi.org/10.1109/TSMCC.2004.826268 CrossRefGoogle Scholar
  11. Brown JD (2002) The Cronbach alpha reliability estimate. JALT Test Eval SIG Newsl 6(1):17–19Google Scholar
  12. Brysbaert M, Lange M, Van Wijnendaele I (2000) The effects of age-of-acquisition and frequency-of-occurrence in visual word recognition: further evidence from the Dutch language. Eur J Cognit Psychol.  https://doi.org/10.1080/095414400382208 CrossRefGoogle Scholar
  13. Caliñski T, Harabasz J (1974) A Dendrite method foe cluster analysis. Commun Stat 3(1):1–27.  https://doi.org/10.1080/03610927408827101 zbMATHCrossRefGoogle Scholar
  14. Caviola L, Everett JAC, Faber NS (2018) The moral standing of animals: towards a psychology of speciesism. J Personal Soc Psychol.  https://doi.org/10.1037/pspp0000182 CrossRefGoogle Scholar
  15. Collins AM, Loftus EF (1975) Spreading-activation theory of semantic memory. Psychol Rev 82(6):407–428.  https://doi.org/10.1037/0033-295X.82.6.407 CrossRefGoogle Scholar
  16. Costa C, Abal M, López-López R, Muinelo-Romay L (2014) Biosensors for the detection of circulating tumour cells. Sens (Switz) 14(3):4856–4875.  https://doi.org/10.3390/s140304856 CrossRefGoogle Scholar
  17. Cree GS, McRae K, McNorgan C (1999) An attractor model of lexical conceptual processing: simulating semantic priming. Cognit Sci.  https://doi.org/10.1207/s15516709cog2303_4 CrossRefGoogle Scholar
  18. Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika.  https://doi.org/10.1007/BF02310555 zbMATHCrossRefGoogle Scholar
  19. Davis JW (2008) Handbook of univariate and multivariate data analysis and interpretation with SPSS. Am Stat 62:268.  https://doi.org/10.1198/000313008x332287 CrossRefGoogle Scholar
  20. Dehaene S, Naccache L, Le Clec’H G, Koechlin E, Mueller M, Dehaene-Lambertz G, Le Bihan D et al (1998) Imaging unconscious semantic priming. Nature 395(6702):597–600.  https://doi.org/10.1038/26967 CrossRefGoogle Scholar
  21. Durkheim E (1912) Les formes élémentaires de la vie religieuse [The elementary forms of religious life]. Alcan, ParisGoogle Scholar
  22. Echterhoff G, Bohner G, Siebler F (2006) “Social Robotics” und Mensch-Maschine-Interaktion. Aktuelle Forschung und Relevanz fuer die Sozialpsychologie., social robotics and human-machine interaction: current research and relevance for social psychology. Zeitschrift Fuer Sozialpsychologie.  https://doi.org/10.1024/0044-3514.37.4.219 CrossRefGoogle Scholar
  23. Epley N, Waytz A, Cacioppo JT (2007) On seeing human: a three-factor theory of anthropomorphism. Psychol Rev 114(4):864–886.  https://doi.org/10.1037/0033-295X.114.4.864 CrossRefGoogle Scholar
  24. Exline JJ, Wood BT, Worthington EL, McMinn MR, Yali AM, Aten JD (2010) Development, refinement, and psychometric properties of the attitudes toward god scale (ATGS-9). Psychol Relig Spiritual 2(3):148–167.  https://doi.org/10.1037/a0018753 CrossRefGoogle Scholar
  25. Eyssel F, Kuchenbrandt D (2011) Manipulating anthropomorphic inferences about NAO: The role of situational and dispositional aspects of effectance motivation. In: Proceedings—IEEE International Workshop on Robot and Human Interactive Communication, pp 467–472.  https://doi.org/10.1109/ROMAN.2011.6005233
  26. Eyssel F, Kuchenbrandt D (2012) Social categorization of social robots: anthropomorphism as a function of robot group membership. Br J Soc Psychol 51(4):724–731.  https://doi.org/10.1111/j.2044-8309.2011.02082.x CrossRefGoogle Scholar
  27. Eyssel F, Kuchenbrandt D, Hegel F, De Ruiter L (2012) Activating elicited agent knowledge: how robot and user features shape the perception of social robots. In: Proceedings—IEEE International Workshop on Robot and Human Interactive Communication.  https://doi.org/10.1109/ROMAN.2012.6343858
  28. Fazio RH, Jackson JR, Dunton BC, Williams CJ (1995) Variability in automatic activation as an unobtrusive measure of racial attitudes: a bona fide pipeline? J Personal Soc Psychol 69(6):1013–1027.  https://doi.org/10.1037/0022-3514.69.6.1013 CrossRefGoogle Scholar
  29. Fiske ST, Neuberg SL (1990) A continuum of impression formation, from category-based to individuating processes: influences of information and motivation on attention and interpretation. Adv Exp Soc Psychol 23(C):1–74.  https://doi.org/10.1016/S0065-2601(08)60317-2 CrossRefGoogle Scholar
  30. Fiske ST, Cuddy AJC, Glick P (2007) Universal dimensions of social cognition: warmth and competence. Trends Cognit Sci 11(2):77–83.  https://doi.org/10.1016/j.tics.2006.11.005 CrossRefGoogle Scholar
  31. Geraci RM (2008) Apocalyptic AI: religion and the promise of artificial intelligence. J Am Acad Relig 76(1):138–166.  https://doi.org/10.1093/jaarel/lfm101 MathSciNetCrossRefGoogle Scholar
  32. Gervais WM (2013) Perceiving minds and gods. Perspect Psychol Sci 8(4):380–394.  https://doi.org/10.1177/1745691613489836 CrossRefGoogle Scholar
  33. Goetz J, Kiesler S, Powers A (2003) Matching robot appearance and behavior to tasks to improve human-robot cooperation. In: Proceedings—IEEE International Workshop on Robot and Human Interactive Communication, PP 55–60.  https://doi.org/10.1109/ROMAN.2003.1251796
  34. Gray K, Wegner DM (2010) Blaming god for our pain: human suffering and the divine mind. Personal Soc Psychol Rev 14(1):7–16.  https://doi.org/10.1177/1088868309350299 CrossRefGoogle Scholar
  35. Greenwald AG, Banaji MR (1995) Implicit Social cognition: attitudes, self-esteem, and stereotypes. Psychol Rev 102(1):4–27.  https://doi.org/10.1037/0033-295X.102.1.4 CrossRefGoogle Scholar
  36. Greenwald AG, McGhee DE, Schwartz JLK (1998) Measuring individual differences in implicit cognition: the implicit association test. J Personal Soc Psychol 74(6):1464–1480.  https://doi.org/10.1037/0022-3514.74.6.1464 CrossRefGoogle Scholar
  37. Haggard P (2017) Sense of agency in the human brain. Nat Rev Neurosci 18:197–208.  https://doi.org/10.1038/nrn.2017.14 CrossRefGoogle Scholar
  38. Häring M, Kuchenbrandt D, André E (2014) Would you like to play with me? In: Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction—HRI’14, pp 9–16.  https://doi.org/10.1145/2559636.2559673
  39. Haslam N (2006) Dehumanization: an integrative review. Personal Soc Psychol Rev 10:252–264.  https://doi.org/10.1207/s15327957pspr1003_4 CrossRefGoogle Scholar
  40. Haslam N, Loughnan S (2014) Dehumanization and infrahumanization. Ann Rev Psychol 65:399–423CrossRefGoogle Scholar
  41. Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2(4):361–375.  https://doi.org/10.1007/s12369-010-0068-5 CrossRefGoogle Scholar
  42. Hegel F, Krach S, Kircher T, Wrede B, Sagerer G (2008) Understanding social robots: a user study on anthropomorphism. In: Proceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN, pp 574–579.  https://doi.org/10.1109/ROMAN.2008.4600728
  43. Heine SJ, Proulx T, Vohs KD (2006) The meaning maintenance model: on the coherence of social motivations. Personal Soc Psychol Rev 10(2):88–110.  https://doi.org/10.1207/s15327957pspr1002_1 CrossRefGoogle Scholar
  44. Heiphetz L, Lane JD, Waytz A, Young LL (2016) How children and adults represent God’s mind. Cogn Sci 40(1):121–144CrossRefGoogle Scholar
  45. Helbing D, Frey BS, Gigerenzer G, Hafen E, Hagner M, Hofstetter Y, van den Hoven J, Zicari RV, Zwitter A (2019) Will democracy survive big data and artificial intelligence? In: Towards digital enlightenment. Springer, Cham, pp 73–98CrossRefGoogle Scholar
  46. Johnson KA, Li YJ, Cohen AB, Okun MA (2013) Friends in high places: the influence of authoritarian and benevolent god-concepts on social attitudes and behaviors. Psychol Relig Spiritual 5(1):15–22.  https://doi.org/10.1037/a0030138 CrossRefGoogle Scholar
  47. Kaplan F (2004) Who is afraid of the humanoid? Investigating cultural differences in the acceptance of robots. Int J Hum Robot 01(03):465–480.  https://doi.org/10.1142/S0219843604000289 CrossRefGoogle Scholar
  48. Krauss SL, Hopper SD (2001) From Dampier to DNA: the 300-year-old mystery of the identity and proposed allopolyploid origin of Conostylis stylidioides (Haemodoraceae). Aust J Bot.  https://doi.org/10.1071/BT00072 CrossRefGoogle Scholar
  49. Krystal H (2006) Shattered assumptions: towards a new psychology of trauma. J Nerv Ment Dis.  https://doi.org/10.1097/00005053-199303000-00017 CrossRefGoogle Scholar
  50. Lawless WF, Mittu R, Russell S, Sofge D (2017) Autonomy and artificial intelligence: a threat or savior? Auton Artif Intell A Threat Savior.  https://doi.org/10.1007/978-3-319-59719-5 CrossRefGoogle Scholar
  51. Lin JSC, Hsieh PL (2007) The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Comput Human Behav 23(3):1597–1615.  https://doi.org/10.1007/s11696-017-0294-5 CrossRefGoogle Scholar
  52. Lucas M (2000) Semantic priming without association: a meta-analytic review. Psychon Bull Rev.  https://doi.org/10.3758/BF03212999 CrossRefGoogle Scholar
  53. MacDorman KF, Vasudevan SK, Ho CC (2009) Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures. AI Soc 23(4):485–510.  https://doi.org/10.1007/s00146-008-0181-2 CrossRefGoogle Scholar
  54. Madden DJ (1988) Adult age differences in the effects of sentence context and stimulus degradation during visual word recognition. Psychol Aging 3(2):167–172CrossRefGoogle Scholar
  55. Mara M, Appel M (2015) Science fiction reduces the eeriness of android robots: a field experiment. Comput Human Behav 48:156–162.  https://doi.org/10.1016/j.chb.2015.01.007 CrossRefGoogle Scholar
  56. Martin CD (1997) The media equation: how people treat computers, television and new media like real people and places [Book Review]. IEEE Spectr.  https://doi.org/10.1109/MSPEC.1997.576013 CrossRefGoogle Scholar
  57. Medin DL, Atran S (2004) The native mind: biological categorization and reasoning in development and across cultures. Psychol Rev 111:960–983.  https://doi.org/10.1037/0033-295X.111.4.960 CrossRefGoogle Scholar
  58. Meltzoff AN (2007) The “like me” framework for recognizing and becoming an intentional agent. Acta Physiol (Oxf) 124(1):26–43.  https://doi.org/10.1016/j.actpsy.2006.09.005 CrossRefGoogle Scholar
  59. Menary R (2010) Dimensions of mind. Phenomenol Cogni Sci 9(4):561–578.  https://doi.org/10.1007/s11097-010-9186-7 CrossRefGoogle Scholar
  60. Mlynář J, Alavi HS, Verma H, Cantoni L (2018) Towards a sociological conception of artificial intelligence. Artificial general intelligence: 11th International Conference, AGI 2018, pp 130–139.  https://doi.org/10.1007/978-3-319-97676-1_13
  61. Müller VC, Bostrom N (2016) Future progress in artificial intelligence: a survey of expert opinion. Fundam Issues Artif Intell.  https://doi.org/10.1007/978-3-319-26485-1_33 CrossRefGoogle Scholar
  62. Nass C, Moon Y (2000) Machines and mindlessness: social responses to computers. J Soc Issues 56(1):81–103.  https://doi.org/10.1111/0022-4537.00153 CrossRefGoogle Scholar
  63. Nass C, Reeves B, Leshner G (1996) Technology and roles: a tale of two TVs. J Commun 46(2):121–128.  https://doi.org/10.1111/j.1460-2466.1996.tb01477.x CrossRefGoogle Scholar
  64. Neely JH (1977) Semantic priming and retrieval from lexical memory: roles of inhibitionless spreading activation and limited-capacity attention. J Exp Psychol Gen 106(3):226–254.  https://doi.org/10.1037/0096-3445.106.3.226 CrossRefGoogle Scholar
  65. New B., Pallier C., Ferrand L., & Matos R. (2001). Une base de données lexicales du français contemporain sur internet: LEXIQUE. L’Année Psychologique Google Scholar
  66. Nomura T, Kanda T, Suzuki T, Kato K (2005) Psychology in human-robot communication: an attempt through investigation of negative attitudes and anxiety toward robots. RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog no. 04TH8759), pp 35–40.  https://doi.org/10.1109/roman.2004.1374726
  67. Nomura T, Kanda T, Suzuki T (2006a) Experimental investigation into influence of negative attitudes toward robots on human–robot interaction. AI Soc 20(2):138–150CrossRefGoogle Scholar
  68. Nomura T, Suzuki T, Kanda T, Kato K (2006b) Altered attitudes of people toward robots: investigation through the negative attitudes toward robots scale. In: Proceedings AAAI-06 Workshop on Human Implications of Human-Robot Interaction, (Chaplin 1991), pp 29–35. Retrieved from http://www.aaai.org/Papers/Workshops/2006/WS-06-09/WS06-09-006.pdf
  69. Nowak KL, Rauh C (2005) The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction. J Comput Med Commun.  https://doi.org/10.1111/j.1083-6101.2006.tb00308.x CrossRefGoogle Scholar
  70. Nyangoma EN, Olson CK, Painter JA, Posey DL, Stauffer WM, Naughton M, Benoit SR et al (2017) Syphilis among US-bound refugees, 2009–2013. J Immigr Minor Health 19(4):835–842.  https://doi.org/10.1007/s10903-016-0397-z CrossRefGoogle Scholar
  71. Parasuraman A (2007) Technology readiness index (Tri). J Serv Res 2(4):307–320.  https://doi.org/10.1177/109467050024001 CrossRefGoogle Scholar
  72. Parasuraman A, Colby CL (2015) An updated and streamlined technology readiness index: TRI 2.0. J Serv Res 18(1):59–74.  https://doi.org/10.1177/1094670514539730 CrossRefGoogle Scholar
  73. Polkinghorne DE (2013) Narrative knowing and the human sciences. Choice Rev Online.  https://doi.org/10.5860/choice.26-0378 CrossRefGoogle Scholar
  74. Ray C, Mondada F, Siegwart R (2008) What do people expect from robots? 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp 3816–3821.  https://doi.org/10.1109/IROS.2008.4650714
  75. Riether N, Hegel F, Wrede B, Horstmann G (2012) Social facilitation with social robots? In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction—HRI’12, pp 41.  https://doi.org/10.1145/2157689.2157697
  76. Robertson J (2007) Robo sapiens Japanicus: humanoid robots and the posthuman family. Criti Asian Stud 39(3):369–398.  https://doi.org/10.1080/14672710701527378 CrossRefGoogle Scholar
  77. Rosen JB, Donley MP (2006) Animal studies of amygdala function in fear and uncertainty: relevance to human research. Biol Psychol 73(1):49–60.  https://doi.org/10.1016/j.biopsycho.2006.01.007 CrossRefGoogle Scholar
  78. Rossiter M (1999) A narrative approach to development: implications for adult education. Adult Educ Q 50(1):56–71.  https://doi.org/10.1177/07417139922086911 CrossRefGoogle Scholar
  79. Rugg MD (1985) The effects of semantic priming and word repetition on event-related potentials. Psychophysiology 22(6):642–647.  https://doi.org/10.1111/j.1469-8986.1985.tb01661.x CrossRefGoogle Scholar
  80. Salomon G, Perkins DN, Globerson T (2007) Partners in cognition: extending human intelligence with intelligent technologies. Educ Res 20(3):2–9.  https://doi.org/10.3102/0013189x020003002 CrossRefGoogle Scholar
  81. Segal HP (1998) Technological heaven. Nature 391:244.  https://doi.org/10.1038/34582 CrossRefGoogle Scholar
  82. Sirkin D, Ju W (2012) Consistency in physical and on-screen action improves perceptions of telepresence robots. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction—HRI’12, pp 57.  https://doi.org/10.1145/2157689.2157699
  83. Spatola N, Urbanska K (2018) Conscious machines: robots rights. Science 359(6374):400Google Scholar
  84. Spatola N, Belletier C, Normand A, Chausse P, Monceau S, Augustinova M, Ferrand L et al (2018) Not as bad as it seems: when the presence of a threatening humanoid robot improves human performance. Sci Robot 3(21):eaat5843.  https://doi.org/10.1126/scirobotics.aat5843 CrossRefGoogle Scholar
  85. Spatola N, Belletier C, Normand A, Chausse P, Barra V, Augustinova M, Huguet P et al (2019) Improved cognitive control in presence of anthropomorphized robots. Int J Soc Robot.  https://doi.org/10.1007/s12369-018-00511-w CrossRefGoogle Scholar
  86. Stroope S, Draper S, Whitehead AL (2013) Images of a loving god and sense of meaning in life. Soc Ind Res 111(1):25–44.  https://doi.org/10.1007/s11205-011-9982-7 CrossRefGoogle Scholar
  87. Sundar SS, Waddell TF, Jung EH (2016) The hollywood robot syndrome: media effects on older adults’ attitudes toward robots and adoption intentions. ACM/IEEE International Conference on Human-Robot Interaction, 2016–April, pp 343–350.  https://doi.org/10.1109/HRI.2016.7451771
  88. Syrdal DS, Dautenhahn K, Koay K, Walters ML (2009) The negative attitudes towards robots scale and reactions to robot behaviour in a live human–robot interaction study. In: Proceedings of the AISB symposium on new frontiers in human–robot interaction. Edinburgh, UK, pp 109–115Google Scholar
  89. Tanaka K, Nakanishi H, Ishiguro H (2014) Comparing video, avatar, and robot mediated communication: pros and cons of embodiment. Collab Technol Soc Comput 460:96–110.  https://doi.org/10.1007/978-3-662-44651-5_9 CrossRefGoogle Scholar
  90. Thompson-Schill SL, Kurtz KJ, Gabrieli JDE (1998) Effects of semantic and associative relatedness on automatic priming. J Mem Lang.  https://doi.org/10.1006/jmla.1997.2559 CrossRefGoogle Scholar
  91. Vimonses V, Lei S, Jin B, Chow CWK, Saint C (2009) Adsorption of congo red by three Australian kaolins. Appl Clay Sci 43(3–4):465–472.  https://doi.org/10.1016/j.clay.2008.11.008 CrossRefGoogle Scholar
  92. Wainer J, Feil-Seifer DJ, Shell DA, Matarić MJ (2006) The role of physical embodiment in human-robot interaction. In: Proceedings—IEEE International Workshop on Robot and Human Interactive Communication, pp 117–122.  https://doi.org/10.1109/ROMAN.2006.314404
  93. Walters ML, Syrdal DS, Dautenhahn K, Te Boekhorst R, Koay KL (2008) Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion. Auton Robots 24(2):159–178.  https://doi.org/10.1007/s10514-007-9058-3 CrossRefGoogle Scholar
  94. Waytz A, Cacioppo J, Epley N (2010a) Who sees human? The stability and importance of individual differences in anthropomorphism. Perspect Psychol Sci 5(3):219–232.  https://doi.org/10.1177/1745691610369336 CrossRefGoogle Scholar
  95. Waytz A, Gray K, Epley N, Wegner DM (2010b) Causes and consequences of mind perception. Trends Cognit Sci 14:383–388.  https://doi.org/10.1016/j.tics.2010.05.006 CrossRefGoogle Scholar
  96. Wiederhold BK, Baños RM, Botella C, Gaggioli A, Riva G (2011) Positive technology: using interactive technologies to promote positive functioning. Cyberpsychol Behav Soc Netw 15(2):69–77.  https://doi.org/10.1089/cyber.2011.0139 CrossRefGoogle Scholar
  97. Woolgar S (1985) Why not a sociology of machines? the case of sociology and artificial intelligence. Sociology.  https://doi.org/10.1177/0038038585019004005 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratoire de Psychologie Sociale et Cognitive (CNRS UMR 6024)Université Clermont AuvergneClermont-FerrandFrance
  2. 2.Department of PsychologyUniversity of SheffieldSheffieldUK

Personalised recommendations