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Effects of a Social Robot's Autonomy and Group Orientation on Human Decision-Making

DOI: 10.1155/2013/263721

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Abstract:

Social attributes of intelligent robots are important for human-robot systems. This paper investigates influences of robot autonomy (i.e., high versus low) and group orientation (i.e., ingroup versus outgroup) on a human decision-making process. We conducted a laboratory experiment with 48 college students and tested the hypotheses with MANCOVA. We find that a robot with high autonomy has greater influence on human decisions than a robot with low autonomy. No significant effect is found on group orientation or on the interaction between group orientation and autonomy level. The results provide implications for social robot design. 1. Introduction Robots play an increasingly important role in our daily life. More and more robots move out from laboratories into everyday life providing services or decision support for human beings. Recent research exploring the influence of a robot’s attributes on human decision-making has shed some light on designing social robots, from their physical attributes to the organization of the human-robot team (e.g., [1–3]). The current research focuses on two of the less explored but important factors in the human-robot decision-making process: robot autonomy and group orientation. Current technology allows for different levels of robotic autonomy, which describes to what degree a robot can act on its own accord. Determining a proper level of autonomy can benefit the interaction between a human and a robot. Autonomy has been studied comprehensively in designing industrial robots (e.g., [3, 4]). However, determining an appropriate autonomy level of social robots that are in close interaction with human remains to be solved. Additionally, as the social robot is increasingly endowed with human natures (e.g., voice, appearance, and motion), it is necessary to define its social identity, as we generally do with human beings. Group orientation of a robot toward its human partner is one of the essential identities, especially in a collaborative decision-making process. Prior research about human decision-making process has revealed that humans tend to have contrasting attitudes toward ingroup and outgroup members [5]. Similarly, when a social robot is perceived as an ingroup member by the interacting human, it may receive different evaluations and exert different levels of influence on human decisions compared with a robot that is perceived as an outgroup member. Therefore, this study investigates the influences of autonomy level and group orientation of a social robot on a human decision-making process and on human’s subjective

References

[1]  P. L. P. Rau, D. Li, and Y. Li, “A cross-cultural study: effect of robot appearance and task,” International Journal of Social Robotics, vol. 2, no. 2, pp. 175–186, 2010.
[2]  A. Powers and S. Kiesler, “The advisor robot: tracing people's mental model from a robot's physical attributes,” in Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction (HRI 2006), pp. 218–225, Salt Lake City, Utah, USA, March 2006.
[3]  T. Kaupp and A. Makarenko, “Measuring human-robot team effectiveness to determine an appropriate autonomy level,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '08), pp. 2146–2151, Pasadena, Calif, USA, May 2008.
[4]  F. Heger and S. Singh, “Sliding autonomy for complex coordinated multi-robot tasks: analysis & experiments,” in Proceedings of the Robotics: Systems and Science, August 2006.
[5]  H. Tajfel, M. Billig, R. Bundy, and C. Flament, “Social categorization and intergroup behavior,” European Journal of Social Psychology, vol. 1, no. 2, pp. 149–178, 1971.
[6]  B. Mutlu, S. Osman, J. Forlizzi, J. Hodgins, and S. Kiesler, “Task structure and user attributes as elements of human-robot interaction design,” in Proceedings of the 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN '06), pp. 74–79, Hatfield, UK, September 2006.
[7]  D. S. Syrdal, K. Dautenhahn, S. N. Woods, M. L. Walters, and K. L. Koay, “Looking good? Appearance preferences and robot personality inferences at zero acquaintance,” in Proceedings of the AAAI Spring Symposium, pp. 86–92, Multidisciplinary Collaboration for Socially Assistive Robotics, Stanford, Calif, USA, March 2007.
[8]  W. Lin, P. P. Rau, V. Evers, B. Robinson, and P. Hinds, “Responsiveness to robots: effects of ingroup orientation & communication style on HRI in China,” in Proceedings of the 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI '09), pp. 247–248, Los Angeles, Calif, USA, March 2009.
[9]  V. Evers, H. C. Maldonado, T. L. Brodecki, and P. J. Hinds, “Relational versus group self-construal: untangling the role of national culture in HRI,” in Proceedings of the 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI '08), pp. 255–262, Amsterdam, The Netherlands, March 2008.
[10]  M. A. Goodrich and A. C. Schultz, “Human-robot interaction: a survey,” Foundations and Trends in Human-Computer Interaction, vol. 1, no. 3, pp. 203–275, 2007.
[11]  T. B. Sheridan and W. L. Verplank, Human and Computer Control for Undersea Teleoperators, MIT Man-Machine Systems Laboratory, 1978.
[12]  B. Sellner, F. W. Heger, L. M. Hiatt, R. Simmons, and S. Singh, “Coordinated multiagent teams and sliding autonomy for large-scale assembly,” Proceedings of the IEEE, vol. 94, no. 7, pp. 1425–1444, 2006.
[13]  H. C. Triandis, R. Bontempo, M. J. Villareal, M. Asai, and N. Lucca, “Individualism and collectivism: cross-cultural perspectives on self-ingroup relationships,” Journal of Personality and Social Psychology, vol. 54, no. 2, pp. 323–338, 1988.
[14]  W. Gudykunst and M. Bond, “Intergroup relations across cultures,” in Handbook of Cross-Cultural Psychology, vol. 3, pp. 119–161, 1997.
[15]  T. Yamagishi, N. Jin, and A. S. Miller, “In-group bias and culture of collectivism,” Asian Journal of Social Psychology, vol. 1, no. 3, pp. 315–328, 1998.
[16]  A. M. Ahmed, “Group identity, social distance and intergroup bias,” Journal of Economic Psychology, vol. 28, no. 3, pp. 324–337, 2007.
[17]  M. B. Brewer, “In-group bias in the minimal intergroup situation: a cognitive-motivational analysis,” Psychological Bulletin, vol. 86, no. 2, pp. 307–324, 1979.
[18]  Y. Chen, J. Brockner, and X. Chen, “Individual-collective primacy and ingroup favoritism: enhancement and protection effects,” Journal of Experimental Social Psychology, vol. 38, no. 5, pp. 482–491, 2002.
[19]  R. Kramer and L. Goldman, “Helping the group or helping yourself? Social motives and group identity in resource dilemmas,” in DSocial Dilemmas: Perspectives on Individuals and Groups, A. Schroeder, Ed., pp. 49–67, Greenwood Publishing Group, Westport, Conn, USA, 1995.
[20]  D. J. Terry and M. A. Hogg, “Group norms and the attitude-behavior relationship: a role for group identification,” Personality and Social Psychology Bulletin, vol. 22, no. 8, pp. 776–793, 1996.
[21]  L. Wang, P. P. Rau, V. Evers, B. K. Robinson, and P. Hinds, “When in Rome: the role of culture and context in adherence to robot recommendations,” in Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI '10), pp. 359–366, Osaka, Japan, March 2010.
[22]  L. Wheeless and J. Grotz, “The measurement of trust and its relationship to self-disclosure,” Human Communication Research, vol. 3, no. 3, pp. 250–257, 1977.
[23]  R. B. Rubin, P. Palmgreen, and H. E. Sypher, Communication Research Measures: A Sourcebook, The Guilford Press, New York, NY, USA, 1994.
[24]  D. Berlo, J. Lemert, and R. Mertz, “Dimensions for evaluating the acceptability of message sources,” Public Opinion Quarterly, vol. 33, no. 4, pp. 563–576, 1969.
[25]  J. McCroskey and T. Young, “Ethos and credibility: the construct and its measurement after three decades,” Communication Studies, vol. 32, pp. 24–34, 1981.
[26]  S. Hart and L. Staveland, “Development of NASA-TLX (task load index): results of empirical and theoretical research,” in Human Mental Workload, vol. 1, pp. 139–183, 1988.
[27]  R. Parasuraman, T. B. Sheridan, and C. D. Wickens, “Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs,” Journal of Cognitive Engineering and Decision Making, vol. 2, no. 2, pp. 140–160, 2008.
[28]  A. Freedy, E. DeVisser, G. Weltman, and N. Coeyman, “Measurement of trust in human-robot collaboration,” in Proceedings of the International Symposium on Collaborative Technologies and Systems (CTS '07), pp. 106–114, Orlando, Fla, USA, May 2007.
[29]  J. Lee and N. Moray, “Trust, control strategies and allocation of function in human-machine systems,” Ergonomics, vol. 35, no. 10, pp. 1243–1270, 1992.
[30]  R. Parasuraman, T. B. Sheridan, and C. D. Wickens, “A model for types and levels of human interaction with automation,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 30, no. 3, pp. 286–297, 2000.
[31]  C. Breazeal and M. Siegel, Persuasive robotics: how robots change our minds [M.S. thesis], Massachusetts Institute of Technology, Cambridge, Mass, USA, 2009.
[32]  C. D. Kidd and C. Breazeal, “Human-robot interaction experiments: lessons learned,” in Proceedings of the Artificial Intelligence and Simulation of Behaviour (AISB '05), pp. 141–142, April 2005.
[33]  A. Steinfeld, T. Fong, and D. Kaber, “Common metrics for human-robot interaction,” in Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction (HRI '06), Salt Lake City, Utah, USA, 2006.
[34]  Department of Defense, “Chapter 16—sea survival,” in US Army Survival Manual: FM 21-76, 1992.
[35]  H. C. Kraemer and S. Thiemann, How Many Subjects? Statistical Power Analysis in Research, Sage, London, UK, 1987.
[36]  M. Madsen and S. Gregor, “Measuring human-computer trust,” in Proceedings of the 11th Australasian Conference on Information Systems, Brisbane, Australia, 2000.
[37]  Y. M. Xiao, Evaluation of mental workload and its application [Ph.D. thesis], Sichuan University, Chengdu, China, 2005 (Chinese).
[38]  T. Nomura, T. Suzuki, T. Kanda, and K. Kato, “Measurement of negative attitudes toward robots,” Interaction Studies, vol. 7, no. 3, pp. 437–454, 2006.
[39]  M. B. Brewer and Y. Chen, “Where (Who) are collectives in collectivism? Toward conceptual clarification of individualism and collectivism,” Psychological Review, vol. 114, no. 1, pp. 133–151, 2007.
[40]  T. Nomura, T. Kanda, T. Suzuki, and K. Kato, “Prediction of human behavior in human—robot interaction using psychological scales for anxiety and negative attitudes toward robots,” IEEE Transactions on Robotics, vol. 24, no. 2, pp. 442–451, 2008.
[41]  C. Bartneck, T. Suzuki, T. Kanda, and T. Nomura, “The influence of people's culture and prior experiences with Aibo on their attitude towards robots,” AI and Society, vol. 21, no. 1, pp. 217–230, 2007.
[42]  C. Sedikides and M. B. Brewer, “Individual self, relational self, and collective self: partners, opponents, or strangers?” in Individual Self, Relational Self, Collective Self, C. Sedikides and M. B. Brewer, Eds., Psychology Press, Philadelphia, Pa, USA, 2001.
[43]  C. F. DiSalvo, F. Gemperle, J. Forlizzi, and S. Kiesler, “All robots are not created equal: the design and perception of humanoid robot heads,” in Proceedings of the 4th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 321–326, London, UK, June 2002.
[44]  A. Aron, E. N. Aron, and D. Smollan, “Inclusion of other in the self scale and the structure of interpersonal closeness,” Journal of Personality and Social Psychology, vol. 63, no. 4, pp. 596–612, 1992.
[45]  J. Nunally and I. Bernstein, Psychometric Theory, McGraw-Hill, New York, NY, USA, 1978.
[46]  K. P. Weinfurt, “Multivariate analysis of variance,” in Reading and Understanding Multivariate Statistics, L. G. Grimm and P. R. Yarnold, Eds., pp. 245–276, American Psychological Association, Washington, DC, USA, 1995.
[47]  J. P. Boldizar and D. M. Messick, “Intergroup fairness biases: is ours the fairer sex?” Social Justice Research, vol. 2, no. 2, pp. 95–111, 1988.
[48]  M. Foddy, M. J. Platow, and T. Yamagishi, “Group-based trust in strangers: the role of stereotypes and expectations,” Psychological Science, vol. 20, no. 4, pp. 419–422, 2009.
[49]  H. R. Markus and S. Kitayama, “Culture and the self: implications for cognition, emotion, and motivation,” Psychological Review, vol. 98, no. 2, pp. 224–253, 1991.

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