This study examines to what extent mixed groups of computers and humans are able to produce conformity effects in human interaction partners. Previous studies reveal that nonhuman groups can induce conformity under certain circumstances, but it is unknown to what extent mixed groups of human and nonhuman agents are able to produce similar effects. It is also unknown how varying the number of human agents per group can affect conformity. Participants were assigned to one of five groups varying in their proportion of human to nonhuman agent composition and were asked to complete a social and analytical task with the assigned group. These task types were chosen to represent tasks which humans (i.e., social task) or computers (i.e., analytical task) may be perceived as having greater expertise in, as well as roughly approximating real-world tasks humans may complete. A mixed analysis of variance (ANOVA) revealed higher rates of conformity (i.e., percentage of time participants answered in line with their group on critical trials) with the group opinion for the analytical versus the social task. In addition, there was an impact of the ratio of human to nonhuman agents per group on conformity on the social task, with higher conformity with the group opinion as the number of humans in the group increased. No such effect was observed for the analytical task. The findings suggest that mixed groups produce different levels of conformity depending on group composition and task type. Designers of systems should be aware that group composition and task type may influence compliance and should design systems accordingly.
AlvaroE. M.CranoW. D. (1997). Indirect minority influence: Evidence for leniency in source evaluation and counterargumentation. Journal of Personality and Social Psychology, 72, 949–964.
2.
AminiM.StrömstenF. (2010). Conformity, gender and the sex composition of the group (Unpublished thesis). Stockholm School of Economics, Stockholm, Sweden.
3.
AndristS.SpannanE.MutluB. (2013). Rhetorical robots: Making robots more effective speakers using linguistic cues of expertise In Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (pp. 341–348). Tokyo, Japan: IEEE Press.
4.
AschS. (1951). Effects of group pressure upon the modification and distortion of judgments. In GuetzkowH. (Ed.), Groups, leadership and men (pp. 177–190). Oxford, UK: Carnegie Press.
5.
AschS. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs: General and Applied, 70(9), 1–70.
6.
BaronR. S.VandelloJ. A.BrunsmanB. (1996). The forgotten variable in conformity research: Impact of task importance on social influence. Journal of Personality and Social Psychology, 71, 915–927.
7.
Baron-CohenS.WheelwrightS.HillJ.RasteY.PlumbI. (2001). The “Reading the Mind in the Eyes” test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. Journal of Child Psychology and Psychiatry, 42, 241–251.
8.
BondR. (2005). Group size and conformity. Group Processes & Intergroup Relations, 8, 331–354.
9.
BondR.SmithP. B. (1996). Culture and conformity: A meta-analysis of studies using Asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119, 111–137.
10.
BradshawJ.FeltovichP.JohnsonM. (2011). Human-agent interaction. In BoyG. (ed.), Handbook of human-machine interaction (pp. 283–302). Farnham, UK: Ashgate.
11.
BrandstetterJ.RáczP.BecknerC.SandovalE. B.HayJ.BartneckC. (2014). A peer pressure experiment: Recreation of the Asch conformity experiment with robots. In IEEE International Conference on Intelligent Robots and Systems (pp. 1335–1340). doi:10.1109/IROS.2014.6942730
12.
BrewerM. B. (2003). Optimal distinctiveness, social identity, and the self. In LearyM. R.TangneyJ. P. (Eds.), Handbook of self and identity (pp. 480–491). New York, NY: Guilford Press.
13.
ChenJ. Y. C.BarnesM. J. (2014). Human-agent teaming for multi-robot control: A review of human factors issues. IEEE Transactions on Human-Machine Systems, 44, 13–29.
14.
ChenJ. Y. C.TerrenceP. (2009). Effects of imperfect automation and individual differences on concurrent performance of military and robotics tasks in a simulated multitasking environment. Ergonomics, 52, 907–920.
15.
CialdiniR. B.GoldsteinN. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621.
16.
CooperH. M. (1979). Statistically combining independent studies: A meta-analysis of sex differences in conformity research. Journal of Personality and Social Psychology, 37(1), 131–146.
17.
CrutchfieldR. S. (1955). Conformity and character. American Psychologist, 10, 191–198.
18.
CummingsM. L.ClareA.HartC. (2010). The role of human-automation consensus in multiple unmanned vehicle scheduling. Human Factors, 52, 17–27.
19.
DavidB.TurnerJ. C. (2001). Majority and minority influence: A single process self-categorization analysis. In De DreuC. K. W.De VriesN. K. (Eds.), Group consensus and minority influence: Implications for innovation (pp. 91–121). Malden, MA: Blackwell.
20.
DemirM.McNeeseN. J.CookeN. J. (2018). Team synchrony in human-autonomy teaming. In ChenJ. (Eds.), Advances in human factors in robots and unmanned systems—Proceedings of the AHFE 2017 International Conference on Human Factors in Robots and Unmanned Systems (pp. 303–312). New York, NY: Springer Verlag.
21.
DeutschM.GerardH. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51, 629–636. doi:10.1037/h0046408
22.
de VisserE.ParasuramanR. (2011). Adaptive aiding of human-robot teaming: Effects of imperfect automation on performance, trust, and workload. Journal of Cognitive Engineering and Decision Making, 5, 209–231.
23.
de VisserE. J.KruegerF.McKnightP.ScheidS.SmithM.ChalkS.ParasuramanR. (2012). The world is not enough: Trust in cognitive agents. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 56, No. 1, pp. 263–267). Los Angeles, CA: SAGE.
24.
de VisserE. J.MonfortS. S.GoodyearK.LuL.O’HaraM.LeeM. R. . . . KruegerF. (2017). A little anthropomorphism goes a long way: Effects of oxytocin on trust, compliance, and team performance with automated agents. Human Factors, 59, 116–133.
25.
de VisserE. J.MonfortS. S.McKendrickR.SmithM. A.McKnightP. E.KruegerF.ParasuramanR. (2016). Almost human: Anthropomorphism increases trust resilience in cognitive agents. Journal of Experimental Psychology: Applied, 22, 331–349.
26.
de VisserE. J.PakR.ShawT. H. (2018). From “automation” to “autonomy”: The importance of trust repair in human-machine interaction. Ergonomics, 61, 1–33. doi:10.1080/00140139. 2018.1457725
27.
de VriesP.MiddenC.BouwhuisD. (2003). The effects of errors on system trust, self-confidence, and the allocation of control in route planning. International Journal of Human-Computer Studies, 58, 719–735.
28.
DijkstraJ. J.LiebrandW. B. G.TimmingaE. (1998). Persuasiveness of expert systems. Behavior & Information Technology, 17, 155–163.
29.
DzindoletM. T.PierceL. G.BeckH. P.DaweL. A.AndersonB. W. (2001). Predicting misuse and disuse of combat identification systems. Military Psychology, 13, 147–164.
30.
GrayH. M.GrayK.WegnerD. M. (2007). Dimensions of mind perception. Science, 315, 619.
31.
HairJ. F.BlackW. C.BabinB. J.AndersonR. E. (2014). Multivariate data analysis. Harlow, UK: Pearson Education.
32.
HertzN.WieseE. (2016). Influence of agent type and task ambiguity on conformity in social decision making. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60, 313–317. doi:10.1177/1541931213601071
33.
HertzN.WieseE. (2018). Under pressure: Examining social conformity with computer and robot groups. Human Factors, 60, 1207–1218. doi:10.1177/0018720818788473
34.
HoytC. L.BlascovichJ.SwinthK. R. (2003). Social inhibition in immersive virtual environments. Presence: Teleoperators & Virtual Environments, 12, 183–195.
35.
JianJ.BisantzA.DruryA. (2000). Foundations for an empirically determined scale of trust in automated systems. International Journal of Cognitive Ergonomics, 4, 53–71.
36.
KellerD.RiceS. (2010). System-wide versus component-specific trust using multiple aids. The Journal of General Psychology: Experimental, Psychological, and Comparative Psychology, 137, 114–128.
37.
KyrlitsiasC.Michael-GrigoriouD. (2018). Asch conformity experiment using immersive virtual reality. Computer Animation and Virtual Worlds, e1804. doi:10.1002/cav.1804
38.
LeeJ. D.MorayN. (1994). Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human-Computer Studies, 40, 153–184.
39.
LesterJ. C.ConverseS. A.KahlerS. E.BarlowS. T.StoneB. A.BhogalR. S. (1997). The persona effect: Affective impact of animated pedagogical agents (pp. 359–366). ACM Press. doi:10.1145/258549.258797
40.
LevyL. (1960). Studies in conformity. Journal of Psychology, 50, 38–41.
41.
LewandowskyS.MundyM.TanG. P. A. (2000). The dynamics of trust: Comparing humans to automation. Journal of Experimental Psychology: Applied, 6(2), 104–123.
42.
LyonsJ. B.StokesC. K. (2012). Human–human reliance in the context of automation. Human Factors, 54, 112–121.
43.
MadhavanP.WiegmannD. A. (2007). Similarities and differences between human-human and human-automation trust: An integrative review. Theoretical Issues in Ergonomics Science, 8(4), 277–301.
44.
MarbleJ. L.BruemmerD. J.FewD. A.DudenhoefferD. D. (2004). Evaluation of supervisory vs. peer-peer interaction with human-robot teams. In System sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference. IEEE. doi:10.1109/HICSS.2004.1265326
45.
McKendrickR.ShawT.de VisserE.SaqerH.KidwellB.ParasuramanR. (2014). Team performance in networked supervisory control of unmanned air vehicles: Effects of automation, working memory, and communication content. Human Factors, 56, 463–475.
46.
MerrittS. M.UnnerstallJ. L.LeeD.HuberK. (2015). Measuring individual differences in the perfect automation schema. Human Factors, 57, 740–753.
47.
MosierK. L.SkitkaL. .J. (1996). Human decision makers and automated decision aids: Made for each other? In ParasuramanR.MoulouaM. (Eds.), Human factors in transportation. Automation and human performance: Theory and applications (pp. 201–220). Hillsdale, NJ, US: Lawrence Erlbaum Associates, Inc.
48.
NassC.FoggB. J.MoonY. (1996). Can computers be teammates?International Journal of Human-Computer Studies, 45(6), 669–678.
49.
NassC.MoonY.CarneyP. (1999). Are people polite to computers? Responses to computer-based interviewing systems. Journal of Applied Social Psychology, 29, 1093–1110.
50.
NassC.SteuerJ.TauberE. (1994). Computers are social actors. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 94, 72–78.
51.
NordholmL. A. (1975). Effects of groups size and stimulus ambiguity on conformity. The Journal of Social Psychology, 97, 123–130.
52.
ParasuramanR.ManzeyD. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52, 381–410.
53.
ParkS.CatramboneR. (2007). Social facilitation effects of virtual humans. Human Factors: The Journal of the Human Factors and Ergonomics Society, 49, 1054–1060. doi:10.1518/001872007X249910
54.
Rayborn-ReevesR.WuJ.WilsonS.KraemerB.KraemerP. (2013). Do as we do, not as you think: The effect of group influence on individual choices in a virtual environment. Journal of Virtual Worlds Research, 6(1), 1–11.
55.
ReevesB.NassC. (1996). The media equation. New York, NY: Cambridge University Press.
56.
RietherN.HegelF.WredeB.HorstmannG. (2012). Social facilitation with social robots? In Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 41–48). New York, NY: Association for Computing Machinery.
57.
RosanderM.ErikssonO. (2012). Conformity on the Internet—The role of task difficulty and gender differences. Computers in Human Behavior, 28(5), 1587–1595.
58.
SalomonsN.van der LindenM.SeboS. S.ScassellatiB. (2018). Humans conform to robots: Disambiguating trust, truth, and conformity. In Proceedings of HIR’18 (pp. 187–195). New York, NY: Association for Computing Machinery.
59.
SchnuerchR.GibbonsH. (2014). A review of neurocognitive mechanisms of social conformity. Social Psychology, 45, 466–478.
60.
ShawT.EmfieldA.GarciaA.de VisserE.MillerC.ParasuramanR.FernL. (2010, September). Evaluating the benefits and potential costs of automation delegation for supervisory control of multiple UAVs. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 54, No. 19, pp. 1498–1502). SAGE. doi:10.1177/154193121005401930
61.
ShiomiM.HagitaK. (2016). Do synchronized multiple robots exert peer pressure? In Proceedings of the Fourth International Conference on Human Agent Interaction (pp. 27–33). New York, NY: Association for Computing Machinery.
62.
SmithM. A.AllahamM. M.WieseE. (2016). Trust in automated agents is modulated by the combined influence of agent and task type. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 60, No. 1, pp. 206–210). Los Angeles, CA: SAGE.
63.
SmithP. J.McCoyC. E.LaytonC. (1997). Brittleness in the design of cooperative problem-solving systems: The effects on user performance. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 27, 360–371.
64.
WalliserJ. C.de VisserE. J.ShawT. H. (2016). Application of a system-wide trust strategy when supervising multiple autonomous agents. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 60, No. 1, pp. 133–137). Los Angeles, CA: SAGE.
65.
WiegmannD. A.RichA.ZhangH. (2001). The effects of aid reliability on users’ trust and reliance. Theoretical Issues in Ergonomics Science, 2, 352–367.
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