A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction

Objective: We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI). Background: To date, reviews of trust in HRI have been qualitative or descriptive. Our quantitative review provides a fundamental empirical foundation to advance both theory and practice. Method: Meta-analytic methods were applied to the available literature on trust and HRI. A total of 29 empirical studies were collected, of which 10 met the selection criteria for correlational analysis and 11 for experimental analysis. These studies provided 69 correlational and 47 experimental effect sizes. Results: The overall correlational effect size for trust was r̄ = +0.26, with an experimental effect size of d̄ = +0.71. The effects of human, robot, and environmental characteristics were examined with an especial evaluation of the robot dimensions of performance and attribute-based factors. The robot performance and attributes were the largest contributors to the development of trust in HRI. Environmental factors played only a moderate role. Conclusion: Factors related to the robot itself, specifically, its performance, had the greatest current association with trust, and environmental factors were moderately associated. There was little evidence for effects of human-related factors. Application: The findings provide quantitative estimates of human, robot, and environmental factors influencing HRI trust. Specifically, the current summary provides effect size estimates that are useful in establishing design and training guidelines with reference to robot-related factors of HRI trust. Furthermore, results indicate that improper trust calibration may be mitigated by the manipulation of robot design. However, many future research needs are identified.

[1]  Susan R. Fussell,et al.  Comparing a computer agent with a humanoid robot , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[2]  Jessie Y.C. Chen,et al.  Effects of imperfect automation and individual differences on concurrent performance of military and robotics tasks in a simulated multitasking environment , 2009, Ergonomics.

[3]  Richard P. DeShon,et al.  Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. , 2002, Psychological methods.

[4]  William Rogers,et al.  Pilot Opinions on Cockpit Automation issues , 1998 .

[5]  Holly A. Yanco,et al.  Improving Human-Robot Interaction through Interface Evolution , 2010 .

[6]  M. Goodrich,et al.  Neglect Tolerant Teaming: Issues and Dilemmas , 2003 .

[7]  Cynthia Breazeal,et al.  Effect of a robot on user perceptions , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[8]  P. Hancock,et al.  A Dynamic Model of Stress and Sustained Attention , 1989, Human factors.

[9]  Luke J. Chang,et al.  Seeing is believing: Trustworthiness as a dynamic belief , 2010, Cognitive Psychology.

[10]  Brian Scassellati,et al.  The effect of presence on human-robot interaction , 2008, RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication.

[11]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[12]  Holly A. Yanco,et al.  Development and Testing of a Robotic Wheelchair System for Outdoor Navigation , 2001 .

[13]  Pamela J. Hinds,et al.  Whose Job Is It Anyway? A Study of Human-Robot Interaction in a Collaborative Task , 2004, Hum. Comput. Interact..

[14]  Ben J. A. Kröse,et al.  Assessing Acceptance of Assistive Social Agent Technology by Older Adults: the Almere Model , 2010, Int. J. Soc. Robotics.

[15]  J. Cacioppo,et al.  On seeing human: a three-factor theory of anthropomorphism. , 2007, Psychological review.

[16]  Stephanie J. Lackey,et al.  Recommended roles for uninhabited team members within mixed-initiative combat teams , 2010, 2010 International Symposium on Collaborative Technologies and Systems.

[17]  Pei-Luen Patrick Rau,et al.  When in Rome: The role of culture & context in adherence to robot recommendations , 2010, 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[18]  P. Hinds,et al.  When in Rome: the role of culture & context in adherence to robot recommendations , 2010, HRI 2010.

[19]  Ewart de Visser,et al.  Measurement of trust in human-robot collaboration , 2007, 2007 International Symposium on Collaborative Technologies and Systems.

[20]  A. Freedy,et al.  A Comprehensive Methodology for Assessing Human-Robot Team Performance for Use in Training and Simulation , 2006 .

[21]  Takayuki Kanda,et al.  Nonverbal leakage in robots: Communication of intentions through seemingly unintentional behavior , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[22]  David P. Biros,et al.  The Influence of Task Load and Automation Trust on Deception Detection , 2004 .

[23]  V. Groom,et al.  Can robots be teammates?: Benchmarks in human–robot teams , 2007 .

[24]  Mark A. Neerincx,et al.  Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors , 2010, Int. J. Hum. Comput. Stud..

[25]  Jessie Y. C. Chen,et al.  Supervisory Control of Multiple Robots: Human-Performance Issues and User-Interface Design , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[26]  Xiao-Hua Zhou,et al.  Statistical Methods for Meta‐Analysis , 2008 .

[27]  P.A. Hancock,et al.  Individuation: the N = 1 revolution , 2009 .

[28]  Susan R. Fussell,et al.  Anthropomorphic Interactions with a Robot and Robot–like Agent , 2008 .

[29]  Cory D. Kidd,et al.  Sociable robots : the role of presence and task in human-robot interaction , 2003 .

[30]  H. Goldstein,et al.  The rise of the body bots [robotic exoskeletons] , 2005, IEEE Spectrum.

[31]  Ferdinando Fornara,et al.  Robots in a domestic setting: a psychological approach , 2005, Universal Access in the Information Society.

[32]  T. Natsoulas.  What are perceptual reports about? , 1967, Psychological bulletin.

[33]  P A Hancock,et al.  Effects of control order, augmented feedback, input device and practice on tracking performance and perceived workload. , 1996, Ergonomics.

[34]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[35]  R.R. Murphy,et al.  Robot-assisted medical reachback: a survey of how medical personnel expect to interact with rescue robots , 2004, RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759).

[36]  Raja Parasuraman,et al.  Complacency and Bias in Human Use of Automation: An Attentional Integration , 2010, Hum. Factors.

[37]  Boris E. R. de Ruyter,et al.  Assessing the effects of building social intelligence in a robotic interface for the home , 2005, Interact. Comput..

[38]  Pei-Luen Patrick Rau,et al.  A Cross-cultural Study: Effect of Robot Appearance and Task , 2010, Int. J. Soc. Robotics.

[39]  Alex M. Andrew,et al.  Humans and Automation: System Design and Research Issues , 2003 .

[40]  Eui Park,et al.  MEASURING TRUST OF HUMAN OPERATORS IN NEW GENERATION RESCUE ROBOTS , 2008 .

[41]  Kate E Decleene,et al.  Publication Manual of the American Psychological Association , 2011 .

[42]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[43]  Regina A. Pomranky,et al.  The role of trust in automation reliance , 2003, Int. J. Hum. Comput. Stud..

[44]  Jessie Y. C. Chen,et al.  Human Performance Issues and User Interface Design for Teleoperated Robots , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[45]  Pei-Luen Patrick Rau,et al.  Effects of communication style and culture on ability to accept recommendations from robots , 2009, Comput. Hum. Behav..

[46]  J. M. Ross.  Moderators Of Trust And Reliance Across Multiple Decision Aids , 2008 .

[47]  Katherine M. Tsui,et al.  Considering the bystander's perspective for indirect human-robot interaction , 2010, 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[48]  Imre Kiss,et al.  Sharing secrets: Oxytocin and trust in schizophrenia , 2009, Social neuroscience.

[49]  Jean Scholtz,et al.  Common metrics for human-robot interaction , 2006, HRI '06.

[50]  Mark W. Lipsey,et al.  Practical Meta-Analysis , 2000 .

[51]  Vanessa Evers,et al.  Relational vs. group self-construal: Untangling the role of national culture in HRI , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[52]  Rolf Dieter Schraft,et al.  Care-O-bot II—Development of a Next Generation Robotic Home Assistant , 2004, Auton. Robots.

[53]  Samuel B. Williams,et al.  ASSOCIATION FOR COMPUTING MACHINERY , 2000 .

[54]  Ingram Olkin,et al.  Estimation of a Single Effect Size: Parametric and Nonparametric Methods , 1985 .

[55]  Raja Parasuraman,et al.  Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload , 2009 .

[56]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[57]  D. Wiegmann,et al.  Similarities and differences between human–human and human–automation trust: an integrative review , 2007 .

[58]  George Mason.  Situation Awareness, Mental Workload, and Trust in Automation:Viable, Empirically Supported Cognitive Engineering Constructs , 2011 .