Rik van den Brule
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Abstract
The present research was aimed at investigating whether human-robot interaction (HRI) can be improved by a robot's nonverbal warning signals. Ideally, when a robot signals that it cannot guarantee good performance, people could take preventive actions to ensure the successful completion of the robot's task. In two experiments, participants learned either that a robot's gestures predicted subsequent poor performance, or they did not. Participants evaluated a robot that uses predictive gestures as more trustworthy, understandable, and reliable compared to a robot that uses gestures that are not predictive of their performance. Finally, participants who learned the relation between gestures and performance improved collaboration with the robot through prevention behavior immediately after a predictive gesture. This limits the negative consequences of the robot's mistakes, thus improving the interaction.
- Aldebaran Robotics (2013). WeBots for Nao. Retrieved from http://doc.aldebaran.com/1-14/software/webots/webots_index.htmlGoogle Scholar
- Aron, A., Aron, E., & Smollan, D. (1992). Inclusion of other in the self scale and the structure of interpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596--612. Retrieved from http://psycnet.apa.org/journals/psp/63/4/596/Google ScholarCross Ref
- Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71--81.Google ScholarCross Ref
- Bluemke, M., & Friese, M. (2008). Reliability and validity of the Single-Target IAT (ST-IAT): Assessing automatic affect towards multiple attitude objects. European Journal of Social Psychology, 38, 977--997.Google ScholarCross Ref
- Chapman, L. J., & Chapman, J. P. (1967). Genesis of popular but erroneous psychodiagnostic observations. Journal of Abnormal Psychology, 72(3), 193--204.Google ScholarCross Ref
- Dautenhahn, K. (2007). Methodology and themes of human-robot interaction: A growing research field. International Journal of Advanced Robotic Systems, 4(1), 103--108.Google ScholarCross Ref
- De Jong, P. J., Merckelbach, H., & Arntz, A. (1990). Illusory correlation, on-line probability estimates, and electrodermal responding in a (quasi)-conditioning paradigm. Biological Psychology, 31, 201--212.Google ScholarCross Ref
- DeSteno, D., Breazeal, C., Frank, R. H., Pizarro, D., Baumann, J., Dickens, L., & Lee, J. J. (2012). Detecting the trustworthiness of novel partners in economic exchange. Psychological Science, 20(10), 1--8.Google Scholar
- Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicity prejudice and interractial interaction. Journal of Personality and Social Psychology, 82(1), 62--68.Google ScholarCross Ref
- Ellis, N. (2008). Implicit and explicit knowledge about language. In J. Cenoz & N. H. Hornberger (Eds.), Encyclopedia of language and education (2nd ed., Vol. 6, pp. 1878--1890). US: Springer.Google Scholar
- Ekman, P., Friesen, W. V., & O'Sullivan, M. (1988). Smiles when lying. Journal of Personality and Social Psychology, 54(3), 414--420.Google ScholarCross Ref
- Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175--91. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/17695343Google ScholarCross Ref
- Feldman, R. S., & Rimé, B. (1991). Fundamentals of nonverbal behavior. Cambridge, MA: Cambridge University Press.Google Scholar
- Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3), 143--166.Google ScholarCross Ref
- Grillon, C., Baas, J. P., Lissek, S., Smith, K., & Milstein, J. (2004). Anxious responses to predictable and unpredictable aversive events. Behavioral Neuroscience, 118(5), 916--924.Google ScholarCross Ref
- Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517--527.Google ScholarCross Ref
- Hanley, J., & McNeil, B. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148(3), 839--843. Retrieved from http://www.medicine.mcgill.ca/epidemiology/hanley/Reprints/Method_of_Comparing_1983.pdfGoogle ScholarCross Ref
- Hanley, J., & McNeil, B. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29--36. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:The+Meaning+and+Use+of+the+Area+under+a+Receiver+Operating+Characteristic+(ROC)+Curve#2Google ScholarCross Ref
- Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1--55.Google ScholarCross Ref
- Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000). Foundations for an empirically determined scale of trust in automated systems. International Journal of Cognitive Ergonomics, 4(1), 53--71.Google ScholarCross Ref
- Jung, M. F., Sirkin, D., Gür, T. M., & Steinert, M. (2015). Displayed uncertainty improves driving experience and behavior: The case of range anxiety in an electric car. In Proceedings of the ACM CHI'15 Conference on Human Factors in Computing Systems (Vol. 1, pp. 2201--2210). Seoul, South Korea: Crossings. Google ScholarDigital Library
- Kervyn, N., Yzerbyt, V. Y., Demoulin, S., & Judd, C. M. (2008). Competence and warmth in context: The compensatory nature of stereotypic views of national groups. European Journal of Social Psychology, 83, 1175--1183.Google ScholarCross Ref
- Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50--80.Google ScholarCross Ref
- Ligthart, M., Van den Brule, R., & Haselager, W. F. G. (2013). Human-robot trust: Is motion fluency an effective behavioral style for regulating robot trustworthiness? In K. Hindriks, M. De Weerdt, B. Van Riemsdijk, & M. Warnier (Eds.), Proceedings of the 25th Benelux Conference on Artificial Intelligence (BNAIC) (pp. 112--119). Delft, The Netherlands.Google Scholar
- Madsen, M., & Gregor, S. (2000). Measuring human-computer trust. In Gable, G., Viatle, M. (Eds.). Proceedings of the 11 th Australasian Conference on Information Systems (pp. 6--8). Brisbane, Australia.Google Scholar
- Mayer, R., Davis, J., & Schoorman, F. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709--734.Google ScholarCross Ref
- Moon, A., Panton, B., Van der Loos, H. F. M., & Croft, E. A. (2010). Using hesitation gestures for safe and ethical human-robot interaction. In Proceedings of ICRA (pp. 11--13). Retrieved from http://www.sites.mech.ubc.ca/~caris/Publications/Using Hesitation Gestures for Safe and Ethical Human-Robot Interaction.pdfGoogle Scholar
- Muir, B. M. (1994). Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37(11), 1905--1922.Google ScholarCross Ref
- Oosterhof, N. N., & Todorov, A. (2008). The functional basis of face evaluation. Proceedings of the National Academy of Sciences, 105(32), 11087--11092.Google ScholarCross Ref
- Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230--253.Google ScholarCross Ref
- Pavlov, I. P. (1927). Conditional reflexes: An investigation of the psychological activity of the cerebral cortex. (C. D. Green, Ed.). Oxford University Press. Retrieved from http://psychclassics.yorku.ca/PavlovGoogle Scholar
- Payne, B. K., & Gawronski, B. (2010). A history of implicit social cognition: Where is it coming from? Where is it now? Where is it going? In B. Gawronski & B. K. Payne (Eds.). Handbook of implicit social cognition: Measurement, theory, and applications (pp.1--14). New York, NY: Guilford Press.Google Scholar
- Rescorla, R. A. (1968). Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative and Physiological Psychology, 66(1), 1--5.Google ScholarCross Ref
- Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1--36.Google ScholarCross Ref
- Sanders, T. L., Oleson, K. E., Billings, D. R., Chen, J. Y. C., & Hancock, P. A. (2011). A model of human-robot trust: Theoretical model development. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55(1), 1432--1436.Google ScholarCross Ref
- Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23--74.Google Scholar
- Schlencker, B. R., Helm, B., & Tedeschi, J. T. (1973). Interpersonal trust, promise credibility, and behavioral trust. Journal of Personality and Social Psychology, 25, 419--427.Google Scholar
- Shanks, D. R. (2007). Associationism and cognition: Human contingency learning at 25. Quarterly Journal of Experimental Psychology, 60(3), 291--309.Google ScholarCross Ref
- Sheridan, T. B. (1988). Trustworthiness of command and control systems. In Proceedings of the IFAC/IFIP/IEA/IFORS Conference on Analysis (pp. 427--431). Pergamon, Elmsford, NY.Google ScholarCross Ref
- Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological science, 20(10), 1--8.Google Scholar
- Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31(1), 137--149. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10495845Google ScholarCross Ref
- Stanton, C., & Stevens, C. J. (2014, October). Robot pressure: The impact of robot eye gaze and lifelike bodily movements upon decision-making and trust. In Proceedings of the International Conference on Social Robotics (pp. 330--339). Sydney, NSW, Australia: Springer International Publishing.Google Scholar
- Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review: An Official Journal of the Society for Personality and Social Psychology, Inc, 8(3), 220--247.Google Scholar
- Sung, J., Grinter, R. E., & Christensen, H. I. (2010). Domestic robot ecology. International Journal of Social Robotics, 2(4), 417--429.Google ScholarCross Ref
- Syrdal, D. S., Koay, K. L., Gácsi, M., Walters, M. L., & Dautenhahn, K. (2010). Video prototyping of dog-inspired non-verbal affective communication for an appearance constrained robot. In Proceedings of the 19th IEEE International Symposium on Robot and Human Interative Communication (pp. 632--637). Principe de Piemonte, Italy.Google ScholarCross Ref
- Takayama, L., Dooley, D., & Ju, W. (2011). Expressing thought: Improving robot readability with animation principles. In Proceedings of Human-Robot Interaction Conference: HRI 2011 (pp. 69--76). Lausanne, Switzerland. Google ScholarDigital Library
- Tellex, S., Knepper, R., Li, A., Howard, T., Rus, D., & Roy, N. (2014). Asking for help using inverse semantics. Robotics: Science and Systems, 2, 3. Retrieved from http://cs.brown.edu/courses/csci2951-k/papers/tellex14.pdfGoogle Scholar
- Torrey, C., Fussell, S. R., & Kiesler, S. (2013). How a robot should give advice. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (pp. 275--282). Google ScholarDigital Library
- Van den Brule, R., Bijlstra, G., Dotsch, R., Wigboldus, D. H. J., & Haselager, W. F. G. (2013). Signaling robot trustworthiness: Effects of behavioral cues as warnings. In G. Herrmann, M. Pearson, A. Lenz, P. Bremner, A. Spiers, & U. Leonards (Eds.), LNCS 8239: Social Robotics (pp. 583--584). Springer.Google Scholar
- Van den Brule, R., Dotsch, R., Bijlstra, G., Wigboldus, D. H. J., & Haselager, W. F. G. (2014). Do robot performance and behavioral style affect human trust? A multi-method approach. International Journal of Social Robotics, 6(4), 519--531.Google ScholarCross Ref
- Wigboldus, D. H. J., Holland, R. W., & Van Knippenberg, A. (2006). Single target implicit associations. Unpublished manuscript.Google Scholar
- Woods, S. N., Walters, M. L., Koay, K. L., & Dautenhahn, K. (2006). Comparing human robot interaction scenarios using live and video based methods: Towards a novel methodological approach. In Proceedings of the 9th IEEE International Workshop on Advanced Motion Control (AMC'06) (pp. 750--755). Istanbul, Turkey: New York, NY: IEEE Press.Google ScholarCross Ref
- Xu, Q., Ng, J., Tan, O., Huang, Z., Tay, B., & Park, T. (2014). Methodological issues in scenario-based evaluation of human--robot interaction. International Journal of Social Robotics.Google Scholar
- Yamaji, Y., Miyake, T., Yoshiike, Y., de Silva, P. R. S., & Okada, M. (2011). STB: Child-dependent sociable trash box. International Journal of Social Robotics, 3(4), 359--370.Google ScholarCross Ref
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