Abstract
The presence of a robot in our everyday life can generate both positive and negative effects on us. While performing a difficult task, the presence of a robot can generate a negative effect on the performance and it can also increase the stress and anxiety levels. In order to minimize these undesired effects, we propose the use of user’s motivation, based on the Regulatory Focus Theory. We analyze the effects of using Regulatory oriented strategies in a robot speech, when giving a person the instructions of how to perform a Stroop Test. We found evidence that matching the Chronic Regulatory state of the participants with the Regulatory oriented strategy of the robot improves the user’s performance, and a mismatch leads to an increase of cognitive load and stress in the participants.
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References
Agrigoroaie, R., Tapus, A.: Contactless physiological data analysis for users quality of life improving by using a humanoid social robot. Accepted in 19th Conference on Image Analysis and Processing (2017)
Andrist, S., Mutlu, B., Tapus, A.: Look like me: matching robot personality via gaze to increase motivation. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3603–3612. ACM (2015)
Bartneck, C., Kulić, D., Croft, E., Zoghbi, S.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Social Robot. 1(1), 71–81 (2009)
Breazeal, C.: Social robots: from research to commercialization. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, p. 1. ACM (2017)
Chevalier, P., Raiola, G., Martin, J.-C., Isableu, B., Bazile, C., Tapus, A.: Do sensory preferences of children with autism impact an imitation task with a robot? In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 177–186 (2017)
Crowe, E., Higgins, E.T.: Regulatory focus and strategic inclinations: Promotion and prevention in decision-making. Organ. Behav. Hum. Decis. Process. 69(2), 117–132 (1997)
Cruz-Maya, A., Ferland, F., Tapus, A.: Social facilitation in a game-like human-robot interaction using synthesized emotions and episodic memory. In: Proceedings of the International Conference on Social Robotics, pp. 164–173 (2015)
Fasola, J., Mataric, M.: A socially assistive robot exercise coach for the elderly. J. Hum.-Robot Interact. 2(2), 3–32 (2013)
Faur, C., Martin, J.-C., Clavel, C.: Matching artificial agents’ and users’ personalities: designing agents with regulatory-focus and testing the regulatory fit effect. In: CogSci (2015)
Faur, C., Martin, J.-C., Clavel, C.: Measuring chronic regulatory focus with proverbs: the developmental and psychometric properties of a french scale. Personal. Individ. Diff. 107, 137–145 (2017)
Grassmann, M., Vlemincx, E., von Leupoldt, A,. Mittelstädt, J.M., Van den Bergh, O.: Respiratory changes in response to cognitive load: a systematic review. Neural Plasticity (2016)
Hamada, T., Kagawa, Y., Onari, H., Naganuma, M., Hashimoto, T., Yoneoka, T.: Study on transition of elderly people’s reactions in robot therapy. In: The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 431–432 (2016)
Higgins, E.T., Friedman, R.S., Harlow, R.E., Idson, L.C., Ayduk, O.N., Taylor, A.: Achievement orientations from subjective histories of success: Promotion pride versus prevention pride. Eur. J. Soc. Psychol. 31(1), 3–23 (2001)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
Lockwood, P., Jordan, C.H., Kunda, Z.: Motivation by positive or negative role models: regulatory focus determines who will best inspire us. J. Pers. Soc. Psychol. 83(4), 854 (2002)
Osawa, H., Orszulak, J., Godfrey, K.M., Coughlin, J.F.: Maintaining learning motivation of older people by combining household appliance with a communication robot. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5310–5316. IEEE (2010)
Stroop, J.: Studies of interference in serial verbal reactions. J. Exploratory Psychol. 18, 643–662 (1935)
Süssenbach, L., Riether, N., Schneider, S., Berger, I., Kummert, F., Lütkebohle, I., Pitsch, K.: A robot as fitness companion: towards an interactive action-based motivation model. In: RO-MAN: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 286–293. IEEE (2014)
Tazhigaliyeva, N., Diyas, Y., Brakk, D., Aimambetov, Y., Sandygulova, A.: Learning with or from the robot: exploring robot roles in educational context with children. In: Agah, A., Cabibihan, J.-J., Howard, A.M., Salichs, M.A., He, H. (eds.) ICSR 2016. LNCS (LNAI), vol. 9979, pp. 650–659. Springer, Cham (2016). doi:10.1007/978-3-319-47437-3_64
Zajonc, R.B., et al.: Social Facilitation. Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor (1965)
Acknowledgment
The first author thanks to the Mexican Council of Science and Technology for the grant CONACYT-French Government (no. 382035). This work has been partially funded by EU Horizon2020 ENRICHME project (no. 643691C).
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Cruz-Maya, A., Agrigoroaie, R., Tapus, A. (2017). Improving User’s Performance by Motivation: Matching Robot Interaction Strategy with User’s Regulatory State. In: Kheddar, A., et al. Social Robotics. ICSR 2017. Lecture Notes in Computer Science(), vol 10652. Springer, Cham. https://doi.org/10.1007/978-3-319-70022-9_46
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DOI: https://doi.org/10.1007/978-3-319-70022-9_46
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