Look Like Me: Matching Robot Personality via Gaze to Increase Motivation
Abstract
Socially assistive robots are envisioned to provide social and cognitive assistance where they will seek to motivate and engage people in therapeutic activities. Due to their physicality, robots serve as a powerful technology for motivating people. Prior work has shown that effective motivation requires adaption to user needs and characteristics, but how robots might successfully achieve such adaptation is still unknown. In this paper, we present work on matching a robot's personality-expressed via its gaze behavior-to that of its users. We confirmed in an online study with 22 participants that the robot's gaze behavior can successfully express either an extroverted or introverted personality. In a laboratory study with 40 participants, we demonstrate the positive effect of personality matching on a user's motivation to engage in a repetitive task. These results have important implications for the design of adaptive robot behaviors in assistive human-robot interaction.
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Much of the relevant research has focused on imbuing voice-based, embodied, and robot-based chatbots with personalities using nonverbal cues such as gestures, gaze, voice tone and speed, and proximity.
“Much of the relevant research has focused on imbuing voice-based, embodied, and robot-based chatbots with personalities using nonverbal cues, such as gestures [20,21] gaze [22], voice tone and speed [23], and proximity [24].”
From Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior (2022) · cites this paper as [22]
It is possible to convey personality in embodied, robot-based, and voice-based chatbots using body language and nonverbal cues such as voice pitch and speed, gaze, proximity, and gestures.
“There is a growing body of literature suggesting that it is possible to convey personality in embodied, robot-based, and voice-based chatbots using body language and nonverbal cues, such as voice pitch and speed [23,58], gaze [22], proximity [24], and gestures [20,59].”
From Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior (2022) · cites this paper as [22]
Cites
Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions
Ryan, R. M., & Deci, E. L. (2000) · Contemporary Educational Psychology · doi:10.1006/ceps.1999.1020 · Cited by 18,666 · see citing works
Osterberg, L., & Blaschke, T. (2005) · New England Journal of Medicine · doi:10.1056/nejmra050100 · Cited by 7,974 · see citing works
Vallerand, R. J., & Reid, G. (1984) · Journal of Sport Psychology · doi:10.1123/jsp.6.1.94 · Cited by 527 · cited 1× · see citing works
“Providing these hints and feedback has been shown in previous work to positively affect people’s motivation to engage in a task [48].”
BackgroundImproving automotive safety by pairing driver emotion and car voice emotion
Nass, C., Jonsson, I.-M., Harris, H., Reaves, B., Endo, J., Brave, S., & Takayama, L. (2005) · CHI '05 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1056808.1057070 · Cited by 249 · see citing works
Acceptance and usability of a relational agent interface by urban older adults
Bickmore, T. W., Caruso, L., & Clough-Gorr, K. (2005) · CHI '05 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1056808.1056879 · Cited by 142 · cited 1× · see citing works
“Relational agents have also been found to be effective for delivering health communication and health behavior change interventions to older adults, especially those with low functional health, reading, or computer literacy [2].”
BackgroundEffects of adaptive robot dialogue on information exchange and social relations
Torrey, C., Powers, A., Marge, M., Fussell, S. R., & Kiesler, S. (2006) · Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction · doi:10.1145/1121241.1121264 · Cited by 93 · see citing works
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Interacting with educational chatbots: A systematic review
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11177-3 · Cited by 841 · see citing works
Human–robot similarity and willingness to work with a robotic co-worker
You, S., & Robert Jr., L. P. (2018) · Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3171221.3171281 · Cited by 118 · see citing works
A Systematic Review of Ten Years of Research on Human Interaction with Social Robots
Lambert, A., Norouzi, N., Bruder, G., & Welch, G. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1801172 · Cited by 89 · see citing works
Kuhail, M. A., Thomas, J., Alramlawi, S., Shah, S. J. H., & Thornquist, E. (2022) · Informatics · doi:10.3390/informatics9040081 · Cited by 59 · see citing works
Examining User Preference for Agreeableness in Chatbots
Völkel, S. T., & Kaya, L. (2021) · CUI 2021 - 3rd Conference on Conversational User Interfaces · doi:10.1145/3469595.3469633 · Cited by 31 · see citing works
- Authors
- Andrist, S., Mutlu, B., & Tapus, A.
- Affiliations
- University of Wisconsin–Madison (US)
- École Nationale Supérieure de Techniques Avancées (FR)
- Venue
- Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2015-04-18
- Pages
- 3603-3612
- OpenAlex
- W2041459575
- Semantic Scholar
- 195f7ce4abb191e09b71af911f001ff63300432f
- Cited by
- 159
- References
- 6
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 20 · 2023: 14 · 2022: 21 · 2021: 19 · 2020: 20 · 2019: 14 · 2018: 12 · 2017: 12 · 2016: 3
- FWCI
- 15.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- socially assistive robots, gaze behavior, personality matching, extraversion-introversion, user motivation, repetitive work, adaptive robot behavior, human-robot interaction, treatment engagement
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