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Vulnerable robots positively shape human conversational dynamics in a human–robot team
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Social robots are becoming increasingly influential in shaping the behavior of humans with whom they interact. Here, we examine how the actions of a social robot can influence human-to-human communication, and not just robot–human communication, using groups of three humans and one robot playing 30 rounds of a collaborative game ( n = 51 groups). We find that people in groups with a robot making vulnerable statements converse substantially more with each other, distribute their conversation somewhat more equally, and perceive their groups more positively compared to control groups with a robot that either makes neutral statements or no statements at the end of each round. Shifts in robot speech have the power not only to affect how people interact with robots, but also how people interact with each other, offering the prospect for modifying social interactions via the introduction of artificial agents into hybrid systems of humans and machines.
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“Conversely, Traeger et al. find automated agents that are trained to perform poorly at collaborative tasks can actually improve the behavior of human team members (29).”
From Can Generative AI improve social science? (2024) · cites this paper as [29]
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Xiong, F., Yu, X., & Leong, H. W. (2024) · International Conference on Intelligent Education and Intelligent Research (IEIR) · doi:10.1109/ieir62538.2024.10960181 · Cited by 6
Identification and Engagement of Passive Subjects in Multiparty Conversations by a Humanoid Robot
Ayllón, D., Chou, T.-S., King, A., & Shen, Y. (2021) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3434074.3447229 · Cited by 4
Metadata
- Authors
- Traeger, M. L., Strohkorb Sebo, S., Jung, M., Scassellati, B., & Christakis, N. A.
- Affiliations
- Yale University (US)
- Department of Computer Science, Yale University, New Haven, CT 06520;
- Cornell University (US)
- Corresponding author
- Christakis, Nicholas A.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2020-03-09
- Volume
- 117
- Issue
- 12
- Pages
- 6370-6375
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC7104178
- OpenAlex
- W3009999177
- Cited by
- 140
- References
- 49
- Citations by year
- 2026: 16 · 2025: 22 · 2024: 23 · 2023: 13 · 2022: 27 · 2021: 25 · 2020: 13
- FWCI
- 21.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Robert Wood Johnson Foundation (75421)
- National Science Foundation (1139078, GR104893)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- social robots, human-robot teams, human-to-human communication, conversational dynamics, collaborative games, group interaction, robot speech, hybrid human-machine systems
- MeSH terms
- Communication, Cooperative Behavior, Group Processes, Humans, Interpersonal Relations, Robotics, Social Behavior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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