The DOI matches its Crossref record.
How anthropomorphism affects empathy toward robots
Abstract
A long-standing question within the robotics community is about the degree of human-likeness robots ought to have when interacting with humans. We explore an unexamined aspect of this problem: how people empathize with robots along the anthropomorphic spectrum. We conducted an experiment that measured how people empathized with robots shown to be experiencing mistreatment by humans. Our results indicate that people empathize more strongly with more human-looking robots and less with mechanicallooking robots.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Anthropomorphism and human likeness in the design of robots and human-robot interaction
Fink, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34103-8_20 · Cited by 455
Putting the art in artificial: Aesthetic responses to computer-generated art.
Chamberlain, R., Mullin, C., Scheerlinck, B., & Wagemans, J. (2018) · Psychology of Aesthetics, Creativity, and the Arts · doi:10.1037/aca0000136 · Cited by 225
Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction
Natarajan, M., & Gombolay, M. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374839 · Cited by 248
Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00820-y · Cited by 122
Corti, K., & Gillespie, A. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.12.039 · Cited by 118
Boy, J., Pandey, A. V., Emerson, J., Satterthwaite, M., Nov, O., & Bertini, E. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025512 · Cited by 104
Aroyo, A. M., Rea, F., Sandini, G., & Sciutti, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2856272 · Cited by 85
Trust repair in human-agent teams: the effectiveness of explanations and expressing regret
Kox, E. S., Kerstholt, J. H., Hueting, T. F., & de Vries, P. W. (2021) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-021-09515-9 · Cited by 83
Sundar, S. S., Waddell, T. F., & Jung, E. H. (2016) · 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2016.7451771 · Cited by 92
Nomura, T., Uratani, T., Kanda, T., Matsumoto, K., Kidokoro, H., Suehiro, Y., & Yamada, S. (2015) · Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts · doi:10.1145/2701973.2701977 · Cited by 58
Can a Machine Tend to Teenagers' Emotional Needs?: A Study with Conversational Agents
Kim, J., Kim, Y., Kim, B., Yun, S., Kim, M., & Lee, J. (2018) · Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3170427.3188548 · Cited by 51
Do You Mind? User Perceptions of Machine Consciousness
Scott, A. E., Neumann, D., Niess, J., & Woźniak, P. W. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581296 · Cited by 46
Genie in the Bottle: Anthropomorphized Perceptions of Conversational Agents
Kuzminykh, A., Sun, J., Govindaraju, N., Avery, J., & Lank, E. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376665 · Cited by 41
Fani, S., Bianchi, M., Jain, S., Pimenta Neto, J. S., Boege, S., Grioli, G., Bicchi, A., & Santello, M. (2016) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2016.00011 · Cited by 31
Metadata
- Authors
- Riek, L. D., Rabinowitch, T.-C., Chakrabarti, B., & Robinson, P.
- Venue
- Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
- Publisher
- ACM
- Published
- 2009-03-09
- Pages
- 245-246
- OpenAlex
- W2152536828
- Cited by
- 307
- References
- 0
- FWCI
- 5.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
