The DOI matches its Crossref record.
How the mind explains behavior: folk explanations, meaning, and social interaction
Abstract
The dual nature of behavior explanation: a tool of social cognition and an instrument for managing social interaction.
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
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Toward a theory of motivational interviewing
Miller, W. R., & Rose, G. S. (2009) · American Psychologist · doi:10.1037/a0016830 · Cited by 1,603
Keil, F. C. (2006) · Annual Review of Psychology · doi:10.1146/annurev.psych.57.102904.190100 · Cited by 1,350
Mind Perception Is the Essence of Morality
Gray, K., Young, L., & Waytz, A. (2012) · Psychological Inquiry · doi:10.1080/1047840x.2012.651387 · Cited by 1,008
Communal and Agentic Content in Social Cognition
Abele, A. E., & Wojciszke, B. (2014) · Advances in Experimental Social Psychology · doi:10.1016/b978-0-12-800284-1.00004-7 · Cited by 669
Malle, B. F., Guglielmo, S., & Monroe, A. E. (2014) · Psychological Inquiry · doi:10.1080/1047840x.2014.877340 · Cited by 632
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564
Poortinga, W., Whitmarsh, L., & Suffolk, C. (2013) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2013.09.001 · Cited by 276
Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation
Zhu, J., Liapis, A., Risi, S., Bidarra, R., & Youngblood, G. M. (2018) · 2018 IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2018.8490433 · Cited by 255
Attributions on the brain: Neuro-imaging dispositional inferences, beyond theory of mind
Harris, L. T., Todorov, A., & Fiske, S. T. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.05.021 · Cited by 149
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
Liao, Q. V., & Varshney, K. R. (2021) · arXiv · doi:10.48550/arxiv.2110.10790 · Cited by 120
Teunissen, P. W. (2015) · Advances in Health Sciences Education · doi:10.1007/s10459-014-9556-y · Cited by 113
Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
Kaur, H., Adar, E., Gilbert, E., & Lampe, C. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533135 · Cited by 92
You Complete Me: Human-AI Teams and Complementary Expertise
Zhang, Q., Lee, M. L., & Carter, S. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517791 · Cited by 85
Six challenges for human-AI Co-learning
van den Bosch, K., Schoonderwoerd, T., Blankendaal, R., & Neerincx, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22341-0_45 · Cited by 50
Architecture of explanatory inference in the human prefrontal cortex
Barbey, A. K., & Patterson, R. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00162 · Cited by 40
An explanation is not an excuse: Trust calibration in an age of transparent robots
Wagner, A. R., & Robinette, P. (2021) · Trust in Human-Robot Interaction · doi:10.1016/b978-0-12-819472-0.00009-5 · Cited by 20
Communicating Missing Causal Information to Explain a Robot’s Past Behavior
Han, Z., & Yanco, H. (2023) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3568024 · Cited by 11
Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 2005-06-01
- Volume
- 42
- Issue
- 10
- Pages
- 42-6170-42-6170
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W1751198022
- Cited by
- 436
- References
- 0
- Type
- book review
- 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.
