The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Explanatory coherence
Abstract
This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life. The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they are contradictory. Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is accepted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherence and incoherence relations are encoded by excitatory and inhibitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lavoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy.
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
Cites
Telling more than we can know: Verbal reports on mental processes.
Nisbett, R. E., & Wilson, T. D. (1977) · Psychological Review · doi:10.1037/0033-295x.84.3.231 · Cited by 11,317
Fodor, J. A. (1983) · doi:10.7551/mitpress/4737.001.0001 · Cited by 7,734
Representing and Intervening: Introductory Topics in the Philosophy of Natural Science
Hacking, I. (1983) · doi:10.1017/cbo9780511814563 · Cited by 4,814
Fraassen, B. C. V. (1980) · doi:10.1093/0198244274.001.0001 · Cited by 4,682
The Essential Tension: Selected Studies in Scientific Tradition and Change
Kuhn, T. S. (1977) · doi:10.7208/chicago/9780226217239.001.0001 · Cited by 3,861
Confirmation, disconfirmation, and information in hypothesis testing
Klayman, J., & Ha, Y.-W. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.211 · Cited by 2,094
Explanation-Based Generalization: A Unifying View
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986) · Machine Learning · doi:10.1023/a:1022691120807 · Cited by 1,382
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
Trope, Y., & Liberman, N. (2003) · Psychological Review · doi:10.1037/0033-295x.110.3.403 · Cited by 3,006
The emerging conceptualization of groups as information processors.
Hinsz, V. B., Tindale, R. S., & Vollrath, D. A. (1997) · Psychological Bulletin · doi:10.1037/0033-2909.121.1.43 · Cited by 1,428
Chinn, C. A., & Brewer, W. F. (1993) · Review of Educational Research · doi:10.3102/00346543063001001 · Cited by 1,393
Varieties of Confirmation Bias
Klayman, J. (1995) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60315-1 · Cited by 755
Dead and Alive: Beliefs in Contradictory Conspiracy Theories
Wood, M. J., Douglas, K. M., & Sutton, R. M. (2012) · Social Psychological and Personality Science · doi:10.1177/1948550611434786 · Cited by 660
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631
Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267
Explanation in Case-Based Reasoning—Perspectives and Goals
Sørmo, F., Cassens, J., & Aamodt, A. (2005) · Artificial Intelligence Review · doi:10.1007/s10462-005-4607-7 · Cited by 239
Explanation and abductive inference
Lombrozo, T. (2012) · The Oxford Handbook of Thinking and Reasoning · doi:10.1093/oxfordhb/9780199734689.013.0014 · Cited by 242
Explanatory Preferences Shape Learning and Inference
Lombrozo, T. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.08.001 · Cited by 229
Doris, J. M., & The Moral Psychology Research Group (2010) · doi:10.1093/acprof:oso/9780199582143.001.0001 · Cited by 227
Kim, B., Park, J., & Suh, J. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113302 · Cited by 182
Conviction Narrative Theory: A theory of choice under radical uncertainty
Johnson, S. G. B., Bilovich, A., & Tuckett, D. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22001157 · Cited by 131
The Instrumental Value of Explanations
Lombrozo, T. (2011) · Philosophy Compass · doi:10.1111/j.1747-9991.2011.00413.x · Cited by 118
Kammrath, L. K., Mendoza-Denton, R., & Mischel, W. (2005) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.88.4.605 · Cited by 109
The role of domain expertise in trusting and following explainable AI decision support systems
Bayer, S., Gimpel, H., & Markgraf, M. (2022) · Journal of Decision Systems · doi:10.1080/12460125.2021.1958505 · Cited by 103
Explainable Activity Recognition for Smart Home Systems
Das, D., Nishimura, Y., Vivek, R. P., Takeda, N., Fish, S. T., Plötz, T., & Chernova, S. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3561533 · Cited by 70
Artificial agents’ explainability to support trust: considerations on timing and context
Papagni, G., de Pagter, J., Zafari, S., Filzmoser, M., & Koeszegi, S. T. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01462-7 · Cited by 52
Danry, V., Pataranutaporn, P., Groh, M., & Epstein, Z. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713408 · Cited by 35
Yilmaz, L., Chakladar, S., & Doud, K. (2016) · 2016 Winter Simulation Conference (WSC) · doi:10.1109/wsc.2016.7822160 · Cited by 14
Chen, Y., Shi, Y., & Lu, Y. (2025) · Psychotherapy Research · doi:10.1080/10503307.2024.2447781 · Cited by 1
Metadata
- Authors
- Thagard, P.
- Venue
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Published
- 1989-09
- Volume
- 12
- Issue
- 3
- Pages
- 435-467
- ISSN
- 0140-525X (ISSN-L)
- 1469-1825
- OpenAlex
- W2913008767
- Cited by
- 1,043
- References
- 7
- FWCI
- 184.86
- Type
- journal article
- 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.