The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.
Explanation and Abductive Inference
Abstract
Abstract Everyday cognition reveals a sophisticated capacity to seek, generate, and evaluate explanations for the social and physical worlds around us. Why are we so driven to explain, and what accounts for our systematic explanatory preferences? This chapter reviews evidence from cognitive psychology and cognitive development concerning the structure and function of explanations, with a focus on the role of explanations in learning and inference. The findings highlight the value of understanding explanation and abductive inference both as phenomena in their own right and for the insights they provide concerning foundational aspects of human cognition, such as representation, learning, and inference.
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“Thus, the outlined classification-oriented frameworks are evaluated on classifiers based on logistic regression [55], [136], [153], [158], decision trees [46], [80], [122], [140], [150], [155], [159], gradient boosted decision trees [147], support vector machines [131], [138], [146], random forests [81], [86], [142]-[144], neural networks [6], [48], [49], [91], [129], [130], [133], [135], [139], [”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [136]
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Cited by
Sorted by citations
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
Expanding Explainability: Towards Social Transparency in AI systems
Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445188 · Cited by 538
Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376
“For instance, Lombrozo treated counterfactual and contrastive explanations as equivalent assuming hypothesized events non-occurred in reality to be ‘‘counterfactual cases’’ where a subset of these cases forms a contrastive explanation [10].”
BackgroundHendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_17 · Cited by 171
Making Sense of Generative Learning
Fiorella, L. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09769-7 · Cited by 135
Metadata
- Authors
- Lombrozo, T.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Lombrozo, Tanya
- Venue
- The Oxford Handbook of Thinking and Reasoning
- Publisher
- Oxford University Press
- Published
- 2012-11-21
- Pages
- 260-276
- OpenAlex
- W2627159663
- Cited by
- 242
- References
- 120
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 11 · 2023: 25 · 2022: 24 · 2021: 31 · 2020: 13 · 2019: 22 · 2018: 15 · 2017: 16 · 2016: 18 · 2015: 16 · 2014: 13 · 2013: 7 · 2012: 4
- FWCI
- 30.42
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Social Sciences › Education › Science Education and Pedagogy
- Keywords
- explanation, abductive reasoning, cognitive development, learning, inference, representation, human cognition, social cognition
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