Teaching Reasoning
Open accessAbstract
Twentieth-century psychologists have been pessimistic about teaching reasoning, prevailing opinion suggesting that people may possess only domain-specific rules, rather than abstract rules; this would mean that training a rule in one domain would not produce generalization to other domains. Alternatively, it was thought that people might possess abstract rules (such as logical ones) but that these are induced developmentally through self-discovery methods and cannot be trained. Research suggests a much more optimistic view: even brief formal training in inferential rules may enhance their use for reasoning about everyday life events. Previous theorists may have been mistaken about trainability, in part because they misidentified the kind of rules that people use naturally.
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Even brief training in statistical principles improves people's solutions on everyday reasoning problems related to those statistical principles.
“Even brief training in statistical principles improves people's solutions on everyday reasoning problems related to those statistical principles (Nisbett et al., 1987).”
From The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. (1993) · cites this paper as Nisbett et al., 1987
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- Authors
- Nisbett, R. E., Fong, G. T., Lehman, D. R., & Cheng, P. W.
- Affiliations
- University of Michigan (US)
- ORCID
- Fong, Geoffrey T.
- Venue
- Science
- Published
- 1987-10-30
- Volume
- 238
- Issue
- 4827
- Pages
- 625-631
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4249755205
- Cited by
- 272
- References
- 11
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 9 · 2022: 7 · 2021: 5 · 2020: 7 · 2019: 6 · 2018: 5 · 2017: 7 · 2016: 5 · 2015: 5 · 2014: 2 · 2013: 10 · 2012: 1
- FWCI
- 50.47
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Institutes of Health (5r01mh038466-03)
- National Institute of Mental Health (1 RO1 MH38466)
- Topics
- Social Sciences › Education › Education and Critical Thinking Development
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- reasoning, reasoning training, inference rules, abstract rules, domain-specific rules, transfer of training, rule induction, formal instruction, everyday reasoning
- MeSH terms
- Education, Humans, Learning, Logic, Statistics as Topic
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 ›
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