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Record W4387948710 · doi:10.1037/xlm0001293

Can we change how people reason? Effects of instructions to reason differently and reasoning strategy.

2023· article· en· W4387948710 on OpenAlexafffund
Henry Markovits, Valerie A. Thompson

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsUniversity of SaskatchewanUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCounterexampleProbabilistic logicPsycINFODual (grammatical number)PsychologyCognitive psychologyPreferenceMental representationDual process theory (moral psychology)Psychology of reasoningComputer scienceArtificial intelligenceSocial psychologyCognitionModel-based reasoningKnowledge representation and reasoningMathematicsMoral reasoningLinguisticsMEDLINEStatistics

Abstract

fetched live from OpenAlex

= 885) explicit instructions to reason either using a counterexample strategy or a probabilistic strategy. In two studies, we observed that the ability to follow these instructions was constrained by people's spontaneous strategy use, and that the effect of instructions carried over to two subsequent forms of reasoning (a) belief-biased inferences and (b) base-rate judgments. Finally, the ability to follow instructions was correlated with reasoning accuracy on both tasks. These results provide strong evidence for the underlying reality of the dual strategy model and show that explicit instructions to reason differently can modify performance on different forms of reasoning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.409
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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