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Record W4391610744 · doi:10.1080/20445911.2024.2313566

Reasoning strategy moderates the transition between slow and fast reasoning

2024· article· en· W4391610744 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Cognitive Psychology · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCounterexampleSet (abstract data type)PsychologyConstraint (computer-aided design)Cognitive psychologyLogical reasoningLimit (mathematics)Computer scienceArtificial intelligenceMathematicsDiscrete mathematics

Abstract

fetched live from OpenAlex

Reasoning faster is often assumed to be less “logical” than slow reasoning. The Dual strategy model of reasoning, which distinguishes between Counterexample and Statistical strategies, suggests a more nuanced way of understanding the effects of time constraints. Previous studies suggest that Statistical reasoners are using a broad form of intuitive processing, while Counterexample reasoners use more working-memory intensive processes, suggesting that time constraint should have less of an effect on the former. In the following study, participants were initially given a set of belief-biased inferences with unlimited time, followed by another version of the same inferences with a 4 s limit, along with the Strategy diagnostic and measures of IQ, CRT and AOT. Consistent with predictions, results show that having less time produced less logical responding in Counterexample reasoners but had no effect on Statistical reasoners. Results also show the existence of reasoners using mostly belief or validity that were directly related to reasoning strategy.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.134
GPT teacher head0.458
Teacher spread0.324 · 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