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Record W2588156857 · doi:10.1037/xlm0000372

Rule-based reasoning is fast and belief-based reasoning can be slow: Challenging current explanations of belief-bias and base-rate neglect.

2017· article· en· W2588156857 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.

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBelief revisionDual process theory (moral psychology)NeglectCognitive psychologyAnalytic reasoningPsychologyPsychology of reasoningDual (grammatical number)Verbal reasoningBase (topology)False beliefProcess (computing)Knowledge baseReasoning systemComputer scienceArtificial intelligenceCognitionMathematicsTheory of mind

Abstract

fetched live from OpenAlex

It is commonly assumed that belief-based reasoning is fast and automatic, whereas rule-based reasoning is slower and more effortful. Dual-Process theories of reasoning rely on this speed-asymmetry explanation to account for a number of reasoning phenomena, such as base-rate neglect and belief-bias. The goal of the current study was to test this hypothesis about the relative speed of belief-based and rule-based processes. Participants solved base-rate problems (Experiment 1) and conditional inferences (Experiment 2) under a challenging deadline; they then gave a second response in free time. We found that fast responses were informed by rules of probability and logical validity, and that slow responses incorporated belief-based information. Implications for Dual-Process theories and future research options for dissociating Type I and Type II processes are discussed. (PsycINFO Database Record

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)0.002
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 score0.877
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.154
GPT teacher head0.426
Teacher spread0.272 · 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