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Record W2947169605 · doi:10.31219/osf.io/m43gn

What does the cognitive reflection test really measure: A process dissociation investigation.

2018· preprint· en· W2947169605 on OpenAlexaff
Jonathon McPhetres

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsIntuitionCognitionPsychologyCognitive psychologyDissociation (chemistry)EpistemologyComputer scienceCognitive sciencePhilosophy

Abstract

fetched live from OpenAlex

The cognitive reflection test (CRT) is a series of brain-teaser type questions believed to measure intuitive versus reflective thinking. However, those measures are confounded by the single-continuum scoring method whereby a decrease in reflective thinking also results in a simultaneous increase in intuitive thinking, making interpretation of the scores difficult. This confound also precludes testing of the relation between the two processes—whether intuition and reflection operate in serial or in parallel. The present studies directly address these limitations using process dissociation (PD) to quantify and manipulate each process independently. If the CRT measures both intuition and reflection then using PD to isolate each score should provide unique information about each process and allow for testing of models describing the relation between the two processes. However, results of four studies (two preregistered) call in to question whether the CRT actually measures intuition (studies 1-3) and provides some limited evidence for a serial processing model of cognition (studies 3-4). Moving forward, it is recommended that researchers 1) consider alternative measures of cognitive reflection, 2) are cognizant of the phrasing used when describing intuitions as inferred from the CRT, and 3) move towards various conceptual measures of intuition.

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 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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.074
GPT teacher head0.340
Teacher spread0.266 · 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 teacher head, not a consensus.

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

Citations5
Published2018
Admission routes1
Has abstractyes

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