What does the cognitive reflection test really measure: A process dissociation investigation.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".