Commentary: Cognitive reflection vs. calculation in decision making
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.
Bibliographic record
Abstract
Sinayev and Peters (2015; hereafter S&P) present two competing hypotheses to explain performance on the Cognitive Reflection Test (CRT). They dub the first the "Cognitive Reflection Hypothesis" and attribute it to other researchers: "Each of these researchers assumes that differences in CRT performance indicated differences in the ability to detect and correct incorrect intuitions. . . " and ". . . implicitly assume that numerical ability is an irrelevant detail when it comes to solving CRT and related problems" (p. 2). They contrast this with their "Numeracy Hypothesis" which states that "the CRT is primarily a measure of numeric ability" (p. 3). S&P report two studies whose results, they argue, favor the Numeracy Hypothesis over the Cognitive Reflection Hypothesis. They conclude that numeric ability is "the key mechanism" that explains the association between CRT performance and decision making (p. 1), although they also state that the ability to detect and correction intuitions (apart from numeracy) plays a role in CRT performance. Both of the hypotheses presented by S&P emphasize the role of cognitive ability in CRT performance. In this commentary we introduce an alternative hypothesis that was not discussed by S&P; namely, that the propensity or disposition to think analytically plays an important role in CRT performance (Pennycook et al., 2015b). We discuss recent empirical evidence that supports the claim that the CRT is more than just a measure of numeracy or, more generally, cognitive ability.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it