Do smart people have better intuitions?
Bibliographic record
Abstract
There is much evidence that high-capacity reasoners perform better on a variety of reasoning tasks (Stanovich, 1999), a phenomenon that is normally attributed to differences in either the efficacy or the probability of deliberate (Type II) engagement (Evans, 2007). In contrast, we hypothesized that intuitive (Type I) processes may differentiate high- and low-capacity reasoners. To test this hypothesis, reasoners were given a reasoning task modeled on the logic of the Stroop Task, in which they had to ignore one dimension of a problem when instructed to give an answer based on the other dimension (Handley, Newstead, & Trippas, 2011). Specifically, in Experiment 1, 112 reasoners were asked to give judgments consistent with beliefs or validity for 2 different types of deductive reasoning problems. In Experiment 2, 224 reasoners gave judgments consistent with beliefs (i.e., stereotypes) or statistics (i.e., base-rates) on a base rate task; half responded under a strict deadline. For all 3 problem types and regardless of the deadline, high-capacity reasoners performed better for logic/statistics than did belief judgments when the 2 conflicted, whereas the reverse was true for low-capacity reasoners. In other words, for high-capacity reasoners, statistical information interfered with their ability to make belief-based judgments, suggesting that, for them, probabilities may be more intuitive than stereotypes. Thus, at least part of the accuracy-capacity relationship observed in reasoning may be because of intuitive (Type I) processes. (PsycINFO Database Record
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".