Reasoning strategies explain individual differences in social reasoning.
Bibliographic record
Abstract
The dual-strategy model of reasoning suggests that when people reason they can either use (a) a statistical strategy which generates an estimation of conclusion likelihood using a rapid form of associative processing or (b) a counterexample strategy which identifies potential counterexamples to a conclusion using a more conscious working memory intensive process. Previous results suggest that strategy use is a strong individual difference that represents a broad distinction in the way that information is processed that goes beyond deductive reasoning. In 3 studies, we examined if this model could predict individual differences in the processing of social information by examining socially relevant cognitive biases. Study 1 found that strategy use predicted the extent of the self-serving bias. Study 2 found that strategy use predicted use of racist stereotypes even when need for closure was accounted for. Study 3 found that an essentialist prime resulted in a higher level of gender bias among statistical reasoners but that this prime had no effect on counterexample reasoners. These results indicate that the processing distinction between the 2 reasoning strategies underlies individual differences in social biases such as stereotypes, sexism, and racism. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".