Who Thinks How? Social Patterns in Reliance on Automatic and Deliberate Cognition
Bibliographic record
Abstract
Sociologists increasingly use insights from dual-process models to explain how people think and act. These discussions generally emphasize the influence of cultural knowledge mobilized through automatic cognition, or else show how the use of automatic and deliberate processes vary according to the task at hand or the context. Drawing on insights from sociological theory and suggestive research from social and cognitive psychology, we argue that socially structured experiences also shape general, individual-level preferences (or propensities) for automatic and deliberate thinking. Using a meta-analysis of 63 psychological studies (N = 25,074) and a new multivariate analysis of nationally representative data, we test the hypothesis that the use of automatic and deliberate cognitive processes is socially patterned. We find that education consistently predicts preferences for deliberate processing and that gender predicts preferences for both automatic and deliberate processing. We find that age is a significant but likely nonlinear predictor of preferences for automatic and deliberate cognition, and we find weaker evidence for differences by income, marital status, and religion. These results underscore the need to consider group differences in cognitive processing in sociological explanations of culture, action, and inequality.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".