Is Deliberate Control of Behavior Rare? A Test of the Automaticity Dominance Perspective
Bibliographic record
Abstract
The “automaticity dominance” perspective on cognition and behavior holds that automatic processes guide most behavior because deliberate processing is slow, inefficient, and therefore rare, typically restricted to “problematic” situations. Other scholars argue on both theoretical and empirical grounds that deliberate processing is more common. In this study, the authors test automaticity dominance by using multinomial processing tree models to examine donation decisions in an online sample of 1,027 respondents. Using a mixture of preregistered and exploratory analyses on both experimental and observational data, the authors find that (1) the processes underlying donation behavior execute efficiently and rapidly, but key processes are also controllable; (2) deliberate cognition increases in problematic situations but also operates when levels of problematicity are low; and (3) respondents deliberately control (at a minimum) a substantial minority of their decisions. These results indicate that deliberate cognition might not be as rare as an automaticity dominance perspective suggests.
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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 | 0.021 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".