A Multi-Site Collaborative Study of the Hostile Priming Effect
Bibliographic record
Abstract
In a now-classic study by Srull and Wyer (1979), people who were exposed to phrases with hostile content subsequently judged a man as being more hostile. And this “hostile priming effect” has had a significant influence on the field of social cognition over the subsequent decades. However, a recent multi-lab collaborative study (McCarthy et al., 2018) that closely followed the methods described by Srull and Wyer (1979) found a hostile priming effect that was nearly zero, which casts doubt on whether these methods reliably produce an effect. To address some limitations with McCarthy et al. (2018), the current multi-site collaborative study included data collected from 29 labs. Each lab conducted a close replication (total N = 2,123) and a conceptual replication (total N = 2,579) of Srull and Wyer’s methods. The hostile priming effect for both the close replication (d = 0.09, 95% CI [-0.04, 0.22], z = 1.34, p = .16) and the conceptual replication (d = 0.05, 95% CI [-0.04, 0.15], z = 1.15, p = .58) were not significantly different from zero and, if the true effects are non-zero, were smaller than what most labs could feasibly and routinely detect. Despite our best efforts to produce favorable conditions for the effect to emerge, we did not detect a hostile priming effect. We suggest that researchers should not invest more resources into trying to detect a hostile priming effect using methods like those described in Srull and Wyer (1979).
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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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".