Awareness of Implicit Attitudes Revisited: A Meta-Analysis on Replications Across Samples and Settings
Bibliographic record
Abstract
A long-standing debate in social psychology is whether the cognitions reflected on implicit measures are unconscious. Research by Hahn et al. (2014) has documented that people are able to predict the patterns of their results on Implicit Association Tests (IATs) towards five pairs of social groups prospectively. The present article presents a meta-analysis of 17 published and unpublished exact replication studies conducted by or in close supervision of the original author. Replicating Hahn et al., participants in all 17 studies were able to accurately predict the patterns of their IAT results (meta-analytical within-subject correlation: b = .44; corrected average within-subjects correlation r = .56). This prediction accuracy effect was smaller for online (b = .27; corrected r = .37) than lab (b = .47; corrected r = .61) studies, as well as for general-public (b = .27; corrected r = .36) as opposed to student samples (b = .47; corrected r = .60). Moreover, predictions fully explained implicit-explicit relations, and they seemed to reflect unique insights into participants’ own cognitions beyond mere knowledge about normatively expected patterns of implicit responses. This pattern of results remained the same across samples, settings, countries (Canada, US, and Germany), and languages (English vs. German). Further analyses suggested that lower prediction accuracy in online samples seems to partly reflect a suppression effect from higher consistency between traditional explicit evaluations and predictions. Controlling for explicit evaluations (which exerted a negative unique effect on IAT scores beyond IAT score predictions) reduced the difference between online and lab studies substantially. Together, the results strengthen the hypothesis that the cognitions reflected on implicit evaluations are largely accessible to conscious awareness.
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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 | 0.087 | 0.182 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".