What Drives Priming Effects in the Affect Misattribution Procedure?
Bibliographic record
Abstract
The affect misattribution procedure (AMP) is one of the most promising implicit measures to date, showing high reliability and large effect sizes. The current research tested three potential sources of priming effects in the AMP: affective feelings, semantic concepts, and prepotent motor responses. Ruling out prepotent motor responses as a driving force, priming effects on evaluative and semantic target responses occurred regardless of whether the key assignment in the task was fixed or random. Moreover, priming effects emerged for affect-eliciting primes in the absence of semantic knowledge about the primes. Finally, priming effects were independent of the order in which primes and targets were presented, suggesting that AMP effects are driven by misattribution rather than biased perceptions of the targets. Taken together, these results support accounts that attribute priming effects in the AMP to a general misattribution mechanism that can operate on either affective feelings or semantic concepts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".