Prevention of Intention Invention in the Affect Misattribution Procedure
Bibliographic record
Abstract
The affect misattribution procedure (AMP) is one of the most promising indirect measures, showing high reliability and large effect sizes. However, the AMP has been recently criticized for being susceptible to explicit influences, in that priming effects are larger and more reliable among participants who report that they intentionally responded to the primes instead of the targets. Consistent with interpretations of these effects in terms of retrospective confabulation, two experiments obtained reliable priming effects when (a) participants lacked meta-cognitive knowledge about their responses to the primes and (b) participants’ attention was directed away from response-eliciting features of the primes. Under either of these conditions, priming effects were unrelated to self-reported intentionality, although self-reported intentionality was positively related to priming effects under control conditions. The findings highlight the contribution of meta-cognitive inferences to retrospective self-reports of intentionality and suggest an effective procedure to rule out explicit influences in the AMP.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".