Constraints on the observation of partial match costs: Implications for transfer-appropriate processing approaches to immediate priming.
Bibliographic record
Abstract
According to a transfer-appropriate processing framework, immediate priming costs arise from a match between a prime and probe event on 1 dimension and a difference between those 2 events on some other dimension (i.e., a partial match). In Experiment 1, the authors used a Stroop priming procedure to generate 6 variants of partial match, yet only 1 of these 6 conditions yielded a partial match cost. Experiment 2 demonstrates that, for some of these conditions, an underlying partial match cost was obscured by the contribution of an independent source of facilitation to performance. In Experiment 3, however, a partial match was observed to have produced an immediate priming cost only when the selective attention demands of the probe task were high. Overall, the results reveal a limitation in current applications of the transfer-appropriate processing framework to immediate priming phenomena.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".