Influence of probe-trial selection on the location negative priming effect.
Bibliographic record
Abstract
Using the location variant of the typical negative priming procedure, participants were cued (100% reliable) before (Experiment 1) or after (Experiment 2) the prime trial as to whether a distractor would or would not accompany the target on the probe trial. The crucial results were that on cued trials, the predictable absence, produced the removal of the negative priming effect (disengagement), and that this disengagement of the priming process, motivated by the predictable absence of a probe-trial distractor, could take place on-line. These findings demonstrated the "selection-state" dependency (probe trial) of the location negative priming process, supporting inhibition-based and episodic retrieval models in their contention that the ultimate function of this process is to enhance the efficiency of future distractor processing, and hence selection. The disengagement results revealed an adaptive feature of a process that can be detrimental or irrelevant to upcoming processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".