The Influence of Distractor-Only Prime Trials on the Location Negative Priming Mechanism
Bibliographic record
Abstract
Two experiments were conducted that examined the influence of distractor-only prime trials on the "location" negative priming (NP) effect. In all experiments, the probe trial always lacked a distractor. We showed that the predictable absence of a probe distractor caused the elimination of the location NP effect when the prime trial contained both a target and a distractor event (T + D-->T), but not when the prime contained only a to-be-ignored distractor event (D-->T) (Milliken, Tipper, Houghton, & Lupianez, 2000). The preservation of the NP effect seen with the distractor-only prime trials (D-->T) was not the result of its lacking a prime-trial selection, nor was it the consequence of its representing a higher level of episodic similarity than the T + D-->T condition. Finally, the location NP effect observed for the D-->T condition is seemingly consistent with the view that location NP and the inhibition-of-return effects share a common underlying process (Milliken et al., 2000).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".