Context-specific control in the single-prime negative-priming procedure
Bibliographic record
Abstract
The current paper examines the applicability of the context-specific control principle to the probe selection dependence of negative-priming effects using the single-prime procedure. In a series of experiments, we highlight the applicability of the context-specific control principle, first by illustrating a key result that implicates the role of context-specific control and challenges the contextual similarity principle. Following this, we show the importance of distinct probe contexts in the single-prime negative-priming procedure and report a novel finding that illustrates a learning effect that can occur within an experimental session. Finally, we test the relation of our novel learning effect to a related learning proposal offered by Frings and Wentura (2006), and we demonstrate that the learning involved in context-specific control is not dependent on contingency learning. Overall, the patterns of results highlight the role of context-sensitive memory in controlling how current perception and action are integrated with prior experience.
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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 | 0.000 | 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".