Context-specific control and the Stroop negative priming effect
Bibliographic record
Abstract
The present study highlights the utility of context-specific learning for different probe types in accounting for the commonly observed dependence of negative priming on probe selection. Using a Stroop priming procedure, Experiments 1a and 1b offered a demonstration that Stroop priming effects can differ qualitatively for selection and no-selection probes when probe selection is manipulated between subjects, but not when it is manipulated randomly from trial to trial within subject (see also Moore, 1994). In Experiments 2 and 3, selection and no-selection probes served as two contexts that varied randomly from trial to trial, but for which proportion repeated was manipulated separately. A context-specific proportion repeated effect was observed in Experiment 2, characterized by modest quantitative shifts in the repetition effects as a function of the context-specific proportion repeated manipulation. However, with a longer intertrial interval in Experiment 3, a context-specific proportion repeated manipulation that focused on the no-selection probes changed the repetition effect qualitatively, from negative priming when the proportion repeated was .25 to positive priming when the proportion repeated was .75. The results are discussed with reference to the role of rapid, context-specific learning processes in the integration of prior experiences with current perception and action.
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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 | 0.001 |
| 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".