Effects of Response Priming and Inhibition on Movement Planning and Execution
Bibliographic record
Abstract
The authors used a precueing method to examine the effects of response priming and inhibition on goal-directed action. Participants (N = 18) completed aiming movements to 1 of 2 locations following predictive (80% cued), nonpredictive (50% cued), and antipredictive (20% cued) precues at 1 of the 2 possible target locations. Consistent with previous research, participants responded more quickly to targets at cued locations than to targets at uncued locations in the 80% condition, and more quickly to targets presented at the uncued than to those presented at cued locations in the 50% and 20% conditions. As predicted by models of action-centered selective attention, movement trajectories deviated away from the cued location in the 50% condition. Movement trajectories were also altered in the 80% and the 20% conditions. Movements directed to the uncued location deviated away from the cued location in the 80% condition, whereas movements directed to the cued location deviated away from the uncued location in the 20% condition. The authors explain the latter trajectory results as a strategy of overcompensation.
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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".