Refining the understanding of inhibitory processes: how response prepotency is created and overcome
Bibliographic record
Abstract
Understanding (a) how responses become prepotent provides insights into when inhibition is needed in everyday life. Understanding (b) how response prepotency is overcome provides insights for helping children develop strategies for overcoming such tendencies. Concerning (a), on tasks such as the day-night Stroop-like task, is the difficulty with inhibiting saying the name of the stimulus due to the name being semantically related to the correct response or to its being a valid response on the task (i.e. a member of the response set) though incorrect for this stimulus? Experiment 1 (with 40 4-year-olds) suggests that prepotency is caused by membership in the response set and not semantic relation. Concerning (b), Diamond, Kirkham and Amso (2002) found that 4-year-olds could succeed on the day-night task if the experimenter sang a ditty after showing the stimulus card, before the child was to respond. They concluded that it was because delaying children's responses gave them time to compute the correct answer. However, Experiment 2 (with 90 3-year-olds) suggests that such a delay helps because it gives the incorrect, prepotent response time to passively dissipate, not because of active computation during the delay.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.019 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".