The influence of practice on response inhibition and its inhibitory after-effects in visual, location-based tasks
Bibliographic record
Abstract
To-be-ignored distractor events are nonetheless deeply processed, to the point of activating their associated response which subsequently undergo inhibition. These inhibited responses resist future execution, thereby delaying later target processing that requires their production (i.e., time is needed to 'override' this resistance). The question here is whether this detrimental inhibitory after-effect can be reduced/removed with practice – i.e., can override time be reduced? Method: 30 individuals undertook a classis spatial negative priming (SNP) task (SNP effect is solely caused by earlier response inhibition after-effects and so indexes the latter), completing 16 sessions of 224 prime-probe trial pairs, including a total of 448 ignored-repetition trials, which require override. Results: An ANOVA with Probe Trial Type (t+d, t-only), Sessions (3-14) and SNP (ignored-repetition, Control) as factors, showed a significant SNP effect, F(1, 29)= 179.79, p< 0.01), which did not interact with the other factors. Distractor interference (RT[t+d] – RT[t-only]) declined significantly over sessions. Conclusions: (1) override time is not shortened due to practice, so the negative impact on target processing caused by recently inhibited responses cannot be set aside on this account, and, (2) while distractor potency declines over practice (i.e., produces less interference), this is not associated with faster override times.Acknowledgments: NSERC
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".