Executive control in set switching: Residual switch cost and task-set inhibition.
Bibliographic record
Abstract
Executive processes necessary for flexibly switching between different tasks were studied using a set switching paradigm that requires participants to rapidly switch between different tasks across consecutive trials. Switch cost reflects poorer performance for task-switch trials than for consecutive same-task trials. Significant switch cost was observed even with considerable preparation time before a task-switch, an effect known as residual switch cost. This study tested the hypothesis that one process underlying residual switch cost is inhibition of the previous task-set. We used semantic categorization tasks to compare switch cost between alternating task series (ABA) and nonalternating series (ABC) in order to test the generality of a task-set inhibition effect previously observed with perceptual judgment tasks (Mayr & Keele, in press). The results yielded significant switch cost only for alternating tasks, in both response times and errors resulting from performance of the wrong task. Thus, resolving inhibition associated with previously abandoned task-sets may be the main process underlying residual switch costs, suggesting that task-set inhibition is an important executive control process.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".