Impact of effort exertion on cognitive flexibility and stability.
Bibliographic record
Abstract
Impact of effort exertion on cognitive flexibility and stabilityAnna Mini Jos, Myles LoParco, A. Ross Otto*Department of Psychology, McGill University, Montral, CanadaEfficient task execution requires attention to task requirements while inhibiting distractors (cognitive stability) and adapt-ing to changes (flexibility). Previous studies have shown that individuals differ in their application of stable versus flexibleprocessing modes. Our study examined the impact of prior effort exertion on flexibility/stability trade-off.Participants performed a stability-flexibility paradigm, with pupil recording, before and after effort and no-effort manipula-tions were induced using different tasks. We analyzed the resulting change in preferences for stability/flexibility (voluntaryswitch rate).We found that the no-effort condition evoked a higher voluntary switch rate than baseline or after effort exertion. Partici-pants in the effort condition also showed higher response times and lower accuracy across trials. Pupil data shows that aftereffort exertion participants exert less effort in spontaneous switches and repeats. Additionally, the relationship betweenswitch cost (on forced-switch trials) and spontaneous switching rate increased after effort exertion. These results suggestthat stability/flexibility preferences can vary with prior effort exertion.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".