Cognitive flexibility and resilience measured through a residual approach
Bibliographic record
Abstract
Background and Objectives: Resilience refers to the process through which individuals show better outcomes than what would be expected based on the adversity they experienced.Several theories have proposed that variation in resilience is underpinned by cognitive flexibility, however, no study has investigated this using an outcomebased measure of resilience.Design: We used a residual-based approach to index resilience, which regresses a measure of mental health difficulties onto a measure of adversity experienced.The residuals obtained from this regression constitute how much better or worse someone is functioning relative to what is predicted by the adversity they have experienced.Methods: A total of 463 undergraduate participants completed questionnaires of mental health difficulties and adversity, as well as a number-letter task-switching task to assess cognitive flexibility.Results: Multiple regression analyses showed that better cognitive flexibility was not associated with greater resilience.Conclusions: Our findings do not support theoretical models that propose the existence of a relationship between cognitive flexibility and resilience.Future research may serve to refine the residual-based approach to measure resilience, as well as investigate the contribution of "hot" rather than "cold" cognitive flexibility to individual differences in resilience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".