Systematic adaptive and maladaptive giving-up strategies in cognitive problem-solving
Bibliographic record
Abstract
Currently, there is little understanding of how individuals give up, whether such strategies are used systematically and whether they are adaptive or maladaptive for performance. Grounded in the meta-reasoning model, this study examines giving-up tendencies based on time and frequency. A university student sample (N = 370) were given three different fluid intelligence tasks to capture these metrics. These tasks also captured solvability and confidence judgments, performance accuracy, and variables within a reward system (points and bets to maximise them). Academic performance and self-reported decision outcomes and styles were also collected. Using latent profile analyses, three unique giving-up profiles emerged—(1) fast and frequent, (2) fast and rare, and (3) slow and rare. Three additional profiles based on never giving up also emerged: (4) for solvable items, (5) for unsolvable items, and (6) for all items. The two “fast” profiles were characterised as maladaptive, having lower points and bets compared to the adaptive “slow” profile. However, findings associated with those who never gave up were weak and mixed. Our results suggest that individuals are systematically predisposed towards adaptive and maladaptive giving-up strategies. However, the predictive validity of these strategies in the broader context remains unclear.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".