Emotional Clarity and Stress Vulnerability as Predictors of Cognitive Failures
Bibliographic record
Abstract
To investigate the relationships between emotional clarity, vulnerability to stress, and cognitive failures, and to determine the predictive value of emotional clarity and vulnerability to stress on cognitive failures. This cross-sectional study included 330 participants selected based on the Morgan and Krejcie table. Data were collected using the Cognitive Failures Questionnaire (CFQ), the Trait Meta-Mood Scale (TMMS) for emotional clarity, and the Perceived Stress Scale (PSS). Pearson correlation and linear regression analyses were conducted using SPSS version 27. Descriptive statistics indicated mean scores of 35.24 (SD = 9.87) for cognitive failures, 26.78 (SD = 7.65) for emotional clarity, and 29.43 (SD = 8.31) for vulnerability to stress. Pearson correlation analysis showed significant relationships between cognitive failures and emotional clarity (r = -0.45, p < 0.001) and vulnerability to stress (r = 0.53, p < 0.001). Regression analysis revealed that both emotional clarity (B = -0.48, SE = 0.09, β = -0.32, t = -5.31, p < 0.001) and vulnerability to stress (B = 0.67, SE = 0.08, β = 0.45, t = 8.37, p < 0.001) were significant predictors of cognitive failures, accounting for 38% of the variance (R² = 0.38, F(2, 327) = 99.65, p < 0.001). The study found that higher emotional clarity is associated with fewer cognitive failures, while higher vulnerability to stress is linked to increased cognitive failures. Both emotional clarity and vulnerability to stress significantly predict cognitive failures, highlighting their importance in cognitive performance. These findings suggest that interventions aimed at enhancing emotional clarity and managing stress could reduce cognitive failures.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".