Examining the Role of Failure and Success Experiences on Task Persistence and Neurocognition in Schizophrenia-Spectrum Disorders
Bibliographic record
Abstract
BACKGROUND: Recent theoretical models suggest that a variety of psychological and contextual factors account for a significant proportion of the observed neurocognitive impairment in schizophrenia-spectrum disorders (SSD). Numerous non-neurocognitive mechanisms of neurocognitive functioning have been proposed that warrant investigation; however, few studies have empirically examined these factors. This cross-over study examined whether the experience of failure or success affects task persistence and neurocognition differentially between individuals with SSD and healthy controls. METHODS: = 10.72) completed success and failure inductions, psychological questionnaires, an anagram persistence task, and brief neurocognitive testing remotely at two time-points. RESULTS: Both groups demonstrated significantly lower persistence and worse decision-making skills in the failure condition relative to the success condition. Individuals with SSD demonstrated slower processing speed, but this was not affected by prior failure or success. CONCLUSIONS: This study demonstrates that the experience of failure is similarly detrimental to persistence and decision-making in healthy controls and individuals with SSD but does not contribute to processing speed performance. This suggests that higher-order executive functions are more susceptible to manipulation by contextual factors compared to lower-order cognitive functions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".