The influence of nocebo information on fatigue and urge to stop: An experimental investigation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Fatigue is an adaptive state after prolonged effort and often goes hand in hand with changes in behavior and motivation, such as the urge to stop exerting further effort. However, fatigue may become chronic in nature, as seen in multiple psychiatric disorders and chronic diseases, thereby losing its adaptive function. The etiology of fatigue symptoms remains poorly understood. We aimed to investigate whether nocebo information about the fatigue inducing nature of a cognitive task may contribute to the experience of fatigue and the motivational urge to stop. METHODS: Participants (N = 46) repeatedly rated currently experienced fatigue while engaging in cognitive effort (working memory task). Crucially, half of participants received nocebo instructions prior to this task, whereas the other half only received neutral information. RESULTS: Over the entire sample, results showed an increase in fatigue and urge to stop as the task progressed. Crucially, participants in the nocebo condition reported a higher urge to stop throughout the task relative to participants in the neutral condition. No significant effects were found for fatigue. Interestingly however, after controlling for baseline differences between conditions in negative affect, there was a significant Condition*Task block interaction effect on fatigue. LIMITATIONS: Limitations include the relatively short experimental protocol and the underrepresentation of male relative to female participants. CONCLUSIONS: These findings suggest that heightened awareness among clinicians and therapists about potential nocebo effects in their communication is warranted.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".