Does napping enhance the effects of Cognitive Bias Modification-Appraisal training? An experimental study
Bibliographic record
Abstract
Posttraumatic Stress Disorder (PTSD) is characterised by dysfunctional appraisals of the trauma and its consequences including one's own symptoms. Experimental studies have shown that Cognitive Bias Modification-Appraisal (CBM-App) training can reduce dysfunctional interpretations and analog trauma symptoms. One important question is how to enhance the effects of CBM-App. Following work suggesting that sleep has beneficial effects on consolidation processes and can thus improve learning, the present study investigated whether a brief period of sleep (i.e., a nap) enhances the effects of CBM-App. All participants watched a stressful movie as an analogue trauma induction. After that, participants received either positive or negative CBM-App training. Within each training, half of the participants then had a 90-minute nap or watched a neutral movie. Results showed that the CBM training induced training-congruent appraisals. Sleep did not enhance this effect. Participants who slept, however, experienced fewer intrusive memories of the analogue trauma, but this effect was independent of the CBM condition. These results provide valuable information about the effects of sleep during a 90-minute nap period on encoding of analogue trauma and emotional learning in the context of appraisal, and highlight the importance of sleep as a focus for continued research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".