Elevated perseveration errors on a verbal fluency task in frequent nightmare recallers: a replication
Bibliographic record
Abstract
A recent study reported that individuals recalling frequent idiopathic nightmares (NM) produced more perseveration errors on a verbal fluency task than did control participants (CTL), while not differing in overall verbal fluency. Elevated scores on perseveration errors, an index of executive dysfunction, suggest a cognitive inhibitory control deficit in NM participants. The present study sought to replicate these results using a French-speaking cohort and French language verbal fluency tasks. A phonetic verbal fluency task using three stimulus letters (P, R, V) and a semantic verbal fluency task using two stimulus categories (female and male French first names) were administered to 23 participants with frequent recall of NM (≥2 NM per week, mean age = 24.4 ± 4.0 years), and to 16 CTL participants with few recalled NM (≤ 1 NM per month, mean age = 24.5 ± 3.8 years). All participants were French-speaking since birth and self-declared to be in good mental and physical health apart from their NM. As expected, groups did not differ in overall verbal fluency, i.e. total number of correct words produced in response to stimulus letters or categories (P = 0.97). Furthermore, groups exhibited a difference in fluency perseveration errors, with the NM group having higher perseveration than the CTL group (P = 0.03, Cohen's d = 0.745). This replication suggests that frequent NM recallers have executive inhibitory dysfunction during a cognitive association task and supports a neurocognitive model which posits fronto-limbic impairment as a neural correlate of disturbed dreaming.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".