The effects of sleep deprivation on emotional empathy
Bibliographic record
Abstract
Previous studies have shown that sleep loss has a detrimental effect on the ability of the individuals to process emotional information. In this study, we tested the hypothesis that this negative effect extends to the ability of experiencing emotions while observing other individuals, i.e. emotional empathy. To test this hypothesis, we assessed emotional empathy in 37 healthy volunteers who were assigned randomly to one of three experimental groups: one group was tested before and after a night of total sleep deprivation (sleep deprivation group), a second group was tested before and after a usual night of sleep spent at home (sleep group) and the third group was tested twice during the same day (day group). Emotional empathy was assessed by using two parallel versions of a computerized test measuring direct (i.e. explicit evaluation of empathic concern) and indirect (i.e. the observer's reported physiological arousal) emotional empathy. The results revealed that the post measurements of both direct and indirect emotional empathy of participants in the sleep deprivation group were significantly lower than those of the sleep and day groups; post measurement scores of participants in the day and sleep groups did not differ significantly for either direct or indirect emotional empathy. These data are consistent with previous studies showing the negative effect of sleep deprivation on the processing of emotional information, and extend these effects to emotional empathy. The findings reported in our study are relevant to healthy individuals with poor sleep habits, as well as clinical populations suffering from sleep disturbances.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".