Effects of Sleep Loss on Team Decision Making: Motivational Loss or Motivational Gain?
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of 30 hr of sleep loss and continuous cognitive work on performance in a distributed team decision-making environment. BACKGROUND: To date, only a few studies have examined the effect of sleep loss on distributed team performance, and only one other to our knowledge has examined the relationship between sleep loss and social-motivational aspects of teams (Hoeksema-van Orden, Gaillard, & Buunk, 1998). METHOD: Sixteen teams participated; each comprised 4 members. Three team members made threat assessments on a military surveillance task and then forwarded their judgments electronically to a team leader, who made a final assessment on behalf of the team. RESULTS: Sleep loss had an antagonistic effect on team decision-making accuracy and decision time. However, the performance loss associated with fatigue attributable to sleep loss was mediated by being part of a team, as compared with performing the same task individually - that is, we found evidence of a "motivational gain" effect in these sleepy teams. We compare these results with those of Hoeksema-van Orden et al. (1998), who found clear evidence of a "social loafing" effect in sleepy teams. CONCLUSION: The divergent results are discussed in the context of the collective effort model (Karau & Williams, 1993) and are attributable in part to a difference between independent and interdependent team tasks. APPLICATION: The issues and findings have implications for a wide range of distributed, collaborative work environments, such as military network-enabled operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".