Human Performance in a Realistic Instrument-Control Task during Short-Term Microgravity
Bibliographic record
Abstract
Previous studies have documented the detrimental effects of microgravity on human sensorimotor skills. While that work dealt with simple, laboratory-type skills, we now evaluate the effects of microgravity on a complex, realistic instrument-control skill. Twelve participants controlled a simulated power plant during the short-term microgravity intervals of parabolic flight as well as during level flight. To this end they watched multiple displays, made strategic decisions and used multiple actuators to maximize their virtual earnings from the power plant. We quantified control efficiency as the participants' net earnings (revenue minus expenses), motor performance as hand kinematics and dynamics, and stress as cortisol level, self-assessed mood and self-assessed workload. We found that compared to normal gravity, control efficiency substantially decreased in microgravity, hand velocity slowed down, and cortisol level and perceived physical strain increased, but other stress and motor scores didn't change. Furthermore, control efficiency was not correlated with motor and stress scores. From this we conclude that realistic instrument control was degraded in short-term microgravity. This degradation can't be explained by the motor and/or stress indicators under study, and microgravity affected motor performance differently in our complex, realistic skill than in the simple, laboratory-type skills of earlier studies.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".