Isolated core vs. superficial cooling effects on virtual maze navigation.
Bibliographic record
Abstract
INTRODUCTION: Cold impairs cognitive performance and is a common occurrence in many survival situations. Altered behavior patterns due to impaired navigation abilities in cold environments are potential problems in lost-person situations. We investigated the separate effects of low core temperature and superficial cooling on a spatially demanding virtual navigation task. METHODS: There were 12 healthy men who were passively cooled via 15 degrees C water immersion to a core temperature of 36.0 degrees C, then transferred to a warm (40 degrees C) water bath to eliminate superficial shivering while completing a series of 20 virtual computer mazes. In a control condition, subjects rested in a thermoneutral (approximately 35 degrees C) bath for a time-matched period before being transferred to a warm bath for testing. Superficial cooling and distraction were achieved by whole-body immersion in 35 degree water for a time-matched period, followed by lower leg immersion in 10 degree C water for the duration of the navigational tests. RESULTS: Mean completion time and mean error scores for the mazes were not significantly different (p > 0.05) across the core cooling (16.59 +/- 11.54 s, 0.91 +/- 1.86 errors), control (15.40 +/- 8.85 s, 0.82 +/- 1.76 errors), and superficial cooling (15.19 +/- 7.80 s, 0.77 +/- 1.40 errors) conditions. DISCUSSION: Separately reducing core temperature or increasing cold sensation in the lower extremities did not influence performance on virtual computer mazes, suggesting that navigation is more resistive to cooling than other, simpler cognitive tasks. Further research is warranted to explore navigational ability at progressively lower core and skin temperatures, and in different populations.
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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 | 0.000 |
| 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) | 0.003 | 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".