Passive heat acclimation does not modulate processing speed and executive functions during cognitive tasks performed at fixed levels of thermal strain
Bibliographic record
Abstract
This study evaluated if passive controlled hyperthermia heat acclimation modulates cognitive performance during passive heat stress. Eight healthy adults (25 ± 4 years) underwent 7 consecutive days of hot water immersion (core temperature ≥38.6 °C) and a 7-day time-control period. On days 1 and 7 of heat acclimation, participants performed a digital Stroop test at baseline, when core temperature reached 38.6 °C, and after 60 minutes at a core temperature ≥38.6 °C to evaluate reaction time during tasks targeting processing speed (reading and counting) and executive functions (inhibition and switching). On days 1 and 7 of the time-control intervention, participants performed the Stroop test with equivalent amounts of time separating each task as for heat acclimation. During day 1 of heat acclimation, reaction time was quicker during the reading (–44 ms [–71 to –17], P < 0.01) and counting (–39 ms [–76 to –2], P = 0.04) tasks when the rectal temperature reached 38.6 °C, but after a further 60 minutes of heat exposure, reaction time only remained quicker during the reading task (–56 ms [–83 to –29], P < 0.01). Changes in reaction time during heat exposure were unaffected by subsequent heat acclimation (interaction, all P ≥ 0.09). In conclusion, 7 days of heat acclimation does not modulate processing speed and executive functions during passive heat exposure. Novelty: Whether heat acclimation improves cognitive performance during heat exposure remains unclear. We tested the hypothesis that heat acclimation modulates reaction time during cognitive tasks performed at matched levels of thermal strain. Despite the classical signs of heat acclimation, reaction time during heat exposure is unaffected by heat acclimation.
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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) | 0.001 | 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".