Now you see it, now you don't: Testing environments modulate the association between hippocampal volume and cortisol levels in young and older adults
Bibliographic record
Abstract
The hypothalamic pituitary adrenal axis production of the stress hormone cortisol interacts with the hippocampal formation and impacts memory function. A growing interest is to determine whether hippocampal volume (HV) predicts basal and/or reactive cortisol levels in young and older adults. Recent evidence shows that contextual features in testing environments might be stressful and inadvertently induce a stress response in young and/or older populations. This latter result suggests that variations in testing environments might influence associations between HV and cortisol levels in young and older adults. To this end, we investigated 28 healthy young adults (ages 18-35) and 32 healthy older adults (ages 60-75) in two different environments constructed to be more or less stressful for each age group (Favoring-Young versus Favoring-Old conditions). Cortisol levels were repeatedly assessed in each environment, and young and older participants underwent an anatomical magnetic resonance imaging scan for subsequent assessment of HV. Results in both age groups showed that HV was significantly associated with cortisol levels only in the unfavorable stressful testing conditions specific for each age group. This association was absent when testing environments were designed to decrease stress for each age group. These findings are fundamental in showing that unless the nature of the testing environment is taken into consideration, detected associations between HV and cortisol levels in both young and older populations might be confounded by environmental stress.
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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 | 0.001 |
| 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".