Psychosocial Stress Evoked by a Virtual Audience: Relation to Neuroendocrine Activity
Bibliographic record
Abstract
A modified version of the Trier Social Stress Test (TSST) was employed to determine whether exposure to a virtual audience using virtual reality (VR) technology would prompt an increase of neuroendocrine activity comparable to that prompted by a real audience. Following an anticipatory period, participants completed a speech or a speech-plus-math challenge in front of either a virtual audience, a panel of judges they were led to believe was behind a one-way mirror, or an audience comprised of confederates. An additional group that had prepared a speech was simply directed to observe the virtual audience but did not deliver the speech. Finally, a control group completed questionnaires for the duration of the experiment. Cortisol samples were obtained upon arrival to the laboratory, just before the challenge, and 15 and 30 minutes after the task. Participants also completed a measure assessing stressor appraisals of the task before and after the challenge. Anticipation of the task was associated with a modest increase of cortisol levels, and a further rise of cortisol was evident in response to the challenge. The neuroendocrine changes evoked by the virtual audience were comparable to those elicited by the imagined audience (behind the one-way mirror) but less than changes evoked by the panel of confederates. Stressor appraisals were higher post-challenge compared to those reported prior to the task; however, appraisals were similar across each group. These data suggest that VR technology may be amenable to evaluating the impact of psychosocial stressors such as the TSST.
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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) | 0.002 | 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".