Neuroendocrine Response and State Anxiety Due to Psychosocial Stress Decrease after a Training with Subject’s Own (but Not Another) Virtual Body: An RCT Study
Bibliographic record
Abstract
Previous research involving healthy participants has reported that seeing a moving virtual body from the first person perspective induces the illusion of ownership and agency over that virtual body. When a person is sitting and the virtual body runs, it is possible to measure physiological, behavioral and cognitive reactions that are comparable to those that occur during actual movement. Capitalizing on this evidence, we hypothesized that virtual training could also induce neuroendocrine effects that prompt a decreased psychosocial stress response, as occurs after physical training. While sitting, 26 healthy young adults watched a virtual avatar running for 30 min from the first person perspective (experimental group), while another 26 participants watched the virtual body from the third person perspective (control group). We found a decreased salivary alpha-amylase concentration (a biomarker for the stress response) after the virtual training among the experimental group only, as well as a decreased subjective feeling of state anxiety (but no difference in heart rate). We argue that the virtual illusion of a moving body from the first person perspective can initiate a cascade of events, from the perception of the visual illusion to physiological activation that triggers other biological effects, such as the neuroendocrine stress response.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".