Multimodal assessment of acute stress dynamics using an Aversive Video Paradigm (AVP)
Bibliographic record
Abstract
This study aims to explore the efficacy of the Aversive Video Paradigm (AVP) in inducing psychological stress and its impact on both psychological and physiological processes in the human body. Stress has become increasingly prominent in modern society, affecting cognition, emotion, and various physiological systems, including the cardiac system, endocrine function, brain activity, and immune response. Investigating stress across multiple modalities—subjective, physiological, and neurophysiological measures—provides a deeper understanding of how these systems interact under stress. To study stress in a controlled setting, it is essential to have reliable methods for stress induction. Traditional paradigms like the Trier Social Stress Test (TSST) and Montreal Imaging Stressor Task (MIST) are effective but complex to administer and require extensive training. To address this, we use the AVP, where participants are exposed to uncontrollable and aversive video clips to induce psychological stress without requiring active engagement or social evaluation. AVP is widely used due to the novelty, unpredictability, and uncontrollability of the clips, and it is easy to administer with minimal experimenter involvement. It also offers a straightforward control condition using neutral video clips. However, limited research has examined the specific stress-inducing components of AVP in terms of brain dynamics, which is a key focus of this study. We are collecting various data to capture both subjective and physiological responses to stress. Subjective data includes self-reported measures of anxiety using the State-Trait Anxiety Inventory (STAI-S) and positive and negative emotions using the Positive and Negative Affect Schedule (PANAS). Physiological data includes heart rate (HR), heart rate variability (HRV), cytokines, and salivary cortisol levels. Neurophysiological data is obtained through electroencephalography (EEG) to measure brain activity. We expect to observe changes in brain oscillations and connectivity resulting from the AVP.
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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) | 0.001 | 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 | 0.001 |
| 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".