The underwood project: A virtual environment for eliciting ambiguous threat
Bibliographic record
Abstract
Threatening environments can be unpredictable in many different ways. The nature of threats, their timing, and their locations in a scene can all be uncertain, even when one is acutely aware of being at risk. Prior research demonstrates that both temporal unpredictability and spatial uncertainty of threats elicit a distinctly anxious psychological response. In the paradigm presented here, we further explore other facets of ambiguous threat via an environment in which there are no concrete threats, predictable or otherwise, but which nevertheless elicits a building sense of danger. By incorporating both psychological research and principles of emotional game design, we constructed this world and then tested its effects in three studies. In line with our goals, participants experienced the environment as creepy and unpredictable. Their subjective and physiological response to the world rose and fell in line with the presentation of ambiguously threatening ambient cues. Exploratory analyses further suggest that this ambiguously threatening experience influenced memory for the virtual world and its underlying narrative. Together the data demonstrate that naturalistic virtual worlds can effectively elicit a multifaceted experience of ambiguous threat with subjective and cognitive consequences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".