The Sensitivity of Physiological Measures to Phobic and Nonphobic Fear Intensity
Bibliographic record
Abstract
We investigated whether amygdala activation, autonomic responses, respiratory responses, and facial muscle activity (measured over the brow and cheek [fear grin] regions) are all sensitive to phobic versus nonphobic fear and, more importantly, whether effects in these variables vary as a function of both phobic and nonphobic fear intensity. Spider-phobic and comparably low spider-fearful control participants imagined encountering different animals and rated their subjective fear while their central and peripheral nervous system activity was measured. All measures included in our study were sensitive to variations in subjective fear, but were related to different ranges and positions on the subjective fear level continuum. Left amygdala activation, heart rate, and facial muscle activity over the cheek region captured fear intensity variations even within narrowly described regions on the fear level continuum (here within extremely low levels of fear and within considerable phobic fear). Skin conductance and facial muscle activity over the brow region did not capture fear intensity variations within low levels of fear: skin conductance mirrored only extreme levels of fear, and activity over the brow region distinguished phobic from nonphobic fear but also low-to-moderate and high phobic fear. Finally, respiratory measures distinguished phobic from nonphobic fear with no further differentiation within phobic and nonphobic fear. We conclude that a careful consideration of the measures to be used in an investigation and the population to be examined can be critical in order to obtain significant results.
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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 | 0.002 |
| 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".