Multivariate fMRI pattern analysis of fear perception across modalities
Bibliographic record
Abstract
The emotional expression of fear can be processed through a number of modalities, and of varying forms, however, much of the functional imaging literature has centered on investigating fear as expressed through faces. Findings point to an active involvement of the amygdala, and remain fairly consistent in other studies of unimodal fear perception; however, few studies have looked at within-subject cross-modal responses to fear. Thus, we approached this inquiry by testing 30 healthy young adults with fast, high-resolution fMRI, recording the neural responses of fear perception, as expressed through faces, bodies, prosody, and vocalizations. The study was analyzed using a multivariate approach (multi-voxel pattern analysis) and yielded a significant distinction in the responses associated with the perception of fearful vs. neutral emotions. Calculated weights highlighted areas in the amygdala and surrounding subcortical structures as contributing the greatest to the discrimination; however, a whole-brain analysis was necessary to obtain above-chance classification accuracy, suggesting that processing fear across modalities likely involves a broad, distributed network. Thus, our findings support a multivariate approach to studying a highly complex construct such as emotion, as it accounts for multiple voxels simultaneously and can accommodate the high subject-level variability that oftentimes comes with studying emotion perception.
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 | 0.000 | 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) | 0.001 | 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".