Interaction Between the Prefrontal and Visual Cortices Supports Subjective Fear
Bibliographic record
Abstract
It has been reported that threatening and non-threatening visual stimuli can be distinguished based on multi-voxel patterns of hemodynamic activity in the human ventral visual stream. Do these findings mean that there may be evolutionarily hardwired mechanisms within early perception, for the fast and automatic detection of threat, and maybe even for the generation of the subjective experience of fear? In this study, we provide evidence using human neuroimaging that the ventral visual stream may represent affectively neutral visual features that are statistically associated with fear ratings of participants, without representing the subjective experience of fear itself. More specifically, we show that patterns of hemodynamic activity predictive of a specific “fear profile” (i.e., fear ratings reported by a given participant) can be observed in the ventral visual stream whether a participant reports being afraid of the stimuli or not. Further, we found that the multivariate information transmission between ventral visual areas and prefrontal regions distinguished participants who reported being subjectively afraid of the stimuli from those who did not. Together, these findings support the view that the subjective experience of fear may depend on the relevant visual information triggering implicit metacognitive mechanisms in the prefrontal cortex.
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) | 0.002 | 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".