Cortical functional connectivity decodes subconscious, task-irrelevant threat-related emotion processing
Bibliographic record
Abstract
It is currently unclear to what extent cortical structures are required for and engaged during subconscious processing of biologically salient affective stimuli (i.e. the 'low-road' vs. 'many-roads' hypotheses). Here we show that cortical-cortical and cortical-subcortical functional connectivity (FC) contain substantially more information, relative to subcortical-subcortical FC (i.e. 'subcortical alarm' and other limbic regions), that predicts subliminal fearful face processing within individuals using training data from separate subjects. A plot of classification accuracy vs. number of selected whole-brain FC features revealed 92% accuracy when learning was based on the top 8 features from each training set. The most informative FC was between right amygdala and precuneus, which increased during subliminal fear conditions, while left and right amygdala FC decreased, suggesting a bilateral decoupling of this key limbic region during processing of subliminal fear-related stimuli. Other informative FC included angular gyrus, middle temporal gyrus and cerebellum. These findings identify FC that decodes subliminally perceived, task-irrelevant affective stimuli, and suggest that cortical structures are actively engaged by and appear to be essential for subliminal fear processing.
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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) | 0.000 | 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".