SURPRISING THREATS ACCELERATE EVIDENCE ACCUMULATION FOR CONSCIOUS PERCEPTION
Bibliographic record
Abstract
ABSTRACT Our survival depends on how well we can rapidly detect threats in our environment. To facilitate this, the brain is faster to bring threatening or rewarding visual stimuli into conscious awareness than neutral stimuli. Unexpected events may indicate a potential threat, and yet we tend to respond slower to unexpected than expected stimuli. It is unclear if or how these effects of emotion and expectation interact with one’s conscious experience. To investigate this, we presented neutral and fearful faces with different probabilities of occurance in a breaking continuous flash suppression (bCFS) paradigm. Across two experiments, we discovered that fulfilled prior expectations hastened responses to neutral faces but had either no significant effect (Experiment 1) or the opposite effect (Experiment 2) on fearful faces. Drift diffusion modelling revealed that, while prior expectations accelerated stimulus encoding time (associated with the visual cortex), evidence was accumulated at an especially rapid rate for unexpected fearful faces (associated with activity in the right inferior frontal gyrus). Hence, these findings demonstrate a novel interaction between emotion and expectation during bCFS, driven by a unique influence of surprising fearful stimuli that expedites evidence accumulation in a fronto-occipital network.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".