Surprising Threats Accelerate Conscious Perception
Bibliographic record
Abstract
The folk psychological notion that "we see what we expect to see" is supported by evidence that we become consciously aware of visual stimuli that match our prior expectations more quickly than stimuli that violate our expectations. Similarly, "we see what we want to see," such that more biologically-relevant stimuli are also prioritised for conscious perception. How, then, is perception shaped by biologically-relevant stimuli that we did not expect? Here, we conducted two experiments using breaking continuous flash suppression (bCFS) to investigate how prior expectations modulated response times to neutral and fearful faces. In both experiments, we found that prior expectations for neutral faces hastened responses, whereas the opposite was true for fearful faces. This interaction between emotional expression and prior expectations was driven predominantly by participants with higher trait anxiety. Electroencephalography (EEG) data collected in Experiment 2 revealed an interaction evident in the earliest stages of sensory encoding, suggesting prediction errors expedite sensory encoding of fearful faces. These findings support a survival hypothesis, where biologically-relevant fearful stimuli are prioritised for conscious access even more so when unexpected, especially for people with high trait anxiety.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".