“Blindsight” and subjective awareness of fearful faces: Inversion reverses the deficits in fear perception associated with core psychopathic traits
Bibliographic record
Abstract
Though emotional faces preferentially reach awareness, the present study utilised both objective and subjective indices of awareness to determine whether they enhance subjective awareness and "blindsight". Under continuous flash suppression, participants localised a disgusted, fearful or neutral face (objective index), and rated their confidence (subjective index). Psychopathic traits were also measured to investigate their influence on emotion perception. As predicted, fear increased localisation accuracy, subjective awareness and "blindsight" of upright faces. Coldhearted traits were inversely related to subjective awareness, but not "blindsight", of upright fearful faces. In a follow-up experiment using inverted faces, increased localisation accuracy and awareness, but not "blindsight", were observed for fear. Surprisingly, awareness of inverted fearful faces was positively correlated with coldheartedness. These results suggest that emotion enhances both pre-conscious processing and the qualitative experience of awareness, but that pre-conscious and conscious processing of emotional faces rely on at least partially dissociable cognitive mechanisms.
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 | 0.001 |
| 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) | 0.002 | 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".