Are emotional stimuli prioritised in visual awareness?
Bibliographic record
Abstract
Although various features of visual stimuli have been shown to affect access to awareness, it is still unclear whether and to what extent emotional stimuli have privileged access to visual awareness. We conducted three online experiments, using a breaking Repeated Masked Suppression (bRMS) paradigm, to assess whether positively and negatively valenced/high-arousal photographs would emerge from masking (break out of forward/backward masking by image-unique masks) faster than would neutral/low-arousal photographs. Experiment 1 found that positively-valenced high-arousal pictures were faster to break out of repeated masked suppression. Experiment 2 showed that this was not because such pictures were substantially more memorable. Experiment 3 used a verbal rather than pictorial method to check for accuracy of breakout, and found similar results to Experiment 1. We also found that various pictorial qualities had either no effect, or only a minor effect, on our results. Importantly, images of people and animals consistently showed the shortest breakout times, indicating that such stimuli might be especially available to conscious access. However, a difference in the distribution of images of people (fastest) and scenes (slowest) images across the valence categories may have also contributed to the overall shorter breakout times of positively-valenced images.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".