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Record W4409149679 · doi:10.1080/02699931.2025.2483288

Are emotional stimuli prioritised in visual awareness?

2025· article· en· W4409149679 on OpenAlexafffund
Mike Doswell, AS Abe, Christy Oi Ting Kwok, Lawrence M. Ward

Bibliographic record

VenueCognition & Emotion · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.184
GPT teacher head0.428
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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