Examining visual prior entry of semantic affective valences: positive is biased over negative
Bibliographic record
Abstract
Affective valences of stimuli (e.g. positive or negative) influence perceptual prioritisation, with emotionally charged stimuli often attended to over neutral ones. However, there are two critical issues in interpreting the previous findings on affective biases: (1) reliance on Reaction Time (RT) measures, which have limitations in capturing subtle cognitive biases at different phases of visual processing, and (2) potential confounds from low-level visual features that can carry affective valence. To address these issues, we used a Temporal Order Judgement (TOJ) paradigm with semantic stimuli and Chinese characters due to their minimised perceptual variations. In this task, two stimuli were presented nearly simultaneously, and participants indicated which appeared first. If an engagement bias is present, participants would consistently perceive the biased stimulus as first appearing even when it was veridically the second (Prior Entry Effect). We specifically examined the direct competition between positive and negative valences at the semantic level. Our results revealed a consistent positive bias over negative stimuli, although its magnitude was smaller than biases observed with facial stimuli in prior research. These findings suggest that affective biases might occur at both semantic and visual levels, offering a more nuanced understanding of emotional attention.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".