You see first what you like most: Visually prioritizing positive over negative semantic stimuli
Bibliographic record
Abstract
In our complex world, we often encounter situations with multiple objects almost simultaneously entering our visual fields. Identifying the temporal order of these stimuli is thus crucial for scene and event segmentation, and guiding task prioritization. Attending to a stimulus has been found to make it perceived earlier than others, with various attention-modulating factors contributing to this advantage (e.g., reward, ownership, perceived spatial depth). However, the impact of affective valences (positivity or negativity), a significant subjective factor influencing attention selection and processing speed, on temporal order perception remains unexplored. To investigate this issue, we used a cueless Temporal Order Judgement (TOJ) task in three experiments. Observers always saw two Chinese characters on the left and right sides of a central fixation, and there was a variable onset delay between the two characters, ranging between 0 ms and 100 ms (in 20 ms intervals). The observers were instructed to indicate which of the two stimuli appeared first. Different pairs of stimuli valences were used in each experiment: positive and negative (Experiment 1), positive and neutral (Experiment 2), and negative and neutral (Experiment 3). The results of the first and second experiments indicated that people reliably perceived positive stimuli earlier than negative stimuli but not neutral stimuli; the third experiment showed that neutral stimuli were perceived earlier when presented with another negative one. Our findings revealed a general temporal prioritization towards semantically positive stimuli modulated by the strength of affective contrasts.
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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 | 0.002 |
| 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".