You see first what you like most: Visually prioritizing positive over negative semantic stimuli
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it