Bibliographic record
Abstract
The current study explored the role of valence and self-relevance in size estimation of neutral and aversive animals. In Experiment 1, participants who were highly fearful of spiders and participants with low fear of spiders rated the size and unpleasantness of spiders and other neutral animals (birds and butterflies). We found that although individuals with both high and low fear of spiders rated spiders as highly unpleasant, only the highly fearful participants rated spiders as larger than butterflies. Experiment 2 included additional pictures of wasps (not self-relevant, but unpleasant) and beetles. The results of this experiment replicated those of Experiment 1 and showed a similar bias in size estimation for beetles, but not for wasps. Mediation analysis revealed that in the high-fear group both relevance and valence influenced perceived size, whereas in the low-fear group only valence affected perceived size. These findings suggest that the effect of highly relevant stimuli on size perception is both direct and mediated by valence.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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; both teacher heads agree on what is shown here.
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".