Positive and negative contexts predict duration of pig vocalisations
Bibliographic record
Abstract
Emotions are mental states occurring in response to external and internal stimuli and thus form an integral part of an animal's behaviour. Emotions can be mapped in two dimensions based on their arousal and valence. Whilst good indicators of arousal exist, clear indicators of emotional valence, particularly positive valence, are still rare. However, positively valenced emotions may play a crucial role in social interactions in many species and thus, an understanding of how emotional valence is expressed is needed. Vocalisations are a potential indicator of emotional valence as they can reflect the internal state of the caller. We experimentally manipulated valence, using positive and negative cognitive bias trials, to quantify changes in pig vocalisations. We found that grunts were shorter in positive trials than in negative trials. Interestingly, we did not find differences in the other measured acoustic parameters between the positive and negative contexts as reported in previous studies. These differences in results suggest that acoustic parameters may differ in their sensitivity as indicators of emotial valence. However, it is important to understand how similar contexts are, in terms of their valence, to be able to fully understand how and when acoustic parameters reflect emotional states.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".