Hear them roar: A comparison of black-capped chickadee (Poecile atricapillus) and human (Homo sapiens) perception of arousal in vocalizations across all classes of terrestrial vertebrates.
Bibliographic record
Abstract
Recently, evidence for acoustic universals in vocal communication was found by demonstrating that humans can identify levels of arousal in vocalizations produced by species across three biological classes (Filippi et al., 2017). Here, we extend this work by testing whether two vocal learning species, humans and chickadees, can discriminate vocalizations of high and low arousal using operant discrimination go/no-go tasks. Stimuli included vocalizations from nine species: giant panda, American alligator, common raven, hourglass treefrog, African elephant, Barbary macaque, domestic pig, black-capped chickadee, and human. Subjects were trained to respond to high or low arousal vocalizations, then tested with additional high and low arousal vocalizations produced by each species. Chickadees (Experiment 1) and humans (Experiment 2) learned to discriminate between high and low arousal stimuli and significantly transferred the discrimination to additional panda, human, and chickadee vocalizations. Finally, we conducted discriminant function analyses using four acoustic measures, finding evidence suggesting that fundamental frequency played a role in responding during the task. However, these analyses also suggest roles for other acoustic factors as well as familiarity. In sum, the results from these studies provide evidence that chickadees and humans are capable of perceiving arousal in vocalizations produced by multiple species. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.001 |
| 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) | 0.001 | 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 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".