Songbirds as Objective Listeners: Zebra Finches (Taeniopygia guttata) Can Discriminate Infant-directed Song and Speech in Two Languages
Bibliographic record
Abstract
Despite their acoustic similarities, human infants are able to discriminate between infant-directed song (as produced by human adults) and infant-directed speech in both English and Russian. However, experimenters are somewhat limited in what they can test using the preference paradigm with infants. As a complement to a previous infant study (Tsang et al. 2016), we asked whether a songbird, the zebra finch, could discriminate infant directed song and speech in English and Russian, and tested responses to stimuli that humans could not categorize as either type. Male and female zebra finches learned to discriminate the stimuli in both languages equally well, although females were slightly faster at learning the discrimination, and generalized responses to untrained stimuli of the same categories. Bird responses to stimuli that humans could not categorize likewise did not follow a clear pattern. Our results show that infant-directed song and speech are discriminable as categories by non-humans, that song and speech are as easy to discriminate in English and Russian, and that comparative studies together can provide more complete answers to research questions about auditory perception and acoustic features used for discrimination than using one species or one language alone.
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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 | 0.000 |
| 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 | 0.001 |
| 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".