Phonological perception by birds: budgerigars can perceive lexical stress
Bibliographic record
Abstract
Metrical phonology is the perceptual "strength" in language of some syllables relative to others. The ability to perceive lexical stress is important, as it can help a listener segment speech and distinguish the meaning of words and sentences. Despite this importance, there has been little comparative work on the perception of lexical stress across species. We used a go/no-go operant paradigm to train human participants and budgerigars (Melopsittacus undulatus) to distinguish trochaic (stress-initial) from iambic (stress-final) two-syllable nonsense words. Once participants learned the task, we presented both novel nonsense words, and familiar nonsense words that had certain cues removed (e.g., pitch, duration, loudness, or vowel quality) to determine which cues were most important in stress perception. Members of both species learned the task and were then able to generalize to novel exemplars, showing categorical learning rather than rote memorization. Tests using reduced stimuli showed that humans could identify stress patterns with amplitude and pitch alone, but not with only duration or vowel quality. Budgerigars required more than one cue to be present and had trouble if vowel quality or amplitude were missing as cues. The results suggest that stress patterns in human speech can be decoded by other species. Further comparative stress-perception research with more species could help to determine what species characteristics predict this ability. In addition, tests with a variety of stimuli could help to determine how much this ability depends on general pattern learning processes versus vocalization-specific cues.
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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) | 0.000 | 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 teacher head, 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".