Learning to pause: Fidelity of and biases in the developmental acquisition of gaps in the communicative signals of a songbird
Bibliographic record
Abstract
The temporal organization of sounds used in social contexts can provide information about signal function and evoke varying responses in listeners (receivers). For example, music is a universal and learned human behavior that is characterized by different rhythms and tempos that can evoke disparate responses in listeners. Similarly, birdsong is a social behavior in songbirds that is learned during critical periods in development and used to evoke physiological and behavioral responses in receivers. Recent investigations have begun to reveal the breadth of universal patterns in birdsong and their similarities to common patterns in speech and music, but relatively little is known about the degree to which biological predispositions and developmental experiences interact to shape the temporal patterning of birdsong. Here, we investigated how biological predispositions modulate the acquisition and production of an important temporal feature of birdsong, namely the duration of silent pauses ("gaps") between vocal elements ("syllables"). Through analyses of semi-naturally raised and experimentally tutored zebra finches, we observed that juvenile zebra finches imitate the durations of the silent gaps in their tutor's song. Further, when juveniles were experimentally tutored with stimuli containing a wide range of gap durations, we observed biases in the prevalence and stereotypy of gap durations. Together, these studies demonstrate how biological predispositions and developmental experiences differently affect distinct temporal features of birdsong and highlight similarities in developmental plasticity across birdsong, speech, and music. RESEARCH HIGHLIGHTS: The temporal organization of learned acoustic patterns can be similar across human cultures and across species, suggesting biological predispositions in acquisition. We studied how biological predispositions and developmental experiences affect an important temporal feature of birdsong, namely the duration of silent intervals between vocal elements ("gaps"). Semi-naturally and experimentally tutored zebra finches imitated the durations of gaps in their tutor's song and displayed some biases in the learning and production of gap durations and in gap variability. These findings in the zebra finch provide parallels with the acquisition of temporal features of speech and music in humans.
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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".