Temporal variability enhances the acquisition of stereotyped communication signals
Bibliographic record
Abstract
Species-typical behaviors are organized into species-typical patterns, and deviations away from these patterns often diminish the strength of behavioral and sensory responses to such stimuli. In songbirds like the zebra finch, species-typical songs consist of acoustic elements (syllables) arranged into stereotyped (i.e., highly predictable) sequences with stereotyped timing. However, the degree to which deviations away from these stereotyped temporal patterns modulate the strength of vocal learning (i.e., the fidelity of vocal imitation) remains unknown. Here we tutored 123 juvenile zebra finches with stimuli that varied in the stereotypy of syllable sequencing and timing. In contrast to the prediction that deviations away from species-typical stereotypy would diminish vocal learning, deviations from sequence or timing stereotypy did not decrease how well juveniles imitated the acoustic structure of syllables. Moreover, presenting syllables in species-atypical sequences (i.e., randomized syllable sequences) enhanced vocal imitation in birds that were tutored later in development. This unexpected enhancement of birdsong learning by sequence variability resembles the effects of contextual diversity on speech acquisition and indicates that such variability can benefit learning even for very stereotyped behaviors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".