Courtship song preferences in female zebra finches are shaped by developmental auditory experience
Bibliographic record
Abstract
The performance of courtship signals provides information about the behavioural state and quality of the signaller, and females can use such information for social decision-making (e.g. mate choice). However, relatively little is known about the degree to which the perception of and preference for differences in motor performance are shaped by developmental experiences. Furthermore, the neural substrates that development could act upon to influence the processing of performance features remains largely unknown. In songbirds, females use song to identify males and select mates. Moreover, female songbirds are often sensitive to variation in male song performance. Consequently, we investigated how developmental exposure to adult male song affected behavioural and neural responses to song in a small, gregarious songbird, the zebra finch. Zebra finch males modulate their song performance when courting females, and previous work has shown that females prefer the high-performance, female-directed courtship song. However, unlike females allowed to hear and interact with an adult male during development, females reared without developmental song exposure did not demonstrate behavioural preferences for high-performance courtship songs. Additionally, auditory responses to courtship and non-courtship song were altered in adult females raised without developmental song exposure. These data highlight the critical role of developmental auditory experience in shaping the perception and processing of song performance.
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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 | 0.000 |
| 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".