Forebrain circuits underlying the social modulation of vocal communication signals
Bibliographic record
Abstract
Across vertebrate species, signalers alter the structure of their communication signals based on the social context. For example, male Bengalese finches produce faster and more stereotyped songs when directing song to females (female-directed [FD] song) than when singing in isolation (undirected [UD] song), and such changes have been found to increase the attractiveness of a male's song. Despite the importance of such social influences, little is known about the mechanisms underlying the social modulation of communication signals. To this end, we analyzed differences in immediate early gene (EGR-1) expression when Bengalese finches produced FD or UD song. Relative to silent birds, EGR-1 expression was elevated in birds producing either FD or UD song throughout vocal control circuitry, including the interface nucleus of the nidopallium (NIf), HVC, the robust nucleus of the arcopallium (RA), Area X, and the lateral magnocellular nucleus of the anterior nidopallium (LMAN). Moreover, EGR-1 expression was higher in HVC, RA, Area X, and LMAN in males producing UD song than in males producing FD song, indicating that social context modulated EGR-1 expression in these areas. However, EGR-1 expression was not significantly different between males producing FD or UD song in NIf, the primary vocal motor input into HVC, suggesting that context-dependent changes could arise de novo in HVC. The pattern of context-dependent differences in EGR-1 expression in the Bengalese finch was highly similar to that in the zebra finch and suggests that social context affects song structure by modulating activity throughout vocal control nuclei.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".