Catecholaminergic contributions to vocal communication signals
Bibliographic record
Abstract
Social context affects behavioral displays across a variety of species. For example, social context acutely influences the acoustic and temporal structure of vocal communication signals such as speech and birdsong. Despite the prevalence and importance of such social influences, little is known about the neural mechanisms underlying the social modulation of communication. Catecholamines are implicated in the regulation of social behavior and motor control, but the degree to which catecholamines influence vocal communication signals remains largely unknown. Using a songbird, the Bengalese finch, we examined the extent to which the social context in which song is produced affected immediate early gene expression (EGR-1) in catecholamine-synthesising neurons in the midbrain. Further, we assessed the degree to which administration of amphetamine, which increases catecholamine concentrations in the brain, mimicked the effect of social context on vocal signals. We found that significantly more catecholaminergic neurons in the ventral tegmental area and substantia nigra (but not the central grey, locus coeruleus or subcoeruleus) expressed EGR-1 in birds that were exposed to females and produced courtship song than in birds that produced non-courtship song in isolation. Furthermore, we found that amphetamine administration mimicked the effects of social context and caused many aspects of non-courtship song to resemble courtship song. Specifically, amphetamine increased the stereotypy of syllable structure and sequencing, the repetition of vocal elements and the degree of sequence completions. Taken together, these data highlight the conserved role of catecholamines in vocal communication across species, including songbirds and 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 | 0.000 |
| 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".