Sensory-motor tuning allows generic features of conspecific acoustic scenes to guide rapid, adaptive, call-timing responses in túngara frogs
Bibliographic record
Abstract
Male frogs court females from within crowded choruses, selecting for mechanisms allowing them to call at favourable times relative to the calls of rivals and background chorus noise. To accomplish this, males must continuously evaluate the fluctuating acoustic scene generated by their competitors for opportune times to call. Túngara frogs produce highly frequency- and amplitude-modulated calls from within dense choruses. We used similarly frequency- and amplitude-modulated playback tones to investigate the sensory basis of their call-timing decisions. Results revealed that different frequencies present throughout this species' call differed in their degree of call inhibition, and that lower-amplitude tones were less inhibitory. Call-timing decisions were then driven by fluctuations in inhibition arising from underlying frequency- and amplitude-modulation patterns, with tone transitions that produced steeper decreases in inhibition having higher probabilities of triggering calls. Interactions between the varied behavioural sensitivities to different conspecific call frequencies revealed here, and the stereotyped amplitude- and frequency-modulation patterns present in this species' calls, can explain previously surprising patterns observed in túngara frog choruses. This highlights the importance of understanding the specific sensory drivers underpinning conspecific signalling interactions, and reveals how sensory systems can mediate the interplay between signal perception and production to facilitate adaptive communication strategies.
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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) | 0.002 | 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".