Supplementary material from "Measuring rhythms of vocal interactions: a proof of principle in harbour seal pups"
Bibliographic record
Abstract
Rhythmic patterns in interactive contexts characterize human behaviours such as conversational turn-taking. These timed patterns are also present in other animals, and often described as rhythm. Understanding fine-grained temporal adjustments in interaction requires complementary quantitative methodologies. Here, we showcase how vocal interactive rhythmicity in a non-human animal can be quantified using a multi-method approach. We record vocal interactions in harbour seal pups (Phoca vitulina) under controlled conditions. We analyse these data by combining analytical approaches, namely categorical rhythm analysis, circular statistics and time series analyses. We test whether pups' vocal rhythmicity varies across behavioural contexts depending on the absence or presence of a calling partner. Four research questions illustrate which analytical approaches are complementary versus orthogonal. For our data, circular statistics and categorical rhythms suggest that a calling partner affects a pup's call timing. Granger causality suggests that pups predictively adjust their call timing when interacting with a real partner. Lastly, the ADaptation and Anticipation Model estimates statistical parameters for a potential mechanism of temporal adaptation and anticipation. Our analytical complementary approach constitutes a proof of concept; it shows feasibility in applying typically unrelated techniques to seals to quantify vocal rhythmic interactivity across behavioural contexts.This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.370 | 0.088 |
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".