Associative Syntax and Maximal Repetitions reveal context-dependent complexity in fruit bat communication

TL;DR

This paper introduces an unsupervised method to analyze fruit-bat vocalizations, revealing context-dependent associative syntax and complex repetitive structures that vary with behavioral scenarios, especially during conflicts.

Contribution

It presents a novel unsupervised approach combining manifold learning and syntax analysis to uncover complexity in fruit-bat communication patterns.

Findings

Evidence for associative syntax rather than combinatorial syntax.

Syllable use varies significantly with behavioral context.

Higher complexity and longer repetitions in conflict scenarios.

Abstract

This study presents an unsupervised method to infer discreteness, syntax and temporal structures of fruit-bats vocalizations, as a case study of graded vocal systems, and evaluates the complexity of communication patterns in relation with behavioral context. The method improved the baseline for unsupervised labeling of vocal units (i.e. syllables) through manifold learning, by investigating how dimensionality reduction on mel-spectrograms affects labeling, and comparing it with unsupervised labels based on acoustic similarity. We then encoded vocalizations as syllabic sequences to analyze the type of syntax, and extracted the Maximal Repetitions (MRs) to evaluate syntactical structures. We found evidence for: i) associative syntax, rather than combinatorial (context classification is unaffected by permutation of sequences, F 1 > 0.9); ii) context-dependent use of syllables (Wilcoxon rank-sum tests, p-value < 0.05); iii) heavy-tail distribution of MRs (truncated power-law, exponent {\alpha} < 2), indicative of mechanism encoding combinatorial complexity. Analysis of MRs and syllabic transition networks revealed that mother-pupil interactions were characterized by repetitions, while communication in conflict-contexts exhibited higher complexity (longer MRs and more interconnected vocal sequences) than non-agonistic contexts. We propose that communicative complexity is higher in scenarios of disagreement, reflecting lower compressibility of information.