Authors
Mandyam Dhati, A., Wewhare, N., Devasthale, N. S., Sundar, S., Parulekar, N., Krishnan, A.
Abstract
Sequencing and syntax in complex animal vocal signals have been studied using several mathematical analyses. Commonly, sequences of vocalizations are assumed to follow a first-order Markov process, where each state depends on the state immediately before it. However, more recent computational analyses challenge this assumption, suggesting alternative processes may be important in vocal sequences. Open-ended vocal learners such as parrots possess complex, variable vocal sequences that may be dynamically modified throughout their lives and contain information about group membership and identity. Although this makes them important systems in which to identify general patterns within vocal sequences, parrot vocalizations remain generally understudied compared to passerine birds. Here, we examined vocal sequence structure in six species of parrots (Psittaculidae). We fit various metrics of sequence structure to those generated by multiple simulated processes, including Markov, random, hidden Markov and renewal processes. Two metrics exhibited the best fit to a renewal process (where a note repeats a certain number of times before transitioning to a new note), and two others to Markov processes. Importantly, all analyses were broadly concordant across species, and multiple metrics indicated an elevated probability of note repetition. Reconciling these results, we suggest a general vocal mechanism across the highly variable vocal sequences of parrots, where both note repetition and Markov processes are important. Note repetition patterns could help communicate individual or group identity, as well as social and behavioral context. Our study thus extends a simulation-based approach using diverse metrics to comparatively examine the complex, variable vocal sequences of these open-ended vocal learners.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 24 Oct 2025.
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