WhaleLM: Finding Structure and Information in Sperm Whale Vocalizations and Behavior with Machine Learning

Abstract

Language models (LMs), which are neural sequence predictors trained to model distributions over natural language texts, have come to play a central role in human language technologies like machine translation and information retrieval. They have also contributed to the scientific study of human language itself, enabling progress on long-standing questions about the learnability, optimality, and universality of key features of human languages. Many analogous questions exist in the study of communication between non-human animals---for which, in many cases, we have only a preliminary understanding of signals' structure and use. Can neural sequence models help us understand these animal communication systems as well? We use these models to characterize the structure and information content of sperm whale vocalizations. Sperm whales (Physeter macrocephalus) engage in complex, coordinated behaviours like foraging and navigation in the darkness of the ocean while exchanging sequences of rhythmic clicks known as codas. However, little is known about whether there are any systematic patterns governing coda production, or how codas influence group decision-making and behaviour. To begin to answer these questions, we first train a neural sequence model (a `sperm whale language model') to predict whales' future vocalizations from their conversational history. By systematically manipulating the information available to this model, and measuring the change in predictive accuracy, we show that sperm whale vocalizations exhibit order dependence, long-range dependencies on up to the past eight codas in an exchange, and predictable turn-taking. Second, we train the sequence model to predict whales' behaviour from their vocal exchanges, and find that both current behavioural context and future actions are predictable, with accuracies of 72% and 86% respectively, from coda sequences. Our study provides the first evidence that sperm whale vocalizations contain information that could be used to coordinate behaviour. More generally, it offers a framework for using modern machine learning tools for hypothesis generation and to assist in investigating the structure and function of unknown communication systems.