A Functionalist Framework for Machine Learning in Animal Communication

Jack Terwilliger, Stephan Paul Kaufhold, Federico Rossano

Keywords: animal communication, multimodal representation, dataset design, intentional communication, ethology, turn-taking

TL;DR: We propose a functionalist, context-sensitive approach for animal communication ML models using multimodal, longitudinal data and behavior-prediction evaluation, to model what signals do rather than what information they transmit.

Abstract:

Recent breakthroughs in natural language processing inspire optimism that similar
methods could decode animal communication systems. But machine learning approaches import assumptions from human language, which could undermine these
efforts. In this proposal, we argue that non-human animal communication systems
do not have self-contained distributional semantics, are largely non-referential,
and function primarily to manipulate the behavior of others rather than exchange
information. Not only do these assumptions constrain our ability to investigate
signal semantics, but also risk confounding discoveries of signal syntax. To hedge
against this possibility, we propose that machine learning efforts should adopt
a functionalist framework. This foregrounds ecological and social contexts and
the interactional contingencies that give signals their meaning. Our framework
provides recommendations about how to account for these variables when building
datasets.

Submission Number: 30