Many morphs: parsing gesture signals from the noise

Abstract

Parsing signals from noise is a general problem for signallers and recipients, as well as for researchers studying communicative systems. Substantial research efforts have been invested in comparing how other species encode information and meaning in their signals, and how signalling is structured. However, our ability to do so depends on identifying and discriminating signals that represent meaningful units of analysis. Early approaches to defining signal repertoires applied top- down approaches, classifying cases into predefined signal types. Recently, more labour-intensive methods have taken a bottom-up approach describing the features of each signal in detail and clustering cases into types based on patterns of similarity between them in multi-dimensional feature-space that were previously undetectable. Nevertheless, it remains essential to assess whether the resulting repertoires are composed of relevant units from the perspective of the species using them, and redefining repertoires when additional data makes more detailed analyses feasible. In this paper we provide a framework that takes data from the largest set of wild chimpanzee (Pan troglodytes) gestures currently available, splitting gesture types at a fine scale based on modifying features of gesture expression and then determining whether this splitting process increases the information content of the communication system. Our method allows different features of interest to be incorporated into the splitting process, providing substantial future flexibility across - for example - species, populations, and levels of signal granularity. In doing so we provide a powerful tool allowing researchers interested in gestural communication to establish repertoires of relevant units for subsequent analyses within and between systems of communication.