Many morphs: Parsing gesture signals from the noise

TY - JOUR

T1 - Many morphs

T2 - Parsing gesture signals from the noise

AU - Mielke, Alexander

AU - Badihi, Gal

AU - Graham, Kirsty E.

AU - Grund, Charlotte

AU - Hashimoto, Chie

AU - Piel, Alex K.

AU - Safryghin, Alexandra

AU - Slocombe, Katie E.

AU - Stewart, Fiona

AU - Wilke, Claudia

AU - Zuberbühler, Klaus

AU - Hobaiter, Catherine

N1 - Funding Information: AM was funded by a Leverhulme Early Career Fellowship. CH, GB, KEG, CG, and AS were supported by funding from the European Research Council under Gestural Origins Grant No: 802719. KS and CW were supported by funding from the European Research Council under Grant No: ERC_CoG 2016_724608. We thank all the staff of the Budongo Conservation Field Station, its founder Vernon Reynolds, and the Royal Zoological Society of Scotland who provide core funding. We thank the directors of the Kibale Chimpanzee Project for permission to use video data archives. We thank the Uganda Wildlife Authority, the National Forestry Authority, the President's Office, and the Uganda National Council for Science and Technology for providing research permits and permissions to conduct research in Budongo, Kalinzu, and Kanyawara. The Issa project (GMERC) is grateful for long-term support provided from the UCSD/Salk Center for Academic Research and Training in Anthropogeny (CARTA). We thank the Tanzanian Wildlife Research Institute (TAWIRI), Commission for Science and Technology (COSTECH), and Tanganyika District for permission to conduct research in the Issa Valley. Publisher Copyright: © The Author(s) 2024.

PY - 2024/3/4

Y1 - 2024/3/4

N2 - Parsing signals from noise is a general problem for signallers and recipients, and for researchers studying communicative systems. Substantial efforts have been invested in comparing how other species encode information and meaning, and how signalling is structured. However, research 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 detailed features of each signal and clustering cases based on patterns of similarity 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 become available. 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 using latent class analysis (a model-based cluster detection algorithm for categorical variables), and then determining whether this splitting process reduces uncertainty about the goal or community of the gesture. 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. 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.

AB - Parsing signals from noise is a general problem for signallers and recipients, and for researchers studying communicative systems. Substantial efforts have been invested in comparing how other species encode information and meaning, and how signalling is structured. However, research 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 detailed features of each signal and clustering cases based on patterns of similarity 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 become available. 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 using latent class analysis (a model-based cluster detection algorithm for categorical variables), and then determining whether this splitting process reduces uncertainty about the goal or community of the gesture. 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. 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.

KW - Chimpanzees

KW - Gesture

KW - Latent class analysis

KW - Morph

KW - Repertoire

UR - http://www.scopus.com/inward/record.url?scp=85186539224&partnerID=8YFLogxK

U2 - 10.3758/s13428-024-02368-6

DO - 10.3758/s13428-024-02368-6

M3 - Article

AN - SCOPUS:85186539224

SN - 1554-351X

JO - Behavior research methods

JF - Behavior research methods

ER -