Mielke, Alexander;
Badihi, Gal;
Graham, Kirsty E;
Grund, Charlotte;
Hashimoto, Chie;
Piel, Alex K;
Safryghin, Alexandra;
... Hobaiter, Catherine; + view all
(2024)
Many morphs: Parsing gesture signals from the noise.
Behavior Research Methods
10.3758/s13428-024-02368-6.
(In press).
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Many morphs: Parsing gesture signals from the noise |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3758/s13428-024-02368-6 |
| Publisher version: | http://dx.doi.org/10.3758/s13428-024-02368-6 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Chimpanzees, Gesture, Latent class analysis, Morph, Repertoire |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Anthropology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10188883 |
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