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dc.contributor.authorMielke, Alexander
dc.contributor.authorBadihi, Gal
dc.contributor.authorGraham, Kirsty E.
dc.contributor.authorGrund, Charlotte
dc.contributor.authorHashimoto, Chie
dc.contributor.authorPiel, Alex K.
dc.contributor.authorSafryghin, Alexandra
dc.contributor.authorSlocombe, Katie E.
dc.contributor.authorStewart, Fiona
dc.contributor.authorWilke, Claudia
dc.contributor.authorZuberbühler, Klaus
dc.contributor.authorHobaiter, Catherine
dc.date.accessioned2024-03-05T15:30:06Z
dc.date.available2024-03-05T15:30:06Z
dc.date.issued2024-03-04
dc.identifier299990316
dc.identifierb4e7ce2f-f31b-4872-b5cc-b3226ae41ab0
dc.identifier85186539224
dc.identifier.citationMielke , A , Badihi , G , Graham , K E , Grund , C , Hashimoto , C , Piel , A K , Safryghin , A , Slocombe , K E , Stewart , F , Wilke , C , Zuberbühler , K & Hobaiter , C 2024 , ' Many morphs : parsing gesture signals from the noise ' , Behavior Research Methods , vol. First Online . https://doi.org/10.3758/s13428-024-02368-6en
dc.identifier.issn1554-3528
dc.identifier.otherRIS: urn:8E1F2AC2DEC7B592F06EE79E6B47A259
dc.identifier.otherRIS: Mielke2024
dc.identifier.otherORCID: /0000-0002-7422-7676/work/155068979
dc.identifier.otherORCID: /0000-0001-8378-088X/work/155069020
dc.identifier.otherORCID: /0000-0002-3893-0524/work/155069078
dc.identifier.urihttps://hdl.handle.net/10023/29433
dc.descriptionAM 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.en
dc.description.abstractParsing 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.
dc.format.extent18
dc.format.extent883140
dc.language.isoeng
dc.relation.ispartofBehavior Research Methodsen
dc.subjectChimpanzeesen
dc.subjectGestureen
dc.subjectRepertoireen
dc.subjectLatent class analysisen
dc.subjectMorphen
dc.subjectBF Psychologyen
dc.subjectDASen
dc.subject.lccBFen
dc.titleMany morphs : parsing gesture signals from the noiseen
dc.typeJournal articleen
dc.contributor.sponsorThe Leverhulme Trusten
dc.contributor.sponsorEuropean Research Councilen
dc.contributor.institutionUniversity of St Andrews. School of Psychology and Neuroscienceen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. Centre for Social Learning & Cognitive Evolutionen
dc.identifier.doi10.3758/s13428-024-02368-6
dc.description.statusPeer revieweden
dc.identifier.grantnumberECF-2021-642en
dc.identifier.grantnumber802719en


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