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A machine learning approach to infant distress calls and maternal behaviour of wild chimpanzees

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Distress calls are an acoustically variable group of vocalizations ubiquitous in mammals and other animals. Their presumed function is to recruit help, but there has been much debate on whether the nature of the disturbance can be inferred from the acoustics of distress calls. We used machine learning to analyse episodes of distress calls of wild infant chimpanzees. We extracted exemplars from those distress call episodes and examined them in relation to the external event triggering them and the distance to the mother. In further steps, we tested whether the acoustic variants were associated with particular maternal responses. Our results suggest that, although infant chimpanzee distress calls are highly graded, they can convey information about discrete problems experienced by the infant and about distance to the mother, which in turn may help guide maternal parenting decisions. The extent to which mothers rely on acoustic cues alone (versus integrate other contextual-visual information) to decide upon intervening should be the focus of future research.
Original languageEnglish
JournalAnimal Cognition
Early online date22 Oct 2020
DOIs
Publication statusEarly online - 22 Oct 2020

Documents

  • A machine learning approach

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Animal Cognition. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10071-020-01437-5.

    Accepted author manuscript (Post-print), 470 KB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 22/10/21

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