Assessing the potential information content of multicomponent visual signals: A machine learning approach

William L. Allen, James P. Higham

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Careful investigation of the form of animal signals can offer novel insights into their function. Here, we deconstruct the face patterns of a tribe of primates, the guenons (Cercopithecini), and examine the information that is potentially available in the perceptual dimensions of their multicomponent displays. Using standardized colour-calibrated images of guenon faces, we measure variation in appearance both within and between species. Overall face pattern was quantified using the computer vision ‘eigenface’ technique, and eyebrow and nose-spot focal traits were described using computational image segmentation and shape analysis. Discriminant function analyses established whether these perceptual dimensions could be used to reliably classify species identity, individual identity, age and sex, and, if so, identify the dimensions that carry this information. Across the 12 species studied, we found that both overall face pattern and focal trait differences could be used to categorize species and individuals reliably, whereas correct classification of age category and sex was not possible. This pattern makes sense, as guenons often form mixed-species groups in which familiar conspecifics develop complex differentiated social relationships but where the presence of heterospecifics creates hybridization risk. Our approach should be broadly applicable to the investigation of visual signal function across the animal kingdom.

    Original languageEnglish (US)
    Article number20142284
    JournalProceedings of the Royal Society B: Biological Sciences
    Volume282
    Issue number1802
    DOIs
    StatePublished - Feb 4 2015

    Keywords

    • Face recognition
    • Guenon
    • Individual recognition
    • Multicomponent signals
    • Species recognition
    • Visual signalling

    ASJC Scopus subject areas

    • Biochemistry, Genetics and Molecular Biology(all)
    • Immunology and Microbiology(all)
    • Environmental Science(all)
    • Agricultural and Biological Sciences(all)

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