One model of signal evolution is based on the notion that behaviours become increasingly detached from their original biological functions to obtain a communicative value. Selection may not always favour the evolution of such transitions, for instance, if signalling is costly due to predators usurping signal production. Here, we collected inertial movement sensing data recorded from multiple locations in free-ranging horses (Equus caballus), which we subjected to a machine learning algorithm to extract kinematic gestalt profiles. This yielded surprisingly rich and multi-layered sets of information. In particular, we were able to discriminate identity, breed, sex and some personality traits from the overall movement patterns of freely moving subjects. Our study suggests that, by attending to movement gestalts, domestic horses, and probably many other group-living animals, have access to rich social information passively but reliably made available by conspecifics, a finding that we discuss in relation with current signal evolution theories.
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