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Social information in equine movement gestalts.
Abstract: 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.
Publication Date: 2018-05-23 PubMed ID: 29796720DOI: 10.1007/s10071-018-1193-zGoogle Scholar: Lookup The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Journal Article
Summary
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The study explores how domestic horses are capable of distinguishing social information including identity, breed, sex, and personality traits from the kinematic gesture profiles or overall movement patterns of other horses.
Understanding the Research
The abstract provides an insight into a research study based on signal evolution theory where behaviours are analyzed for their communicative value over their biological function. The primary subjects of this study are domestic horses (Equus caballus), and the main research process involved gathering inertial movement sensing data from free-ranging horses. The information collected was then processed through a machine learning algorithm to identify unique kinematic gestalt profiles.
- The examination aimed at viewing the communicative functions of behavioural patterns, specifically in how these patterns transfer social information.
- A data set was created by using inertial sensors to track the movement of free-ranging horses. This data covered various aspects such as speeds, directions, and patterns of movement, among other things.
- This data was then processed using a machine learning algorithm to create unique gestalt profiles. Gestalt profiles provide a holistic view of the subjects’ behaviour, emphasizing the overall function rather than an isolated analysis of its parts.
Findings
The kinematic gestalt profiles derived from the machine learning algorithm provided rich and nuanced sets of information. The profiles could be discriminated into identity, breed, sex, and even some personality traits based on the overall movement patterns.
- The results indicate that horses (and likely other group-living animals) can obtain complex social information about their kin simply by observing their movement patterns.
- The implications of these findings are discussed in relation to current theories on signal evolution, suggesting that more refined communication behaviors can evolve when passive but robust social information is available.
Cite This Article
APA
Dahl CD, Wyss C, Zuberbühler K, Bachmann I.
(2018).
Social information in equine movement gestalts.
Anim Cogn, 21(4), 583-594.
https://doi.org/10.1007/s10071-018-1193-z Publication
Researcher Affiliations
- Institute of Biology, University of Neuchâtel, Rue Emile-Argand 11, 2000, Neuchâtel, Switzerland. christoph.dahl@unine.ch.
- Agroscope, Swiss National Stud Farm (SNSF), Les Longs-Prés, 1580 Avenches, Switzerland.
- Institute of Biology, University of Neuchâtel, Rue Emile-Argand 11, 2000, Neuchâtel, Switzerland.
- School of Psychology and Neuroscience, University of St Andrews, Westburn Lane, St Andrews, Scotland, UK.
- Agroscope, Swiss National Stud Farm (SNSF), Les Longs-Prés, 1580 Avenches, Switzerland.
MeSH Terms
- Animals
- Breeding
- Horses
- Movement
- Social Behavior
Grant Funding
- PZ00P3_154741 / Schweizerischer Nationalfonds zur Fu00f6rderung der Wissenschaftlichen Forschung
- 31003A_166458 / Schweizerischer Nationalfonds zur Fu00f6rderung der Wissenschaftlichen Forschung
Citations
This article has been cited 2 times.- Dezecache G, Zuberbühler K, Davila-Ross M, Dahl CD. A machine learning approach to infant distress calls and maternal behaviour of wild chimpanzees. Anim Cogn 2021 May;24(3):443-455.
- Yan X, Wang X, Zhao Y, Zhu Q, Yang L, Li Z. Collective decision-making and spatial patterns in orientation of an endemic ungulate on the Tibetan Plateau. Curr Zool 2024 Feb;70(1):45-58.
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