Inter-individual coordination in walking chimpanzees

Schweinfurth, Manon K.; Baldridge, Dylan B.; Finnerty, Kyle; Call, Josep; Knoblich, Günther

doi:10.1016/j.cub.2022.09.059

Cited by 8 publications

(6 citation statements)

References 52 publications

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“…For example, analyses of rhythmic movements, such as gait, lip‐smacking, or drumming (cf. Eleuteri et al, 2022; Pereira, Kavanagh, et al, 2020; Schweinfurth et al, 2022); analyses of gestural signals would benefit from systematic descriptions of variation in movement patterns within and between action types, or features such as emphasis and arousal (cf. Graham et al, 2022; Grund et al, 2023); and analyses of variation in movement and the efficiency of motion paths could be applied to questions about the ontogenetic development and acquisition of tool‐use.…”

Section: Discussionmentioning

confidence: 99%

DeepWild: Application of the pose estimation tool DeepLabCut for behaviour tracking in wild chimpanzees and bonobos

Wiltshire,

Lewis‐Cheetham,

Komedová

et al. 2023

Journal of Animal Ecology

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Studying animal behaviour allows us to understand how different species and individuals navigate their physical and social worlds. Video coding of behaviour is considered a gold standard: allowing researchers to extract rich nuanced behavioural datasets, validate their reliability, and for research to be replicated. However, in practice, videos are only useful if data can be efficiently extracted. Manually locating relevant footage in 10,000 s of hours is extremely time‐consuming, as is the manual coding of animal behaviour, which requires extensive training to achieve reliability. Machine learning approaches are used to automate the recognition of patterns within data, considerably reducing the time taken to extract data and improving reliability. However, tracking visual information to recognise nuanced behaviour is a challenging problem and, to date, the tracking and pose‐estimation tools used to detect behaviour are typically applied where the visual environment is highly controlled. Animal behaviour researchers are interested in applying these tools to the study of wild animals, but it is not clear to what extent doing so is currently possible, or which tools are most suited to particular problems. To address this gap in knowledge, we describe the new tools available in this rapidly evolving landscape, suggest guidance for tool selection, provide a worked demonstration of the use of machine learning to track movement in video data of wild apes, and make our base models available for use. We use a pose‐estimation tool, DeepLabCut, to demonstrate successful training of two pilot models of an extremely challenging pose estimate and tracking problem: multi‐animal wild forest‐living chimpanzees and bonobos across behavioural contexts from hand‐held video footage. With DeepWild we show that, without requiring specific expertise in machine learning, pose estimation and movement tracking of free‐living wild primates in visually complex environments is an attainable goal for behavioural researchers.

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Section: Discussionmentioning

confidence: 99%

DeepWild: Application of the pose estimation tool DeepLabCut for behaviour tracking in wild chimpanzees and bonobos

Wiltshire,

Lewis‐Cheetham,

Komedová

et al. 2023

Journal of Animal Ecology

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“…These factors might sometimes be overlooked, and potentially lead to false negative results [ 3 ]. The other avenue that has been taken is relying on more naturalistic observation where rhythmic abilities from a species-specific behavioral repertoire are used (e.g., chimpanzee walking: [ 42 ]). Critically, this approach can be further enhanced when combined with mathematical modeling [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Marmosets mutually compensate for differences in rhythms when coordinating vigilance

Phaniraj,

Brügger,

Burkart

2024

PLoS Comput Biol

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Synchronization is widespread in animals, and studies have often emphasized how this seemingly complex phenomenon can emerge from very simple rules. However, the amount of flexibility and control that animals might have over synchronization properties, such as the strength of coupling, remains underexplored. Here, we studied how pairs of marmoset monkeys coordinated vigilance while feeding. By modeling them as coupled oscillators, we noted that (1) individual marmosets do not show perfect periodicity in vigilance behaviors, (2) nevertheless, marmoset pairs started to take turns being vigilant over time, a case of anti-phase synchrony, (3) marmosets could couple flexibly; the coupling strength varied with every new joint feeding bout, and (4) marmosets could control the coupling strength; dyads showed increased coupling if they began in a more desynchronized state. Such flexibility and control over synchronization require more than simple interaction rules. Minimally, animals must estimate the current degree of asynchrony and adjust their behavior accordingly. Moreover, the fact that each marmoset is inherently non-periodic adds to the cognitive demand. Overall, our study provides a mathematical framework to investigate the cognitive demands involved in coordinating behaviors in animals, regardless of whether individual behaviors are rhythmic or not.

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“…These factors might sometimes be overlooked, especially when encountering negative results [3]. The other avenue that has been taken is relying on more naturalistic observation where rhythmic abilities from a species-specific behavioural repertoire are used (e.g., chimpanzee walking: [42]). Critically, this approach can be further enhanced when combined with mathematical modelling [2].…”

Section: Introductionmentioning

confidence: 99%

Marmosets mutually compensate for differences in rhythms when coordinating vigilance

Phaniraj,

Brügger,

Burkart

2023

Preprint

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Synchronisation is widespread in animals, and studies have often emphasised how this seemingly complex phenomenon can emerge from very simple rules. However, the amount of flexibility and control that animals might have over synchronisation properties, such as the strength of coupling, remains underexplored. Here, we studied how pairs of marmoset monkeys coordinated vigilance while feeding. By modelling them as coupled oscillators, we noted that (1) individual marmosets do not show perfect periodicity in vigilance behaviours, (2) even then, pairs of marmosets developed a tendency to take turns being vigilant, a case of anti-phase synchrony, (3) marmosets could couple flexibly; the coupling strength varied with every new joint feeding bout, and (4) marmosets could control the coupling strength; dyads showed increased coupling if they began in a more desynchronised state. Such flexibility and control over synchronisation require more than simple interaction rules. Minimally, animals must estimate the current degree of asynchrony and adjust their behaviour accordingly. Moreover, the fact that each marmoset is inherently non-periodic adds to the cognitive demand. Overall, our study taps into the cognitive aspects of synchronisation and provides a mathematical framework to investigate the phenomenon more widely, where individuals may not display perfectly rhythmic behaviours.

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Inter-individual coordination in walking chimpanzees

Cited by 8 publications

References 52 publications

DeepWild: Application of the pose estimation tool DeepLabCut for behaviour tracking in wild chimpanzees and bonobos

DeepWild: Application of the pose estimation tool DeepLabCut for behaviour tracking in wild chimpanzees and bonobos

Marmosets mutually compensate for differences in rhythms when coordinating vigilance

Marmosets mutually compensate for differences in rhythms when coordinating vigilance

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