Consensus of travel direction is achieved by simple copying, not voting, in free-ranging goats

Sankey, Daniel W. E.; O'Bryan, Lisa R.; Garnier, Simon; Cowlishaw, Guy; Hopkins, Philip F.; Holton, Mark D.; Fürtbauer, Ines; King, Andrew J.

doi:10.1098/rsos.201128

Cited by 16 publications

(27 citation statements)

References 59 publications

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“…For example, flocks of juvenile northern bald ibis (Geronticus eremita) take turns leading their V-formation during migration (Voelkl et al, 2015;Voelkl and Fritz, 2017). Additionally, leaders can emerge through simple behavioral rules by followers following the movements of their neighbors (Herbert-Read et al, 2011;King et al, 2011;Rosenthal et al, 2015;Torney et al, 2018;Sankey et al, 2021). Thus, the individuals that initiate movement may have a large influence on group navigation.…”

Section: Leadershipmentioning

confidence: 99%

The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals

2021

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Cognition, defined as the processes concerned with the acquisition, retention and use of information, underlies animals’ abilities to navigate their local surroundings, embark on long-distance seasonal migrations, and socially learn information relevant to movement. Hence, in order to fully understand and predict animal movement, researchers must know the cognitive mechanisms that generate such movement. Work on a few model systems indicates that most animals possess excellent spatial learning and memory abilities, meaning that they can acquire and later recall information about distances and directions among relevant objects. Similarly, field work on several species has revealed some of the mechanisms that enable them to navigate over distances of up to several thousand kilometers. Key behaviors related to movement such as the choice of nest location, home range location and migration route are often affected by parents and other conspecifics. In some species, such social influence leads to the formation of aggregations, which in turn may lead to further social learning about food locations or other resources. Throughout the review, we note a variety of topics at the interface of cognition and movement that invite further investigation. These include the use of social information embedded in trails, the likely important roles of soundscapes and smellscapes, the mechanisms that large mammals rely on for long-distance migration, and the effects of expertise acquired over extended periods.

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Section: Leadershipmentioning

confidence: 99%

The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals

2021

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“…One such mechanism is to attain a specific number of individuals (a quorum) notifying a preference. For example, African buffalo (Prins 1996), wild dogs (Walker et al 2017), hamadryas baboons (Kummer 1968) or Tonkean macaques (Sueur et al 2010) are reported to use body orientation to vote and indicate their preferred direction to achieve a consensus on travel direction, while golden shiners (Herbert-Read et al 2011;Katz et al 2011) or goats (Sankey et al 2021) achieve consensus of direction by responding to the movement cues of their neighbours. In voting processes, long negotiation processes happen during the collective decision to reach a quorum showing implication of theory of mind, particularly described in primates (Kummer 1968;Strandburg-Peshkin et al 2015).…”

Section: The Different Systems Used To Aggregate Individual Preferencesmentioning

confidence: 99%

Improving human collective decision-making through animal and artificial intelligence

Bousquet¹,

Espinosa²,

Deneubourg³

et al. 2021

Peer Community Journal

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Whilst fundamental to human societies, collective decision-making such as voting systems can lead to non-efficient decisions, as past climate policies demonstrate. Current systems are harshly criticised for the way they consider voters needs and knowledge. Collective decision-making is central in human societies but also occurs in animal groups mostly when animals need to choose when and where to move. In these societies, animals balance between the needs of the group members and their own needs and rely on each individuals (partial) knowledge. We argue that non-human animals and humans share similar collective decision processes, among which are agenda-setting, deliberation and voting. Recent works in artificial intelligence have sought to improve decisionmaking in human groups, sometimes inspired by animals decision-making systems. We discuss here how our societies could benefit from recent advances in ethology and artificial intelligence to improve our collective decision-making system.

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“…Previous studies have investigated leader-follower relationships. For example, the existences of the leadership have been investigated via the correlation in movement with time delay (e.g., [52,64]) and via global physical (e.g., [4]) and statistical properties [56]. Meanwhile, methods for data-driven biological multi-agent motion modeling have been intensively investigated for pedestrian (e.g., [1,24]), vehicles (e.g., [5,61,70]), animals [16,27], and athletes (e.g., [80,36]).…”

Section: Related Workmentioning

confidence: 99%

Learning interaction rules from multi-animal trajectories via augmented behavioral models

Fujii¹,

Takeishi²,

Tsutsui³

et al. 2021

Preprint

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Extracting the interaction rules of biological agents from moving sequences pose challenges in various domains. Granger causality is a practical framework for analyzing the interactions from observed time-series data; however, this framework ignores the structures of the generative process in animal behaviors, which may lead to interpretational problems and sometimes erroneous assessments of causality. In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models. We adopt an approach for augmenting incomplete multi-agent behavioral models described by time-varying dynamical systems with neural networks. For efficient and interpretable learning, our model leverages theory-based architectures separating navigation and motion processes, and the theory-guided regularization for reliable behavioral modeling. This can provide interpretable signs of Granger-causal effects over time, i.e., when specific others cause the approach or separation. In experiments using synthetic datasets, our method achieved better performance than various baselines. We then analyzed multi-animal datasets of mice, flies, birds, and bats, which verified our method and obtained novel biological insights.

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Consensus of travel direction is achieved by simple copying, not voting, in free-ranging goats

Cited by 16 publications

References 59 publications

The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals

The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals

Improving human collective decision-making through animal and artificial intelligence

Learning interaction rules from multi-animal trajectories via augmented behavioral models

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