Collective learning from individual experiences and information transfer during group foraging

Falcón-Cortés, Andrea; Boyer, Denis; Ramos‐Fernández, Gabriel

doi:10.1098/rsif.2018.0803

Cited by 32 publications

(39 citation statements)

References 82 publications

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“…It is The copyright holder for this preprint this version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.09.443340 doi: bioRxiv preprint group structure becoming dispersed. Close-knit group structure can be beneficial in terms of defence from predation [49], foraging [50], and, as we have demonstrated, for efficient navigation.…”

Section: Case Studysupporting

confidence: 52%

Modelling collective navigation via nonlocal communication

Johnston

Painter

2021

Preprint

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Collective migration occurs throughout the animal kingdom, and demands both the interpretation of navigational cues and the perception of other individuals within the group. Navigational cues orient individuals toward a destination, while it is hypothesised that communication between individuals enhances navigation through a reduction in orientation error. We develop a mathematical model of collective navigation that synthesises navigational cues and perception of other individuals. Crucially, this approach incorporates the uncertainty inherent to cue interpretation and perception in the decision making process, which can arise due to noisy environments. We demonstrate that collective navigation is more efficient than individual navigation, provided a threshold number of other individuals are perceptible. This benefit is even more pronounced in low navigation information environments. In navigation ``blindspots'', where no information is available, navigation is enhanced through a relay that connects individuals in information-poor regions to individuals in information-rich regions. As an expository case study, we apply our framework to minke whale migration in the North East Atlantic Ocean, and quantify the decrease in navigation ability due to anthropogenic noise pollution.

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Section: Case Studysupporting

confidence: 52%

Modelling collective navigation via nonlocal communication

Johnston

Painter

2021

Preprint

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“…Gil et al, 2018 ). Exchanging information about available patches when foragers disperse and learning about the location and availability of different patches increases the foraging success of the whole group (Falcón-Cortés et al, 2019 ). The circuit of individual strategies that we infer here is, at least in part, a reflection of information sharing about available patches.…”

Section: Discussionmentioning

confidence: 99%

Collective Computation in Animal Fission-Fusion Dynamics

et al. 2020

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Recent work suggests that collective computation of social structure can minimize uncertainty about the social and physical environment, facilitating adaptation. We explore these ideas by studying how fission-fusion social structure arises in spider monkey (Ateles geoffroyi) groups, exploring whether monkeys use social knowledge to collectively compute subgroup size distributions adaptive for foraging in variable environments. We assess whether individual decisions to stay in or leave subgroups are conditioned on strategies based on the presence or absence of others. We search for this evidence in a time series of subgroup membership. We find that individuals have multiple strategies, suggesting that the social knowledge of different individuals is important. These stay-leave strategies provide microscopic inputs to a stochastic model of collective computation encoded in a family of circuits. Each circuit represents an hypothesis for how collectives combine strategies to make decisions, and how these produce various subgroup size distributions. By running these circuits forward in simulation we generate new subgroup size distributions and measure how well they match food abundance in the environment using transfer entropies. We find that spider monkeys decide to stay or go using information from multiple individuals and that they can collectively compute a distribution of subgroup size that makes efficient use of ephemeral sources of nutrition. We are able to artificially tune circuits with subgroup size distributions that are a better fit to the environment than the observed. This suggests that a combination of measurement error, constraint, and adaptive lag are diminishing the power of collective computation in this system. These results are relevant for a more general understanding of the emergence of ordered states in multi-scale social systems with adaptive properties-both natural and engineered.

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“…Systems with many impurity sites should therefore deserve further study, and could also be used to test the ability of non-Markovian diffusing elements to solve complex optimization problems. Recent numerical results with multiple searchers suggest promising applications in this field [56]. where b k = (1−q) cos(k).…”

Section: Discussionmentioning

confidence: 99%

Anderson-like localization transition of random walks with resetting

Boyer¹,

Falcón-Cortés²,

Giuggioli³

et al. 2019

J. Stat. Mech.

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We study several lattice random walk models with stochastic resetting to previously visited sites which exhibit a phase transition between an anomalous diffusive regime and a localization regime where diffusion is suppressed. The localized phase settles above a critical resetting rate, or rate of memory use, and the probability density asymptotically adopts in this regime a non-equilibrium steady state similar to that of the well known problem of diffusion with resetting to the origin. The transition occurs because of the presence of a single impurity site where the resetting rate is lower than on other sites, and around which the walker spontaneously localizes. Near criticality, the localization length diverges with a critical exponent that falls in the same class as the self-consistent theory of Anderson localization of waves in random media. The critical dimensions are also the same in both problems. Our study provides analytically tractable examples of localization transitions in path-dependent, reinforced stochastic processes, which can be also useful for understanding spatial learning by living organisms.

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Collective learning from individual experiences and information transfer during group foraging

Cited by 32 publications

References 82 publications

Modelling collective navigation via nonlocal communication

Modelling collective navigation via nonlocal communication

Collective Computation in Animal Fission-Fusion Dynamics

Anderson-like localization transition of random walks with resetting

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