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Open accessJournal ArticleDOI: 10.1038/S41598-021-84542-W

Efficient Lévy walks in virtual human foraging.

04 Mar 2021-Scientific Reports (Springer Science and Business Media LLC)-Vol. 11, Iss: 1, pp 5242-5242
Abstract: Efficient foraging depends on decisions that account for the costs and benefits of various activities like movement, perception, and planning. We conducted a virtual foraging experiment set in the foothills of the Himalayas to examine how time and energy are expended to forage efficiently, and how foraging changes when constrained to a home range. Two hundred players foraged the human-scale landscape with simulated energy expenditure in search of naturally distributed resources. Results showed that efficient foragers produced periods of locomotion interleaved with perception and planning that approached theoretical expectations for Levy walks, regardless of the home-range constraint. Despite this constancy, efficient home-range foraging trajectories were less diffusive by virtue of restricting locomotive search and spending more time instead scanning the environment to plan movement and detect far-away resources. Altogether, results demonstrate that humans can forage efficiently by arranging and adjusting Levy-distributed search activities in response to environmental and task constraints.

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Topics: Foraging (59%)
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Open accessPosted Content
Abstract: The Levy walk is a non-Brownian random walk model that has been found to describe anomalous dynamic phenomena in diverse fields ranging from biology over quantum physics to ecology. Recurrently occurring problems are to examine whether observed data are successfully quantified by a model classified as Levy walks or not and extract the best model parameters in accordance with the data. Motivated by such needs, we propose a hidden Markov model for Levy walks and computationally realize and test the corresponding Bayesian inference method. We introduce a Markovian decomposition scheme to approximate a renewal process governed by a power-law waiting time distribution. Using this, we construct the likelihood function of Levy walks based on a hidden Markov model and the forward algorithm. With the Levy walk trajectories simulated at various conditions, we perform the Bayesian inference for parameter estimation and model classification. We show that the power-law exponent of the flight-time distribution can be successfully extracted even at the condition that the mean-squared displacement does not display the expected scaling exponent due to the noise or insufficient trajectory length. It is also demonstrated that the Bayesian method performs remarkably inferring the Levy walk trajectories from given unclassified trajectory data set if the noise level is moderate.

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Topics: Random walk (59%), Hidden Markov model (57%), Bayesian inference (56%) ... show more

1 Citations


Open accessPosted ContentDOI: 10.1101/2021.04.29.442031
29 Apr 2021-bioRxiv
Abstract: Central-place foraging, where foragers return to a central location (or home), is a key feature of hunter-gatherer social organization. Central-place foraging could have significantly changed hunter-gatherers9 use of space and mobility, and altered social networks and increased opportunities for information exchange. We evaluated whether central-place foraging patterns facilitate information transmission and considered the potential roles of environmental conditions and mobility strategies. We built an agent-based central-place foraging model where agents move according to a simple optimal foraging rule, and can encounter other agents as they move across the environment. They either forage close to their home within a given radius or move the location of their home to new areas. We analyzed the interaction networks arising across different environments and mobility strategies. We found that, at intermediate levels of environmental heterogeneity and mobility, central-place foraging increased global and local network efficiencies as well as the rate of contagion-based information transmission (simple and complex). We also assessed the effect of population density on the resultant networks and found that central-place mobility strategies can further improve information transmission in larger populations. Our findings suggest that the combination of foraging and movement strategies, as well as the underlying environmental conditions that characterized early human societies, may have been a crucial precursor in our species9 unique capacity to innovate, accumulate and rely on complex culture.

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Topics: Foraging (64%), Optimal foraging theory (59%)

1 Citations


Open accessPosted ContentDOI: 10.1101/2021.11.10.468137
12 Nov 2021-bioRxiv
Abstract: Search requires balancing exploring for more options and exploiting the ones previously found. Individuals foraging in a group face another trade-off: whether to engage in social learning to exploit the solutions found by others or to solitarily search for unexplored solutions. Social learning can decrease the costs of finding new resources, but excessive social learning can decrease the exploration for new solutions. We study how these two trade-offs interact to influence search efficiency in a model of collective foraging under conditions of varying resource abundance, resource density, and group size. We modeled individual search strategies as Levy walks, where a power-law exponent () controlled the trade-off between exploitative and explorative movements in individual search. We modulated the trade-off between individual search and social learning using a selectivity parameter that determined how agents responded to social cues in terms of distance and likely opportunity costs. Our results show that social learning is favored in rich and clustered environments, but also that the benefits of exploiting social information are maximized by engaging in high levels of individual exploration. We show that selective use of social information can modulate the disadvantages of excessive social learning, especially in larger groups and with limited individual exploration. Finally, we found that the optimal combination of individual exploration and social learning gave rise to trajectories with {approx} 2 and provide support for the general optimality such patterns in search. Our work sheds light on the interplay between individual search and social learning, and has broader implications for collective search and problem-solving.

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Topics: Social learning (66%), Social cue (51%)

Open accessPosted ContentDOI: 10.1101/2021.05.12.443799
Jones Sa1, Barfield Jh1, Woodrow L. Shew1Institutions (1)
13 May 2021-bioRxiv
Abstract: Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure, thought to confer functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here we show that scale-free dynamics of behavior and certain subsets of cortical neurons are one-to-one related. Surprisingly, the scale-free neural subsets exhibit stochastic winner-take-all competition with other neural subsets, inconsistent with prevailing theory of scale-free neural systems. We develop a computational model which accounts for known cell-type-specific circuit structure and explains our findings. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity, which was previously thought to represent background noise in cerebral cortex. One-Sentence Summary Winner-take-all competition among cortical neurons underpins shared fractal spatiotemporal dynamics of behavior and brain.

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84 results found


Open accessJournal ArticleDOI: 10.1137/070710111
01 Nov 2009-Siam Review
Abstract: Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data, while in others the power law is ruled out.

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Topics: Zipf's law (54%), Model selection (52%), Probability and statistics (51%) ... show more

8,065 Citations


Open accessJournal ArticleDOI: 10.2307/1941447
Simon A. Levin1Institutions (1)
01 Dec 1992-Ecology
Abstract: This book is the second of two volumes in a series on terrestrial and marine comparisons, focusing on the temporal complement of the earlier spatial analysis of patchiness and pattern (Levin et al. 1993). The issue of the relationships among pattern, scale, and patchiness has been framed forcefully in John Steele’s writings of two decades (e.g., Steele 1978). There is no pattern without an observational frame. In the words of Nietzsche, “There are no facts… only interpretations.”

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5,508 Citations


Open accessJournal ArticleDOI: 10.3758/S13428-011-0124-6
Winter Mason1, Siddharth Suri1Institutions (1)
Abstract: Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this article is to demonstrate how to use this Web site for conducting behavioral research and to lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments, and faster iteration between developing theory and executing experiments. While other methods of conducting behavioral research may be comparable to or even better than Mechanical Turk on one or more of the axes outlined above, we will show that when taken as a whole Mechanical Turk can be a useful tool for many researchers. We will discuss how the behavior of workers compares with that of experts and laboratory subjects. Then we will illustrate the mechanics of putting a task on Mechanical Turk, including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform, including techniques for conducting synchronous experiments, methods for ensuring high-quality work, how to keep data private, and how to maintain code security.

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Topics: Poison control (51%)

2,295 Citations


Open accessJournal ArticleDOI: 10.1038/S41467-018-04768-7
Zhen Liu1, Zongyang Lu2, Guang Yang2, Shisheng Huang2  +10 moreInstitutions (2)
Abstract: A recently developed adenine base editor (ABE) efficiently converts A to G and is potentially useful for clinical applications. However, its precision and efficiency in vivo remains to be addressed. Here we achieve A-to-G conversion in vivo at frequencies up to 100% by microinjection of ABE mRNA together with sgRNAs. We then generate mouse models harboring clinically relevant mutations at Ar and Hoxd13, which recapitulates respective clinical defects. Furthermore, we achieve both C-to-T and A-to-G base editing by using a combination of ABE and SaBE3, thus creating mouse model harboring multiple mutations. We also demonstrate the specificity of ABE by deep sequencing and whole-genome sequencing (WGS). Taken together, ABE is highly efficient and precise in vivo, making it feasible to model and potentially cure relevant genetic diseases. CRISPR-based base editors allow for single nucleotide genome editing in a range of organisms. Here the authors demonstrate the in vivo generation of mouse models carrying clinically relevant mutations using C→T and A→G editors.

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Topics: Genome editing (52%)

2,094 Citations


Open accessJournal ArticleDOI: 10.1073/PNAS.0800375105
Ran Nathan1, Wayne M. Getz2, Eloy Revilla3, Marcel Holyoak4  +3 moreInstitutions (6)
Abstract: Movement of individual organisms is fundamental to life, quilting our planet in a rich tapestry of phenomena with diverse implications for ecosystems and humans. Movement research is both plentiful and insightful, and recent methodological advances facilitate obtaining a detailed view of individual movement. Yet, we lack a general unifying paradigm, derived from first principles, which can place movement studies within a common context and advance the development of a mature scientific discipline. This introductory article to the Movement Ecology Special Feature proposes a paradigm that integrates conceptual, theoretical, methodological, and empirical frameworks for studying movement of all organisms, from microbes to trees to elephants. We introduce a conceptual framework depicting the interplay among four basic mechanistic components of organismal movement: the internal state (why move?), motion (how to move?), and navigation (when and where to move?) capacities of the individual and the external factors affecting movement. We demonstrate how the proposed framework aids the study of various taxa and movement types; promotes the formulation of hypotheses about movement; and complements existing biomechanical, cognitive, random, and optimality paradigms of movement. The proposed framework integrates eclectic research on movement into a structured paradigm and aims at providing a basis for hypothesis generation and a vehicle facilitating the understanding of the causes, mechanisms, and spatiotemporal patterns of movement and their role in various ecological and evolutionary processes. "Now we must consider in general the common reason for moving with any movement whatever." (Aristotle, De Motu Animalium, 4th century B.C.).

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Topics: Movement (music) (50%), Conceptual framework (50%)

1,788 Citations


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20215