848,553 research outputs found
Interactive rhythms across species: The evolutionary biology of animal chorusing and turn-taking
The study of human language is progressively moving toward comparative and interactive frameworks, extending the concept of turn‐taking to animal communication. While such an endeavor will help us understand the interactive origins of language, any theoretical account for cross‐species turn‐taking should consider three key points. First, animal turn‐taking must incorporate biological studies on animal chorusing, namely how different species coordinate their signals over time. Second, while concepts employed in human communication and turn‐taking, such as intentionality, are still debated in animal behavior, lower level mechanisms with clear neurobiological bases can explain much of animal interactive behavior. Third, social behavior, interactivity, and cooperation can be orthogonal, and the alternation of animal signals need not be cooperative. Considering turn‐taking a subset of chorusing in the rhythmic dimension may avoid overinterpretation and enhance the comparability of future empirical work
Scaling and Intermittency in Animal Behavior
Scale-invariant spatial or temporal patterns and L\'evy flight motion have
been observed in a large variety of biological systems. It has been argued that
animals in general might perform L\'evy flight motion with power law
distribution of times between two changes of the direction of motion. Here we
study the temporal behaviour of nesting gilts. The time spent by a gilt in a
given form of activity has power law probability distribution without finite
average. Further analysis reveals intermittent eruption of certain periodic
behavioural sequences which are responsible for the scaling behaviour and
indicates the existence of a critical state. We show that this behaviour is in
close analogy with temporal sequences of velocity found in turbulent flows,
where random and regular sequences alternate and form an intermittent sequence.Comment: 10 page
Collective Animal Behavior from Bayesian Estimation and Probability Matching
Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior
Probabilistic models of individual and collective animal behavior
Recent developments in automated tracking allow uninterrupted,
high-resolution recording of animal trajectories, sometimes coupled with the
identification of stereotyped changes of body pose or other behaviors of
interest. Analysis and interpretation of such data represents a challenge: the
timing of animal behaviors may be stochastic and modulated by kinematic
variables, by the interaction with the environment or with the conspecifics
within the animal group, and dependent on internal cognitive or behavioral
state of the individual. Existing models for collective motion typically fail
to incorporate the discrete, stochastic, and internal-state-dependent aspects
of behavior, while models focusing on individual animal behavior typically
ignore the spatial aspects of the problem. Here we propose a probabilistic
modeling framework to address this gap. Each animal can switch stochastically
between different behavioral states, with each state resulting in a possibly
different law of motion through space. Switching rates for behavioral
transitions can depend in a very general way, which we seek to identify from
data, on the effects of the environment as well as the interaction between the
animals. We represent the switching dynamics as a Generalized Linear Model and
show that: (i) forward simulation of multiple interacting animals is possible
using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii)
formulated properly, the maximum likelihood inference of switching rate
functions is tractably solvable by gradient descent; (iii) model selection can
be used to identify factors that modulate behavioral state switching and to
appropriately adjust model complexity to data. To illustrate our framework, we
apply it to two synthetic models of animal motion and to real zebrafish
tracking data.Comment: 26 pages, 11 figure
Social norms and farm animal protection
Social change is slow and difficult. Social change for animals is formidably slow and difficult. Advocates and scholars alike have long tried to change attitudes and convince the public that eating animals is wrong. The topic of norms and social change for animals has been neglected, which explains in part the relative failure of the animal protection movement to secure robust support reflected in social and legal norms. Moreover, animal ethics has suffered from a disproportionate focus on individual attitudes and behavior at the expense of collective behavior, social change, and empirical psychology. If what we want to change is behavior on a large scale, norms are important tools. This article reviews an account of social norms that provides insights into the possibility and limitations of social change for animals, approaching animal protection as a problem of reverse social engineering. It highlights avenues for future work from this neglected perspective
Neural Correlates of Social Behavior in Mushroom Body Extrinsic Neurons of the Honeybee Apis mellifera
The social behavior of honeybees (Apis mellifera) has been extensively investigated, but little is known about its neuronal correlates. We developed a method that allowed us to record extracellularly from mushroom body extrinsic neurons (MB ENs) in a freely moving bee within a small but functioning mini colony of approximately 1,000 bees. This study aimed to correlate the neuronal activity of multimodal high-order MB ENs with social behavior in a close to natural setting. The behavior of all bees in the colony was video recorded. The behavior of the recorded animal was compared with other hive mates and no significant differences were found. Changes in the spike rate appeared before, during or after social interactions. The time window of the strongest effect on spike rate changes ranged from 1 s to 2 s before and after the interaction, depending on the individual animal and recorded neuron. The highest spike rates occurred when the experimental animal was situated close to a hive mate. The variance of the spike rates was analyzed as a proxy for high order multi-unit processing. Comparing randomly selected time windows with those in which the recorded animal performed social interactions showed a significantly increased spike rate variance during social interactions. The experimental set-up employed for this study offers a powerful opportunity to correlate neuronal activity with intrinsically motivated behavior of socially interacting animals. We conclude that the recorded MB ENs are potentially involved in initiating and controlling social interactions in honeybees
Phoresy by Pseudoscorpions
Excerpt: Phoretic behavior involving a non-parasitic association of a larger animal by a smaller animal resulting in transportation is well-documented in some pseudoscorpions. Muchmore (1971) summarized the records of pseudoscorpion phoresy in Central and North America as presented by Beier and as published since 1948
- …