Couzin, Iain D.
0000-0001-8556-4558 Berufsbeschreibung
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Individual and collective encoding of risk in animal groups
The need to make fast decisions under risky and uncertain conditions is a widespread problem in the natural world. While there has been extensive work on how individual organisms dynamically modify their behavior to respond appropriately to changing environmental conditions (and how this is encoded in the brain), we know remarkably little about the corresponding aspects of collective information processing in animal groups. For example, many groups appear to show increased "sensitivity" in the presence of perceived threat, as evidenced by the increased frequency and magnitude of repeated cascading waves of behavioral change often observed in fish schools and bird flocks under such circumstances. How such context-dependent changes in collective sensitivity are mediated, however, is unknown. Here we address this question using schooling fish as a model system, focusing on 2 nonexclusive hypotheses: 1) that changes in collective responsiveness result from changes in how individuals respond to social cues (i.e., changes to the properties of the "nodes" in the social network), and 2) that they result from changes made to the structural connectivity of the network itself (i.e., the computation is encoded in the "edges" of the network). We find that despite the fact that perceived risk increases the probability for individuals to initiate an alarm, the context-dependent change in collective sensitivity predominantly results not from changes in how individuals respond to social cues, but instead from how individuals modify the spatial structure, and correspondingly the topology of the network of interactions, within the group. Risk is thus encoded as a collective property, emphasizing that in group-living species individual fitness can depend strongly on coupling between scales of behavioral organization.
Machine behaviour
Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.
From single steps to mass migration : the problem of scale in the movement ecology of the Serengeti wildebeest
A central question in ecology is how to link processes that occur over different scales. The daily interactions of individual organisms ultimately determine community dynamics, population fluctuations and the functioning of entire ecosystems. Observations of these multiscale ecological processes are constrained by various technological, biological or logistical issues, and there are often vast discrepancies between the scale at which observation is possible and the scale of the question of interest. Animal movement is characterized by processes that act over multiple spatial and temporal scales. Second-by-second decisions accumulate to produce annual movement patterns. Individuals influence, and are influenced by, collective movement decisions, which then govern the spatial distribution of populations and the connectivity of meta-populations. While the field of movement ecology is experiencing unprecedented growth in the availability of movement data, there remain challenges in integrating observations with questions of ecological interest. In this article, we present the major challenges of addressing these issues within the context of the Serengeti wildebeest migration, a keystone ecological phenomena that crosses multiple scales of space, time and biological complexity.This article is part of the theme issue 'Collective movement ecology'.
Counteracting estimation bias and social influence to improve the wisdom of crowds
Aggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here, we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three new aggregation measures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We show that the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities and across different methods for averaging social information. Using knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2× with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
Conserved behavioral circuits govern high-speed decision-making in wild fish shoals
To evade their predators, animals must quickly detect potential threats, gauge risk, and mount a response. Putative neural circuits responsible for these tasks have been isolated in laboratory studies. However, it is unclear whether and how these circuits combine to generate the flexible, dynamic sequences of evasion behavior exhibited by wild, freely moving animals. Here, we report that evasion behavior of wild fish on a coral reef is generated through a sequence of well-defined decision rules that convert visual sensory input into behavioral actions. Using an automated system to present visual threat stimuli to fish in situ, we show that individuals initiate escape maneuvers in response to the perceived size and expansion rate of an oncoming threat using a decision rule that matches dynamics of known loom-sensitive neural circuits. After initiating an evasion maneuver, fish adjust their trajectories using a control rule based on visual feedback to steer away from the threat and toward shelter. These decision rules accurately describe evasion behavior of fish from phylogenetically distant families, illustrating the conserved nature of escape decision-making. Our results reveal how the flexible behavioral responses required for survival can emerge from relatively simple, conserved decision-making mechanisms.
Collective animal navigation and migratory culture : from theoretical models to empirical evidence
Animals often travel in groups, and their navigational decisions can be influenced by social interactions. Both theory and empirical observations suggest that such collective navigation can result in individuals improving their ability to find their way and could be one of the key benefits of sociality for these species. Here, we provide an overview of the potential mechanisms underlying collective navigation, review the known, and supposed, empirical evidence for such behaviour and highlight interesting directions for future research. We further explore how both social and collective learning during group navigation could lead to the accumulation of knowledge at the population level, resulting in the emergence of migratory culture.
Modular structure within groups causes information loss but can improve decision accuracy
Many animal groups exhibit signatures of persistent internal modular structure, whereby individuals consistently interact with certain groupmates more than others. In such groups, information relevant to a collective decision may spread unevenly through the group, but how this impacts the quality of the resulting decision is not well understood. Here, we explicitly model modularity within animal groups and examine how it affects the amount of information represented in collective decisions, as well as the accuracy of those decisions. We find that modular structure necessarily causes a loss of information, effectively silencing the input from a fraction of the group. However, the effect of this information loss on collective accuracy depends on the informational environment in which the decision is made. In simple environments, the information loss is detrimental to collective accuracy. By contrast, in complex environments, modularity tends to improve accuracy. This is because small group sizes typically maximize collective accuracy in such environments, and modular structure allows a large group to behave like a smaller group (in terms of its decision-making). These results suggest that in naturalistic environments containing correlated information, large animal groups may be able to exploit modular structure to improve decision accuracy while retaining other benefits of large group size. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.
Methods for the effective study of collective behavior in a radial arm maze
Collective behaviors are observed throughout nature, from bacterial colonies to human societies. Important theoretical breakthroughs have recently been made in understanding why animals produce group behaviors and how they coordinate their activities, build collective structures, and make decisions. However, standardized experimental methods to test these findings have been lacking. Notably, easily and unambiguously determining the membership of a group and the responses of an individual within that group is still a challenge. The radial arm maze is presented here as a new standardized method to investigate collective exploration and decision-making in animal groups. This paradigm gives individuals within animal groups the opportunity to make choices among a set of discrete alternatives, and these choices can easily be tracked over long periods of time. We demonstrate the usefulness of this paradigm by performing a set of refuge-site selection experiments with groups of fish. Using an open-source, robust custom image-processing algorithm, we automatically counted the number of animals in each arm of the maze to identify the majority choice. We also propose a new index to quantify the degree of group cohesion in this context. The radial arm maze paradigm provides an easy way to categorize and quantify the choices made by animals. It makes it possible to readily apply the traditional uses of the radial arm maze with single animals to the study of animal groups. Moreover, it opens up the possibility of studying questions specifically related to collective behaviors.
Synchronization, coordination and collective sensing during thermalling flight of freely migrating white storks
Exploring how flocks of soaring migrants manage to achieve and maintain coordination while exploiting thermal updrafts is important for understanding how collective movements can enhance the sensing of the surrounding environment. Here we examined the structural organization of a group of circling white storks (Ciconia ciconia) throughout their migratory journey from Germany to Spain. We analysed individual high-resolution GPS trajectories of storks during circling events, and evaluated each bird's flight behaviour in relation to its flock members. Within the flock, we identified subgroups that synchronize their movements and coordinate switches in their circling direction within thermals. These switches in direction can be initiated by any individual of the subgroup, irrespective of how advanced its relative vertical position is, and occur at specific horizontal locations within the thermal allowing the storks to remain within the thermal. Using the motion of all flock members, we were able to examine the dynamic variation of airflow within the thermals and to determine the specific environmental conditions surrounding the flock. With an increasing amount of high-resolution GPS tracking, we may soon be able to use these animals as distributed sensors providing us with a new means to obtain a detailed knowledge of our environment.This article is part of the theme issue 'Collective movement ecology'.