Trends in Cognitive Sciences
ReviewCollective cognition in animal groups
Introduction
It is little wonder that the behavior of animal groups, such as schools of fish, flocks of birds or swarms of insects has been associated with the concept of having a ‘collective mind’ [1]. Grouping individuals often have to make rapid decisions about where to move or what behavior to perform, in uncertain and dangerous environments. Decision-making by individuals within such aggregates is so synchronized and intimately coordinated that it has previously been considered to require telepathic communication among group members or the synchronized response to commands given, somehow, by a leader 2, 3.
In fact, individuals base their movement decisions on locally acquired cues such as the positions, motion, or change in motion, of others [2], making the collective response all the more remarkable. Each organism typically has only relatively local sensing ability (further limited in large aggregates by crowding). Groups are, therefore, often composed of individuals that differ with respect to their informational status and individuals are usually not aware of the informational state of others, such as whether they are knowledgeable about a pertinent resource, or of a threat 1, 2, 4, 5.
Recent studies have begun to elucidate how the repeated interactions among grouping animals scale to collective behavior, and have revealed, remarkably, that collective decision-making mechanisms across a wide range of animal group types, from insects to birds (and even among humans in certain circumstances) seem to share similar functional characteristics 2, 4, 5. Furthermore, at a certain level of description, collective decision-making by organisms shares essential common features with mechanisms of decision-making within the brain 1, 6. Although many details differ, there is good reason for increased communication between researchers interested in collective animal behavior and those in cognitive science.
Section snippets
Collective motion
It is usually not possible to scale reliably from individual to group behavior through verbal argument alone. Consequently, considerable progress in revealing the principles of collective behavior has been made using mathematical modeling techniques, such as computer simulation (Box 1). Some of the earliest theoretical approaches were inspired by particle physics 2, 7. These introduced the influential concept of using equations to characterize individual movements and interactions (as ‘social
Feedback processes
The social context created by highly integrated behavior strongly affects the way information is acquired, transmitted and processed by group members. Specifically, it can facilitate the collective amplification and damping of information and, thus, the adaptive tuning of collective behavior in response to external stimuli and/or internal state.
Alignment among individuals (a tendency to move in the same direction as near-neighbors, Box 1), for example, can enable information about a change in
Group size and collective decision-making
Group size can also have an important role in decision-making. If individuals have access to the same information, but it is inaccurately represented or processed, then averaging response with others (as is inherent in many schooling or flocking strategies; Box 1), will improve the decisions of the group-members. This can enable individuals to avoid the time costs associated with temporal integration and has sometimes been referred to as ‘the many-wrongs hypothesis’ [29] or ‘the wisdom of the
Leadership and coming to a consensus decision
Although leadership is not a pre-condition for group coordination it does frequently emerge in animal groups 2, 4, 31 such as when only relatively few ‘informed’ individuals have salient information [31]. Using computational modeling, Couzin et al. [31] revealed that information transfer within groups requires neither individual recognition nor signaling. If relatively few informed individuals bias grouping tendency with a desired direction of travel (such as towards a resource or away from a
Collective cognition through environmental modification: foraging ants
In highly related grouping organisms, such as the social insects (e.g. ants, bees, wasps etc.), collective cognition can be particularly sophisticated because individual behavior and interactions have evolved to benefit the colony reproductive success (thus reducing inter-individual conflict), a functional integration so tight that they have been termed ‘super-organisms’ [36]. This is exemplified by ant species that use chemical pheromone trails to coordinate foraging activities [36].
By
Finding a new home
In addition to selecting among potential food sources, social insects need to choose where to live. This process has been studied extensively in two, apparently very different, organisms; a species of small ant, Temnothorax albipennis, which lives in colonies of between ∼50 to 200 individuals in naturally weathered cracks in rocks (these ants are similar to those shown in Box 2, Figure I), and the honeybee, Apis mellifera, which typically lives in colonies of tens of thousands of individuals
Conclusions and future research
Through collective action, animals of many species can enhance their capacity to detect and respond to salient features of the environment. Interactions with others can enable individuals to circumvent their own cognitive limitations, giving them access to context-dependent and spatially and temporally integrated information. This can result in more accurate decision-making even in the face of distractions and uncertainty. Collective behavior allows access to important higher-order
Acknowledgements
I.D.C. gratefully acknowledges support from a Searle Scholar Award and also a DARPA grant #HR001–05–1-0057 to Princeton University. Insightful suggestions from three anonymous referees much improved the manuscript. Sepideh Bazazi, Adrian De Froment, Vishwesha Guttal, Christos Ioannou, Yael Katz, Liliana Salvador and Allison Shaw provided valuable feedback.
References (75)
- et al.
Self-organization and collective behavior in vertebrates
Adv. Stud. Behav.
(2003) - et al.
Consensus decision-making in animals
Trends Ecol. Evol.
(2005) Collective memory and spatial sorting in animal groups
J. Theor. Biol.
(2002)- et al.
Neural computations that underlie decisions about sensory stimuli
Trends Cognit. Sci.
(2001) Probabilistic decision making by slow reverberation in cortical circuits
Neuron
(2002)- et al.
Psychology and neurobiology of simple decisions
Trends Neurosci.
(2004) - et al.
The organizing principles of neuronal avalanches: cell assemblies in the cortex?
Trends Neurosci.
(2007) - et al.
Group transmission of predator avoidance behavior in a marine insect: the Trafalgar effect
Anim. Behav.
(1981) Many wrongs: the advantage of group navigation
Trends Ecol. Evol.
(2004)Social relationships and reproductive state influence leadership roles in movements of plains zebra, Equus burcellii
Anim. Behav.
(2007)
From compromise to leadership in pigeon homing
Curr. Biol.
Consensus decision-making in human crowds
Anim. Behav.
Path efficiency of ant foraging trails in an artificial network
J. Theor. Biol.
Modulation of pheromone trail strength with food quality in Pharaoh's ant, Monomorium pharaonis
Anim. Behav.
Communication in ants
Curr. Biol.
Longevity and detection of persistent foraging trails in Pharoah's ants, Monomorium pharaonis (L.)
Anim. Behav.
Random behavior, amplification processes and number of participants: how they contribute to the foraging properties of ants
Physica D
Strategies for choosing between alternatives with different attributes: exemplified by house-hunting ants
Anim. Behav.
Collective motion and cannibalism in locust marching bands
Curr. Biol.
Collective behaviour of random-activated mobile cellular automata
Physica D
The synchronization of recruitment-based activities in ants
Biosystems
Collective minds
Nature
Thought Transference (Or What?) in Birds
The principles of collective animal behavior
Philos. Trans. R. Soc. Lond. B Biol. Sci.
Swarm cognition in honey bees
Behav. Ecol. Sociobiol.
Equations descriptive of fish schools and other animal aggregations
Ecology
A simulation study on the schooling mechanism in fish
Bull. Jap. Soc. Sci. Fish
Flocks, herds and schools: a distributed behavioral model
Comput. Graph.
Motion perception: seeing and deciding
Proc. Natl. Acad. Sci. U. S. A.
Retinal waves are governed by collective network properties
J. Neurosci.
Neural basis of deciding, choosing and acting
Nat. Neurosci.
The time course of perceptual choice: the leaky, competing accumulator model
Psychol. Rev.
Neuronal oscillations in cortical networks
Science
Optimal decision-making theories: linking neurobiology with behavior
Trends Cogn. Sci.
Dynamical principles in neuroscience
Rev. Mod. Phys.
Dynamical synapses causing self-organized criticality in neural networks
Nature Phys.
Schooling in the Ecology of Fsh
Cited by (689)
Selective preferences and behavioral adaptation strategy of Pacific abalone in response to different water flow velocities
2024, Global Ecology and ConservationInformation cascades spread adaptive and maladaptive behaviours in group-living animals
2024, Animal BehaviourDelay-induced phase transitions in active matter
2024, Physica A: Statistical Mechanics and its ApplicationsA stochastic differential equation model for predator-avoidance fish schooling
2024, Mathematical BiosciencesChimpanzees make tactical use of high elevation in territorial contexts
2023, PLoS BiologyIdentifying a developmental transition in honey bees using gene expression data
2023, PLoS Computational Biology