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Modeling Collective Animal Behavior with a Cognitive Perspective: A Methodological Framework
- Source :
- PLoS ONE, PLoS ONE, Public Library of Science, 2012, 7 (6), pp.e38588. ⟨10.1371/journal.pone.0038588⟩, PLoS ONE, Vol 7, Iss 6, p e38588 (2012)
- Publication Year :
- 2012
- Publisher :
- Public Library of Science (PLoS), 2012.
-
Abstract
- International audience; The last decades have seen an increasing interest in modeling collective animal behavior. Some studies try to reproduce as accurately as possible the collective dynamics and patterns observed in several animal groups with biologically plausible, individual behavioral rules. The objective is then essentially to demonstrate that the observed collective features may be the result of self-organizing processes involving quite simple individual behaviors. Other studies concentrate on the objective of establishing or enriching links between collective behavior researches and cognitive or physiological ones, which then requires that each individual rule be carefully validated. Here we discuss the methodological consequences of this additional requirement. Using the example of corpse clustering in ants, we first illustrate that it may be impossible to discriminate among alternative individual rules by considering only observational data collected at the group level. Six individual behavioral models are described: They are clearly distinct in terms of individual behaviors, they all reproduce satisfactorily the collective dynamics and distribution patterns observed in experiments, and we show theoretically that it is strictly impossible to discriminate two of these models even in the limit of an infinite amount of data whatever the accuracy level. A set of methodological steps are then listed and discussed as practical ways to partially overcome this problem. They involve complementary experimental protocols specifically designed to address the behavioral rules successively, conserving group-level data for the overall model validation. In this context, we highlight the importance of maintaining a sharp distinction between model enunciation, with explicit references to validated biological concepts, and formal translation of these concepts in terms of quantitative state variables and fittable functional dependences. Illustrative examples are provided of the benefits expected during the often long and difficult process of refining a behavioral model, designing adapted experimental protocols and inversing model parameters.
- Subjects :
- 0106 biological sciences
State variable
Collective behavior
Population Dynamics
computer.software_genre
Bioinformatics
Choice Behavior
01 natural sciences
Cognition
Morphogenesis
[PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech]
Animal Management
Physics
0303 health sciences
Multidisciplinary
Animal Behavior
Behavior, Animal
[SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior
Applied Mathematics
[SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences
Agriculture
Complex Systems
Behavioral modeling
Medicine
Research Article
[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]
Markov Model
Process (engineering)
Science
Decision Making
Context (language use)
Environment
Machine learning
Models, Biological
010603 evolutionary biology
03 medical and health sciences
Cadaver
Animals
Social Behavior
[NLIN.NLIN-AO]Nonlinear Sciences [physics]/Adaptation and Self-Organizing Systems [nlin.AO]
Set (psychology)
Cluster analysis
Biology
Theoretical Biology
030304 developmental biology
Evolutionary Biology
Models, Statistical
Population Biology
Ants
business.industry
Computational Biology
Probability Theory
Animal Cognition
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Nonlinear Dynamics
Collective animal behavior
Artificial intelligence
business
Zoology
computer
Mathematics
Developmental Biology
Neuroscience
[SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....7daf45458ccd70df3777f6cc98ef3808