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Collective Learning and Optimal Consensus Decisions in Social Animal Groups

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Collective Learning and Optimal Consensus Decisions in Social Animal Groups
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003762
Pubmed ID
Authors

Albert B. Kao, Noam Miller, Colin Torney, Andrew Hartnett, Iain D. Couzin

Abstract

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

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The data shown below were compiled from readership statistics for 265 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
Brazil 1 <1%
Mexico 1 <1%
Germany 1 <1%
Russia 1 <1%
China 1 <1%
Spain 1 <1%
Luxembourg 1 <1%
Unknown 252 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 64 24%
Researcher 39 15%
Student > Master 35 13%
Student > Bachelor 25 9%
Student > Doctoral Student 14 5%
Other 48 18%
Unknown 40 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 77 29%
Psychology 24 9%
Computer Science 20 8%
Physics and Astronomy 20 8%
Neuroscience 14 5%
Other 58 22%
Unknown 52 20%