Skill learning and the evolution of social learning mechanisms

BMC Evolutionary Biology

Research Article

Kevin N. Laland¹  Daniel J. van der Post¹  Mathias Franz² 

[1]Center for Social Learning and Cognitive Evolution, School of Biology, St Andrews University, Harold Mitchell Building, KY16 9TH, St Andrews, UK

[2]Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Straße 17, 10315, Berlin, Germany

关键词: Multi-scale approach;    Agent-based model;    Information parasitism;    Mechanism specificity;    Group foragers;    Self-organization;   

DOI  :  10.1186/s12862-016-0742-9

 received in 2016-02-19, accepted in 2016-08-16,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundSocial learning is potentially advantageous, but evolutionary theory predicts that (i) its benefits may be self-limiting because social learning can lead to information parasitism, and (ii) these limitations can be mitigated via forms of selective copying. However, these findings arise from a functional approach in which learning mechanisms are not specified, and which assumes that social learning avoids the costs of asocial learning but does not produce information about the environment. Whether these findings generalize to all kinds of social learning remains to be established. Using a detailed multi-scale evolutionary model, we investigate the payoffs and information production processes of specific social learning mechanisms (including local enhancement, stimulus enhancement and observational learning) and their evolutionary consequences in the context of skill learning in foraging groups.ResultsWe find that local enhancement does not benefit foraging success, but could evolve as a side-effect of grouping. In contrast, stimulus enhancement and observational learning can be beneficial across a wide range of environmental conditions because they generate opportunities for new learning outcomes.ConclusionsIn contrast to much existing theory, we find that the functional outcomes of social learning are mechanism specific. Social learning nearly always produces information about the environment, and does not always avoid the costs of asocial learning or support information parasitism. Our study supports work emphasizing the value of incorporating mechanistic detail in functional analyses.

【 授权许可】

CC BY   
© The Author(s) 2016

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