William Hoppitt & Kevin N. Lala 
Social Learning [EPUB ebook] 
An Introduction to Mechanisms, Methods, and Models

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Many animals, including humans, acquire valuable skills and knowledge by copying others. Scientists refer to this as social learning. It is one of the most exciting and rapidly developing areas of behavioral research and sits at the interface of many academic disciplines, including biology, experimental psychology, economics, and cognitive neuroscience. Social Learning provides a comprehensive, practical guide to the research methods of this important emerging field. William Hoppitt and Kevin N. Lala define the mechanisms thought to underlie social learning and demonstrate how to distinguish them experimentally in the laboratory. They present techniques for detecting and quantifying social learning in nature, including statistical modeling of the spatial distribution of behavior traits. They also describe the latest theory and empirical findings on social learning strategies, and introduce readers to mathematical methods and models used in the study of cultural evolution. This book is an indispensable tool for researchers and an essential primer for students.





  • Provides a comprehensive, practical guide to social learning research



  • Combines theoretical and empirical approaches



  • Describes techniques for the laboratory and the field



  • Covers social learning mechanisms and strategies, statistical modeling techniques for field data, mathematical modeling of cultural evolution, and more





€67.99
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About the author

William Hoppitt is senior lecturer in zoology at Anglia Ruskin University.
Kevin N. Lala is professor of behavioral and evolutionary biology at the University of St. Andrews. His books include
Culture Evolves and
Niche Construction: The Neglected Process in Evolution
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Language English ● Format EPUB ● Pages 320 ● ISBN 9781400846504 ● File size 5.5 MB ● Publisher Princeton University Press ● City Princeton ● Country US ● Published 2013 ● Downloadable 24 months ● Currency EUR ● ID 6491636 ● Copy protection Adobe DRM
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