William Hoppitt and Kevin N. Laland
- Published in print:
- 2013
- Published Online:
- October 2017
- ISBN:
- 9780691150703
- eISBN:
- 9781400846504
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691150703.003.0005
- Subject:
- Biology, Animal Biology
This chapter describes statistical methods for inferring and quantifying social transmission in groups of animals in the wild, or in “captive” groups of animals in naturalistic social environments. ...
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This chapter describes statistical methods for inferring and quantifying social transmission in groups of animals in the wild, or in “captive” groups of animals in naturalistic social environments. In particular, it considers techniques for analyzing time-structured data on the occurrence of a particular behavior pattern, or behavioral trait, in one or more groups. For the most part, the focus is on cases where a novel trait spreads through one or more groups. Following standard terminology in the field of social learning, the spread of a trait through a group is referred to as a diffusion, and the resulting data as diffusion data. The methods include diffusion curve analysis and network-based diffusion analysis. For the latter, inclusion of individual-level variables is taken into account, along with model selection and inference, modeling of multiple diffusions, choosing a social network, and “untransmitted” social effects. The chapter also examines the spatial spread of a behavioral trait.Less
This chapter describes statistical methods for inferring and quantifying social transmission in groups of animals in the wild, or in “captive” groups of animals in naturalistic social environments. In particular, it considers techniques for analyzing time-structured data on the occurrence of a particular behavior pattern, or behavioral trait, in one or more groups. For the most part, the focus is on cases where a novel trait spreads through one or more groups. Following standard terminology in the field of social learning, the spread of a trait through a group is referred to as a diffusion, and the resulting data as diffusion data. The methods include diffusion curve analysis and network-based diffusion analysis. For the latter, inclusion of individual-level variables is taken into account, along with model selection and inference, modeling of multiple diffusions, choosing a social network, and “untransmitted” social effects. The chapter also examines the spatial spread of a behavioral trait.
William Hoppitt and Kevin N. Laland
- Published in print:
- 2013
- Published Online:
- October 2017
- ISBN:
- 9780691150703
- eISBN:
- 9781400846504
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691150703.003.0010
- Subject:
- Biology, Animal Biology
This concluding chapter summarizes the different social learning concepts and methods explored in the book, beginning with definitions of some key terms such as social learning, social transmission, ...
More
This concluding chapter summarizes the different social learning concepts and methods explored in the book, beginning with definitions of some key terms such as social learning, social transmission, imitation, and innovation. The book has discussed the history of social learning research, methods for studying social learning in the laboratory, social learning mechanisms, statistical methods for diffusion data, repertoire-based data, and developmental approaches. It has also examined social learning strategies and some of the mathematical models that can be applied to investigate social learning, cultural evolution, and gene-culture coevolution. A key emphasis throughout the book has been that mathematical and statistical modeling is at its most powerful when tightly integrated with empirical research.Less
This concluding chapter summarizes the different social learning concepts and methods explored in the book, beginning with definitions of some key terms such as social learning, social transmission, imitation, and innovation. The book has discussed the history of social learning research, methods for studying social learning in the laboratory, social learning mechanisms, statistical methods for diffusion data, repertoire-based data, and developmental approaches. It has also examined social learning strategies and some of the mathematical models that can be applied to investigate social learning, cultural evolution, and gene-culture coevolution. A key emphasis throughout the book has been that mathematical and statistical modeling is at its most powerful when tightly integrated with empirical research.
