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/9780691150703.003.0008
- Subject:
- Biology, Animal Biology
This chapter focuses on social learning strategies—functional rules specifying what, when, and who to copy. There are many plausible social learning strategies. Individuals might disproportionately ...
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This chapter focuses on social learning strategies—functional rules specifying what, when, and who to copy. There are many plausible social learning strategies. Individuals might disproportionately copy when asocial learning would be difficult or costly, when they are uncertain of what to do, when the environment changes, when established behavior proves unproductive, and so forth. Likewise, animals might preferentially copy the dominant individual, the most successful individual, or a close relative. This chapter presents evidence for some of the better-studied learning heuristics and describes statistical procedures for identifying which social learning strategies are being deployed in a data set. It examines “who” strategies, which cover frequency-dependent biases, success biases, and kin and age biases, as well as “what” strategies, random copying, and statistical methods for detecting social learning strategies. Finally, it evaluates meta-strategies, best strategies, and hierarchical control.Less
This chapter focuses on social learning strategies—functional rules specifying what, when, and who to copy. There are many plausible social learning strategies. Individuals might disproportionately copy when asocial learning would be difficult or costly, when they are uncertain of what to do, when the environment changes, when established behavior proves unproductive, and so forth. Likewise, animals might preferentially copy the dominant individual, the most successful individual, or a close relative. This chapter presents evidence for some of the better-studied learning heuristics and describes statistical procedures for identifying which social learning strategies are being deployed in a data set. It examines “who” strategies, which cover frequency-dependent biases, success biases, and kin and age biases, as well as “what” strategies, random copying, and statistical methods for detecting social learning strategies. Finally, it evaluates meta-strategies, best strategies, and hierarchical control.
William Hoppitt and Kevin N. Laland
- Published in print:
- 2013
- Published Online:
- October 2017
- ISBN:
- 9780691150703
- eISBN:
- 9781400846504
- Item type:
- book
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691150703.001.0001
- Subject:
- Biology, Animal Biology
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 ...
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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. This book provides a comprehensive, practical guide to the research methods of this important emerging field. It defines the mechanisms thought to underlie social learning and demonstrate how to distinguish them experimentally in the laboratory. It presents techniques for detecting and quantifying social learning in nature, including statistical modeling of the spatial distribution of behavior traits. It also describes the latest theory and empirical findings on social learning strategies, and introduces 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.Less
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. This book provides a comprehensive, practical guide to the research methods of this important emerging field. It defines the mechanisms thought to underlie social learning and demonstrate how to distinguish them experimentally in the laboratory. It presents techniques for detecting and quantifying social learning in nature, including statistical modeling of the spatial distribution of behavior traits. It also describes the latest theory and empirical findings on social learning strategies, and introduces 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.
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, ...
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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.
Alberto Acerbi
- Published in print:
- 2019
- Published Online:
- January 2020
- ISBN:
- 9780198835943
- eISBN:
- 9780191873331
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198835943.003.0002
- Subject:
- Psychology, Evolutionary Psychology
Cultural evolution is a diverse field of research, but some similarities can be found: cultural evolutionists defend a quantitative, naturalistic, and interdisciplinary approach to the study of human ...
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Cultural evolution is a diverse field of research, but some similarities can be found: cultural evolutionists defend a quantitative, naturalistic, and interdisciplinary approach to the study of human culture. Importantly, cultural evolutionists are committed to develop sound hypotheses about the individual psychology that drives our cultural behavior. Although there are different nuances, a common idea is that human cognition is specialized for processing social interactions, communication, and learning from others. From an evolutionary point of view, the cognitive mechanisms involved should produce, on average, adaptive outcomes. From this perspective, social learning strategies (a series of relatively simple, general-domain, heuristics to choose when, what, and from whom to copy) provide a first boundary to indiscriminate social influence. I critically examine the concept of social learning strategies, and I discuss how cultural evolutionists may have overestimated both the effect of social influence and, possibly, our reliance of social learning itself. I also discuss the perspective from epistemic vigilance theory, which gives more weight to the possibility of explicit deception, and proposes that we apply sophisticated cognitive operations when deciding whether to trust information coming from others.Less
Cultural evolution is a diverse field of research, but some similarities can be found: cultural evolutionists defend a quantitative, naturalistic, and interdisciplinary approach to the study of human culture. Importantly, cultural evolutionists are committed to develop sound hypotheses about the individual psychology that drives our cultural behavior. Although there are different nuances, a common idea is that human cognition is specialized for processing social interactions, communication, and learning from others. From an evolutionary point of view, the cognitive mechanisms involved should produce, on average, adaptive outcomes. From this perspective, social learning strategies (a series of relatively simple, general-domain, heuristics to choose when, what, and from whom to copy) provide a first boundary to indiscriminate social influence. I critically examine the concept of social learning strategies, and I discuss how cultural evolutionists may have overestimated both the effect of social influence and, possibly, our reliance of social learning itself. I also discuss the perspective from epistemic vigilance theory, which gives more weight to the possibility of explicit deception, and proposes that we apply sophisticated cognitive operations when deciding whether to trust information coming from others.