scholarly journals Social learning research outside the laboratory: How and why?

2010 ◽  
Vol 38 (3) ◽  
pp. 187-194 ◽  
Author(s):  
Rachel L. Kendal ◽  
Bennett G. Galef ◽  
Carel P. Van Schaik
Author(s):  
William Hoppitt ◽  
Kevin N. Laland

This chapter provides a brief historical background to social learning research. The history of research into social learning and imitation dates back to Aristotle, who explicitly made the claim that animals acquire behavior through imitation and other forms of social learning. Aristotle was particularly impressed with the human imitative tendency. The three insights made in the fourth century BC—that humans are uncharacteristically reliant on imitative learning compared to other animals, that young children in particular acquire important aspects of their behavioral repertoire through copying, and that imitation appears intrinsically rewarding to children—are remarkably relevant to contemporary social learning research. The chapter examines how investigations of social learning have been central to research into the evolution of mind, the mechanisms of social learning, animal culture, the diffusion of innovations, child development, and cultural evolution.


2016 ◽  
Vol 55 ◽  
pp. 116-126 ◽  
Author(s):  
Bernd Siebenhüner ◽  
Romina Rodela ◽  
Franz Ecker

Author(s):  
William Hoppitt ◽  
Kevin N. Laland

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.


2014 ◽  
Vol 69 ◽  
pp. 39-47 ◽  
Author(s):  
Georgina Cundill ◽  
Heila Lotz-Sisitka ◽  
Mutizwa Mukute ◽  
Million Belay ◽  
Sheona Shackleton ◽  
...  

2021 ◽  
Vol 8 (9) ◽  
pp. 210450
Author(s):  
Riana Minocher ◽  
Silke Atmaca ◽  
Claudia Bavero ◽  
Richard McElreath ◽  
Bret Beheim

Reproducibility is integral to science, but difficult to achieve. Previous research has quantified low rates of data availability and results reproducibility across the biological and behavioural sciences. Here, we surveyed 560 empirical publications, published between 1955 and 2018 in the social learning literature, a research topic that spans animal behaviour, behavioural ecology, cultural evolution and evolutionary psychology. Data were recoverable online or through direct data requests for 30% of this sample. Data recovery declines exponentially with time since publication, halving every 6 years, and up to every 9 years for human experimental data. When data for a publication can be recovered, we estimate a high probability of subsequent data usability (87%), analytical clarity (97%) and agreement of published results with reproduced findings (96%). This corresponds to an overall rate of recovering data and reproducing results of 23%, largely driven by the unavailability or incompleteness of data. We thus outline clear measures to improve the reproducibility of research on the ecology and evolution of social behaviour.


2020 ◽  
Author(s):  
Riana Minocher ◽  
Silke Atmaca ◽  
Claudia Bavero ◽  
Richard McElreath ◽  
Bret Beheim

Reproducibility is integral to science, but difficult to achieve. We surveyed 560 empirical publications, published between 1955 and 2018 in the social learning literature, a research topic that spans animal behaviour, behavioural ecology, cultural evolution, and evolutionary psychology. Data was recoverable online or through direct data requests for 30% of this sample. Moreover, data recovery declines exponentially with time since publication, halving every 6 years, and up to every 9 years for human experimental data. When data for a publication can be recovered, we estimate a high probability of subsequent data usability (87%), analytical clarity (97%), and agreement of published results with reproduced findings (96%). This corresponds to an overall rate of recovering data and reproducing results of 23%, largely driven by unavailability or incompleteness of data. We thus outline clear measures to improve reproducibility of research on the ecology and evolution of social behaviour.


2020 ◽  
Vol 43 ◽  
Author(s):  
Thibaud Gruber

Abstract The debate on cumulative technological culture (CTC) is dominated by social-learning discussions, at the expense of other cognitive processes, leading to flawed circular arguments. I welcome the authors' approach to decouple CTC from social-learning processes without minimizing their impact. Yet, this model will only be informative to understand the evolution of CTC if tested in other cultural species.


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