Literature DB >> 26006723

The learning of action sequences through social transmission.

Andrew Whalen1, Daniel Cownden, Kevin Laland.   

Abstract

Previous empirical work on animal social learning has found that many species lack the ability to learn entire action sequences solely through reliance on social information. Conversely, acquiring action sequences through asocial learning can be difficult due to the large number of potential sequences arising from even a small number of base actions. In spite of this, several studies report that some primates use action sequences in the wild. We investigate how social information can be integrated with asocial learning to facilitate the learning of action sequences. We formalize this problem by examining how learners using temporal difference learning, a widely applicable model of reinforcement learning, can combine social cues with their own experiences to acquire action sequences. The learning problem is modeled as a Markov decision process. The learning of nettle processing by mountain gorillas serves as a focal example. Through simulations, we find that the social facilitation of component actions can combine with individual learning to facilitate the acquisition of action sequences. Our analysis illustrates that how even simple forms of social learning, combined with asocial learning, generate substantially faster learning of action sequences compared to asocial processes alone, and that the benefits of social information increase with the length of the action sequence and the number of base actions.

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Year:  2015        PMID: 26006723     DOI: 10.1007/s10071-015-0877-x

Source DB:  PubMed          Journal:  Anim Cogn        ISSN: 1435-9448            Impact factor:   3.084


  4 in total

1.  How do horses (Equus caballus) learn from observing human action?

Authors:  Kira Bernauer; Hanna Kollross; Aurelia Schuetz; Kate Farmer; Konstanze Krueger
Journal:  Anim Cogn       Date:  2019-09-17       Impact factor: 2.899

2.  Skill learning and the evolution of social learning mechanisms.

Authors:  Daniel J van der Post; Mathias Franz; Kevin N Laland
Journal:  BMC Evol Biol       Date:  2016-08-24       Impact factor: 3.260

3.  The power of associative learning and the ontogeny of optimal behaviour.

Authors:  Magnus Enquist; Johan Lind; Stefano Ghirlanda
Journal:  R Soc Open Sci       Date:  2016-11-30       Impact factor: 2.963

4.  Deep-Reinforcement Learning-Based Co-Evolution in a Predator-Prey System.

Authors:  Xueting Wang; Jun Cheng; Lei Wang
Journal:  Entropy (Basel)       Date:  2019-08-08       Impact factor: 2.524

  4 in total

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