Imitative and Direct Learning as Interacting Factors in Life History Evolution

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Imitative and Direct Learning as Interacting Factors in Life History Evolution. / Bullinaria, John.

In: Artificial Life, Vol. 23, No. 3, 08.08.2017, p. 374-405.

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@article{a2ac973338d14d808edb110543a7b6a0,
title = "Imitative and Direct Learning as Interacting Factors in Life History Evolution",
abstract = "The idea that lifetime learning can have a significant effect on life history evolution has recently been explored using a series of artificial life simulations. These involved populations of competing individuals evolving by natural selection to learn to perform well on simplified abstract tasks, with the learning consisting of identifying regularities in their environment. In reality, there is more to learning than that type of direct individual experience, because it often includes a substantial degree of social learning that involves various forms of imitation of what other individuals have learned before them. This paper rectifies that omission by incorporating memes and imitative learning into revised versions of the previous approach. To do this reliably requires formulating and testing a general framework for meme-based simulations which will enable more complete investigations of learning as a factor in any life history evolution scenarios. It does that by simulating imitative information transfer in terms of memes being passed between individuals, and developing a process for merging that information with the (possibly inconsistent) information acquired by direct experience, leading to a consistent overall body of learning. The proposed framework is tested on a range of learning variations and a representative set of life history factors to confirm the robustness of the approach. The simulations presented illustrate the types of interactions and trade-offs that can emerge, and indicate the kinds of species specific models that could be developed with this approach in the future. ",
keywords = "Imitation, Memes, Artificial life, Life history, Evolution, Learning",
author = "John Bullinaria",
year = "2017",
month = aug,
day = "8",
doi = "10.1162/ARTL_a_00237",
language = "English",
volume = "23",
pages = "374--405",
journal = "Artificial Life",
issn = "1064-5462",
publisher = "Massachusetts Institute of Technology Press",
number = "3",

}

RIS

TY - JOUR

T1 - Imitative and Direct Learning as Interacting Factors in Life History Evolution

AU - Bullinaria, John

PY - 2017/8/8

Y1 - 2017/8/8

N2 - The idea that lifetime learning can have a significant effect on life history evolution has recently been explored using a series of artificial life simulations. These involved populations of competing individuals evolving by natural selection to learn to perform well on simplified abstract tasks, with the learning consisting of identifying regularities in their environment. In reality, there is more to learning than that type of direct individual experience, because it often includes a substantial degree of social learning that involves various forms of imitation of what other individuals have learned before them. This paper rectifies that omission by incorporating memes and imitative learning into revised versions of the previous approach. To do this reliably requires formulating and testing a general framework for meme-based simulations which will enable more complete investigations of learning as a factor in any life history evolution scenarios. It does that by simulating imitative information transfer in terms of memes being passed between individuals, and developing a process for merging that information with the (possibly inconsistent) information acquired by direct experience, leading to a consistent overall body of learning. The proposed framework is tested on a range of learning variations and a representative set of life history factors to confirm the robustness of the approach. The simulations presented illustrate the types of interactions and trade-offs that can emerge, and indicate the kinds of species specific models that could be developed with this approach in the future.

AB - The idea that lifetime learning can have a significant effect on life history evolution has recently been explored using a series of artificial life simulations. These involved populations of competing individuals evolving by natural selection to learn to perform well on simplified abstract tasks, with the learning consisting of identifying regularities in their environment. In reality, there is more to learning than that type of direct individual experience, because it often includes a substantial degree of social learning that involves various forms of imitation of what other individuals have learned before them. This paper rectifies that omission by incorporating memes and imitative learning into revised versions of the previous approach. To do this reliably requires formulating and testing a general framework for meme-based simulations which will enable more complete investigations of learning as a factor in any life history evolution scenarios. It does that by simulating imitative information transfer in terms of memes being passed between individuals, and developing a process for merging that information with the (possibly inconsistent) information acquired by direct experience, leading to a consistent overall body of learning. The proposed framework is tested on a range of learning variations and a representative set of life history factors to confirm the robustness of the approach. The simulations presented illustrate the types of interactions and trade-offs that can emerge, and indicate the kinds of species specific models that could be developed with this approach in the future.

KW - Imitation

KW - Memes

KW - Artificial life

KW - Life history

KW - Evolution

KW - Learning

U2 - 10.1162/ARTL_a_00237

DO - 10.1162/ARTL_a_00237

M3 - Article

VL - 23

SP - 374

EP - 405

JO - Artificial Life

JF - Artificial Life

SN - 1064-5462

IS - 3

ER -