Spatial evolution of regularization in learned behavior of animals

Dakari Harris; Dominik Wodarz; Natalia L Komarova

doi:10.1016/j.mbs.2018.03.005

Spatial evolution of regularization in learned behavior of animals

Math Biosci. 2018 May:299:103-116. doi: 10.1016/j.mbs.2018.03.005. Epub 2018 Mar 14.

Authors

Dakari Harris¹, Dominik Wodarz², Natalia L Komarova³

Affiliations

¹ Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA.
² Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA; Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA.
³ Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA; Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA. Electronic address: komarova@uci.edu.

PMID: 29550299
DOI: 10.1016/j.mbs.2018.03.005

Abstract

Stochastic population dynamics of learned traits are studied, where individual learners behave according to a reinforcement learner model, which is a nonlinear version of the Bush-Mosteller model. Depending on a regularization parameter (parameter a), the learners may possess different degrees of overmatching (regularization behavior, 0 ≤ a < 1), frequency matching (corresponding to a=1), or undermatching behavior (a > 1). Both non-spatial and spatial models are considered, to study the interplay of individual heterogeneity of behavior, spatial and temporal effects of learning, and the possibility of emergence of regional culture. In non-spatial models, we observe that populations of individuals learning from each other converge to a universally shared, deterministic rule (either rule "1" or rule "0"), only if they to some extent possess the ability to generalize (a < 1). Otherwise, a low-coherence solution where both rules are used intermittently by everyone, is achieved. If the evolution of the regularization ability is included, then we find that a initially evolves toward lower values, and a shared solution is established when everyone reliably uses the same rule. The spatial (2D) model has two well known limiting cases: if a=0 (the strongest degree of regularization), the model converges to a threshold voter model, and if a=1 (frequency matching), it is equivalent to the discrete diffusion equation. If 0 < a < 1 (the case where individuals regularize), spatial patterns emerge, where patches of different usage of the rule are formed. Smaller values of a lead to sharper and longer lived patches. Values of a < 1 close to unity result in probabilistic outcomes where patches only survive if they are attached to the boundary. Analytical treatment of the 1D case reveals the existence of approximate equilibria that have front structure, where spatially intermittent deterministic usage of one and the other rule are separated by interfaces whose analytical form is derived.

MeSH terms

Animals
Behavior, Animal / physiology*
Learning / physiology*
Models, Theoretical*
Social Behavior*
Spatial Analysis*