The Evolution of Learning Under Environmental Variability

By Kai Olav Ellefsen. 2014

5 citations recorded by the source corpus.

An important unanswered question within the evolution of intelligence is how evolved learning efforts relate to environmental characteristics. Through simulated evolution of simple learning agents, we study two different models of the relationship between environmental variability and evolved learning. We begin with a recently proposed model (the “pqmodel”), which suggests that 1) unreliable reinforcing feedback select against learning and 2) a fixed environment selects for innate strategies, whereas a changing environment selects for learned strategies. The other model we study proposes that, in contrast with point 2 above, intermediate values of environmental stability select for learning, whereas both too stable and too variable environments will select against it. We harmonize these seemingly conflicting models by evolving learning agents across a wide range of environments, varying in levels of stability and reliability of stimuli. Based on our findings, we propose a revised model for how learning evolves under different levels of environmental variability.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “signal is used by one network to learn where is the changing location of a stochastic reward. Figure adapted from Soltoggio et al. (2007). plastic neural networks to affect the evolution of learning (Ellefsen, 2014), revealing a complex relationship between environmental variability and evolved lear”