title: Learning and evolution: a quantitative genetics approach.
abstract: Recent models of the interactions between learning and evolution show that learning increases the rate at which populations find optima in fixed environments. However, learning ability is only advantageous in variable environments. In this study, quantitative genetics models are used to investigate the effects of individual learning on evolution. Two models of populations of learning individuals are constructed and analyzed. In the first model, the effect of learning is represented as an increase in the variance of selection. Dynamical equations and equilibrium conditions are derived for a population of learning individuals under fixed and variable environmental selection. In the second model, the amount of individual learning effort is regulated by a second gene specifying the duration of a critical learning period. The second model includes a model of the learning process to determine the individual fitness costs and benefits accrued during the learning period. Individuals are then selected for the optimal learning investment. The similarities of the results from these two models suggest that the net effects of learning on evolution are relatively independent of the mechanisms underlying the learning process.
authors.affiliation: Viral and Ricketsial Disease Lab, California Department of Health Services, Berkeley, USA.
authors.forename: R W
authors.initials: RW
authors.lastname: Anderson
mesh_terms: D000818:Animals; D005075:Biological Evolution; D003198:Computer Simulation; D007858:Learning; D008433:Mathematics; D008957:Models, Genetic; D010641:Phenotype