How learning can change the course of evolution.

Area of Science:

• Evolutionary Biology
• Machine Learning
• Computational Biology

Background:

• The interplay between phenotypic plasticity (learning) and evolution is a key area in both Evolutionary Biology and Machine Learning.
• While the evolution of learning and using evolution to improve learning are well-studied, the effect of learning on evolution (Baldwin effect) is less understood.
• Existing research suggests learning accelerates evolutionary convergence to environment-induced responses.

Purpose of the Study:

• To investigate the effect of learning on the evolutionary process, specifically the Baldwin effect.
• To determine if learning can alter the genetic outcome of evolution, leading to configurations not matching plastic responses.
• To explore how learning influences the selection of genotypic traits and potential co-evolution between traits.

Main Methods:

• Analytical modeling.
• Agent-based modeling of a foraging task with seasonal resource changes.
• Incorporating evolved and learned trade-offs in foraging success.

Main Results:

• Learning can indeed change the outcome of evolution, producing genetic configurations that diverge from environment-induced plastic responses.
• Demonstrated that learning can alter selection pressures on different traits, suggesting co-evolution within a genome.
• Empirical and analytical results support the novel findings on learning's influence on evolutionary trajectories.

Conclusions:

• Learning's influence on evolution extends beyond adaptation speed, capable of redirecting evolutionary pathways.
• The study highlights a potential co-evolutionary dynamic where learning-enabled traits modify selection on other genetic traits.
• This research offers new perspectives on the Baldwin effect and the intricate relationship between learning and evolution.