Modelling the evolution of learning

The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. Here, we present a novel way of modeling learning using small neural networks and a simple, biology-inspired learning algorithm. We used this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning regularly evolved in our individual-based simulations. However, the evolution of learning was less likely in relatively constant environments(where learning is less important) or in case of short-lived agents (that cannot afford to spend much of their lifetime on exploration). Once learning did evolve, the characteristics of the learning strategy and the average performance after learning were surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, agent lifespan had a strong effect on the evolved learning strategy.Interestingly, a longer learning period did not always lead to a better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, our study shows that even a relatively simple learning mechanism can lead to efficient adaptation