Non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models.

The way in which animals and humans learn new motor skills through free exploratory movements sequences solely governed by success or failure outcomes is not yet fully understood. Recent advances in machine learning techniques for continuous action spaces led us to construct a motor learning model investigate how animals progressively enhance the efficiency of their behaviors through numerous trials and errors. This study conducts a comprehensive comparison between deep learning models and experimental data from monkey behavior. Notably, we show that the progressive refinement of motor sequences, as they are observed in the animals, do not require the implementation of a complete model of their environment. Rather, it merely requires the capacity to anticipate both movement costs and final reward a few steps ahead in the future following a value iteration principle. Furthermore, the systematic deviations exhibited by the monkeys with respect to the computational model inform us on the presence of individual preferences in either minimizing the duration or the energy consumption, and also on the involvement of alternative ”cognitive” strategies.