  What is the role of real-time control and learning in the formation of social
conventions? To answer this question, we propose a computational model that
matches human behavioral data in a social decision-making game that was
analyzed both in discrete-time and continuous-time setups. Furthermore, unlike
previous approaches, our model takes into account the role of sensorimotor
control loops in embodied decision-making scenarios. For this purpose, we
introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded
in the Distributed Adaptive Control (DAC) theory of mind and brain, where
low-level sensorimotor control is modulated through perceptual and behavioral
learning in a layered structure. CRL follows these principles by implementing a
feedback control loop handling the agent's reactive behaviors (pre-wired
reflexes), along with an adaptive layer that uses reinforcement learning to
maximize long-term reward. We test our model in a multi-agent game-theoretic
task in which coordination must be achieved to find an optimal solution. We
show that CRL is able to reach human-level performance on standard
game-theoretic metrics such as efficiency in acquiring rewards and fairness in
reward distribution.
