2026-04-17T12:56:28Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1827072025-12-05T14:16:45Zcom_2072_1057col_2072_478902col_2072_478917

00925njm 22002777a 4500 dc Freire, Ismael T. author Moulin Frier, Clement author Sanchez Fibla, Marti author Arsiwalla, Xerxes D. author Verschure, Paul F. M. J. author 2022-01-27T11:45:21Z 2022-01-27T11:45:21Z 2020-06-22 2022-01-25T06:21:36Z 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. Simulació per ordinador Teoria de jocs Conducta (Psicologia) Human behavior Computer simulation Game theory Modeling the formation of social conventions from embodied real-time interactions