2026-04-17T02:42:21Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/1827072025-12-05T14:16:45Zcom_2072_1057col_2072_478902col_2072_478917
RECERCAT author Freire, Ismael T. author Moulin Frier, Clement author Sanchez Fibla, Marti author Arsiwalla, Xerxes D. author Verschure, Paul F. M. J. 2022-01-27T11:45:21Z2022-01-27T11:45:21Z2020-06-222022-01-25T06:21:36Z 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. cc by (c) Freire, Ismael T. et al., 2020 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess 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 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion