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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/182707
Modeling the formation of social conventions from embodied real-time interactions
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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.
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FREIRE, Ismael T., et al. Modeling the formation of social conventions from embodied real-time interactions. Plos One. 2020. Vol. 15, num. 6, pags. e0234434. ISSN 1932-6203. [consulted: 7 of June of 2026]. Available at: https://hdl.handle.net/2445/182707