A generic self-learning emotional framework for machines Hernández Marcos, Alberto Ros Die, Eduardo Emotions Emotional model Reinforcement learning In nature, intelligent living beings have developed emotions to modulate their behavior as a fundamental evolutionary advantage. However, researchers seeking to endow machines with this advantage lack a clear theory from cognitive neuroscience describing emotional elicitation from first principles, namely, from raw observations to specific affects. As a result, they often rely on casespecific solutions and arbitrary or hard-coded models that fail to generalize well to other agents and tasks. Here we propose that emotions correspond to distinct temporal patterns perceived in crucial values for living beings in their environment (like recent rewards, expected future rewards or anticipated world states) and introduce a fully self-learning emotional framework for Artificial Intelligence agents convincingly associating them with documented natural emotions. Applied in a case study, an artificial neural network trained on unlabeled agent’s experiences successfully learned and identified eight basic emotional patterns that are situationally coherent and reproduce natural emotional dynamics. Validation through an emotional attribution survey, where human observers rated their pleasure-arousal-dominance dimensions, showed high statistical agreement, distinguishability, and strong alignment with experimental psychology accounts. We believe that the framework’s generality and cross-disciplinary language defined, grounded on first principles from Reinforcement Learning, may lay the foundations for further research and applications, leading us toward emotional machines that think and act more like us. 2024-12-03T12:30:57Z 2024-12-03T12:30:57Z 2024-10-28 journal article Hernández Marcos, A. & Ros Die, E. Sci Rep 14, 25858 (2024). [https://doi.org/10.1038/s41598-024-72817-x] https://hdl.handle.net/10481/97662 10.1038/s41598-024-72817-x eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature