Computational models can advance affective science by shedding light onto the interplay between cognition and emotion from an information processing point of view. We propose a computational model of emotion that integrates reinforcement learning (RL) and appraisal theory, establishing a formal relationship between reward processing, goal-directed task learning, cognitive appraisal and emotional experiences. The model achieves this by formalizing evaluative checks from the component process model (CPM) in terms of temporal difference learning updates. We formalized novelty, goal relevance, goal conduciveness, and power. The formalization is task independent and can be applied to any task that can be represented as a Markov decision problem (MDP) and solved using RL. We investigated to what extent CPM-RL enables simulation of emotional responses cased by interactive task events. We evaluate the model by predicting a range of human emotions based on a series of vignette studies, highlighting its potential in improving our understanding of the role of reward processing in affective experiences.

翻译：计算模型能够通过信息处理视角揭示认知与情绪之间的相互作用，从而推动情感科学的发展。我们提出了一种整合强化学习（RL）与评估理论的计算情感模型，建立了奖赏处理、目标导向任务学习、认知评估与情感体验之间的形式化关联。该模型通过将成分过程模型（CPM）中的评估性检验转化为时序差分学习更新来实现这一目标。我们形式化了新颖性、目标相关性、目标促进性以及掌控感这四个评估维度。该形式化方法独立于具体任务，可应用于任何能够被表示为马尔可夫决策问题（MDP）并通过RL求解的任务。我们探究了CPM-RL在多大程度上能够模拟由交互性任务事件引发的情感反应。通过一系列情境故事研究对人类多种情绪进行预测来评估该模型，突显了其在提升我们对奖赏处理在情感体验中作用的理解方面的潜力。