  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.
