  Affective Computing is a rapidly growing field spurred by advancements in
artificial intelligence, but often, held back by the inability to translate
psychological theories of emotion into tractable computational models. To
address this, we propose a probabilistic programming approach to affective
computing, which models psychological-grounded theories as generative models of
emotion, and implements them as stochastic, executable computer programs. We
first review probabilistic approaches that integrate reasoning about emotions
with reasoning about other latent mental states (e.g., beliefs, desires) in
context. Recently-developed probabilistic programming languages offer several
key desidarata over previous approaches, such as: (i) flexibility in
representing emotions and emotional processes; (ii) modularity and
compositionality; (iii) integration with deep learning libraries that
facilitate efficient inference and learning from large, naturalistic data; and
(iv) ease of adoption. Furthermore, using a probabilistic programming framework
allows a standardized platform for theory-building and experimentation:
Competing theories (e.g., of appraisal or other emotional processes) can be
easily compared via modular substitution of code followed by model comparison.
To jumpstart adoption, we illustrate our points with executable code that
researchers can easily modify for their own models. We end with a discussion of
applications and future directions of the probabilistic programming approach.
