# Applying Probabilistic Programming to Affective Computing

## TL;DR

This paper introduces a probabilistic programming framework for affective computing, enabling flexible, modular, and data-driven modeling of emotions grounded in psychological theories, with practical code examples for researchers.

## Contribution

It presents a novel application of probabilistic programming to model emotions based on psychological theories, facilitating theory comparison and data integration.

## Key findings

- Probabilistic programming offers flexibility and modularity for emotion modeling.
- The approach enables efficient inference and learning from naturalistic data.
- Executable code examples promote adoption and customization by researchers.

## Abstract

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.