# Modeling Cognitive-Affective Processes with Appraisal and Reinforcement Learning

Source: [Modeling Cognitive-Affective Processes with Appraisal and Reinforcement Learning](<https://www.sota2.com/research/paper/68fa71081e209131cea4eb95>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:56:42.060Z

Authors: Jiayi Zhang, Joost Broekens, Jussi Jokinen

Published: 2023-09-12T16:30:06.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Macro-level panic propagation prediction on Hurricane Sandy](<https://www.sota2.com/research/sota/macro-level-panic-propagation-prediction-on-hurricane-sandy.md>): Macro-level panic propagation prediction; Hurricane Sandy; Peak Count: 524.

- [Panic Classification on Hurricane Sandy dataset (test)](<https://www.sota2.com/research/sota/panic-classification-on-hurricane-sandy-dataset-test.md>): Panic Classification; Hurricane Sandy dataset (test); Accuracy: 61.42.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2309.06367>)

[PDF](<https://arxiv.org/pdf/2309.06367.pdf>)