Functionalist Emotion Modeling in Biomimetic Reinforcement Learning

TL;DR

This paper develops a biologically inspired reinforcement learning framework based on functionalist emotion modeling, aiming to explain emotional valence and apply it to psychological phenomena.

Contribution

It introduces a novel functionalist approach to emotion modeling in reinforcement learning, emphasizing utility function construction grounded in biological plausibility.

Findings

Framework aligns utility function with emotional valence.

Applied to humor, psychopathy, and advertising phenomena.

Demonstrates broad explanatory power of the model.

Abstract

We explore a functionalist approach to emotion by employing an ansatz -- an initial set of assumptions -- that a hypothetical concept generation model incorporates unproven but biologically plausible traits. From these traits, we mathematically construct a theoretical reinforcement learning framework grounded in functionalist principles and examine how the resulting utility function aligns with emotional valence in biological systems. Our focus is on structuring the functionalist perspective through a conceptual network, particularly emphasizing the construction of the utility function, not to provide an exhaustive explanation of emotions. The primary emphasis is not of planning or action execution, but such factors are addressed when pertinent. Finally, we apply the framework to psychological phenomena such as humor, psychopathy, and advertising, demonstrating its breadth of explanatory power.