Inverse Reinforcement Learning to Study Motivation in Mouse Behavioral Paradigms

Abstract

Motivation describes the underlying goals that drive animal and agent behavior. In Neuroscience, behavioral paradigms are used to quantify the motivations of mice and used to gain insights into traits and diseases which can be translated to humans. In recent years, Computer Vision models are becoming widely adopted by Neuroscientists to score mouse behavior associated with motivations such as hunger and anxiety. However, a single motivation can be expressed by multiple different behaviors, and a single behavior can be linked to multiple motivations. Therefore the ideal analysis of motivational paradigms would attempt to directly recover the underlying motivations guiding behavior, rather than indirectly score their associated behaviors. In this paper, we move towards this goal by applying Inverse Reinforcement Learning to study the underlying motivations that drive mouse behavior.