A computational account of need states, reward and wanting

Abstract

Need states are internal states that arise from deprivation of crucial biological stimuli. They direct motivation, independently of external learning. Despite their separate origin, they interact with reward processing systems that respond to external stimuli. This paper aims to illuminate the functioning of the needing system through the lens of active inference, a framework for understanding brain and cognition. We propose that need states exert a pervasive influence on the organism, which in active inference terms translates to a "pervasive surprise" - a measure of the distance from the organisms preferred state. Crucially, we define needing as an active inference process that seeks to reduce this pervasive surprise. Through a series of simulations, we demonstrate that our proposal successfully captures key aspects of the phenomenology and neurobiology of needing. We show that as need states increase, the tendency to occupy preferred states strengthens, independently of external reward prediction. Furthermore, need states increase the precision of states (stimuli and actions) leading to preferred states, suggesting their ability to amplify the value of reward cues and rewards themselves. Collectively, our model and simulations provide valuable insights into the directional and underlying influence of need states, revealing how this influence amplifies the wanting or liking associated with relevant stimuli.