A computational account of needing and wanting

Needing is related to deprivation of something biologically significant, and wanting is linked to reward prediction and dopamine and usually has more power on behavioral activation than need states alone. However, it is unclear why sometimes there is association and sometimes dissociation between needing and wanting. The paper aims to clarify their relations using active inference. In this approach needing is related to a deviation from preferred states that living things tend to occupy in order to reduce their surprise, while wanting, i.e. reward prediction, is related to precision over policy leading to rewards. Through a series of simulations, we demonstrate the interplay between needing and wanting systems. Specifically, our simulations show that when need states increase, the tendency to occupy preferred states is enhanced independently of wanting (or reward prediction), showing a dissociation between needing and wanting. Furthermore, the simulations show that when need states increase, the value of cues that signal reward achievement and the precision of the policies that lead to preferred states increase, suggesting that need can amplify the value of a reward and its wanting. Taken together, our model and simulations help clarifying the directional and underlying influence of need states separately from reward prediction, i.e. wanting, and at the same time show how this same underlying influence of need amplifies wanting, i.e. increases the precision of reward cues that lead to the preferred state.