title: From prediction error to incentive salience: mesolimbic computation of reward motivation.
abstract: Reward contains separable psychological components of learning, incentive motivation and pleasure. Most computational models have focused only on the learning component of reward, but the motivational component is equally important in reward circuitry, and even more directly controls behavior. Modeling the motivational component requires recognition of additional control factors besides learning. Here I discuss how mesocorticolimbic mechanisms generate the motivation component of incentive salience. Incentive salience takes Pavlovian learning and memory as one input and as an equally important input takes neurobiological state factors (e.g. drug states, appetite states, satiety states) that can vary independently of learning. Neurobiological state changes can produce unlearned fluctuations or even reversals in the ability of a previously learned reward cue to trigger motivation. Such fluctuations in cue-triggered motivation can dramatically depart from all previously learned values about the associated reward outcome. Thus, one consequence of the difference between incentive salience and learning can be to decouple cue-triggered motivation of the moment from previously learned values of how good the associated reward has been in the past. Another consequence can be to produce irrationally strong motivation urges that are not justified by any memories of previous reward values (and without distorting associative predictions of future reward value). Such irrationally strong motivation may be especially problematic in addiction. To understand these phenomena, future models of mesocorticolimbic reward function should address the neurobiological state factors that participate to control generation of incentive salience.
authors.affiliation: Department of Psychology, University of Michigan, Ann Arbor, MI 48109-1043, USA. berridge@umich.edu
authors.forename: Kent C
authors.initials: KC
authors.lastname: Berridge
mesh_terms: D000818:Animals; D003198:Computer Simulation; D005544:Forecasting; D006801:Humans; D007858:Learning / Q000502:physiology*; D008032:Limbic System / Q000502:physiology*; D008959:Models, Neurological; D009042:Motivation / Q000502:physiology*; D012201:Reward