A Neural Computational Model of Incentive Salience

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Abstract
Incentive salience is a motivational property with ‘magnet-like’ qualities. When attributed to reward-predicting stimuli (cues), incentive salience triggers a pulse of ‘wanting’ and an individual is pulled toward the cues and reward. A key computational question is how incentive salience is generated during a cue re-encounter, which combines both learning and the state of limbic brain mechanisms. Learning processes, such as temporal-difference models, provide one way for stimuli to acquire cached predictive values of rewards. However, empirical data show that subsequent incentive values are also modulated on the fly by dynamic fluctuation in physiological states, altering cached values in ways requiring additional motivation mechanisms. Dynamic modulation of incentive salience for a Pavlovian conditioned stimulus (CS or cue) occurs during certain states, without necessarily requiring (re)learning about the cue. In some cases, dynamic modulation of cue value occurs during states that are quite novel, never having been experienced before, and even prior to experience of the associated unconditioned reward in the new state. Such cases can include novel drug-induced mesolimbic activation and addictive incentive-sensitization, as well as natural appetite states such as salt appetite. Dynamic enhancement specifically raises the incentive salience of an appropriate CS, without necessarily changing that of other CSs. Here we suggest a new computational model that modulates incentive salience by integrating changing physiological states with prior learning. We support the model with behavioral and neurobiological data from empirical tests that demonstrate dynamic elevations in cue-triggered motivation (involving natural salt appetite, and drug-induced intoxication and sensitization). Our data call for a dynamic model of incentive salience, such as presented here. Computational models can adequately capture fluctuations in cue-triggered ‘wanting’ only by incorporating modulation of previously learned values by natural appetite and addiction-related states. Reward cues are potent triggers of desires, ranging from normal appetites to compulsive addictions. Food cues may trigger a sudden desire to eat before lunch, and drug cues may trigger even a ‘recovered addict’ to relapse again into drug taking. But learned cues are not constant in their motivating power. Food cues are more potent when you are hungry, and drug cues may become overwhelmingly potent to an addict who tries to take ‘just one’ drink or hit, precipitating an escalating binge of further relapse. These changes in cue-triggered desire produced by a change in biological state present a challenge to many current computational models of motivation. Such modulation can even be unlearned (though the modulation interacts with cues acquired through learning), in that the modulation instantly follows a physiological or neurobiological change (hunger, drug hit, etc.), altering the cue's ability to trigger desire for a relevant reward. Here we demonstrate concrete examples of instant modulation and propose how to build computational models of cue-triggered ‘wanting’ to better capture the dynamic interaction between learning and physiology that controls the incentive salience mechanism of motivation for rewards.