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Efficient regulation of internal homeostasis and defending it against perturbations requires adaptive behavioral strategies. However, the computational principles mediating the interaction between homeostatic and associative learning processes remain undefined. Here we use a definition of primary rewards, as outcomes fulfilling physiological needs, to build(More)
Despite explicitly wanting to quit, long-term addicts find themselves powerless to resist drugs, despite knowing that drug-taking may be a harmful course of action. Such inconsistency between the explicit knowledge of negative consequences and the compulsive behavioral patterns represents a cognitive/behavioral conflict that is a central characteristic of(More)
Efficient regulation of internal homeostasis and defending it against perturbations requires complex behavioral strategies. However, the computational principles mediating brain's homeostatic regulation of reward and associative learning remain undefined. Here we use a definition of primary rewards, as outcomes fulfilling physiological needs, to build a(More)
Long-term addicts find themselves powerless to resist drugs, despite knowing that drug-taking may be a harmful course of action, and an explicit motivation to quit. In controlled experiments, human addicts show a self-described mistake characterized by an inconsistency between drug-seeking response and their reported subjective value. We provide a unified(More)
Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important aspects of addiction, however, remain to be explained within a single, unified framework that integrates the 2 mechanisms. Building upon a recently developed(More)
Central to the organization of behavior is the ability to predict the values of outcomes to guide choices. The accuracy of such predictions is honed by a teaching signal that indicates how incorrect a prediction was ("reward prediction error," RPE). In several reinforcement learning contexts, such as Pavlovian conditioning and decisions guided by reward(More)
Behavioral and neural evidence reveal a prospective goal-directed decision process that relies on mental simulation of the environment, and a retrospective habitual process that caches returns previously garnered from available choices. Artificial systems combine the two by simulating the environment up to some depth and then exploiting habitual values as(More)
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