Mehdi Keramati

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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)
Reinforcement learning models address animal’s behavioral adaptation to its changing “external” environment, and are based on the assumption that Pavlovian, habitual and goal-directed responses seek to maximize reward acquisition. Negative-feedback models of homeostatic regulation, on the other hand, are concerned with behavioral adaptation in response to(More)
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)
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)
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)
Decision-making in the real world presents the challenge of requiring flexible yet prompt behavior, a balance that has been characterized in terms of a trade-off between a slower, prospective goal-directed model-based (MB) strategy and a fast, retrospective habitual model-free (MF) strategy. Theory predicts that flexibility to changes in both reward values(More)
Drug addiction is a complex behavioral and neurobiological disorder which, in an emergent brain-circuit view, reflects a loss of prefrontal top-down control over subcortical circuits governing drug-seeking and drug-taking. We first review previous computational accounts of addiction, focusing on cocaine addiction and on prevalent dopamine-based(More)