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Beyond dopamine Characterizing the computational functions of midbrain modulato...

Beyond dopamine Characterizing the computational functions of midbrain modulatory neurotransmitter systems in human reinforcement learning using model based pharmacological fMRI Understanding how humans and other animals are able to learn from experience and use this information to select future behavioural strategies to obtain the reinforcers necessary for survival, is a fundamental research question in... ver más

30/09/2010

TRINITY COLLEGE DU...

2M€

Presupuesto del proyecto: 2M€

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THE PROVOST FELLOWS FOUNDATION SCHOLARS THE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2010-09-30 No tenemos la información de la convocatoria

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THE PROVOST FELLOWS FOUNDATION SCHOLARS THE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Understanding how humans and other animals are able to learn from experience and use this information to select future behavioural strategies to obtain the reinforcers necessary for survival, is a fundamental research question in biology. Considerable progress has been made in recent years on the neural computational underpinnings of this process following the observation that the phasic activity of dopamine neurons in the midbrain resembles a prediction error from a formal computational theory known as reinforcement learning (RL). While much is known about the functions of dopamine in RL, much less is known about the computational functions of other modulatory neurotransmitter systems in the midbrain such as the cholinergic, norcpinephrine, and serotonergic systems. The goal of this research proposal to the ERC, is to begin a systematic study of the computational functions of these other neurotransmitter systems (beyond dopamine) in RL. To do this we will combine functional magnetic resonance imaging in human subjects while they perform simple decision making tasks and undergo pharmacological manipulations to modulate systemic levels of these different neurotransmitter systems. We will combine computational model-based analyses with fMRI and behavioural data in order to explore the effects that these pharmacological modulations exert on different parameters and modules within RL. Specifically, we will test the contributions that the cholinergic system makes in setting the learning rate during RL and in mediating computations of expected uncertainty in the distribution of rewards available, we will test for the role of norepinephrine in balancing the rate of exploration and exploitation during decision making, as well as in encoding the level of unexpected uncertainty, and we will explore the possible role of serotonin in setting the rate of temporal discounting for reward, or in encoding prediction errors during aversive as opposed to reward-learning.

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