Objective
Adolescence is defined as the period of life that starts with the biological changes of puberty and ends at the time at which the individual attains a stable, independent role in society. During this period, decisions are characterized by being impulsive and high-risk, and this can have serious consequences, for example accidents caused by dangerous driving and experimentation with alcohol and drugs. It is thus important to understand the neurocognitive processes that underlie decision-making in adolescence.
Decision-making depends on the interaction of several component processes, including the representation of value, response selection and learning. In reinforcement learning all these processes are integrated. Reinforcement learning deals with the ability of learning to improve one’s future choices in order to maximize the occurrence of pleasant events (rewards) and minimize the occurrence of unpleasant events (punishments).
Theoretical models for reinforcement learning postulate the existence of a dual controller for action selection: ‘Model-free’, which is computationally simple, more reflexive and inflexible and ‘Model-based’, which is more reflective and flexible, but computationally complex.
The development of reinforcement learning in human adolescence has been studied only recently. Studies to date have not attempted to disentangle model-free and model-based behaviour. The aims of the current project are: i. to map the development of reinforcement learning systems – via behavioural/computational analysis - and ii. to relate the maturation of model-based reinforcement learning to the functional maturation of specific neural circuits – via functional magnetic resonance experiment.
This study will provide further insights in adolescent reward-based decision-making and its neural bases, within the theoretical framework of reinforcement learning.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-PEOPLE-2012-IEF
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Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
WC1E 6BT LONDON
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.