Descrizione del progetto
Determinazione delle reti di apprendimento causale nel cervello
Comprendere in che modo procede l’apprendimento delle relazioni causali è stato un problema fin dall’antichità. Anche con i progressi di oggi nella tecnologia e nelle neuroscienze, tra gli altri campi, l’esatto percorso che il cervello utilizza per costruire tali convinzioni rimane sfuggente. L’ipotesi operativa del progetto CausalBrain è che diverse regioni del cervello sono coinvolte attraverso interazioni dirette nella formazione di reti per contribuire all’apprendimento da parte del cervello. Utilizzando i dati magnetoencefalografici raccolti durante un’attività di apprendimento causale, il progetto mira a chiarire ulteriormente questo percorso e a testare le teorie di apprendimento causale correnti rispetto ai dati comportamentali e cerebrali.
Obiettivo
Humans have an extraordinary capacity to infer cause-effect relations and form beliefs about the causal effect of actions. This ability provides the basis for rational decision-making and allows people to engage in meaningful life and social interactions. In fact, alterations of cognitive processes involved in causal learning have been found in patients affected by psychiatric disorders such as obsessive-compulsive disorder, schizophrenia and addiction. The formation of causal beliefs relies on learning rules determined by the contingency between actions and outcomes. Although fronto-striatal areas are known to be involved in the cogntive architecture of causal beliefs, it is still unknown how these brain regions interact to learn causal structures. This project aims to unravel the link between functional brain networks and causal reasoning. We hypothesize that causal representation are implemented in a dynamic distributed network of directed functional interactions between brain regions and that this network is shaped by learning. We will characterize the modulations of brain circuits involved in learning phases as well as the brain networks responsible of internal representations of contingency values and associated uncertainty. We are going to pursue these two aims by analyzing magneto-encephalografic and intracranial electro-encephalographic data collected during a causal reasoning task. We will use state-of-the-art methods for dynamic directed connectivity estimation. In addition, we will develop machine learning pipelines to found those subnetworks that implement the cognitive architecture of causal learning. Overall, we will be able to understand whether causal learning and the psychological internal variables predicted by rational theories are reflected in dynamically changing directional influences in whole-brain circuits.
Campo scientifico
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
75794 Paris
Francia