Projektbeschreibung
Forschung an Netzwerken für kausales Lernen im Gehirn
Bereits in der Antike versuchte man herauszufinden, auf welche Weise Kausalzusammenhänge erlernt werden. Doch selbst mit den heutigen technologischen und neurowissenschaftlichen Fortschritten ist der genaue Pfad, über den das Gehirn diese Zusammenhänge herstellt, noch ungeklärt. Die Arbeitshypothese des Projekts CausalBrain lautet, dass mehrere Gehirnregionen durch gezielte Interaktion Netzwerke bilden und so Lernprozesse im Gehirn ermöglichen. Anhand von Daten aus Magnetoenzephalographien (MEG), die bei einer kausalen Lernaufgabe erfasst werden, soll dieser Weg nun weiter geklärt werden. Zudem werden aktuelle kausale Lerntheorien anhand von Verhaltens- und Gehirndaten getestet.
Ziel
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.
Wissenschaftliches Gebiet
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Aufforderung zur Vorschlagseinreichung
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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Koordinator
75794 Paris
Frankreich