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Dynamic directed functional connectivity of causal learning

Descripción del proyecto

Determinación de las redes encefálicas de aprendizaje causal

Comprender cómo aprendemos las relaciones causales ha sido problemático desde la antigüedad. Incluso con los avances actuales en tecnología y neurociencia, entre otros campos, la vía que utiliza el encéfalo para construir estas creencias sigue siendo difícil de encontrar. La hipótesis de trabajo del proyecto CausalBrain es que intervienen distintas regiones encefálicas a través de interacciones dirigidas a la formación de redes que ayudan al encéfalo a aprender. Empleando datos magnetoencefalográficos recogidos durante una tarea de aprendizaje causal, el proyecto tiene como objetivo aclarar esta vía y probar las actuales teorías de aprendizaje causal con los datos conductuales y encefálicos.

Objetivo

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.

Coordinador

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Aportación neta de la UEn
€ 196 707,84
Dirección
RUE MICHEL ANGE 3
75794 Paris
Francia

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Región
Ile-de-France Ile-de-France Paris
Tipo de actividad
Research Organisations
Enlaces
Coste total
€ 196 707,84