Executive function scaffolds reinforcement learning

Reinforcement learning frameworks have contributed tremendously to our better understanding of learning processes in brain and behavior. However, this remarkable success obscures the reality of multiple underlying processes, and in particular hides how executive functions set the frame over which reinforcement learning computations operate. In this talk, I will show that executive functions define the learning substrates (such as choices, stimuli and reinforcers) for other learning mechanisms, setting the stage for what we learn about.

Predictibe and Interpretable: using classic cognitive models and artificial neural networks to understand human learning and decision-making

Quantitative models of behavior are a fundamental tool in cognitive science. Typically, models are hand-crafted to implement specific cognitive mechanisms. Such "classic" models are interpretable by design, but may provide poor fit to experimental data. Artificial neural networks (ANNs), on the contrary, can fit arbitrary datasets at the cost of opaque mechanisms. I will present research in the classic tradition that sheds light on the development of learning during childhood and the teen years, and some studies on hierarchical learning and abstraction.

Towards a foundation model of human cognition

Most cognitive models are domain-specific, meaning that their scope is restricted to a single type of problem. The human mind, on the other hand, does not work like this -- it is a unified system whose processes are deeply intertwined. In this talk, I present our work on building domain-general computational models and using them to understand human cognition. I start by outlining how meta-learning can be used to construct cognitive models across various domains.

Feedback-based motor control can guide plasticity and drive rapid learning

Animals use afferent feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that counteracts its effects. Primary motor cortex (M1) is intimately involved in both processes, integrating inputs from various sensorimotor brain regions to update the motor output. Here, we investigate whether feedback-based motor control and motor adaptation may share a common implementation in M1 circuits.

Grâce à de continuelles avancées technologiques, les neurosciences sont en perpétuel mouvement

Chercheuse au Laboratoire de Neurosciences Cognitives et Computationnelles, Alex Cayco Gajic étudie la façon dont les réseaux neuronaux du cerveau contrôlent le comportement et apprennent de nouvelles tâches, en utilisant des méthodes mathématiques. Des recherches interdisciplinaires à la croisée des nouvelles technologies, qui l’ont amenée à rejoindre récemment le FENS-Kavli Network of Excellence, un prestigieux réseau européen de jeunes chercheurs et chercheuses en neurosciences. À cette occasion, Alex Cayco Gajic revient sur son parcours et ses récents travaux.