ABOUT PCP 2020.

The goal of the first Paris Computational Psychiatry (PCP) Symposium is to provide a broad community of researchers interested in human and animal cognition and its dysfunctions with ongoing work in computational psychiatry and related research fields.

The symposium will take place at the Department of Cognitive Studies at the Ecole Normale Supérieure in central Paris. The event is part of the New Ideas in Decision-Making series, sponsored by the Frontiers in Cognition research program of PSL University and the European Research Council.

ABOUT THE ORGANIZERS.

RENAUD JARDRI

Renaud Jardri is professor of child and adolescent psychiatry at the Lille University school of medicine (France). After graduating a PhD in cognitive neuroscience at Lille University in 2009, he spent 3-years as post-doctoral fellow in computational neuroscience at the Ecole Normale Supérieure, ENS Paris (2009-2011). He currently leads the Plasticity & SubjectivitY team (PSY, INSERM) at the Lille Neuroscience & Cognition Centre, while remaining associate faculty at the ENS, where he can combine behavioral, computational, brain imaging and developmental approaches. He is member of the steering committee of the International Consortium of Hallucinations Research since 2014.

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STEFANO PALMINTERI

My main research topics are reinforcement learning and economic decision-making. More precisely I am interested in understanding the computational, neural bases underlying these processes, and whether and how biases in these processes could explain neuropsychiatric diseases and economic maladaptive behaviours. Broadly speaking  my research can be ascribed to the fields of neuroeconomics and computational psychiatry.

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VALENTIN WYART

Valentin is an Inserm group leader at the Cognitive and Computational Neuroscience Lab of the Ecole Normale Supérieure in Paris. His group studies how we make decisions in uncertain and changing environments, and especially why we make avoidable errors. His research combines computational modeling of behavior with multimodal functional neuroimaging. He has recently applied this line of research to characterize cognitive dysfunctions of learning and decision-making in psychiatric diseases. His work is currently funded by the European Research Council (ERC) and the French National Research Agency (ANR).

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SOPHIE BAVARD

Born in Lyon and raised in Bordeaux (France), Sophie is a PhD student at the LNC² in the Human Reinforcement Learning team. She received a bachelor’s degree in fundamental mathematics and a master’s degree in cellular neuroscience from the Université Pierre et Marie Curie (Paris 6). Her current work in cognitive neuroscience involves computational applications in value-based decision-making. She is interested in the different strategies we use to make decisions, their inter-individual variability, and the neuropathologies emerging from their dysfunction.

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VINCENT BOUTTIER

I am a 3rd year PhD student co-supervised by Sophie Denève (Ecole Normale Supérieure, Paris) and Renaud Jardri (Lille University). I received a Bachelor’s degree from Ecole Polytechnique and a Master’s degree in machine learning from Ecole Normale Supérieure in Cachan. The goal of my PhD is to go towards a more biologically plausible model of how the brain exchanges and represents information. I base this investigation going from the previously developed model "Circular Inference", which uses respectively Belief Propagation and probabilities for that. I am also interested in the disruption of the model, which could explain some of the symptoms of schizophrenia.

JUN SEOK LEE

Jun is a 1st year PhD candidate in the Inference and Decision-Making team under the supervision of Valentin Wyart at the Cognitive and Computational Neuroscience Lab (LNC²) at the Ecole Normale Supérieure (ENS) in Paris. He completed a Bachelor's degree in theoretical physics and pure mathematics at Rutgers University in New Jersey, and a Master's degree in the cognitive sciences at the ENS. His current research project involves investigating the dynamic between multimodal learning mechanisms and the precision of the internal computations one performs leading up to a decision and their neural correlates. 

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