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Sophie Bavard



29 rue d'Ulm
75005 Paris France

Human reinforcement learning
Selected publications
International Journal article  

Bavard, S., Rustichini, A. & Palminteri, S. (2021). Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning. Science Advances, 7(14). doi:10.1126/sciadv.abe0340

International Journal article  

Lebreton, M., Bavard, S., Daunizeau, J. & Palminteri, S. (2019). Assessing inter-individual differences with task-related functional neuroimaging. Nature Human Behaviour. doi:10.1038/s41562-019-0681-8

International Journal article  

Bavard, S., Lebreton, M., Khamassi, M., Coricelli, G. & Palminteri, S. (2018). Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nature Communications, 9(4503)


Born in Lyon and raised in Bordeaux (France), Sophie is a post-doctoral fellow at the LNC² in the Human Reinforcement Learning team. She received a bachelor’s degree in fundamental mathematics, a master’s degree in cellular neuroscience from the Université Pierre et Marie Curie (Paris 6), and a PhD in cognitive and computational neuroscience from the Ecole normale supérieure. Her current work 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.