• Updated
27 April 2022

Sophie Bavard, winner of the 2021 Société des Neurosciences Thesis Award

The Société des Neurosciences awards annual Thesis Prizes for doctoral work in neuroscience. The young researcher was rewarded for her work on decision making. Her thesis entitled "Computational principles of adaptive coding in healthy and impaired reinforcement learning", defended one year ago, was carried out under the supervision of Stefano Palminteri at the Laboratory of Cognitive and Computational Neuroscience (LNC2).




Research on the importance of context in decision making

The young researcher worked in the Human Reinforcement Learning team led by Stefano Palminteri at the LNC2. She is doing her post-doctoral work with Sébastien Gluth at the General Psychology Lab at the University of Hamburg in Germany where she is working on a social aspect of decision making.

Her current research in cognitive neuroscience finvolves 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.

"Computational principles of adaptive coding in healthy and impaired reinforcement learning"

Abstract : Do you think you make all your decisions rationally? Imagine that you can choose between different fruits: you probably have a pre-established order of preferences and you will make your choice accordingly. If you prefer apples to bananas, and bananas to cherries, chances are you prefer apples to cherries. But is this true for economic choices? Can your experience influence your decisions when it comes to money? These are the questions I addressed during my PhD, between cognitive neuroscience, mathematics and psychiatry. Using mathematical models, we showed that our choices are influenced by the context in which the values of the different alternatives were learned. First, we developed existing models and paradigms explaining decision-making strategies in a large sample of healthy volunteer participants, using recent tools such as large-scale online experiments. Then, we used innovative approaches to identify the links between the parameters of our decision-making models and reward-related pathological traits that may affect value learning. In the long term, this research will potentially help to develop new tools to characterize phenotypes of several pathologies and behavioral disorders, as well as improve patients’ treatment at the individual level.