Valentin Guigon

Current position

Dr. Valentin Guigon is a postdoctoral researcher in psychology and neuroscience at the University of Maryland, working in Caroline Charpentier’s Social Learning and Decisions Lab (SLD Lab). He is also an affiliated member of the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM). His research combines behavioral experimentation, computational modeling, and neuroimaging to investigate how people learn, update beliefs, and make decisions under uncertainty, particularly in social contexts.

Dr. Guigon designs experimental paradigms and fits interpretable models of behavior and brain function using Bayesian frameworks, Reinforcement Learning, heuristics, and mixtures of models. His contributions spans from experimental design and data acquisition to model implementation and interpretation - with a strong emphasis on reproducibility, methodological transparency and using cutting-edge methods.

On the side, he is also a) designing an infrastructure to support reproducibility in neuroscience lab experiments, b) experimenting with modular multi-agent systems to support behavioral science workflows - including simulation environments, metadata parsing, and automated documentation -, and c) exploring how cognitive models and AI systems can inform one another.

About me

I was trained in psychology and neuroscience at Aix-Marseille Université and completed a master’s in cognitive science at Université Lumière Lyon 2. I earned my Ph.D. in neuroscience at Université Claude Bernard Lyon 1, under the supervision of Jean-Claude Dreher and Marie Claire Villeval. My doctoral work focused on the neurocognitive mechanisms underlying the transmission of uncertain information in social and economic environments.

Over the years, I’ve adopted a multidisciplinary approach to decision-making - bridging behavioral experiments, computational modeling, and brain imaging to understand belief formation, preference learning, and social inference. I’ve worked on questions ranging from reward learning and moral decision-making to updating beliefs about others, updating trust in others, and networked cognition.

In my current work, I supervise computational neuroscience research projects focused on social learning, particularly examining trust in dynamic and uncertain environments using computational models, fMRI, and behavioral game theory approaches.

Additionally, I contribute to lab-wide infrastructure for data stewardship, modeling workflows, and reproducible pipelines. I’m also interested in the development of AI tools that support scientific reasoning and help structure the production of knowledge. Overall, I strongly believe in, and apply, continuous learning. To that extent, I enjoy working with cutting-edge methods and technologies (e.g., Bayesian inference, LLM and AI agents).

Applied Directions and Broader Interests

• I believe that my duties as a researcher comprise participating in the public discourse when my expertise is relevant. To that extent, I have contributed to discussions on disinformation (fr), echo chambers (fr, en), polarization (fr), critical thinking (en) and belief calibration through public-facing pieces grounded in research.
• I have participated in designing behavioral interventions and incentive-based nudges earlier in my career.
• I participate in public education efforts, such as my invited instruction at the latest 2025 CORTECS summer school on critical thinking.
• I develop systems that support scientific work and reproducibility.
• I am giving classes on fundamentals of cognitive science, first at Université Lumière Lyon 2, now at University at Maryland.

Beyond research, I maintain a strong interest in the role of cognitive science in public policy making, as well as photography, climbing, cinema, music, literature, epistemology, and many more.