René Burghardt

I am a PhD student at the University of Göttingen, under the primary supervision of Fabian Sinz. My research interests lie at the intersection of artificial intelligence and neuroscience, focusing on understanding the mammalian brain through computational approaches.

I earned my M.Sc. in Applied Data Science from the University of Göttingen in 2024, where my research focused on latent variable models to study inter-area brain communication. Before that, I completed my B.Sc. in Computer Science at Humboldt University of Berlin in 2019.

news

Dec 01, 2024	I have officially started my PhD in Fabian Sinz’s lab.
Nov 12, 2024	Our poster abstract for the 2024 Foraging Conference in Lyon has been accepted. See us there at poster #34!
Nov 10, 2024	I have successfully completed my Master’s degree in Applied Data Science.

publications

Freely Foraging Macaques Value Information in Ambiguous Terrains

Neda Shahidi, Zurna Ahmed, Yuliya Badayeva, and 3 more authors

In Foraging Conference, Dec 2024
Predicting Choices in a Dyadic Foraging Task Using Gated Recurrent Networks

Neda Shahidi, Kacper Ksiezak, Fabian Sinz, and 2 more authors

In International Conference on Complex Networks, Nov 2023
Social Brains Meet Robots Perspectives on Using Artificial Agents for Investigating Social and Observational Learning in Humans and Monkeys

Neda Shahidi, Ricarda Bothe, René Burghardt, and 8 more authors

Nov 2024

Abs DOI

Understanding social interactions in human and non-human primates is an actively investigated research topic in psychology, primatology, cognitive-, and neuroscience. Experimental investigations of such interactions could benefit from using human-like artificial agents as they promise a higher degree of control while partially preserving ecological validity. We discuss the potential of artificial agents for studying the aforementioned fields through the lenses of social and observational learning research. We highlight examples of experimental approaches for children and monkeys and share our perspective on various features of artificial agents that are potentially relevant to building novel experimental paradigms for humans and monkeys. Finally, we discuss an intuitive technique for designing artificial agents for experimental behavioral tasks.