In this project, we investigate how new concepts are learned and represented in the brain over multiple days by integrating behavioral experiments, sleep recordings, scalp electroencephalography, single-neuron recordings in the human brain, and computational modeling.
Concepts represent abstract knowledge about the world that is crucial for making sense of new experiences. They allow us to identify commonalities across individual experiences and apply previously acquired knowledge to novel situations. Yet, how conceptual knowledge is acquired and stored in the human brain is still not fully understood.
Current theories suggest that learning relies on the interaction of two brain systems with different learning dynamics. The hippocampus rapidly encodes individual experiences, whereas the neocortex is thought to gradually extract commonalities across experiences. This raises the question of when and where abstract conceptual knowledge emerges. Sleep may play a critical role in this process by consolidating and reorganizing newly acquired memories, thereby supporting the integration of information across experiences.
A further open question concerns how concepts are represented in the brain. Prototype theories propose that concepts are stored by forming an abstract, average prototype representation. In contrast, exemplar theories assume that individual experiences are stored and that new items are categorized based on their similarity to previously encountered examples. Whether and how these representations change as learning progresses remains unclear.
In this project, we investigate the emergence of conceptual knowledge over several days of learning. We combine behavioral experiments, sleep recordings, and scalp electroencephalography in healthy participants with single-neuron recordings in epilepsy patients with implanted brain electrodes. Cognitive and neural network models allow us to compare competing theories of concept learning with human behavior and brain activity. By integrating these approaches, we aim to reveal how individual experiences are transformed into abstract knowledge that allows us to represent and make sense of the world.
Data & Code Repositories
Workshop Contributions
- Using representational similarity analysis to understand how the brain represents information, Ruch S. and Blanco-Cuaresma S., Invited workshop at the Brainfood Peer Mentoring group, Jacobs Center for Productive Youth, Zurich, Switzerland, 2026, https://doi.org/10.17605/OSF.IO/5MXT4
Conference Contributions (Posters)
- The emergence of concept cells in the human medial temporal lobe, Ruch S., Blanco-Cuaresma S., Kunz L., Guth T., Qin Y., Nett L., Borger V., Surges R., Mormann F., and Reber T.P., Poster at the FENS Forum, Barcelona, Spain, 2026, https://fens2026.abstractserver.com/program/#/details/presentations/2151
- Multi-day and multi-modal concept learning in humans: Behavioral dynamics and computational modeling of exemplar–prototype representations, Blanco-Cuaresma S., Reber T.P., and Ruch S., Poster at the FENS Forum, Barcelona, Spain, 2026, https://fens2026.abstractserver.com/program/#/details/presentations/1738
Conference Contributions (Short Talks)
- Concept learning in high-dimensional, multisensory feature spaces, Ruch S. and Reber T.P., Short talk at the 18th conference of the Swiss Psychological Society, Crans Montana, Switzerland, 2025
