Dr. Isabelle Ripp

Personal information

Dr. Isabelle Ripp is a postdoctoral researcher at the Faculty of Philosophy at LMU Munich. She holds a PhD in neuroimaging from LMU and the Technical University of Munich, where she investigated the effects of cognitive training on brain network architectures. She completed her Master's degree in Neuroscience at LMU and received her Bachelor's degree in Neuroscience from the University of Cologne. Prior to her current position, Dr. Ripp was a postdoctoral fellow at Yale University (USA) and has worked at the Fraunhofer-Gesellschaft in Munich, among other institutions.

She leads the AI workshop at the LUISE Cultural Center in Munich, holds seminars on the interaction between human and artificial cognition and is committed to communicating scientific findings to a broad audience.

Research interests

Dr. Isabelle Ripp's research focuses on the concept of cognitive mapping, which explores how the human brain represents and processes information. Dr. Ripp is particularly interested in the differences and similarities in information processing between the human brain and AI systems, a topic that forms the basis for her work at the interface between cognitive science, philosophy, and technological development.

Selected publications

1. Ripp I., Sun W., Borrmann A. et al. Sensory Modality Influence on Human Reinforcement Learning: Different Response Time but Consistent Performance. Scientific Reports (under review).
2. Lizarraga A, Ripp I, Sala A, et al. (2023). Similarity between structural and proxy estimates of brain connectivity. Journal of Cerebral Blood Flow & Metabolism.
3. Ripp, I., Emch, M., Wu, Q. et al. (2022). Adaptive working memory training does not produce transfer effects in cognition and neuroimaging. Translational Psychiatry.
4. Yakushev, I.*, Ripp, I.*, Wang, M. et al. (2022). Mapping covariance in brain FDG uptake to structural connectivity. European Journal of Nuclear Medicine and Molecular Imaging. *equally contributed
5. Ripp I., Wu Q., Wallenwein L. et al. (2022). Neuronal efficiency following n-back training task is accompanied by a higher cerebral glucose metabolism. NeuroImage.
6. Sala A.*, Lizarraga A.*, Ripp I.* et al. (2022). Static versus Functional PET: Making Sense of Metabolic Connectivity. Cerebral Cortex. *equally contributed
7. Ripp I., Wallenwein L., Wu Q. et al. (2021). Working memory task induced neural activation: A simultaneous PET/fMRI study. NeuroImage.
8. Ripp I., Stadhouders T., Savio A. et al. (2020). Integrity of Neurocognitive Networks in Dementing Disorders as Measured with Simultaneous PET/Functional MRI. Journal of Nuclear Medicine.
9. Ripp, I., Zur Nieden A.-N., Blankenagel S. et al. (2018). Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis. Human Brain Mapping.