Learning Memory and Decision Lab
Learning from good and bad experiences, and using those experiences to guide our decisions, are essential for adaptive behavior, and dysfunctions in these mechanisms are common in psychiatric disorders and addiction. Our research focuses on understanding the neurocomputational mechanisms supporting value-based learning, memory, and decision-making. Decisions can be based on different types of memories. For example, innumerable positive experiences with chocolate may bias us toward choosing a chocolate dessert, while a single recent experience of a delicious chocolate cake at a restaurant might bias our choice of where to eat. Our research employs a range of methods, including computational modeling of behavior, decoding neural representations using MEG, and transdiagnostic computational factor analysis. By integrating across research disciplines and incorporating aspects of real-world behavior, our work aims toward translational applications that can address societal challenges.
Wimmer, G. E., Liu, Y., McNamee, D. C., & Dolan, R. J. (2023). Distinct replay signatures for prospective decision-making and memory preservation. Proceedings of the National Academy of Sciences, 120(6), e2205211120. https://doi.org/10.1073/pnas.2205211120
Wimmer, G. E., & Büchel, C. (2021). Reactivation of single-episode pain patterns in the hippocampus and decision making. The Journal of Neuroscience, 41(37), 7894–7908. https://doi.org/10.1523/JNEUROSCI.1350-20.2021
Wimmer, G. E., Liu, Y., Vehar, N., Behrens, T. E. J., & Dolan, R. J. (2020). Episodic memory retrieval success is associated with rapid replay of episode content. Nature Neuroscience, 23(8), 1025–1033. https://doi.org/10.1038/s41593-020-0649-z