Estrogen modulates reward prediction errors and reinforcement learning

This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate Extended Data 6 in the publication: photometry data measured with GRABDA from the caudate putamen, motion-corrected and aligned to the events on the temporal wagering task. All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning. Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.