https://www.selleckchem.com/products/ms1943.html
Learning signals during reinforcement learning and cognitive control rely on valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by surprise magnitude. A core debate in reward learning focuses on whether valenced and non-valenced PEs can be isolated in the human electroencephalogram (EEG). We combine behavioral modeling and single-trial EEG regression to disentangle sequential PEs in an interval timing task dissociating outcome valence, magnitude, and probability. Multiple regression across t
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