Reinforcement learning (RL) is a concept that has been invaluable to research fields including machine learning, neuroscience, and cognitive science. However, what RL entails partly differs between fields, leading to difficulties when interpreting and translating findings.
This paper lays out these differences and zooms in on cognitive (neuro)science, revealing that we often overinterpret RL modeling results, with severe consequences for future research. Specifically, researchers often assume—implicitly—that model parameters generalize between tasks, models, and participant populations, despite overwhelming negative empirical evidence for this assumption. We also often assume that parameters measure specific, unique, and meaningful (neuro)cognitive processes, a concept we call interpretability, for which […]