Topic: neural reinforcement learning

Generative models provide a powerful interpretive framework in neural reinforcement learning. However, given the complexity of behavior, models are often proposed in the absence of structurally different alternatives, poten-tially weakening their explanatory force. We employ a data-driven methodology (choice-wide behavioral associa-tion study; CBAS; Kastner et al. BioRxiv 2024), to examine such limitations. We use a dataset of human subjects performing a two-step task that was designed to test the interplay between model-based and model free learning (Gillan et al. eLife 2016). The subjects al