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I am working on using a Bayesian model to estimate parameters for our reward learning data. I’m extending Nate Haines’ Double Update Model for Reversal Learning (Ahn, Haines, and Zhang 2017). Nate modified the version available in his package hBayesDM to work with our dataset, which is a deterministic Reversal Learning task. I have since incrementally extended it to handle two different tasks (reward and punishment learning) and repeated runs.
