Title : Medial prefrontal cortex as an action - outcome predictor
نویسنده
چکیده
In Simulation 6 in the main text, we simulated the PRO model in a 2-arm bandit task similar to a previously reported study. A reinforcement learning model was then fit to the trial-by-trial choice behavior of the PRO model in order to recover effective learning rates in stable and volatile periods. The reinforcement learning model is described by a learning law that tracks the value (V) of choices i, and an actor component that determines the probability of making a particular response. To learn the value of each choice i, we used a delta-learning rule:
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Action-outcome relationships are represented differently by medial prefrontal and orbitofrontal cortex neurons during action execution.
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