Estimation of prediction errors and their role in endogenous pain modulation
associative learning, pain
To analyze how mechanisms of instrumental learning contribute to the observed choice behavior and how this related to reward-induced pain modulation, we fitted reinforcement learning (RL) models to participants’ choices in test trials of the wheel of fortune game. Such models were initially formulated for associative learning (where The prediction error as the difference between the actual and the expected outcome in trial with In the wheel of fortune game, outcomes were implemented as changes in stimulation intensities. Accordingly, Q values were initiated to zero and calculated separately for choices of the color associated with a higher chance to win pain relief (While RL models traditionally used a softmax choice rule (where 