One of the more prominent computational models, based in part on the mismatch theory, is the reinforcement learning theory of the ERN (RL-ERN; Holroyd & Coles, 2002). According to the RL-ERN, the basal ganglia monitor information from both the environment (external) and self-generated actions (internal), and evaluate on-going events based on learned expectations (Holroyd & Coles, 2002). The RL-ERN is rooted in non-human animal work indicating that the basal ganglia induce an increase or decrease in phasic midbrain dopamine (DA) activity, when events are better or worse than expected, respectively (for review see Barto, 1995; Houk, Adams, & Barto, 1995; Schultz, 2002). The RL-ERN theory proposes that the ERN is the result of disinhibition of the ACC by DA neurons signaling events as worse than anticipated. From this perspective, error signals are important for learning because they are used to predict future rewards and non-rewards and to modify ongoing behavior (Barto, 1995; Montague, Dayan, & Sejnowski, 1996; Schultz, 2002).
