Multiple value signals in dopaminergic midbrain and their role in avoidance contexts

The role of dopaminergic brain regions in avoidance behaviour is unclear. Active avoidance requires motivation, and the latter is linked to increased activity in dopaminergic regions. However, avoidance is also often tethered to the prospect of punishment, a state typically characterized by below baseline levels of dopaminergic function. Avoidance has been considered from the perspective of two-factor theories where the prospect of safety is considered to act as a surrogate for reward, leading to dopamine release and enhanced motivational drive. Using fMRI we investigated predictions from two-factor theory by separating the neural representation of a conventional net expected value, which is negative in the case of avoidance, from an adjusted expected value which factors in a possibility of punishment and is larger for both big rewards and big (predictably avoidable) punishments. We show that neural responses in ventral striatum and ventral tegmental area/substantial nigra (VTA/SN) covaried with net expected value. Activity in VTA/SN also covaried with an adjusted expected value, as did activity in anterior insula. Consistent with two-factor theory models, the findings indicate that VTA/SN and insula process an adjusted expected value during avoidance behaviour

Dissociable feedback valence effects on frontal midline theta during reward gain versus threat avoidance learning

While frontal midline theta (FMθ) has been associated with threat processing, with cognitive control in the context of anxiety, and with reinforcement learning, most reinforcement learning studies on FMθ have used reward rather than threat-related stimuli as reinforcer. Accordingly, the role of FMθ in threat-related reinforcement learning is largely unknown. Here, n = 23 human participants underwent one reward-, and one punishment-, based reversal learning task, which differed only with regard to the kind of reinforcers that feedback was tied to (i.e., monetary gain vs. loud noise burst, respectively). In addition to single-trial EEG, we assessed single-trial feedback expectations based on both a reinforcement learning computational model and trial-by-trial subjective feedback expectation ratings. While participants' performance and feedback expectations were comparable between the reward and punishment tasks, FMθ was more reliably amplified to negative vs. positive feedback in the reward vs. punishment task. Regressions with feedback valence, computationally derived, and self-reported expectations as predictors and FMθ as criterion further revealed that trial-by-trial variations in FMθ specifically relate to reward-related feedback-valence and not to threat-related feedback or to violated expectations/prediction errors. These findings suggest that FMθ as measured in reinforcement learning tasks may be less sensitive to the processing of events with direct relevance for fear and anxiety