Abstract Work in computational psychiatry suggests that mood disorders may stem from aberrant reinforcement learning processes. Specifically, it is proposed that depressed individuals believe that negative events are more informative than positive events, resulting in faster learning from negative outcomes (Pulcu &amp; Browning, 2019). In this proof-of-concept study, we investigated whether learning rates for affective outcomes are malleable using transcranial direct current stimulation (tDCS). Healthy adults completed an established reinforcement learning task (Pulcu &amp; Browning, 2017) in which the information content of reward and loss outcomes was manipulated by varying the volatility of stimulus-outcome associations. Learning rates on the tasks were quantified using computational models. Stimulation over dorsolateral prefrontal cortex (DLPFC) but not motor cortex (M1) specifically increased learning rates for reward outcomes. The effects of prefrontal tDCS were cognitive state-dependent: online stimulation increased learning rates for wins; offline stimulation decreased both win and loss learning rates. A replication study confirmed the key finding that online tDCS to DLPFC specifically increased learning rates for rewards relative to losses. Taken together, these findings demonstrate the potential of tDCS for modulating computational parameters of reinforcement learning relevant to mood disorders. Significance statement Disproportionate learning from negative relative to positive outcomes has been implicated in the development and maintenance of depression. The present work demonstrates that transcranial direct current stimulation (tDCS) to dorsolateral prefrontal cortex can specifically increase learning from positive events in healthy adults. Our results provide preliminary evidence that non-invasive brain stimulation can be used to shape reinforcement learning, indicating a potential novel cognitive neurostimulation intervention strategy for affective disorders.

Stimulating human prefrontal cortex increases reward learning