As an integral ingredient of human sociality, prosocial behavior requires learning what acts can benefit or harm others. However, it remains unknown how individuals adjust prosocial learning to avoid punishment or to pursue reward. Given that arginine vasopressin (AVP) is a neuropeptide that has been involved in modulating various social behaviors in mammals, it could be a crucial neurochemical facilitator that supports prosocial learning.

In 50 placebo controls and 54 participants with AVP administration, we examined the modulation of AVP on the prosocial learning characterized by reward and punishment framework, as well as its underlying neurocomputational mechanisms combining computational modeling, event-related potentials and oscillations.

We found a self-bias that individuals learn to avoid punishment asymmetrically more severely than reward-seeking. Importantly, AVP increased behavioral performances and learning rates when making decisions to avoid losses for others and to obtain gains for self. These behavioral effects were underpinned by larger responses of stimulus-preceding negativity (SPN) to anticipation, as well as higher punishment-related feedback-related negativity (FRN) for prosocial learning and reward-related P300 for proself benefits, while FRN and P300 neural processes were integrated into theta (4–7 Hz) oscillation at the outcome evaluation stage.

These results suggest that AVP context-dependently up-regulates altruism for concerning others' losses and reward-seeking for self-oriented benefits. Our findings provide insight into the selectively modulatory roles of AVP in prosocial behaviors depending on learning contexts between proself reward-seeking and prosocial punishment-avoidance.

Reward dysfunction is a major dimension of depressive symptomatology, but it remains obscure if that dysfunction varies across different reward types. In this study, we focus on the abnormalities in anticipatory/consummatory processing of monetary and social reward associated with depressive symptoms.

Forty participants with depressive symptoms and forty normal controls completed the monetary incentive delay (MID) and social incentive delay (SID) tasks with event-related potential (ERP) recording.

In the SID but not the MID task, both the behavioral hit rate and the ERP component contingent negative variation (CNV; indicating reward anticipation) were sensitive to the interaction between the grouping factor and reward magnitude; that is, the depressive group showed a lower hit rate and a smaller CNV to large-magnitude (but not small-magnitude) social reward cues compared to the control group. Further, these two indexes were correlated with each other. Meanwhile, the ERP components feedback-related negativity and P3 (indicating reward consumption) were sensitive to the main effect of depression across the MID and SID tasks, though this effect was more prominent in the SID task.

Overall, we suggest that depressive symptoms are associated with deficits in both the reward anticipation and reward consumption stages, particularly for social rewards. These findings have a potential to characterize the profile of functional impairment that comprises and maintains depression.

Excessive worry is a defining feature of generalized anxiety disorder and is present in a wide range of other psychiatric conditions. Therefore, individualized predictions of worry propensity could be highly relevant in clinical practice, with respect to the assessment of worry symptom severity at the individual level.

We applied a multivariate machine learning approach to predict dispositional worry based on microstructural integrity of white matter (WM) tracts.

We demonstrated that the machine learning model was able to decode individual dispositional worry scores from microstructural properties in widely distributed WM tracts (mean absolute error = 10.46, p < 0.001; root mean squared error = 12.82, p < 0.001; prediction R2 = 0.17, p < 0.001). WM tracts that contributed to worry prediction included the posterior limb of internal capsule, anterior corona radiate, and cerebral peduncle, as well as the corticolimbic pathways (e.g. uncinate fasciculus, cingulum, and fornix) already known to be critical for emotion processing and regulation.

The current work thus elucidates potential neuromarkers for clinical assessment of worry symptoms across a wide range of psychiatric disorders. In addition, the identification of widely distributed pathways underlying worry propensity serves to better improve the understanding of the neurobiological mechanisms associated with worry.