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Humans need social closeness to prosper. There is evidence that empathy can induce socialcloseness. However, it remains unclear how empathy-related social closeness is formed and howstable it is as time passes. Here we applied an acquisition-extinction paradigm, combined withcomputational modelling and fMRI, to investigate the formation and stability of empathy-relatedsocial closeness. Participants observed painful stimulation of another person with high probability(acquisition) and low probability (extinction), and rated their closeness to the other person. Theresults of two independent studies showed an increase in social closeness in the acquisition blockthat resisted extinction in the extinction block. Providing insights into the underlying mechanisms,reinforcement learning modelling revealed that participants formed social closeness based onobserving other’s pain, but maintained social closeness based on observing pain relief, i.e., lessfrequent pain stimulation in the extinction block. Model comparison revealed that this wasassociated with an individual recalibration of the observed feedback value allowing for an increase insocial closeness based on observing another’s pain as well as non-pain. The results of a control studyshowed that this feedback recalibration was specific for the learning of empathy-related socialcloseness. On the neural level, the recalibration of the feedback signal was associated with neuralresponses in anterior insula and adjacent inferior frontal gyrus and the bilateral superior temporalsulcus/temporo-parietal junction (TPJ). These results reveal that empathy-related social closenessgenerated in bad times, i.e., via empathy with the misfortune of another person, transfers to goodtimes, and thus may form one important basis for stable social relationships.

While navigating a fundamentally uncertain world, humans and animals constantly produce subjective confidence judgments, thereby evaluating the probability of their decisions, actions or statements being correct. Confidence typically correlates with neural activity positively in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement- learning models. Altogether, these results identify the VMPFC as a key node in the neuro- computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.

In economic environments, decision-makers may strategically delay irreversible investments to learn from the actions of others creating socially suboptimal outcomes. We investigate if and how communication mitigates the strategic delay in investment timings. Players choose when to invest in a nonrival project with uncertain returns. The earliest investor bears the costs of investment and all players learn whether the project is of good or bad quality. Informational externalities create free-riding incentives resulting in strategic delays in investment timings. Our theoretical analysis suggests that introducing communication into this setting reduces strategic delay. We implement our model in a laboratory experiment utilizing a 2x2 design, where we vary the availability of communication and the number of agents. We find that communication significantly reduces the strategic delay and leads to earlier investment timings in the two-player case. In the case of four players, communication helps subjects to coordinate and reduce strategic delay significantly in the first period of the experiment, while coordination failures emerge in the following periods sweeping away the beneficial effect of communication at the aggregate level.

We study public persuasion in elections with binary outcomes, like referendums. In our model, one or multiple senders attempt to influence the election outcome by manipulating public information about a payoff-relevant state. We allow for a wide class of sender preferences, ranging from pure self-interest to a broad set of social welfare functions, including utilitarian preferences. Our main result identifies a single-crossing property that ensures the optimality of censorship policies, which reveal intermediate states while censoring extreme states. This holds in large elections under both monopolistic and competitive persuasion. The single-crossing property holds for all self-interested senders and more generally under a mild condition for the distribution of voters’ preferences. We analyze how a sender’s optimal censorship policy changes with his preferences and voting rules, and characterize the asymptotic properties as the electorate size goes to infinity. Finally,our results shed new lights on whether media competition maximizes voter welfare.

How do people attribute responsibility when an outcome is not caused by a single person but results from a decision chain involving several people? We study this question in an experiment, in which five voters sequentially decide on how to distribute money between them and five recipients. The recipients can reward or punish each voter, which measures responsibility attribution. In the aggregate, we find that responsibility is attributed mostly according to the voters’ choices and the pivotality of the decision, but not for being the initial voter. On the individual level, we find substantial heterogeneity with three overall patterns: Little to no responsibility attribution, pivotality-driven, and focus on choices. These patterns are similar when praising voters for good outcomes and blaming voters for bad outcomes.