Extinction of fear responses is critical for adaptive behavior and deficits in this form of safety learning are hallmark of anxiety disorders. However, the neuronal mechanisms that initiate extinction learning are largely unknown. Here we show, using single-unit electrophysiology and cell-type specific fiber photometry, that dopamine neurons in the ventral tegmental area (VTA) are activated by the omission of the aversive unconditioned stimulus (US) during fear extinction. This dopamine signal occurred specifically during the beginning of extinction when the US omission is unexpected, and correlated strongly with extinction learning. Furthermore, temporally-specific optogenetic inhibition or excitation of dopamine neurons at the time of the US omission revealed that this dopamine signal is both necessary for, and sufficient to accelerate, normal fear extinction learning. These results identify a prediction error-like neuronal signal that is necessary to initiate fear extinction and reveal a crucial role of DA neurons in this form of safety learning.

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Online neighborhood social networks (ONSNs) represent an emerging phenomenon among a growing number of niche social networks. These platforms afford users the ability to engage in activities such social interaction with neighbors, sharing of information on local issues or neighborhood volunteering and exhibit promising effects, including improved relationships between neighbors and an increase in neighborly communication. Despite the mounting popularity of platforms such as Nextdoor or nebenan, extant research on ONSNs remains scarce. In this paper, we aim to alleviate this research gap by developing a conceptually and empirically validated taxonomy of ONSNs with a particular focus on their differentiating design properties. We further leverage this taxonomy to derive four distinct archetypes of ONSNs based on a cluster analysis. With our research we provide a first and structured overview on the domain of ONSNs and support researchers and practitioners in analyzing, designing and selecting ONSNs.