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Background: Numerous studies have highlighted the pivotal role of alterations in the monetary reward system in the development and maintenance of substance use disorder (SUD). Although these alterations have been well documented in various forms of SUD, the electrophysiological mechanisms specific to opioid use disorder (OUD) remain underexplored. Understanding these mechanisms is critical for developing targeted interventions and advancing theories of addiction specific to opioid use. Objectives: To explore abnormalities in monetary reward outcome processing in males with OUD. We hypothesized that control individuals would show higher feedback-related negativity (FRN) to losses, unlike those in the OUD group, where FRN to losses and gains would not differ significantly. Methods: Fifty-seven participants (29 male individuals with OUD [heroin] and 28 male controls) were evaluated. A combination of the monetary incentive delay task (MIDT) and event-related potential (ERP) technology was used to investigate electrophysiological differences in monetary reward feedback processing between the OUD and healthy control groups. Results: We observed a significant interaction between group (control vs. OUD) and monetary outcome (loss vs. gain), indicated by p <.05 and η2p = 0.116. Specifically, control participants showed stronger negative FRN to losses than gains (p <.05), unlike the OUD group (p >.05). Conclusion: This study's FRN data indicate that males with OUD show altered processing of monetary rewards, marked by reduced sensitivity to loss. These findings offer electrophysiological insights into why males with OUD may pursue drugs despite potential economic downsides. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence technologies, especially large language models (LLMs) like ChatGPT, are revolutionizing information acquisition and content production across a variety of domains. These technologies have a significant potential to impact participation and content production in online knowledge communities. We provide initial evidence of this, analyzing data from Stack Overflow and Reddit developer communities between October 2021 and March 2023, documenting ChatGPT's influence on user activity in the former. We observe significant declines in both website visits and question volumes at Stack Overflow, particularly around topics where ChatGPT excels. By contrast, activity in Reddit communities shows no evidence of decline, suggesting the importance of social fabric as a buffer against the community-degrading effects of LLMs. Finally, the decline in participation on Stack Overflow is found to be concentrated among newer users, indicating that more junior, less socially embedded users are particularly likely to exit., (© 2024. The Author(s).)