Not all bad decisions are alike: approach and avoidant bad decisions are associated with distinct network organization

Abstract

IntroductionDecisions under ambiguity occurs daily for everyone. Subsequently, we all deliberate upon options to initiate an action most appropriate for current goal demands. Researchers has attempted to identify factors which contribute to risk taking, alongside the neurocircuitry underpinning it. Empirically, uncertain decision making is frequently assessed using the Iowa Gambling Task (IGT). Research have reliably identified varying regions implicating two broader circuits known as the reward and salience networks. However, considerable work has focused on contrasting “good” versus “bad” decisions.MethodsThe present investigation attempted a unique approach to analyzing the modified IGT acquired during fMRI (n = 24) and focused on active and passive bad decisions to identify potential internetwork connectivity, dissociable connectivity patterns between approach and avoidant bad decisions, and their relationship with personality traits, which can be linked with behavioral approach styles.ResultsNetwork cluster analyses revealed general internetwork connectivity when passing (avoiding) good decks; however, the OFC was functionally disconnected from the rest of the selected brain regions when playing (approaching) bad decks. Decreased reward responsiveness was linked to increased functional connectivity between the lateral OFC and aSMG, while drive was associated with increased functional connectivity between dACC and aINS.DiscussionWe report evidence that approach and avoidant bad decisions are associated with distinct neural communication patterns. Avoidant decisions were marked by substantial network integration and coherence, contrasted with the general scarcity of internetwork communication observed for approach decisions. Furthermore, the present investigation observed preliminary evidence of personality traits linked with neural communication between salience and reward evaluative networks.