2018
DOI: 10.1007/s12264-018-0267-x
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Separate Neural Networks for Gains and Losses in Intertemporal Choice

Abstract: An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision-making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounti… Show more

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Cited by 22 publications
(14 citation statements)
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“…a more pronounced devaluation of future rewards. The neural underpinnings of intertemporal choice have been investigated using neuroimaging studies in humans and neurophysiological recordings in primates and other species [ 7 , 23 , 24 , 35 , 37 , 55 , 58 , 59 , 68 , 109 ]. One of the earliest neuroimaging studies of intertemporal choice proposed two neural systems in intertemporal choice [ 58 , 59 ]: a beta system, comprising limbic structures thought to place special weight on immediate rewards, whereas prefrontal cortical structures, the delta system, are thought to mediate deliberate, patient choices.…”
Section: Introductionmentioning
confidence: 99%
“…a more pronounced devaluation of future rewards. The neural underpinnings of intertemporal choice have been investigated using neuroimaging studies in humans and neurophysiological recordings in primates and other species [ 7 , 23 , 24 , 35 , 37 , 55 , 58 , 59 , 68 , 109 ]. One of the earliest neuroimaging studies of intertemporal choice proposed two neural systems in intertemporal choice [ 58 , 59 ]: a beta system, comprising limbic structures thought to place special weight on immediate rewards, whereas prefrontal cortical structures, the delta system, are thought to mediate deliberate, patient choices.…”
Section: Introductionmentioning
confidence: 99%
“…Once the optimal model was selected, the participant-specific parameters for the two frame conditions were averaged across the three runs and entered into group analysis with one-sample and paired-sample t-tests, where appropriate. This allowed us to summarize the consistent findings from the subject-specific DCMs using classical statistics (Cho et al, 2013; Li et al, 2015; Neufang et al, 2016; Wiehler, Petzschner, Stephan, & Peters, 2017; Zhang et al, 2018).…”
Section: Methodsmentioning
confidence: 99%
“…First, we demonstrated that frontocentral beta band oscillations can predict trust behavior. Other important features, such as the resting state and interactions functional regions of the brain, need to be further explored to identify their role in predicting trust [58,59]. In addition, to validate the predictive effect, additional samples can be used as the test set to test the predictive effect of the predictive model in future.…”
Section: The Fast Neural Prediction Modelmentioning
confidence: 99%