Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.
Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.
Elife. 2020 Aug 11;9:e54051. doi: 10.7554/eLife.54051.
Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.
决策需要将个人经验中收集到的知识与他人的建议相结合。信息源之间的仲裁过程的神经基础尚未完全阐明。在这项研究中，我们使用层次贝叶斯建模将仲裁正式定义为每个学习系统提供的预测相对精度。在概率学习任务中，参与者使用更有见识的顾问的建议和/或自我采样结果来预测彩票的结果。决策信心（由参与者在其预测上押注的点数衡量）与我们将仲裁定义为精度比的方式有关。功能神经影像学表明，仲裁信号与决策信心无关，涉及特定于模态的大脑区域。有利于自我收集信息的仲裁激活了背外侧前额叶皮层和中脑，而有利于社会信息的仲裁则涉及腹内侧前额叶皮层和杏仁核。这些发现表明，相对精度在行为和神经水平上都可以捕捉到社会和个体学习系统之间的仲裁。
