Department of Psychiatry and Behavioral Sciences, University of Texas at Austin.
Department of Psychiatry, University of Wisconsin-Madison.
Neuroimage. 2022 Dec 1;264:119709. doi: 10.1016/j.neuroimage.2022.119709. Epub 2022 Oct 22.
Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities. Computational modeling supported a modified reinforcement learning model where participants integrated reward and threat value into a combined total value according to an individually varying policy parameter, which was highly predictive of decisions to approach reward vs avoid threat during trials where the highest reward option was also the highest threat option (i.e., approach-avoidance conflict). ICA analyses demonstrated unique roles for salience, frontoparietal, medial prefrontal, and inferior frontal networks in differential encoding of reward vs threat prediction error and value signals. The left frontoparietal network uniquely encoded degree of conflict between reward and threat value at the time of choice. MVPA demonstrated that delivery of reward and threat could accurately be decoded within salience and inferior frontal networks, respectively, and that decisions to approach reward vs avoid threat were predicted by the relative degree to which these reward vs threat representations were active at the time of choice. This latter result suggests that navigating AAC decisions involves generating mental representations for possible decision outcomes, and relative activation of these representations may bias subsequent decision-making towards approaching reward or avoiding threat accordingly.
许多现实情况需要在奖励和威胁方面进行决策。虽然在理解奖励和威胁的决策机制以及调节神经回路方面已经取得了重大进展,但对于奖励和威胁条件同时存在以产生趋近回避冲突(AAC)的情况,理解有限。在这里,我们利用计算学习模型、独立成分分析(ICA)和多变量模式分析(MVPA)方法来理解一项新任务中的决策,该任务嵌入了同时的奖励和威胁学习,并操纵奖励和威胁概率之间的一致性。计算模型支持了一种经过修改的强化学习模型,其中参与者根据个体变化的策略参数将奖励和威胁值整合到一个组合总价值中,该模型对在最高奖励选项也是最高威胁选项的情况下接近奖励与避免威胁的决策具有高度预测性(即,趋近回避冲突)。ICA 分析表明,在不同的奖励与威胁预测误差和价值信号的编码中,突显、额顶叶、内侧前额叶和下额叶网络具有独特的作用。左额顶叶网络在选择时唯一地编码了奖励和威胁值之间的冲突程度。MVPA 表明,奖励和威胁的传递可以分别在突显和下额叶网络中准确解码,并且接近奖励与避免威胁的决策可以通过这些奖励与威胁表示在选择时的相对活跃程度来预测。后一结果表明,导航 AAC 决策涉及生成可能决策结果的心理表示,并且这些表示的相对激活可能会相应地偏向随后的接近奖励或避免威胁的决策。