Indiana University.
Ghent University.
J Cogn Neurosci. 2017 Oct;29(10):1656-1673. doi: 10.1162/jocn_a_01140. Epub 2017 Apr 21.
Recent work on the role of the ACC in cognition has focused on choice difficulty, action value, risk avoidance, conflict resolution, and the value of exerting control among other factors. A main underlying question is what are the output signals of ACC, and relatedly, what is their effect on downstream cognitive processes? Here we propose a model of how ACC influences cognitive processing in other brain regions that choose actions. The model builds on the earlier Predicted Response Outcome model and suggests that ACC learns to represent specifically the states in which the potential costs or risks of an action are high, on both short and long timescales. It then uses those cost signals as a basis to bias decisions to minimize losses while maximizing gains. The model simulates both proactive and reactive control signals and accounts for a variety of empirical findings regarding value-based decision-making.
近期有关 ACC 在认知中的作用的研究主要集中在选择难度、动作价值、风险规避、冲突解决以及控制的价值等因素上。一个主要的基本问题是 ACC 的输出信号是什么,以及它们对下游认知过程有什么影响?在这里,我们提出了一个关于 ACC 如何影响选择动作的其他大脑区域的认知处理的模型。该模型建立在早期的预测反应结果模型的基础上,表明 ACC 学会了专门表示动作的潜在成本或风险在短时间和长时间尺度上都很高的状态。然后,它使用这些成本信号作为基础,在最大化收益的同时最小化损失,从而做出决策。该模型模拟了主动和被动控制信号,并解释了关于基于价值的决策的各种经验发现。