Electrophysiological measures reveal the role of anterior cingulate cortex in learning from unreliable feedback
Electrophysiological measures reveal the role of anterior cingulate cortex in learning from unreliable feedback
复制标题
电生理学测量揭示了前扣带皮层在从不可靠反馈中学习中的作用
DOI：
10.3758/s13415-018-0615-3
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发表时间：
2018-10-01
影响因子：
2.9
通讯作者：
Holroyd, Clay B.
中科院分区：
文献类型：
--
作者：
Li, Peng;Peng, Weiwei;Holroyd, Clay B.
虽然越来越多的研究已经调查了强化学习的神经机制，但仍然不清楚大脑如何对不可靠的反馈做出反应。最近的一个理论认为，事件相关脑电位（ERP）的奖励积极性（RewP）成分和额叶中线θ（FMT）功率反映了前扣带皮层（ACC）的独立反馈相关处理功能。在本研究中，脑电图（EEG）记录从参与者，因为他们从事的时间估计任务中，反馈的可靠性是操纵跨条件。在每次回应后，他们都会收到一个提示，表明接下来的反馈刺激是100%，75%或50%可靠的。结果表明，被试的时间估计值随反馈信度的变化呈线性变化。此外，提示100%可靠性的呈现比其他线索引起更大的RewP样ERP组件，和反馈呈现引起的RewP的幅度大致相等的所有三个可靠性条件。相比之下，FMT功率引起的负反馈线性下降，从100%的条件下，75%和50%的条件下，只有FMT功率预测的行为调整在以下试验。此外，Beta功率和Beta功率与FMT相位的交叉频率耦合（CFC）分析表明，Beta-FMT通信对运动区的调制是为了调节行为。我们解释了这些发现的层次强化学习帐户的ACC，其中RewP和FMT建议，以反映奖励处理和控制功能的ACC，分别。
Although a growing number of studies have investigated the neural mechanisms of reinforcement learning, it remains unclear how the brain responds to feedback that is unreliable. A recent theory proposes that the reward positivity (RewP) component of the event-related brain potential (ERP) and frontal midline theta (FMT) power reflect separate feedback-related processing functions of anterior cingulate cortex (ACC). In the present study, the electroencephalogram (EEG) was recorded from participants as they engaged in a time estimation task in which feedback reliability was manipulated across conditions. After each response, they received a cue that indicated that the following feedback stimulus was 100%, 75%, or 50% reliable. The results showed that participants’ time estimates adjusted linearly according to the feedback reliability. Moreover, presentation of the cue indicating 100% reliability elicited a larger RewP-like ERP component than the other cues did, and feedback presentation elicited a RewP of approximately equal amplitude for all of the three reliability conditions. By contrast, FMT power elicited by negative feedback decreased linearly from the 100% condition to 75% and 50% condition, and only FMT power predicted behavioral adjustments on the following trials. In addition, an analysis of Beta power and cross-frequency coupling (CFC) of Beta power with FMT phase suggested that Beta-FMT communication modulated motor areas for the purpose of adjusting behavior. We interpreted these findings in terms of the hierarchical reinforcement learning account of ACC, in which the RewP and FMT are proposed to reflect reward processing and control functions of ACC, respectively.