Physiol Rev. 2014 Jan;94(1):35-79. doi: 10.1152/physrev.00041.2012.
Successful goal-directed behavior requires not only correct action selection, planning, and execution but also the ability to flexibly adapt behavior when performance problems occur or the environment changes. A prerequisite for determining the necessity, type, and magnitude of adjustments is to continuously monitor the course and outcome of one's actions. Feedback-control loops correcting deviations from intended states constitute a basic functional principle of adaptation at all levels of the nervous system. Here, we review the neurophysiology of evaluating action course and outcome with respect to their valence, i.e., reward and punishment, and initiating short- and long-term adaptations, learning, and decisions. Based on studies in humans and other mammals, we outline the physiological principles of performance monitoring and subsequent cognitive, motivational, autonomic, and behavioral adaptation and link them to the underlying neuroanatomy, neurochemistry, psychological theories, and computational models. We provide an overview of invasive and noninvasive systemic measures, such as electrophysiological, neuroimaging, and lesion data. We describe how a wide network of brain areas encompassing frontal cortices, basal ganglia, thalamus, and monoaminergic brain stem nuclei detects and evaluates deviations of actual from predicted states indicating changed action costs or outcomes. This information is used to learn and update stimulus and action values, guide action selection, and recruit adaptive mechanisms that compensate errors and optimize goal achievement.
成功的目标导向行为不仅需要正确的行动选择、计划和执行，还需要在出现性能问题或环境变化时灵活地调整行为。确定调整的必要性、类型和幅度的前提是持续监测自己行为的过程和结果。纠正与预期状态偏差的反馈控制回路构成了神经系统各级适应的基本功能原则。在这里，我们回顾了评估行为过程和结果的神经生理学，涉及到它们的效价，即奖励和惩罚，并启动短期和长期的适应、学习和决策。基于人类和其他哺乳动物的研究，我们概述了绩效监测以及随后的认知、动机、自主和行为适应的生理原理，并将其与潜在的神经解剖学、神经化学、心理理论和计算模型联系起来。我们提供了侵入性和非侵入性全身测量方法的概述，如电生理学、神经影像学和损伤数据。我们描述了广泛的脑区网络如何检测和评估实际状态与预测状态之间的偏差，这些偏差表明行动成本或结果发生了变化。这些信息用于学习和更新刺激和动作值，指导动作选择，并招募适应性机制，以补偿错误并优化目标实现。
