Neurophysiology of performance monitoring and adaptive behavior.
Neurophysiology of performance monitoring and adaptive behavior.
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DOI：
10.1152/physrev.00041.2012
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发表时间：
2014
影响因子：
33.6
通讯作者：
M. Ullsperger;C. Danielmeier;G. Jocham
中科院分区：
文献类型：
--
作者：
M. Ullsperger;C. Danielmeier;G. Jocham
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，还需要在出现绩效问题或环境变化时灵活调整行为的能力。确定调整的必要性、类型和幅度的先决条件是持续监控行动的过程和结果。纠正与预期状态的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理。在这里，我们回顾了评估行动过程和结果的效价（即奖励和惩罚）以及启动短期和长期适应、学习和决策的神经生理学。基于对人类和其他哺乳动物的研究，我们概述了表现监测以及随后的认知、动机、自主和行为适应的生理原理，并将它们与潜在的神经解剖学、神经化学、心理学理论和计算模型联系起来。我们提供侵入性和非侵入性系统测量的概述，例如电生理学、神经影像学和病变数据。我们描述了包括额叶皮质、基底神经节、丘脑和单胺能脑干核在内的广泛大脑区域网络如何检测和评估实际与预测状态的偏差，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行动价值，指导行动选择，并招募补偿错误和优化目标实现的自适应机制。
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.