Center for Information and Neural Networks, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.
Neuron. 2019 Mar 20;101(6):1029-1041. doi: 10.1016/j.neuron.2019.01.055.
Since noxious stimulation usually leads to the perception of pain, pain has traditionally been considered sensory nociception. But its variability and sensitivity to a broad array of cognitive and motivational factors have meant it is commonly viewed as inherently imprecise and intangibly subjective. However, the core function of pain is motivational-to direct both short- and long-term behavior away from harm. Here, we illustrate that a reinforcement learning model of pain offers a mechanistic understanding of how the brain supports this, illustrating the underlying computational architecture of the pain system. Importantly, it explains why pain is tuned by multiple factors and necessarily supported by a distributed network of brain regions, recasting pain as a precise and objectifiable control signal.
由于有害刺激通常会导致疼痛感知，因此传统上认为疼痛属于感觉性伤害感受。但由于其可变性和对广泛认知和动机因素的敏感性，疼痛通常被视为固有不精确和难以捉摸的主观感受。然而，疼痛的核心功能是动机性的——引导短期和长期行为远离伤害。在这里，我们举例说明了疼痛的强化学习模型如何提供一种对大脑如何支持这一功能的机制理解，阐明了疼痛系统的基本计算架构。重要的是，它解释了为什么疼痛会受到多种因素的调节，并且必然需要大脑区域的分布式网络来支持，从而将疼痛重新定义为一种精确和可量化的控制信号。
