Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Applied Computational Psychiatry Lab, Max Planck Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology and Mental Health Neuroscience Department, Division of Psychiatry, University College London, London, United Kingdom.
Elife. 2024 Jul 10;12:RP90634. doi: 10.7554/eLife.90634.
The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.
安慰剂和反安慰剂效应突出了期望在调节疼痛感知中的重要性，但在日常生活中，我们不需要外部信息来源来形成对疼痛的期望。大脑可以通过更基本的方式学习预测疼痛，只需简单地经历波动的、非随机的有害输入流，并从中提取其时间规律。这个过程被称为统计学习。在这里，我们解决了一个关键的开放性问题：统计学习是否调节疼痛感知？我们要求 27 名参与者对波动热痛序列中的疼痛强度进行评分和预测。使用一种计算方法，我们表明概率期望和置信度被用于权衡疼痛感知和预测。因此，这项研究超越了将非疼痛线索与疼痛结果相关联的既定条件作用范式，并表明统计学习本身塑造了疼痛体验。这一发现为疼痛调节的大脑机制研究开辟了一条新途径，对于可能功能失调的慢性疼痛具有重要意义。
