Pain inadequate treatment is frequent in modern society, with major medical, ethical, and financial implications. In many healthcare environments, pain is quantified prevalently through subjective measures, such as self-reports from patients or health care providers’ personal experience. Recently, automatic diagnostic tools have been developed to detect and quantify pain more “objectively” from facial expressions. However, it is still unclear if these approaches can distinguish pain from other aversive (but painless) states. In this article, we analyzed the facial responses from a database of video-recorded facial reactions evoked by comparably-unpleasant painful and disgusting stimuli. We modeled this information as function of subjective unpleasantness, as well as the specific state evoked by the stimuli (pain vs. disgust). Results show that a machine learning algorithm could predict subjective pain unpleasantness from facial information, but mistakenly detected unpleasant disgust, especially in those models relying in great extent on the brow lowerer. Importantly, pain and disgust could be disentangled using an ad hoc algorithm that rely on combined information from the eyes and the mouth. Overall, the facial expression of pain contains both specific and unpleasantness-related information shared with disgust. Automatic diagnostic tools should be guided to account for this confounding effect.

中文翻译：

---

面部疼痛反应的特定状态和超序成分

在现代社会中，疼痛治疗不充分是很常见的，具有重大的医学、伦理和经济影响。在许多医疗保健环境中，疼痛通常通过主观测量来量化，例如患者的自我报告或医疗保健提供者的个人经历。最近，已经开发出自动诊断工具来更“客观地”从面部表情中检测和量化疼痛。然而，尚不清楚这些方法是否可以将疼痛与其他厌恶（但无痛）状态区分开来。在这篇文章中，我们分析了由相当不愉快的痛苦和恶心刺激引起的视频记录的面部反应数据库中的面部反应。我们将此信息建模为主观不愉快的函数，以及由刺激引起的特定状态（疼痛与. 厌恶）。结果表明，机器学习算法可以从面部信息中预测主观疼痛不愉快，但会错误地检测出不愉快的厌恶，特别是在那些很大程度上依赖于眉毛较低的模型中。重要的是，可以使用依赖于眼睛和嘴巴组合信息的特殊算法来解开疼痛和厌恶。总体而言，疼痛的面部表情包含与厌恶共享的特定和不愉快相关信息。应引导自动诊断工具来解释这种混杂效应。