Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.

Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.
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将受监督和无监督的方法与人脑,身体和主观经验中的情绪分类进行比较。

DOI:
10.1038/s41598-020-77117-8
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发表时间:
2020-11-20
期刊:
影响因子:
4.6
通讯作者:
Barrett LF
Barrett LF
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Azari B;Westlin C;Satpute AB;Hutchinson JB;Kragel PA;Hoemann K;Khan Z;Wormwood JB;Quigley KS;Erdogmus D;Dy J;Brooks DH;Barrett LF

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机器学习方法提供了强大的工具来将物理测量映射到科学类别。但是,这些方法是否适合于发现心理学范畴的基本事实呢?我们用情感科学作为一个测试案例来探索这个问题。在情绪研究中,研究人员使用受情绪标签指导的监督分类器,试图发现大脑或身体中相应情绪类别的生物标志物。这种做法依赖于这样的假设,即标签指的是可以发现的客观类别。在这里,我们批判性地研究了这种方法在情绪发作期间收集的三个不同的数据集-测量人类大脑,身体和主观体验-并将监督分类解决方案与无监督聚类的解决方案进行比较,其中没有标签分配给数据。最后,我们提出了一系列建议,以指导研究人员在情感科学及其他领域进行有意义的、数据驱动的发现。
Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes—measuring the human brain, body, and subjective experience—and compare supervised classification solutions with those from unsupervised clustering in which no labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.
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