Abstract:
: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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
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 LFdoi
10.1038/s41598-020-77117-8subject
Has Abstractpub_date
2020-11-20 00:00:00pages
20284issue
1issn
2045-2322pii
10.1038/s41598-020-77117-8journal_volume
10pub_type
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