ArticleScientific reports2020
Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- The inadequacy of normative ratings for building stimulus sets in affective science.Emotion (Washington, D.C.) · 2026Article
- Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience.Royal Society open science · 2025Article
- The Affective Science Network: A Fieldwide Map of over 1 Million Citations.Affective science · 2025Article
- The Effect of Evaluating Self's Emotions on Frontal Alpha Asymmetry.Brain and behavior · 2025Article
- Real-world stress and control: integrating ambulatory physiological and ecological momentary assessment technologies to explain daily wellbeing.Frontiers in psychology · 2025Article
- Naturalistic multimodal emotion data with deep learning can advance the theoretical understanding of emotion.Psychological research · 2024Review
- Neural Predictors of Fear Depend on the Situation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Fear-related psychophysiological patterns are situation and individual dependent: A Bayesian model comparison approach.Emotion (Washington, D.C.) · 2024Article
- Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer.Sensors (Basel, Switzerland) · 2023Article
- Improving the study of brain-behavior relationships by revisiting basic assumptions.Trends in cognitive sciences · 2023Review
- Piecing together the puzzle of emotional consciousness.Neuroscience of consciousness · 2023Review
- Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion.Social cognitive and affective neuroscience · 2022Article
- Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science.The American psychologist · 2022Article
- A Computational Neural Model for Mapping Degenerate Neural Architectures.Neuroinformatics · 2022Article
- A new science of emotion: implications for functional neurological disorder.Brain : a journal of neurology · 2022Review
- The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health.Journal of cognitive neuroscience · 2022Article
- Automatic Decision-Making Style Recognition Method Using Kinect Technology.Frontiers in psychology · 2022Article
- Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Comparing supervised and unsupervised approaches to multimodal emotion recognition.PeerJ. Computer science · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.