Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

Kragel, Philip A.; LaBar, Kevin S.

doi:10.1037/a0031820

Cited by 134Emotion and Mood RecognitionHeart Rate Variability and Autonomic ControlNeural and Behavioral Psychology Studies

Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

Open access

Kragel, Philip A., LaBar, Kevin S.·2013·Emotion

DOI 10.1037/a0031820PMC PMC3745776

Open this paper in CoChat — free account

Abstract

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1037/a0031820

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The same is true for studies that successfully created different patterns of autonomic physiology, despite using the same stimuli and experimental method, and sampling from the same population.
“The same is true for studies that successfully created different patterns of autonomic physiology, despite using the same stimuli and experimental method, and sampling from the same population (e.g. Stephens et al., 2010; Kragel and LaBar, 2013).”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Kragel and LaBar, 2013
The Method (Materials and Procedure; page unavailable) says the study used mostly the same emotion-induction stimuli as Stephens et al. (2010), and the Discussion reports successful autonomic classification. But the Method specifies different contentment films, the Discussion identifies methodological and analytical differences, and the copy does not establish that the studies sampled from the same population. Thus, the claim's unqualified sameness of stimuli, method, and population is not supported. (LLM, conf=0.86)
Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
A circumplex model of affect.
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · see citing works
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992 · see citing works
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 11,966 · see citing works
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646 · see citing works
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,861 · see citing works
Statistical pattern recognition: a review
Jain, A. K., Duin, P., & Mao, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824819 · Cited by 6,795 · see citing works
Core affect and the psychological construction of emotion.
Russell, J. A. (2003) · Psychological Review · doi:10.1037/0033-295x.110.1.145 · Cited by 6,113 · see citing works
Handbook of Psychophysiology
(2001) · doi:10.1017/cbo9780511546396 · Cited by 4,482 · see citing works
Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI
Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002) · NeuroImage · doi:10.1006/nimg.2002.1087 · Cited by 3,502 · see citing works
Heart rate variability: A review
Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-006-0119-0 · Cited by 2,820 · see citing works
Autonomic nervous system activity distinguishes among emotions
Ekman, P., Levenson, R. W., & Friesen, W. V. (1983) · Science · doi:10.1126/science.6612338 · Cited by 2,692 · see citing works
The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology
POSNER, J., RUSSELL, J. A., & PETERSON, B. S. (2005) · Development and Psychopathology · doi:10.1017/s0954579405050340 · Cited by 2,602 · see citing works
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334 · see citing works
The brain basis of emotion: a meta-analytic review
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11000446 · Cited by 2,320 · cited 2× · see citing works
“, & Lin, C. J. (2002). A comparison of methods for multiclass”
BackgroundMethod
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271 · see citing works
Choosing Multiple Parameters for Support Vector Machines
Chapelle, O., Vapnik, V. N., Bousquet, O., & Mukherjee, S. (2002) · Machine Learning · doi:10.1023/a:1012450327387 · Cited by 2,215 · see citing works
Emotion elicitation using films
Gross, J. J., & Levenson, R. W. (1995) · Cognition & Emotion · doi:10.1080/02699939508408966 · Cited by 2,179 · see citing works
Are Emotions Natural Kinds?
Barrett, L. F. (2006) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2006.00003.x · Cited by 1,859 · see citing works
Solving the Emotion Paradox: Categorization and the Experience of Emotion
Barrett, L. F. (2006) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr1001_2 · Cited by 1,679 · see citing works
The Nature of Emotions
Plutchik, R. (2001) · American Scientist · doi:10.1511/2001.4.344 · Cited by 1,428 · see citing works
Small sample size effects in statistical pattern recognition: Recommendations for practitioners
Raudys, S. J., & Jain, A. K. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.75512 · Cited by 1,384 · see citing works
Cognitive Neuroscience of Emotion
(1999) · doi:10.1093/oso/9780195118889.001.0001 · Cited by 1,288 · see citing works
Voluntary Facial Action Generates Emotion‐Specific Autonomic Nervous System Activity
Levenson, R. W., Ekman, P., & Friesen, W. V. (1990) · Psychophysiology · doi:10.1111/j.1469-8986.1990.tb02330.x · Cited by 1,248 · see citing works
Valence, gender, and lateralization of functional brain anatomy in emotion: a meta-analysis of findings from neuroimaging
Wager, T. D., Phan, K. L., Liberzon, I., & Taylor, S. F. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00078-8 · Cited by 1,150 · see citing works
Basic Emotions, Natural Kinds, Emotion Schemas, and a New Paradigm
Izard, C. E. (2007) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2007.00044.x · Cited by 1,110 · see citing works
Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability
Brennan, M., Palaniswami, M., & Kamen, P. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.959330 · Cited by 1,044 · see citing works
The Structure of Current Affect: Controversies and Emerging Consensus
Barrett, L. F., & Russell, J. A. (1999) · Current Directions in Psychological Science · doi:10.1111/1467-8721.00003 · Cited by 974 · see citing works
Basic emotions, relations among emotions, and emotion-cognition relations.
Izard, C. E. (1992) · Psychological Review · doi:10.1037/0033-295x.99.3.561 · Cited by 972 · see citing works
The Physiological Differentiation between Fear and Anger in Humans
Ax, A. F. (1953) · Psychosomatic Medicine · doi:10.1097/00006842-195309000-00007 · Cited by 903 · see citing works
Measuring Emotion: Behavior, Feeling, and Physiology
Bradley, M. M., & Lang, P. J. (1999) · doi:10.1093/oso/9780195118889.003.0011 · Cited by 740 · see citing works
Theoretical analysis of an alphabetic confusion matrix
Townsend, J. T. (1971) · Perception & Psychophysics · doi:10.3758/bf03213026 · Cited by 602 · see citing works
Handbook of psychophysiology, 2nd ed.
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (2000) · Cited by 544 · see citing works
Basic emotions are associated with distinct patterns of cardiorespiratory activity
Rainville, P., Bechara, A., Naqvi, N. H., & Damásio, A. R. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2005.10.024 · Cited by 499 · see citing works
Cardiovascular, electrodermal, and respiratory response patterns to fear‐ and sadness‐inducing films
Kreibig, S. D., Wilhelm, F. H., Roth, W. T., & Gross, J. J. (2007) · Psychophysiology · doi:10.1111/j.1469-8986.2007.00550.x · Cited by 495 · see citing works
Short-Term Psychotherapy and Emotional Crisis
Sifneos, P. E. (1972) · Cited by 452 · see citing works
Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms
Bouckaert, R., & Frank, E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24775-3_3 · Cited by 437 · see citing works
Short-Term Psychotherapy and Emotional Crisis
MALAN, D. H. (1973) · Psychosomatic Medicine · doi:10.1097/00006842-197303000-00013 · Cited by 413 · see citing works
Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate approach
Christie, I. C., & Friedman, B. H. (2004) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2003.08.002 · Cited by 347 · see citing works
Untitled work
(1997) · VU Research Portal · Cited by 307 · see citing works
The Embodiment of Emotional Feelings in the Brain
Harrison, N. A., Gray, M. A., Gianaros, P. J., & Critchley, H. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.1725-10.2010 · Cited by 250 · see citing works
Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability
Merri, M., Farden, D. C., Mottley, J. G., & Titlebaum, E. L. (1990) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.43621 · Cited by 248 · see citing works
Autonomic specificity of basic emotions: Evidence from pattern classification and cluster analysis
Stephens, C. L., Christie, I. C., & Friedman, B. H. (2010) · Biological Psychology · doi:10.1016/j.biopsycho.2010.03.014 · Cited by 209 · see citing works
The Autonomic Differentiation of Emotions Revisited: Convergent and Discriminant Validation
Stemmler, G. (1989) · Psychophysiology · doi:10.1111/j.1469-8986.1989.tb03163.x · Cited by 189 · see citing works
Feelings and the body: the Jamesian perspective on autonomic specificity of emotion
Friedman, B. H. (2010) · Biological Psychology · doi:10.1016/j.biopsycho.2009.10.006 · Cited by 178 · see citing works
BioSig: A Free and Open Source Software Library for BCI Research
Schlögl, A., & Brunner, C. (2008) · Computer · doi:10.1109/mc.2008.407 · Cited by 141 · see citing works
Preschool Children's Interpretation of Facial Expressions of Emotion
Bullock, M., & Russell, J. A. (1984) · International Journal of Behavioral Development · doi:10.1177/016502548400700207 · Cited by 135 · see citing works
The motivational organization of emotions : affect-reflex connections
Lang, P. (1994) · Medical Entomology and Zoology · Cited by 132 · see citing works
Analysis of tactile and visual confusion matrices
Loomis, J. M. (1982) · Perception & Psychophysics · doi:10.3758/bf03206199 · Cited by 124 · see citing works
An affective computing approach to physiological emotion specificity: Toward subject‐independent and stimulus‐independent classification of film‐induced emotions
Kolodyazhniy, V., Kreibig, S. D., Gross, J. J., Roth, W. T., & Wilhelm, F. H. (2011) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01170.x · Cited by 122 · see citing works
The motivational organization of emotion: Affect-reflex connections.
Lang, P. J. (1994) · Cited by 82 · see citing works
Evaluation of Ten Psychometric Criteria for Circumplex Structure
Acton, G. S., & Revelle, W. (2004) · RIT Scholar Works (Rochester Institute of Technology) · doi:10.23668/psycharchives.12793 · Cited by 56 · see citing works
The nature and function of emotion
Scherer, K. R. (1982) · Social Science Information · doi:10.1177/053901882021004001 · Cited by 55 · see citing works

Flagged first, then by citations

Research works printed but not linked: 1

Bullock M Russell JA Preschool childrens interpretation of facial expressions of emotion International Journal of Behavioral Development 1984 7 193 214

Sorted by citations

Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019) · Psychological Science in the Public Interest · doi:10.1177/1529100619832930 · Cited by 1,748 · see citing works
The theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw154 · Cited by 1,426 · see citing works
“The same is true for studies that successfully created different patterns of autonomic physiology, despite using the same stimuli and experimental method, and sampling from the same population (e.g. Stephens et al. , 2010; Kragel and LaBar, 2013).”
The theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx060 · Cited by 950 · see citing works
“The same is true for studies that successfully created different patterns of autonomic physiology, despite using the same stimuli and experimental method, and sampling from the same population (e.g. Stephens et al., 2010; Kragel and LaBar, 2013).”
Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories
Siegel, E. H., Sands, M. K., Van den Noortgate, W., Condon, P., Chang, Y., Dy, J., Quigley, K. S., & Barrett, L. F. (2018) · Psychological Bulletin · doi:10.1037/bul0000128 · Cited by 515 · see citing works
Decoding the Nature of Emotion in the Brain
Kragel, P. A., & LaBar, K. S. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.011 · Cited by 388 · see citing works
Self-citation
A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect
Chang, L. J., Gianaros, P. J., Manuck, S. B., Krishnan, A., & Wager, T. D. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002180 · Cited by 336 · see citing works
Experimental Methods for Inducing Basic Emotions: A Qualitative Review
Siedlecka, E., & Denson, T. F. (2018) · Emotion Review · doi:10.1177/1754073917749016 · Cited by 314 · see citing works
Psychological Construction: The Darwinian Approach to the Science of Emotion
Barrett, L. F. (2013) · Emotion Review · doi:10.1177/1754073913489753 · Cited by 311 · see citing works
Beyond happiness: Building a science of discrete positive emotions.
Shiota, M. N., Campos, B., Oveis, C., Hertenstein, M. J., Simon-Thomas, E. R., & Keltner, D. J. (2017) · American Psychologist · doi:10.1037/a0040456 · Cited by 279 · see citing works
What does music express? Basic emotions and beyond
Juslin, P. N. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00596 · Cited by 266 · see citing works
The Default Mode Network’s Role in Discrete Emotion
Satpute, A. B., & Lindquist, K. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.07.003 · Cited by 262 · see citing works
A Bayesian Model of Category-Specific Emotional Brain Responses
Wager, T. D., Kang, J., Johnson, T. D., Nichols, T. E., Satpute, A. B., & Barrett, L. F. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004066 · Cited by 262 · see citing works
The neural basis of one's own conscious and unconscious emotional states
Smith, R. S., & Lane, R. D. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.003 · Cited by 240 · see citing works
Boredom as a seeking state: Boredom prompts the pursuit of novel (even negative) experiences.
Bench, S. W., & Lench, H. C. (2018) · Emotion · doi:10.1037/emo0000433 · Cited by 241 · see citing works
What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures
Cowen, A., Fang, X., Sauter, D. A., & Keltner, D. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910704117 · Cited by 237 · see citing works
Somatic and vicarious pain are represented by dissociable multivariate brain patterns
Krishnan, A., Woo, C., Chang, L. J., Ruzic, L. L., Gu, X., López‐Solà, M., Jackson, P. L., Pujol, J., Fan, J., & Wager, T. D. (2016) · eLife · doi:10.7554/elife.15166 · Cited by 233 · see citing works
Physiological feelings
Pace-Schott, E. F., Amole, M. C., Aue, T., Balconi, M., Bylsma, L. M., Critchley, H., Demaree, H. A., Friedman, B. H., Gooding, A. E. K., Gosseries, O., et al. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.05.002 · Cited by 221 · see citing works
“specificity is supported by rich experimental work (for review, see Kreibig, 2010) and multi-measure studies using statistical classification (Christie and Friedman, 2004; Kragel and Labar, 2013; Stephens et al., 2010).”
Multivariate neural biomarkers of emotional states are categorically distinct
Kragel, P. A., & LaBar, K. S. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv032 · Cited by 218 · see citing works
Self-citation
The nuts and bolts of animal emotion
Kremer, L., Holkenborg, S. E. J. K., Reimert, I., Bolhuis, J. E., & Webb, L. E. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.01.028 · Cited by 198 · see citing works
Distributed affective space represents multiple emotion categories across the human brain
Saarimäki, H., Ejtehadian, L. F., Glerean, E., Jääskeläinen, I. P., Vuilleumier, P., Sams, M., & Nummenmaa, L. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy018 · Cited by 146 · see citing works
Music Communicates Affects, Not Basic Emotions – A Constructionist Account of Attribution of Emotional Meanings to Music
Céspedes-Guevara, J., & Eerola, T. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00215 · Cited by 153 · see citing works
Context-aware experience sampling reveals the scale of variation in affective experience
Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69180-y · Cited by 124 · see citing works
On the deep structure of social affect: Attitudes, emotions, sentiments, and the case of “contempt”
Gervais, M. M., & Fessler, D. M. T. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16000352 · Cited by 129 · see citing works
The Affective Computing Approach to Affect Measurement
D’Mello, S. K., Kappas, A., & Gratch, J. S. (2017) · Emotion Review · doi:10.1177/1754073917696583 · Cited by 127 · see citing works
Grounding Business Models: Cognition, Boundary Objects, and Business Model Change
Shepherd, D. A., Seyb, S., & George, G. (2021) · Academy of Management Review · doi:10.5465/amr.2020.0173 · Cited by 117 · see citing works
Navigating the Science of Emotion
Barrett, L. F. (2016) · Elsevier eBooks · doi:10.1016/b978-0-08-100508-8.00002-3 · Cited by 112 · see citing works
Unconscious emotion: A cognitive neuroscientific perspective
Smith, R. S., & Lane, R. D. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.013 · Cited by 113 · see citing works
Concepts dissolve artificial boundaries in the study of emotion and cognition, uniting body, brain, and mind
Hoemann, K., & Barrett, L. F. (2018) · Cognition & Emotion · doi:10.1080/02699931.2018.1535428 · Cited by 111 · see citing works
Is there consistency and specificity of autonomic changes during emotional episodes? Guidance from the Conceptual Act Theory and psychophysiology
Quigley, K. S., & Barrett, L. F. (2014) · Biological Psychology · doi:10.1016/j.biopsycho.2013.12.013 · Cited by 101 · see citing works
Aesthetic Emotions Reconsidered
Fingerhut, J., & PRINZ, J. J. (2020) · The Monist · doi:10.1093/monist/onz037 · Cited by 98 · see citing works
Emotion, somatovisceral afference, and autonomic regulation
Norman, G. J., Berntson, G. G., & Cacioppo, J. T. (2014) · Emotion Review · doi:10.1177/1754073913512006 · Cited by 97 · see citing works
Multivoxel pattern analysis does not provide evidence to support the existence of basic emotions
Clark-Polner, E., Johnson, T. D., & Barrett, L. F. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw028 · Cited by 97 · see citing works
Development and validation of an unsupervised scoring system (Autonomate) for skin conductance response analysis
Green, S. R., Kragel, P. A., Fecteau, M. E., & LaBar, K. S. (2013) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2013.10.015 · Cited by 96 · see citing works
Self-citation
Exploring the Toolkit of Emotion: What Do Sadness and Anger Do for Us?
Lench, H. C., Tibbett, T. P., & Bench, S. W. (2016) · Social and Personality Psychology Compass · doi:10.1111/spc3.12229 · Cited by 101 · see citing works
Decoding subjective emotional arousal from EEG during an immersive virtual reality experience
Hofmann, S., Klotzsche, F., Mariola, A., Nikulin, V. V., Villringer, A., & Gaebler, M. (2021) · eLife · doi:10.7554/elife.64812 · Cited by 93 · see citing works
Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience
Azari, B., Westlin, C., Satpute, A. B., Hutchinson, J. B., Kragel, P. A., Hoemann, K., Khan, Z., Wormwood, J. B., Quigley, K. S., Deniz Erdogmus, B., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-77117-8 · Cited by 76 · see citing works
Self-citation
A Functionalist Manifesto: Goal-Related Emotions From an Evolutionary Perspective
Lench, H. C., Bench, S. W., Darbor, K. E., & Moore, M. (2014) · Emotion Review · doi:10.1177/1754073914553001 · Cited by 71 · see citing works
Is boredom one or many? A functional solution to the problem of heterogeneity
Elpidorou, A. (2020) · Mind & Language · doi:10.1111/mila.12282 · Cited by 65 · see citing works
Qualitative Analysis of Emotions: Fear and Thrill
Buckley, R. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01187 · Cited by 63 · see citing works
Autonomic nervous system activity during positive emotions: a meta-analytic review
Behnke, M., Kreibig, S. D., Kaczmarek, L. D., Assink, M., & Gross, J. J. (2022) · Emotion Review · doi:10.1177/17540739211073084 · Cited by 60 · see citing works
Navigating the manyverse of skin conductance response quantification approaches – A direct comparison of trough‐to‐peak , baseline correction, and model‐based approaches in Ledalab and PsPM
Kuhn, M., Gerlicher, A. M. V., & Lonsdorf, T. B. (2022) · Psychophysiology · doi:10.1111/psyp.14058 · Cited by 55 · see citing works
Understanding mixed emotions: paradigms and measures
Kreibig, S. D., & Gross, J. J. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.05.016 · Cited by 58 · see citing works
Affective brain-computer interfaces as enabling technology for responsive psychiatric stimulation
Widge, A. S., Dougherty, D. D., & Moritz, C. T. (2014) · Brain-Computer Interfaces · doi:10.1080/2326263x.2014.912885 · Cited by 51 · see citing works
Decoding naturalistic affective behaviour from spectro-spatial features in multiday human iEEG
Bijanzadeh, M., Khambhati, A. N., Desai, M., Wallace, D. L., Shafi, A., Dawes, H. E., Sturm, V. E., & Chang, E. F. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01310-0 · Cited by 48 · see citing works
Advancing Emotion Theory with Multivariate Pattern Classification
Kragel, P. A., & LaBar, K. S. (2014) · Emotion Review · doi:10.1177/1754073913512519 · Cited by 47 · see citing works
Self-citation
Grounding evaluative concepts
Fingerhut, J., & PRINZ, J. J. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0142 · Cited by 44 · see citing works
Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter
Babiker, A., Faye, I., Prehn, K., & Malik, A. S. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01921 · Cited by 44 · see citing works
The Theory of Constructed Emotion: More Than a Feeling
Barrett, L. F., Atzil, S., Bliss‐Moreau, E., Chanes, L., Gendron, M., Hoemann, K., Katsumi, Y., Kleckner, I. R., Lindquist, K. A., Quigley, K. S., et al. (2025) · Perspectives on Psychological Science · doi:10.1177/17456916251319045 · Cited by 55 · see citing works
Association between Ability Emotional Intelligence and Left Insula during Social Judgment of Facial Emotions
Quarto, T., Blasi, G., Maddalena, C., Viscanti, G., Lanciano, T., Soleti, E., Mangiulli, I., Taurisano, P., Fazio, L., Bertolino, A., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148621 · Cited by 44 · see citing works
Neural signatures of dynamic emotion constructs in the human brain
Grootswagers, T., Kennedy, B. L., Most, S. B., & Carlson, T. A. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.10.016 · Cited by 45 · see citing works
Navigating the science of emotion
Barrett, L. F., & Westlin, C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821124-3.00002-8 · Cited by 44 · see citing works
The Psychophysiology of Emotions
Norman, G. J., Necka, E. A., & Berntson, G. G. (2016) · Elsevier eBooks · doi:10.1016/b978-0-08-100508-8.00004-7 · Cited by 33 · see citing works
Autonomic specificity in emotion: The induction method matters
McGinley, J. J., & Friedman, B. H. (2017) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2017.06.002 · Cited by 29 · see citing works
Embodiment, Context-Sensitivity, and Discrete Emotions: A Response to Moors
Eickers, G., Loaiza, J. R., & PRINZ, J. J. (2017) · Psychological Inquiry · doi:10.1080/1047840x.2017.1255492 · Cited by 26 · see citing works
Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology
Pasquini, L., Noohi, F., Veziris, C. R., Kosik, E. L., Holley, S. R., Lee, A. J., Brown, J. A., Roy, A. R. K., Chow, T. E., Allen, I. E., et al. (2022) · Psychophysiology · doi:10.1111/psyp.14218 · Cited by 27 · see citing works
New insights on the correspondence between subjective affective experience and physiological responses from representational similarity analysis
Ventura‐Bort, C., Wendt, J., & Weymar, M. (2022) · Psychophysiology · doi:10.1111/psyp.14088 · Cited by 25 · see citing works
Emotion Distribution Learning Based on Peripheral Physiological Signals
Shu, Y., Yang, P., Liu, N., Zhang, S., Zhao, G., & Liu, Y. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3163609 · Cited by 19 · see citing works
Brain networks subserving functional core processes of emotions identified with componential modeling
Mohammadi, G., Ville, D. V. D., & Vuilleumier, P. O. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad093 · Cited by 28 · see citing works
Moving thoughts: emotion concepts from the perspective of context dependent embodied simulation
Winkielman, P., Davis, J. D., & Seana Coulson, A. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2236731 · Cited by 19 · see citing works
The Anatomy of Boredom
Elpidorou, A. (2025) · doi:10.1093/9780197564363.001.0001
The Future of Affective Science: Introduction to the Special Issue
Shiota, M., Camras, L., & Adolphs, R. (2023) · Affective Science · doi:10.1007/s42761-023-00220-2 · Cited by 16 · see citing works
Fear-related psychophysiological patterns are situation and individual dependent: A Bayesian model comparison approach.
McVeigh, K., Kleckner, I. R., Quigley, K. S., & Satpute, A. B. (2023) · Emotion · doi:10.1037/emo0001265 · Cited by 15 · see citing works
Emotion Recognition in a Multi-Componential Framework: The Role of Physiology
Menétrey, M. Q., Mohammadi, G., Leitão, J., & Vuilleumier, P. O. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.773256 · Cited by 16 · see citing works
Emotion Regulation Can Build Resources: How Amplifying Positive Emotions Is Beneficial for Employees and Organizations
Lapalme, M. L., Rojas-Quiroga, F., Pertuzé, J., Espinoza, P., Rojas-Córdova, C., & Ananias, J. F. (2023) · Journal of business and psychology · doi:10.1007/s10869-023-09875-x · Cited by 15 · see citing works
Complete the triangulation: Quantifying differential fear conditioning with a noninterfering and sensitive behavioral measure along with self‐report and physiological measures
Jiang, X., Burleigh, L. M., & Greening, S. G. (2021) · Psychophysiology · doi:10.1111/psyp.13831 · Cited by 14 · see citing works
The representation of emotional experience from imagined scenarios.
Faul, L., Baumann, M. G., & LaBar, K. S. (2022) · Emotion · doi:10.1037/emo0001192 · Cited by 12 · see citing works
Self-citation
Context-aware experience sampling reveals the scale of variation in affective experiencePreprint
Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020) · doi:10.31234/osf.io/cvjb8 · Cited by 14 · see citing works
Major Theories of Emotion Causation and Their Applicability to Music
Juslin, P. N. (2025) · Music Perception An Interdisciplinary Journal · doi:10.1525/mp.2025.2396878 · Cited by 9 · see citing works
Using Subgroup Discovery to Relate Odor Pleasantness and Intensity to Peripheral Nervous System Reactions
Moranges, M., Plantevit, M., & Bensafi, M. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3173403 · Cited by 9 · see citing works
Brain networks subserving functional core processes of emotions identified with componential modellingPreprint
Mohammadi, G., Ville, D. V. D., & Vuilleumier, P. O. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.10.145201 · Cited by 11 · see citing works
Cerebral topographies of perceived and felt emotions
Saarimäki, H., Nummenmaa, L., Volynets, S., Santavirta, S., Aksiuto, A., Sams, M. E., Jääskeläinen, I. P., & Lahnakoski, J. M. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00517 · Cited by 8 · see citing works
EmoStim: A Database of Emotional Film Clips With Discrete and Componential Assessment
Somarathna, R., Vuilleumier, P. O., & Mohammadi, G. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3328900 · Cited by 7 · see citing works
The Functional and Dysfunctional Aspects of Happiness: Cognitive, Physiological, Behavioral, and Health Considerations
Storbeck, J., & Wylie, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-77619-4_10 · Cited by 7 · see citing works
The Universality of Emotion
Irish, B. J. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009442534 · Cited by 6 · see citing works
Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering
Jolly, E., Farrens, M., Greenstein, N., Eisenbarth, H., Reddan, M. C., Andrews, E. S., Wager, T. D., & Chang, L. J. (2022) · Affective Science · doi:10.1007/s42761-022-00161-2 · Cited by 5 · see citing works
The Cardiac Rhythm of the Unconscious in a Case of Panic Disorder
Fleury, G., Fortin-Langelier, B., & Ben-Cheikh, I. (2016) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.2016.70.3.277 · Cited by 5 · see citing works
A Multi-Component Model of Emotion Response Convergence: Implications for the Development of Psychopathology
Michalska, K. J., & Díaz, D. E. (2025) · Emotion Review · doi:10.1177/17540739251335577 · Cited by 4 · see citing works
A shared structure for emotion experiences from narratives, videos, and everyday life
Han, Y., & Adolphs, R. (2024) · iScience · doi:10.1016/j.isci.2024.110378 · Cited by 4 · see citing works
Dynamic Grounding of Concepts: Implications for Emotion and Social Cognition
Davis, J. D., Seana Coulson, A., Arnold, A. J., & Winkielman, P. (2021) · Springer eBooks · doi:10.1007/978-3-030-78471-3_2 · Cited by 4 · see citing works
Cerebral Topographies of Perceived and Felt EmotionsPreprint
Saarimäki, H., Nummenmaa, L., Volynets, S., Santavirta, S. S., Aksiuto, A., Sams, M. E., Jääskeläinen, I. P., & Lahnakoski, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.08.521183 · Cited by 4 · see citing works
Grit buffers against stress-induced physiological response
Jeon, J., Yoo, C., Park, S., Kim, B. M., Yoo, J., Kim, M. J., & Park, D. (2025) · Personality and Individual Differences · doi:10.1016/j.paid.2025.113390 · Cited by 5 · see citing works
AffectTracker: real-time continuous rating of affective experience in immersive virtual reality
Fourcade, A., Malandrone, F., Roellecke, L., Ciston, A. B., de Mooij, J., Villringer, A., Carletto, S., & Gaebler, M. (2025) · Frontiers in Virtual Reality · doi:10.3389/frvir.2025.1567854 · Cited by 3 · see citing works
Intraindividual Variability Matters for the Correspondence Between Subjective Arousal, Valence, and Physiological Responses
Koppold, A., Lonsdorf, T. B., Kuhn, M., Weymar, M., & Ventura‐Bort, C. (2026) · Psychophysiology · doi:10.1111/psyp.70224 · Cited by 8 · see citing works
Emotions and psychophysiology
Norman, G. J., Necka, E. A., Faig, K. E., & Berntson, G. G. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821124-3.00004-1 · Cited by 3 · see citing works
An Efficacy Analysis of Affect Representation Methodologies Used in EEG-Based Emotion Recognition System: An Unsupervised Approach
Kar, P., & Hazarika, J. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2254626 · Cited by 3 · see citing works
The Feelings of Goals Hypothesis: Emotional Feelings are Non-Conceptual, Non-Motoric Representations of Goals
Kron, A., & Weksler, A. (2022) · Emotion Review · doi:10.1177/17540739221104456 · Cited by 3 · see citing works
Affect and Motion Pictures
PRINZ, J. J. (2019) · Springer eBooks · doi:10.1007/978-3-030-19601-1_38 · Cited by 3 · see citing works
Neurobiology of Positive Emotion Disruption in Neurodegenerative Disease
Sturm, V. E., & LEVENSON, R. W. (2019) · doi:10.1093/oxfordhb/9780190653200.013.29 · Cited by 3 · see citing works
Mood swings in cows : Cognitive and physiological assessments
Kremer, L. (2022) · doi:10.18174/562024 · Cited by 3 · see citing works
Emotion is a multi-componential experience guided by appraisal: evidence from multi-level annotation during naturalistic stimulationPreprint
Morgenroth, E., Somarathna, R., Ville, D. V. D., Mohammadi, G., & Vuilleumier, P. O. (2024) · doi:10.31234/osf.io/qzdbu · Cited by 3 · see citing works
Physiological Harmony or Discord? Unveiling the Correspondence Between Subjective Arousal, Valence and Physiological ResponsesPreprint
Koppold, A., Lonsdorf, T. B., Kuhn, M., Weymar, M., & Ventura‐Bort, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.31.596899 · Cited by 3 · see citing works
Multicomponent approach reveals differences in affective responses among children and adolescents
Konopkina, K., Hirvaskoski, H., Hietanen, J., & Saarimäki, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-94309-2 · Cited by 2 · see citing works
Relationships between subjective experience, electroencephalogram, and heart rate variability during a series of cosmetic behavior
Moriya, H., Machida, A., Munakata, T., Herai, T., & Tagai, K. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1225737 · Cited by 2 · see citing works
How to Study the Structure of Emotions? A Welcome Call to Action and a Pragmatic Proposal
Weidman, A. C., & Tracy, J. L. (2017) · Psychological Inquiry · doi:10.1080/1047840x.2017.1254612 · Cited by 2 · see citing works
Putting Emotional Memories in Context: The Constructionist Model of Emotional Memory
West, J. T., Mulligan, N. W., & Lindquist, K. A. (2026) · Perspectives on Psychological Science · doi:10.1177/17456916261425152 · Cited by 1 · see citing works
Temperamental Contributions to the Development of Psychological Profiles: I. Basic Issues
Kagan, J. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-394427-6.00013-3 · Cited by 1 · see citing works
Psychophysiological activity and reactivity in children and adolescents with conduct problems: A systematic review and meta-analysis
Fanti, K. A., Eisenbarth, H., Goble, P., Demetriou, C. A., Kyranides, M. N., Goodwin, D., Zhang, J., Bobak, B., & Cortese, S. (2020) · doi:10.26686/wgtn.12431021.v1 · Cited by 1 · see citing works
A Conceptualization of a Joyful Leadership Style
McIntyre, K., Graham, W., Mulcahy, R. F., & Lawley, M. (2024) · Research on emotion in organizations · doi:10.1108/s1746-979120240000019010 · Cited by 1 · see citing works
Emotional granularity : definition, measurement, and relationship to cardiovascular physiological activity
Hoemann, K. (2020) · doi:10.17760/d20382016 · Cited by 1 · see citing works
Naturalistic affective behaviors decoded from spectro-spatial features of multi-day human intracranial recordingsPreprint
Bijanzadeh, M., Khambhati, A. N., Desai, M., Wallace, D. L., Shafi, A., Dawes, H. E., Sturm, V. E., & Chang, E. F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.26.400374 · Cited by 1 · see citing works

Advancing Emotion Theory with Multivariate Pattern Classification
Kragel, P. A., & LaBar, K. S. (2014) · Emotion Review · doi:10.1177/1754073913512519 · Cited by 47 · see citing works
Self-citation
Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity
Christie, I. C., Friedman, B. H., Crawford, H. J., & Franchina, J. J. (2002) · VTechWorks (Virginia Tech) · Cited by 16 · see citing works
Multivariate Discrimination of EmotionSpecific Autonomic Nervous System Activity." vol. MSc Thesis
Christie, I. C., Crawford, H. J., & Franchina, J. J. (2002)
Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate approach
Christie, I. C., & Friedman, B. H. (2004) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2003.08.002 · Cited by 347 · see citing works
Pattern Recognition of Self‐Reported Emotional State from Multiple‐Site Facial EMG Activity During Affective Imagery
Fridlund, A. J., SCHWARTZ, G. E. R., & Fowler, S. C. (1984) · Psychophysiology · doi:10.1111/j.1469-8986.1984.tb00249.x · Cited by 131 · see citing works

Authors: Kragel, P. A., & LaBar, K. S.
Affiliations: Duke University (US)
Corresponding author: LaBar, Kevin S.
ORCID: Kragel, Philip A.
LaBar, Kevin S.
Venue: Emotion
Publisher: American Psychological Association (APA)
Published: 2013-08
Volume: 13
Issue: 4
Pages: 681-690
ISSN: 1528-3542 (ISSN-L)
1931-1516
DOI: 10.1037/a0031820
PMC: PMC3745776
OpenAlex: W2168248188
Cited by: 134
References: 53
Citations by year: 2026: 6 · 2025: 25 · 2024: 5 · 2023: 10 · 2022: 16 · 2021: 8 · 2020: 13 · 2019: 5 · 2018: 10 · 2017: 8 · 2016: 12 · 2015: 7 · 2014: 6 · 2013: 3
FWCI: 12.64
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Emotion (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (R01 DA027802)
Duke Institute for Brain Sciences
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords: discrete emotions, emotion classification, autonomic nervous system, electrodermal activity, heart rate, respiration, gastric activity, self-report measures, valence and arousal, emotion structure, fear, anger, sadness, surprise, contentment, amusements
MeSH terms: Adult, Arousal, Autonomic Nervous System, Data Interpretation, Statistical, Electrocardiography, Emotions, Female, Galvanic Skin Response, Humans, Male, Psychophysiology, Stomach, Young Adult, Self Report

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks