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The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology
Open accessAbstract
The circumplex model of affect proposes that all affective states arise from cognitive interpretations of core neural sensations that are the product of two independent neurophysiological systems. This model stands in contrast to theories of basic emotions, which posit that a discrete and independent neural system subserves every emotion. We propose that basic emotion theories no longer explain adequately the vast number of empirical observations from studies in affective neuroscience, and we suggest that a conceptual shift is needed in the empirical approaches taken to the study of emotion and affective psychopathologies. The circumplex model of affect is more consistent with many recent findings from behavioral, cognitive neuroscience, neuroimaging, and developmental studies of affect. Moreover, the model offers new theoretical and empirical approaches to studying the development of affective disorders as well as the genetic and cognitive underpinnings of affective processing within the central nervous system. This work was supported in part by NIMH Grants MH01232, MH59139, MH36197, MHK02-74677, and MH068318; a grant from the National Alliance for Research in Schizophrenia and Affective Disorders (NARSAD); NSF Grant BSC-0421702; and funding from the Thomas D. Klingenstein and Nancy D. Perlman Family Fund and the Suzanne Crosby Murphy Endowment at Columbia University.
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“These opposite effects on ISC fit with the proposed distinction between valence and arousal representations in the brain (31,32) and they also highlight the neurobehavioral functions that emotional arousal and valence may have in human social interaction.”
From Emotions promote social interaction by synchronizing brain activity across individuals (2012) · cites this paper as [32]
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“The arousal- and valence- (AV) space [8] will be used to represent affect because many modalities for affect detection have been shown to project onto the AV space (e.”
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“Even with different test robot platforms, results have indicated significant influence of speed/acceleration on the arousal axis in the circumplex model of affect [38, 55, 63].”
BackgroundBuilding predictive models of emotion with functional near-infrared spectroscopy
Bandara, D., Velipasalar, S., Bratt, S., & Hirshfield, L. (2017) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2017.10.001 · Cited by 51
Flow Activities as a Route to Living Well With Less
Isham, A., Gatersleben, B., & Jackson, T. P. (2018) · Environment and Behavior · doi:10.1177/0013916518799826 · Cited by 49
A review of EEG features for emotion recognition
Minjing, Y. U., Zhang, D., Zhang, G., Zhao, G., Liu, Y., Han, Y., & Guo, C. H. E. N. (2019) · Scientia Sinica Informationis · doi:10.1360/n112018-00337 · Cited by 49
Plasencia, M. L., Taylor, C. T., & Alden, L. E. (2016) · Clinical Psychological Science · doi:10.1177/2167702615622204 · Cited by 48
Theorien der Stressentstehung und -bewältigung
Semmer, N. K., & Zapf, D. (2017) · Springer eBooks · doi:10.1007/978-3-662-49322-9_1 · Cited by 52
“Warr bezieht sich dabei einerseits auf das Konzept des psychischen Wohlbefindens (psychological well-being), das durch verschiedene Kombinationen von Arousal und Valenz gekennzeichnet ist; dabei unterscheidet er, in Anlehnung an den emotional cirumplex (Posner et al. 2005), drei Achsen.”
BackgroundWhen Old Meets New: Emotion Recognition from Speech Signals
Araño, K. A., Gloor, P. A., Orsenigo, C., & Vercellis, C. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09865-2 · Cited by 50
“Different emotion categorization models have been commonly used by the research community, such as the Hourglass of Emotions [15], the Circumplex Model of Affect [16] and Paul Ekman’s emotion model [17], among others.”
MethodGadosey, C. K., Schnettler, T., Scheunemann, A., Fries, S., & Grunschel, C. (2021) · Learning and Individual Differences · doi:10.1016/j.lindif.2021.102013 · Cited by 55
A probabilistic map of emotional experiences during competitive social interactions
Heffner, J., & FeldmanHall, O. (2022) · Nature Communications · doi:10.1038/s41467-022-29372-8 · Cited by 46
Presti, P., Ruzzon, D., Avanzini, P., Caruana, F., Rizzolatti, G., & Vecchiato, G. (2022) · Scientific Reports · doi:10.1038/s41598-022-17689-9 · Cited by 56
Troiano, E., Oberländer, L., & Klinger, R. (2022) · Computational Linguistics · doi:10.1162/coli_a_00461 · Cited by 71
Duval, E. R., Moser, J. S., Huppert, J. D., & Simons, R. F. (2013) · Journal of Psychophysiology · doi:10.1027/0269-8803/a000083 · Cited by 48
Affect recognition using EEG signal
Xu, H., & Plataniotis, K. N. (2012) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2012.6343458 · Cited by 48
“2 where emotions are represented in a 2D plane, with one dimension (x-axis) for judged Valence which stands for one’s judgment about a situation as positive or negative and the other dimension (y-axis) for Arousal spans from calmness to excitement, expressing the degree of one’s excitation.”
BackgroundTiberio, L., Cesta, A., & Belardinelli, M. O. (2013) · Robotics · doi:10.3390/robotics2020092 · Cited by 51
“two fundamental neurophysiological systems, one related to valence (a pleasure-displeasure continuum) and the other to arousal [15,115].”
BackgroundEmotion classification using single-channel scalp-EEG recording
Jalilifard, A., Pizzolato, E. B., & Islam, M. K. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590833 · Cited by 47
“joy, happiness, relaxation) [11], [12] and [13].”
EEG oscillations during caress‐like affective haptic elicitation
Valenza, G., Greco, A., Bianchi, M., Nardelli, M., Rossi, S., & Scilingo, E. P. (2018) · Psychophysiology · doi:10.1111/psyp.13199 · Cited by 49
Curran, M. T., Gordon, J., Lin, L., Sridhar, P., & Chuang, J. (2019) · International Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300844 · Cited by 50
“In psychophysiology research EDA has been shown to be more related to the arousal dimension of affect than valence [27], and thus if the EA task was instead to rate the arousal of the results may be quite different which is worth future exploration.”
BackgroundKim, J., Urbano, J., Liem, C. C. S., & Hanjalić, A. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04076-1 · Cited by 49
Higher education emotions: a scale development exercise
White, C. J. (2012) · Higher Education Research & Development · doi:10.1080/07294360.2012.674496 · Cited by 52
“…Clark, & Tellegen, 1988) and given the close relationship between cognition and affect (Chiu & Wu, 2002; Farley & Stasson, 2003; Homburg, Koschate, & Hoyer, 2006; Posner et al., 2005) and, because participants described them in terms of feelings, these emotion words will be retained in this study.”
BackgroundTseng, A., Bansal, R., Liu, J., Gerber, A. J., Goh, S., Posner, J. E., Colibazzi, T., Algermissen, M., Chiang, I.-C., Russell, J. A., et al. (2013) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-013-1993-6 · Cited by 52
“Previous reviews have presented the many limitations and inconsistencies of this theory, including the absence of oneto-one mappings of individual emotions to specific facial expressions, motor behaviors, and autonomic responses, and the absence of evidence for a core set of emotions from which other emotions derive (…”
InfluentialSelf-citationContinuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models
Mencattini, A., Martinelli, E., Ringeval, F., Schuller, B. W., & Natale, C. D. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2531664 · Cited by 47
“According to Russell’s two dimensional representation of emotions [16], each quadrant of the diagram conveys spe-”
BackgroundMethodLighting modes and their effects on impressions of public squares
Nasar, J., & Bokharaei, S. (2017) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2016.12.007 · Cited by 39
Fanti, K. A., Kyranides, M. N., Petridou, M., Demetriou, C. A., & Georgiou, G. (2018) · Developmental Psychology · doi:10.1037/dev0000505 · Cited by 48
Effects of Group-Level Fit on Group Conflict and Performance
Seong, J. Y., & Choi, J. N. (2014) · Group & Organization Management · doi:10.1177/1059601113517138 · Cited by 51
Does training have to be fun? A review and conceptual model of the role of fun in workplace training
Tews, M. J., & Noe, R. A. (2017) · Human Resource Management Review · doi:10.1016/j.hrmr.2017.11.003 · Cited by 60
A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels
Apicella, A., Arpaïa, P., Isgrò, F., Mastrati, G., & Moccaldi, N. (2022) · IEEE Access · doi:10.1109/access.2022.3219844 · Cited by 48
“…the study, the classes (where H, L, V, and A mean high, low, valence and arousal, respectively), the classification accuracy, how the data are divided during the validation strategy, the validation strategy, and the reduced number of channels. circuitry is not associated with any EEG pattern [190].”
Background
Metadata
- Authors
- POSNER, J., RUSSELL, J. A., & PETERSON, B. S.
- Affiliations
- New York State Psychiatric Institute (US)
- Boston College (US)
- Venue
- Development and Psychopathology
- Publisher
- Cambridge University Press (CUP)
- Published
- 2005-09
- Volume
- 17
- Issue
- 3
- Pages
- 715-734
- ISSN
- 0954-5794 (ISSN-L)
- 1469-2198
- PMC
- PMC2367156
- OpenAlex
- W2165857685
- Cited by
- 2,602
- References
- 149
- Citations by year
- 2026: 161 · 2025: 177 · 2024: 226 · 2023: 213 · 2022: 240 · 2021: 266 · 2020: 222 · 2019: 170 · 2018: 225 · 2017: 174 · 2016: 127 · 2015: 73 · 2014: 58 · 2013: 61 · 2012: 51
- FWCI
- 1.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Development and Psychopathology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (MH‐K02‐74677, MH 36197, R01MH036197, MH59139, R01MH059139, MH-01232, R01MH068318, MH 068318, K08MH001232)
- National Institute on Drug Abuse (R01DA017820)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Clinical Psychology › Child and Adolescent Psychosocial and Emotional Development
- Keywords
- circumplex model of affect, basic emotions, affective neuroscience, cognitive development, mood disorders, neural systems, emotion theory
- MeSH terms
- Affect, Arousal, Mental Disorders, Brain, Cognition, Facial Expression, Humans, Nerve Net, Neurosciences, Psychological Theory, Temperament, Visual Perception
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.
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