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Contempt – Where the modularity of the mind meets the modularity of the brain?

Published online by Cambridge University Press:  30 October 2017

Danilo Bzdok
Affiliation:
Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, 52074 Aachen, Germany. [email protected]://www.danilobzdok.de JARA, Translational Brain Medicine, 52074 Aachen, Germany DFG-IRTG2150, International Research Training Group, 52074 Aachen, Germany Parietal Team, INRIA, Neurospin, 91191 Gif-sur-Yvette, France
Leonhard Schilbach
Affiliation:
Max Planck Institute of Psychiatry, 80804 Munich, Germany. [email protected]://www.leonhardschilbach.de

Abstract

“Contempt” is proposed to be a unique aspect of human nature, yet a non-natural kind. Its psychological construct is framed as a sentiment emerging from a stratification of diverse basic emotions and dispositional attitudes. Accordingly, “contempt” might transcend traditional conceptual levels in social psychology, including experience and recognition of emotion, dyadic and group dynamics, context-conditioned attitudes, time-enduring personality structure, and morality. This strikes us as a modern psychological account of a high-level, social-affective cognitive facet that joins forces with recent developments in the social neuroscience by drawing psychological conclusions from brain biology.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2017 

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References

Barrett, L. F. & Satpute, A. B. (2013) Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain. Current Opinion in Neurobiology 23(3):361–72. doi: 10.1016/j.conb.2012.12.012.Google Scholar
Bzdok, D., Eickenberg, M., Grisel, O., Thirion, B. & Varoquaux, G. (2015) Semi-supervised factored logistic regression for high-dimensional neuroimaging data. In: Advances in Neural Information Processing Systems 28: Proceedings of the 29th Annual Conference on “Neural Information Processing Systems (NIPS) 2015”, ed. Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., & Garnett, R., pp. 3348–56. Neural Information Processing Systems (NIPS).Google Scholar
Wager, T. D., Kang, J., Johnson, T. D., Nichols, T. E., Satpute, A. B. & Barrett, L. F. (2015) A Bayesian model of category-specific emotional brain responses. PLoS Computational Biology 11(4):e1004066. doi: 10.1371/journal.pcbi.1004066.Google Scholar
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. (2011) Large-scale automated synthesis of human functional neuroimaging data. Nature Methods 8(8):665–70. doi: 10.1038/nmeth.1635.Google Scholar