Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-07T22:35:16.043Z Has data issue: false hasContentIssue false

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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