Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-26T14:35:30.218Z Has data issue: false hasContentIssue false
  • English
  • Français

Thinking through others’ emotions: Incorporating the role of emotional state inference in thinking through other minds

Published online by Cambridge University Press:  28 May 2020

Ryan Smith Open the ORCID record for Ryan Smith [Opens in a new window]  and
Richard D. Lane
Ryan Smith
Affiliation:
Laureate Institute for Brain Research, Tulsa, OK74136rsmith@laureateinstitute.org
Richard D. Lane
Affiliation:
Department of Psychiatry, University of Arizona, Tucson, AZ85724. lane@psychiatry.arizona.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

The active inference framework offers an attractive starting point for understanding cultural cognition. Here, we argue that affective dynamics are essential to include when constructing this type of theory. We highlight ways in which interactions between emotional responses and the perception of those responses, both within and between individuals, can play central roles in both motivating and constraining sociocultural practices.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e114
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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

Allen, M., Levy, A., Parr, T. & Friston, K. J. (2019) In the body's eye: The computational anatomy of interoceptive inference. BioRxiv 603928. https://doi.org/10.1101/603928.CrossRefGoogle Scholar
Barrett, L. (2017) How emotions are made: The secret life of the brain. Houghton Mifflin Harcourt.Google Scholar
Lamm, C., Decety, J. & Singer, T. (2011) Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. NeuroImage 54:2492–502. doi:10.1016/J.NEUROIMAGE.2010.10.014.CrossRefGoogle ScholarPubMed
Niedenthal, P. (2007) Embodying emotion. Science 316:1002–1005. doi:10.1126/science.1136930.CrossRefGoogle ScholarPubMed
Smith, R., Alkozei, A., Killgore, W. D. S. & Lane, R. D. (2018a) Nested positive feedback loops in the maintenance of major depression: An integration and extension of previous models. Brain, Behavior, and Immunity 67:374–97. doi:10.1016/j.bbi.2017.09.011.CrossRefGoogle Scholar
Smith, R., Killgore, W. D. S., Alkozei, A. & Lane, R. D. (2018b) A neuro-cognitive process model of emotional intelligence. Biological Psychology 139:131–51. doi:10.1016/j.biopsycho.2018.10.012.CrossRefGoogle Scholar
Smith, R., Killgore, W. D. S. & Lane, R. D. (2018c) The structure of emotional experience and its relation to trait emotional awareness: A theoretical review. Emotion 18(5):670–92. doi:10.1037/emo0000376.CrossRefGoogle Scholar
Smith, R., Lane, R. D., Parr, T. & Friston, K. J. (2019a) Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance. Neuroscience & Biobehavioral Reviews 107:473–91.CrossRefGoogle Scholar
Smith, R., Parr, T. & Friston, K. J. (2019b) Simulating emotions: An active inference model of emotional state inference and emotion concept learning. Frontiers in Psychology 10:2844. doi:10.1101/640813.CrossRefGoogle Scholar
Smith, R., Weihs, K. L., Alkozei, A., Killgore, W. D. S. & Lane, R. D. (2019c) An embodied neurocomputational framework for organically integrating biopsychosocial processes: An application to the role of social support in health and disease. Psychosomatic Medicine 81(2):125–45. doi:10.1097/PSY.0000000000000661.CrossRefGoogle Scholar