JOURNAL ARTICLE
SYMMETRY AND PSYCHOSOCIAL ADAPTATION TO TRAUMA, ILLNESS AND DISABILITY PART I: VIEWS FROM PHYSICS, MEDICINE AND PSYCHOLOGY.
Published In: Symmetry: Culture & Science, 2026, v. 37, n. 1. P. 7 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Livneh, Hanoch 3 of 3
Abstract
This article examines the concept of symmetry, including symmetry breaking (SB) and asymmetry, and their roles in understanding psychosocial adaptation to trauma, illness, and disability from interdisciplinary perspectives in physics, biology, medicine, and psychology. It outlines how symmetry underlies fundamental physical laws, conservation principles, and forces of nature, while SB processes contribute to the emergence of complexity and order in the universe. In biological systems, symmetry and SB interplay to shape molecular structures, organismal development, homeostasis, and disease onset, with life existing in a dynamic balance between order and disorder. Psychologically, symmetry influences perception, personality, attitudes, interpersonal relationships, and mental health, with theories such as psychoanalysis, Lewin’s field theory, chaos and complexity theory, and Dabrowski’s positive disintegration highlighting the dynamic tension between symmetrical equilibrium and asymmetrical adaptation. The paper sets the stage for a subsequent discussion on how these concepts inform the understanding of trauma adaptation, emphasizing the balance between stability and change in human functioning.
Additional Information
- Source:Symmetry: Culture & Science. 2026/01, Vol. 37, Issue 1, p7
- Document Type:Article
- Subject Area:Science
- Publication Date:2026
- ISSN:08654824
- DOI:10.26830/symmetry_2026_1_007
- Accession Number:193185302
- Copyright Statement:Copyright of Symmetry: Culture & Science is the property of Public Foundation for the Advancement of Symmetrology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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