An Embodied Neurocomputational Framework for Organically Integrating Biopsychosocial Processes: An Application to the Role of Social Support in Health and Disease. Issue 2 (February 2019)

• Record Type: Journal Article
• Authors: Smith, Ryan; Weihs, Karen L.; Alkozei, Anna; Killgore, William D.S.; Lane, Richard D.
• Is Part Of: Psychosomatic medicine. Volume 81:Issue 2(2019)
• Publication Date: 2019-02
• Subjects: active inference -- biopsychosocial model -- biomedical model -- computational neuroscience -- embodied cognition -- predictive coding -- PP = predictive processing -- SES = socioeconomic status -- SNS = sympathetic nervous system; Medicine, Psychosomatic -- Periodicals; 616.0805
• DOI: 10.1097/PSY.0000000000000661
• Languages: English
• ISSNs: 0033-3174

Abstract

ABSTRACT: Objective: Two distinct perspectives—typically referred to as the biopsychosocial and biomedical models—currently guide clinical practice. Although the role of psychosocial factors in contributing to physical and mental health outcomes is widely recognized, the biomedical model remains dominant. This is due in part to ( a ) the largely nonmechanistic focus of biopsychosocial research and ( b ) the lack of specificity it currently offers in guiding clinicians to focus on social, psychological, and/or biological factors in individual cases. In this article, our objective is to provide an evidence-based and theoretically sophisticated mechanistic model capable of organically integrating biopsychosocial processes. Methods: To construct this model, we provide a narrative review of recent advances in embodied cognition and predictive processing within computational neuroscience, which offer mechanisms for understanding individual differences in social perceptions, visceral responses, health-related behaviors, and their interactions. We also review current evidence for bidirectional influences between social support and health as a detailed illustration of the novel conceptual resources offered by our model. Results: When integrated, these advances highlight multiple mechanistic causal pathways between psychosocial and biological variables. Conclusions: By highlighting these pathways, the resulting model has important implications motivating a more psychologically sophisticated, person-specific approach to future research and clinical application in the biopsychosocial domain. It also highlights the potential for quantitative computational modeling and the design of novel interventions. Finally, it should aid in guiding future research in a manner capable of addressing the current criticisms/limitations of the biopsychosocial model and may therefore represent an important step in bridging the gap between it and the biomedical perspective.