Beyond Needling: Integrating a Bayesian Brain Model into Acupuncture Treatment
Beomku Kang, Da-Eun Yoon, Yeonhee Ryu, In‐Seon Lee, Younbyoung Chae
Abstract
. But is that all? In this article, we adopt a Bayesian perspective to explore the cognitive and affective aspects of acupuncture beyond needling, specifically, how the body integrates bottom-up sensory signals with top-down predictions of acupuncture perception. We propose that the way in which we discern acupuncture treatment is the result of predictive coding, a probabilistic, inferential process of our brain. Active inference from both prior experience and expectations of acupuncture, when integrated with incoming sensory signals, creates a unique, individual internal generative model of our perception of acupuncture. A Bayesian framework and predictive coding may, therefore, aid in elucidating and quantifying the cognitive components of acupuncture and facilitate understanding of their differential interactions in determining individual expectations of treatment. Thus, a perception-based Bayesian model of acupuncture presented in this article may expand on how we perceive acupuncture treatment, from simply inserting needles into our body to one that encompasses a complex healing process supported by belief and hope of regaining health. By exploring how cognitive factors influence individual responsiveness to acupuncture treatment, this review sheds light on why acupuncture treatment is more effective in some individuals than in others.