Burdening the Predictive Mind: A Predictive Processing Approach to Health-Related Burdens

Abstract

The notion of burden features as a central aspect of research into the challenges faced by patients and their carers, especially in regard to long-term health conditions and multimorbidity. Research in this area has considered the burdens that stem from the presence of disease (e.g., symptom burden) as well as the burden associated with healthcare interventions (e.g., treatment burden). While there have been a number of attempts to theorize burden, there is, at present, little consensus on how burdens ought to be understood. It is, in particular, unclear what makes something a burden, why certain things are perceived as burdensome, what forces and factors moderate the experience of burden, and how burdensome experiences relate to other experiential constructs, such as wellbeing, despair, and suffering. The present paper seeks to advance our understanding of burden by drawing on predictive processing accounts of brain function. All burdens, it is suggested, have their origins in a reduced capacity to fulfill neurally-realized expectations (or predictions). This is marked by a hypothesized increase in a particular form of prediction error, dubbed expected prediction error. In addition to providing a unitary theoretical approach to burden, the present account supports the effort to apply predictive processing to a wider array of clinical and health-related phenomena.