A Comprehensive Behavioral Model of Emotion Rooted in Relational Frame Theory and Contemporary Extensions

There exists a vast literature on affect and emotion in psychological disciplines, yet contemporary conceptualizations and technologies to predict and influence emotion have been slower to emerge in behavior analysis. The current article is an attempt to conceptualize emotional experiencing through a radical behavioral lens using relational frame theory (RFT) and contemporary extensions. RFT provides a behavioral approach to cognitive appraisal within existing models of human emotion by emphasizing derived relational responding, transformation of stimulus function, and generalized reinforcement learning. Relational density theory (RDT) and the hyperdimensional multilevel (HDML) framework both expand upon RFT and may allow for a more complete account of emotional experiencing within complex networks. Synthesizing these two approaches yields multiple testable predictions that are consistent with RDT across levels of the HDML. Moreover, the ROE-M (relating, orienting, and evoking functions within a motivational context) is a dynamical unit that may be readily evident within emotional experiencing as it is generally described within the psychological literature, and compatible with the synthesized model. Taken together, these approaches and emerging research on affective dynamics may provide a starting point to develop a robust and comprehensive analysis of human emotion that can strengthen behavior analysis and therapies