TL;DR

• Full article available here: https://www.researchgate.net/publication/382210415_A_Comprehensive_Behavioral_Model_of_Emotion_Rooted_in_Relational_Frame_Theory_and_Contemporary_Extensions

• Emotions are complex behavioral response classes involving physiological responses, overt behaviors, and cognitive appraisal

• RFT explains how verbal relations transform emotional functions across complex networks

• Relational Density Theory (RDT) and Hyperdimensional Multilevel (HDML) framework extend RFT to account for emotional complexity

• The ROE-M (relating, orienting, evoking within motivational context) provides a dynamic unit for understanding emotion

The Topic

Despite the social and clinical importance of emotional experiencing—with mood disorders representing leading causes of psychiatric hospitalization and significant public health costs—behavior analysis has been slow to develop comprehensive technologies for predicting and influencing emotions. While B.F. Skinner opened the door for functional analysis of private events like emotions, contemporary conceptualizations remain limited. Emotions cannot be reduced to simple topographies; they involve interconnected response classes (physiological arousal, overt behaviors, cognitive appraisals) that are occasioned by environmental contexts and shaped by learning histories. Understanding emotional dysfunction requires a sufficiently complex analysis that accounts for the entanglement of human language, cognition, and affective experience.

The Approach

This theoretical article synthesizes Relational Frame Theory (RFT), Relational Density Theory (RDT), and the Hyperdimensional Multilevel (HDML) framework to create a comprehensive behavioral model of emotion. RFT provides the foundation by explaining how derived relational responding and transformation of stimulus function create complex emotional networks—for instance, how a word paired with shock can generalize fear responses throughout semantically related networks. RDT extends this by introducing equations predicting relational resistance, density, and coherence that explain why some emotional responses are more resistant to change. The HDML framework adds dimensional analysis across five levels (mutual entailment through relating relational networks) and four dimensions (complexity, derivation, coherence, flexibility), with the ROE-M providing a functional behavioral unit integrating relating, orienting, and evoking functions within motivational contexts.

The Verdict

The synthesized framework generates testable predictions about emotional experiencing consistent with both basic research and clinical observations. For example, high-mass negative affective networks with greater relational density should show "emotional inertia" (temporal persistence) characteristic of depression, while low-mass networks should demonstrate affective instability seen in borderline personality disorder. Translational studies using multidimensional scaling have demonstrated that relational stimuli naturally organize around positive/negative valence dimensions in domains like gender stereotypes, racial attitudes, and climate change perceptions. The model explains why exposure therapy, behavioral activation, and ACT-based interventions work through distinct mechanisms—habituation of physiological responses, increased contact with reinforcing experiences, and psychological flexibility that allows valued action despite aversive private events, respectively. Early case studies using HDML-informed verbal functional analysis have shown promising therapeutic outcomes.

Why This Is Important

This framework addresses a critical gap: behavior analysts need conceptual tools sufficiently sophisticated to analyze the complexity of human emotional experiencing while remaining functionally analytic and empirically grounded. Third-wave behavior therapies like ACT are increasingly within the scope of practicing behavior analysts, yet the link between RFT processes and therapeutic mechanisms often remains implicit. By providing equations, testable predictions, and clear conceptual integration across RFT extensions, this model offers a roadmap for developing technologies that can genuinely predict and influence emotional experiences. More fundamentally, it honors the radical behavioral insight that emotions deserve functional analysis while acknowledging that such analysis must account for verbal behavior, relational framing across multiple levels, and dynamical interactions within complex networks. The ultimate pragmatic value lies in helping people not only behave how they want to behave, but feel how they want to feel.

Citation

Belisle, J., Paliliunas, D., Catrone, R., Sickman, E., & Ramakrishnan, A. (2024). A comprehensive behavioral model of emotion rooted in relational frame theory and contemporary extensions. The Psychological Record, 74, 521–539. https://doi.org/10.1007/s40732-024-00603-2