Relational Density in the Lab: Examining Resistance and Coherence, Disinformation and False Beliefs, and Other Translations with Dr. Jordan Belisle & Dr. Caleb Stanley
There is a considerable gap between RFT research in the lab and the relational behavior that clinicians are observing in sessions and that people are observing in their world. RDT attempts to bridge the gap by exploring higher-order, self-organizing properties of relational framing from a theoretical quantitative lens that borrows from RFT and behavior momentum theory. First, we will discuss emerging research on relational resistance, or the likelihood that behavior will change along with changes in the external world. Relational behavior that exhibits greater density (strength of relations) and volume (number of relations) (mass = volume * density) appears to be more resistant to changes in context. Second, we will discuss research on relational coherence by adopting gravity as a metaphor, where relational networks with greater mass are not only more likely to acquire coherent class members with very little training, but may also merge with entire networks to create complex networks (Force = Rm1 * Rm2 / Rdis). Throughout the presentation, we will discuss how these models allow for a functional analysis of relational framing and translational work underway to explore why people believe weird things (misinformation and disinformation) and implications for analyzing global issues such as climate change.
1. Describe the concept of Relational Resistance
2. Describe the concept of Relational Coherence
3. Discuss how framing contributes false beliefs and global issues