Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


50th Annual Convention; Philadelphia, PA; 2024

Event Details

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Symposium #129
CE Offered: BACB
Advanced Translational Research on Derived Relational Responding and the Relational Field
Saturday, May 25, 2024
3:00 PM–4:50 PM
Convention Center, 100 Level, 111 AB
Area: VRB/EAB; Domain: Translational
Chair: Stephanie Vickroy (Missouri State University)
Discussant: Mark R. Dixon (University of Illinois at Chicago)
CE Instructor: Mark R. Dixon, Ph.D.
Abstract: Field theories are foundational to understanding complex systems across scientific branches, including in the analysis of derived relational responding (i.e., relational fields; Barnes-Holmes et al., 2020). Conceptual discussions of complexity in relational responding have been presented, yet direct experimental research has lagged behind (Dixon et al., 2018). Presenters will discuss a series of translational laboratory studies exploring complex forms of relational responding using a variety of experimental methods. The first presentation will explore the concept of relational volume within multi-nodal networks to determine how the development of multiple relational networks influence response probability and resistance. The second and third presentation will explore the role of affective or emotional experiencing on the emergence of complex networks, using multiple analytic approaches to capture implicit and explicit relational responding (e.g., MDS, IRAP). The fourth presentation will evaluate the utility of a network analysis to model the interdependency of relations within multi-nodal networks. The convergence of evidence within these studies will be discussed, centering commonalities and anomalies within the reported results and what they mean for theoretical and applied branches of behavior analysis.
Instruction Level: Advanced
Keyword(s): complex systems, network analysis, relational density, relational framing
Target Audience: Behavior analysts and practitioners
Learning Objectives: At the conclusion of the presentation, participants will be able to: (1) describe Relational Density Theory as an extension of Relational Frame Theory; (2) discuss the role of network analyses in exploring complex relational framing; (3) discuss emotion and affect as transformations of stimulus function.

Relational Volume and Resistance to Change Within Complex Networks

(Basic Research)
RYAN MOSER (Missouri State University), Bentley Elliott (Missouri State University), Jordan Belisle (Missouri State University)

Relational Density Theory (Belisle & Dixon, 2020) provides a quantitative extension of Relational Frame Theory that attempts to model or describe higher-order interactions within relational behavior. RDT has been used in multiple studies to the examine complex relational networks involved with gender stereotyping (Sickman et al, 2023), consumer behavior and climate (Hutchenson et al, 2023), and racial prejudice (Belile et al, 2023). RDT describes these higher-order interactions by using terms such as density (strength of relationship) and volume (size of relational class, or nodal distance). The current study extended upon results reported by Belisle and Dixon (2020) and Cotter and Stewart (2023) by training 3- and 6-member coordinated classes differing in nodal distance. The relative strength of relations in the network was measured using a metric multidimensional scaling procedure that included time-based responding. Results showed that class size and nodal distance differentially influenced the response strength of multiple network relations and allowed for the prediction of resistance to change following counterconditioning. These results have implications for understanding the concept of volume within an RDT extension of Relational Frame Theory.

Transfer of Emotional Functions Through High-Mass Relational Networks
(Basic Research)
AMANDA MIDDLETON (Missouri State University), Jordan Belisle (Missouri State University)
Abstract: Clinically relevant behavior involving relational frame formation has been described to occur in complex ways (Guinther & Dougher, 2015; Belisle & Dixon, 2022). Relational Density Theory proposes a quantitative approach to assessing the mass and volume within these networks (Belisle & Dixon, 2020). The present study sought to evaluate how different schedules of reinforcement can influence the affective function of arbitrary stimuli and transfer within complex relational networks. In initial phases, participants responded to a series of 4 different reinforcement schedules contingent on a symbol presented on the screening signaling a contingency shift. Affective responses suggested that the emotions reported in response to the stimuli were consistent with the reinforcement schedules. In subsequent phases, 4-member relational frames were established using an SPOP procedure with testing to create a 12-member relational network. Results demonstrated the emergence of 4 distinct relational classes consistent with prior RDT analyses and affective functions transferred from the initial stimulus to other class members. Moreover, relational volume effects were evident within the emotional transfer, demonstrating a complex interplay between environmental reinforcement schedules, affective experiencing, and relational framing.
Multidimensional Scaling and Implicit Relational Assessment Procedure Analysis of Emotional Transfer Effects
(Basic Research)
BREANNA LEE (Ulster University), Dermot Barnes-Holmes (Ulster University), Julian C. Leslie (Ulster University), Dana Paliliunas (Missouri State University), Jordan Belisle (Missouri State University)
Abstract: Recent work involving Relational Density Theory (RDT; Belisle & Dixon, 2020) has used the framework for observing relational responding according to properties of relational strength. As such, multidimensional scaling (MDS) procedures have been used to map relational responding, providing routes for understanding the ways in which properties organize (e.g., Belisle & Clayton, 2021). This may contribute to work involving the Implicit Relational Assessment Procedure (IRAP; Barnes-Holmes et al., 2008) and a focus on the Cfunc property of relational responding (e.g., Bortoloti et al., 2019, 2023). The current work involves analog studies for observing emotional reactions to verbal stimuli.  Specifically, the project seeks to understand any overlap between the IRAP and an MDS procedure for organizing relational responding according to ranging levels of valence and arousal. First, stimuli were obtained from a standardized set of images in which ratings of valence and arousal were provided. These stimuli were then included in two separate studies, one using an MDS procedure and a second using an IRAP. The arrangements of responding to stimuli are compared between studies. Results are discussed in terms of understanding how relational density impacts functions involving orienting and evoking to stimuli.
The Utility of Network Analysis for Modeling Verbal Relations
(Basic Research)
CRAIG A MARRER (Endicott College), Mark R. Dixon (University of Illinois at Chicago)
Abstract: Network analysis is a mathematical and graphical method for examining relationships and interactions among variables within complex systems. These variables, represented as nodes (i.e., stimuli), are connected by edges (i.e., relations), forming a network often displayed as a visual structure. Those familiar with stimulus equivalence (e.g., Sidman et al., 1989) or relational frame theory (e.g., Blackledge, 2003) are already familiar with these visual structures. The research discussed here relates to the effectiveness of network analysis in understanding and influencing functional stimulus relations using centrality measures and visual representations of dynamically related nodes within networks. Data will be presented along with key questions, including quantifying, and graphically representing common metrics of relational responding, predicting these metrics using network analysis, using measures of nodal centrality to optimize conditioning or counterconditioning of relations, exploring derivation between nodes (symmetry and transitivity), and improving interventions for individuals with varying language abilities.By quantifying the structure and dynamics of these networks, researchers and analysts can uncover patterns, vulnerabilities, and emergent properties, making it an essential tool for understanding and optimizing research and practice within the field of ABA.



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