Date of Graduation
Spring 2025
Degree
Master of Science in Behavior Analysis & Therapy
Department
Mental Health & Behavioral Science
Committee Chair
Ann Rost
Abstract
Relational Density Theory (Belisle & Dixon, 2020a) is an advanced conceptualization of Stimulus Equivalence (Sidman & Tailby, 1982) and Relational Frame Theory (Hayes et al., 2001) that allows behavior scientists to measure the strength, formation, and malleability of covert behavioral experiences such as attitudes, biases, and probabilities. Relational density combines principles of behavior with the fundamentals of Newtonian classical mechanics to quantify the dimensions of relational framing as a tool for examining behavior. This thesis contains two studies on relational density in context. The first study utilized relational density theory and principles of behavioral economics to measure attitudes and charitable response allocation towards members of the unhoused community. Results indicated emotional relatedness and charitable probability were altered as a function of relational frames and degree of social relatedness. The second study tested for relational density consistent effects in artificial neural networks, a division of machine learning. Results demonstrated the potential for further research into the behavior analysis of machine learning using relational density paradigms.
Keywords
relational density, relational framing, stimulus equivalence, delay discounting, verbal behavior, machine learning, unhoused
Subject Categories
Applied Behavior Analysis | Experimental Analysis of Behavior | Social Psychology
Copyright
© Bentley J. Elliott
Recommended Citation
Elliott, Bentley J., "Applied and Conceptual Implications of Relational Density Theory in Machine Learning and Behavioral Economics" (2025). Graduate Theses/Dissertations. 4067.
https://bearworks.missouristate.edu/theses/4067
Open Access
Included in
Applied Behavior Analysis Commons, Experimental Analysis of Behavior Commons, Social Psychology Commons
