Date of Award

5-1-2018

Degree Name

Doctor of Philosophy

Department

Rehabilitation

First Advisor

Dixon, Mark

Abstract

Relational Density Theory describes quantifiable higher-order properties governing relational framing of verbal organisms. Consistent with Newtonian classical mechanics, the theory posits that relational networks, and relating itself, will demonstrate the higher-order emergent properties of density, volume, and mass. Thus, networks that contain more relations (volume) that are stronger (density) will be more resistant to change (i.e., contain greater mass; mass = volume * density). Consistent with Newton’s law of gravity, networks that contain greater mass will also demonstrate force, accelerating the acquisition of new relations beyond that accounted for by direct acting contingencies, therefore demonstrating emergent self-organization that is highly susceptible to small changes at initial conditions. The current set of experiments provides initial proof of concept data for foundational principles introduced in the theory. Experiment 1 (N = 6) models the volumetric mass density formula, predicting that networks with greater volume and density will be more resistant to change (i.e., contain greater mass) when counterconditioning is applied to a subset of derived relations contained within experimentally established networks. Results were consistent with theoretical predictions based on density on 10 of 12 occasions, and resistance appeared greater for relations operating at greater volume. Experiment 2 (N = 6) extended directly from Experiment 1, generating a density differential through exposure at initial training conditions, and utilizing response time as a measure of relational density. Results again demonstrated successful prediction of resistance corresponding with the emergent density differential on 10 of 12 occasions, along with overall greater resistance corresponding with and volumetric increases. Experiment 3 (N = 9) demonstrated that relational volume can detract from relational density when accurate responding is near 100%, and that network density is predictive of class mergers when no merged responding is ever reinforced, suggesting that network mass can exert force on relational responding in the absence of any experimental conditioning (i.e., gravity). Taken together, results have radical implications for understanding the self-emergent nature of complex human behavior, with applications in therapy and treatment, as well as in understanding the human condition more broadly.

Share

COinS
 

Access

This dissertation is only available for download to the SIUC community. Others should contact the
interlibrary loan department of your local library or contact ProQuest's Dissertation Express service.