Association for Behavior Analysis International

The evolutionary theory of behavior dynamics (ETBD) is a computational model of operant behavior based on the Darwinian evolutionary processes of selection, reproduction, and mutation. There is an extensive body of empirical research demonstrating the ability of the ETBD model to animate artificial organisms that emit behavior that closely mirrors steady-state choice data produced by live organisms. Recently, ETBD has been extended to the analysis of clinically relevant behavior across a number of unique assessment and treatment applications. Ultimately, the application of ETBD to the study of aberrant behavior may yield insights into identifying potential behavioral treatments that are effective. In this symposium, four presenters will discuss emerging applications of ETBD to the study of problem behavior. The first presentation will provide an overview of the concepts and principles of ETBD. The second presentation will discuss the use of ETBD to model the functional assessment and treatment of automatically maintained self-injurious behavior. The third presentation will discuss the use of ETBD to model evidence-based behavioral assessment and treatment procedures for severe problem behavior. The final presentation will discuss the use of ETBD to model behavioral treatment durability and resurgence during schedule thinning

Morris and McDowell (2021) used the evolutionary theory of behavior dynamics (ETBD) to model the subtypes of automatically reinforced self-injurious behavior (ASIB) and identified two models for which behavior generated by the ETBD matched the functional analysis and treatment outcomes characteristic of Subtypes 1 and 2. In the current study, we conducted more stringent evaluation of these models within the framework of precision medicine and aimed to replicate the results of Hagopian et al., (2018). The models’ response to treatment were well predicted by the level of differentiation in the functional analysis, as in Hagopian et al. However, other characteristics of the data (e.g., the exact predictive behavioral marker) suggested that better models for the subtypes of ASIB could still be identified. Thus, we evaluated adjustments to model sensitivity and reinforcer magnitude and their effect on correspondence with human data to identify superior models of Subtypes 1 and 2. The superior models were then utilized to replicate the analyses of Morris and McDowell and to evaluate other candidate predictive behavioral markers. The implications for assessment and treatment of ASIB, research on mechanisms underlying subtype differences, and research on the application of the ETBD are discussed.

Although the evolutionary theory of behavior dynamics (ETBD) is not designed to model the behavior of any particular individual, it can model how organisms will generally behave as function of contingencies operating in the environment. By manipulating certain parameters such as those that affect sensitivity to reinforcement and response variability, it is possible to model specific functional classes of problem behavior – and how they are impacted by treatment. Morris and McDowell (2021) used ETBD to successfully model different subtypes of automatically maintained self-injurious behavior and their differential response to behavioral treatment. The purpose of the present study is to illustrate how ETBD can also be used to model: a) socially-maintained problem behavior, and b) the effects of commonly used clinical procedures. Parameters and schedule arrangements were manipulated to model functional analysis outcomes, effects of response blocking, outcomes of competing stimulus assessments, and effects of treatments involving differential reinforcement and noncontingent reinforcement. Beyond merely modeling functional classes of problem behavior and treatment effects, this approach has potential to support and guide research aimed at understanding problem behavior and elucidating the mechanisms by which treatments bring about behavior change.