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.


48th Annual Convention; Boston, MA; 2022

Event Details

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Symposium #461
Modeling Severe Problem Behavior and Treatment Effects Using Artificial Organisms: A New Frontier
Monday, May 30, 2022
10:00 AM–11:50 AM
Meeting Level 1; Room 153A
Area: EAB; Domain: Translational
Chair: Brianna Laureano (Kennedy Krieger Institute & Johns Hopkins University School of Medicine)
Discussant: Jack J McDowell (Emory University)

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

Instruction Level: Intermediate
Keyword(s): artificial organisms, computerized model, evolutionary theory, self-injurious behavior
Performing Simulated Operant Experiments Using an Evolutionary Theory of Behavior Dynamics: A Tutorial
BRYAN KLAPES (Philadelphia College of Osteopathic Medicine - Georgia)
Abstract: An Evolutionary Theory of Behavior Dynamics (ETBD; McDowell, 2004) is a selectionist theory of dynamic operant behavior. Through a series of computations, ETBD can simulate a wide range of live organism performance with excellent accuracy (McDowell, 2013, 2019). McDowell’s ETBD program is written in the primary object-oriented programming language for Windows OS-based machines (Visual Basic) and typically run using Microsoft’s integrated development environment (Visual Studio). Thus, researchers with limited experience coding in Visual Basic or using Visual Studio may find it difficult to perform their own ETBD experiments. In this presentation, I will first provide a brief review of the ETBD literature. Next, I will demonstrate how to acquire a version of the ETBD program that does not require Visual Studio to run (viz., an “executable” version of the program that can be run from any PC running a Windows OS and has Microsoft Excel installed). Finally, I will show how to use the graphical user interface to successfully and efficiently run ETBD experiments.

Further Modeling of the Subtypes of Automatically-Reinforced Self-Injurious Behavior Within the Framework of Precision Medicine

(Applied Research)
SAMUEL L MORRIS (Southeastern Louisiana University), Sarah Lucia (Southeastern Louisiana University)

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.


Modeling Functional Assessment and Treatment of Severe Problem Behavior With the Evolutionary Theory of Behavior Dynamics

(Applied Research)
LOUIS P. HAGOPIAN (Kennedy Krieger Institute), John Falligant (Kennedy Krieger Institute & Johns Hopkins University School of Medicine)

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.

Modeling Effects of Downshifts in Reinforcement: A Demonstration of Resurgence as Choice Using Artificial Organisms
(Applied Research)
JOHN FALLIGANT (Kennedy Krieger Institute & Johns Hopkins University School of Medicine), Bryan Klapes (Philadelphia College of Osteopathic Medicine - Georgia), Louis P. Hagopian (Kennedy Krieger Institute)
Abstract: Artificial organisms animated by the rules of the evolutionary theory of behavior dynamics (ETBD) were exposed to schedule arrangements comparable to reinforcement schedule thinning during functional communication training (FCT). Responding corresponded to that seen in clinical populations during FCT and schedule thinning, and mirrored findings in animal laboratory studies involving downshifts in reinforcement. That is, FCT produced a shift in allocation of responding from problem behavior to the alternative response, and schedule thinning resulted in resurgence of problem behavior. The Resurgence as Choice (RaC; Shahan & Craig, 2017) model was applied to data generated by the artificial organisms. Findings indicated that resurgence increased as a function the relative downshift in reinforcement rate and magnitude, replicating findings from previous studies with live animals. These results further demonstrate the conceptual and quantitative utility of RaC, and illustrate the use of ETBD for generating data like that produced by live humans and animals.



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