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.

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50th Annual Convention; Philadelphia, PA; 2024

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Symposium #123
Artificial Organisms Worked Hard to Create this Symposium: Further Evaluation and Application of the Evolutionary Theory of Behavior Dynamics
Saturday, May 25, 2024
10:00 AM–11:50 AM
Convention Center, 100 Level, 104 AB
Area: EAB; Domain: Translational
Chair: Hunter King (Department of Behavioral Psychology, Kennedy Krieger Institute, Johns Hopkins School of Medicine)
Discussant: Nathan Call (Emory University School of Medicine, Children's Health Care of Atlanta, Marcus Autism Center)
Abstract:

The evolutionary theory of behavioral dynamics is a computational model of behavior that integrates Darwinian rules of selection and reproduction to study the behavior of artificial organisms animated by the theory. A considerable body of research shows that the behavior of artificial organisms is indistinguishable from the behavior of live organisms under concurrent arrangements, and supports the theory for modeling a wide array of behavioral phenomena. In the past decade, studies have evaluated the degree to which the behavior of artificial organisms corresponds to live organisms under novel conditions. The purpose of this symposium therefore is to discuss several of these novel applications and future research in this area. The first presentation provides an introduction to the evolutionary theory of behavioral dynamics with an overview of supporting evidence. The second presentation discusses the correspondence between the behavior of artificial organisms and live organisms under delay discounting arrangements. The third presentation reviews conceptual and methodological considerations on the application of the theory to clinical phenomena, such as severe behavior in treatment-resistant populations. Finally, the fourth presentation discusses how the theory be applied to investigate patterns of responding during reversal learning tasks in individuals with autism spectrum disorder. Collectively, these presentations describe novel applications of the evolutionary theory of behavioral dynamics and how it can be used to investigate issues of social importance.

Instruction Level: Intermediate
Keyword(s): behavior dynamics, complexity theory, evolutionary dynamics
 
The Evolutionary Theory of Behavior Dynamics
(Basic Research)
JACK J MCDOWELL (Department of Psychology, Emory University)
Abstract: The evolutionary theory of behavior dynamics is a complex systems theory, which means that it is stated in the form of simple low-level rules, the repeated operation of which generates high level outcomes that can be compared to data. The low-level rules of the theory implement Darwinian processes of selection, reproduction, and mutation. This talk is an introduction to the evolutionary theory of behavioral dynamics for a general audience and illustrates how the theory is used to animate artificial organisms that can behave continuously in any experimental environment. Extensive research has shown that the theory generates behavior in artificial organisms that is indistinguishable in qualitative and quantitative detail from the behavior of live organisms in a wide variety of experimental environments. An overview and summary of this supporting evidence is provided. The theory may be understood to be computationally equivalent to the biological nervous system, which means that the algorithmic operation of the theory and the material operation of the nervous system give the same answers. The applied relevance of the theory is also discussed, including the creation of artificial organisms with various forms of psychopathology that can be used to study clinical problems and their treatment.
 
Delay Discounting in Artificial Organisms Animated by the Evolutionary Theory of Behavior Dynamics
(Basic Research)
RYAN HIGGINBOTHAM (University of Florida), Jesse Dallery (Department of Psychology, University of Florida)
Abstract: The evolutionary theory of behavior dynamics is a complexity theory that animates artificial organisms whose behavior is indistinguishable from the behavior of live organisms under a number of different experimental arrangements. We investigated whether artificial organisms would display live organism-like delay discounting using two common procedures. We used adjusting delay and adjusting amount procedures to investigate artificial organisms’ delay discounting. The results show that the artificial organisms discount delayed reinforcers hyperbolically, similarly to live organisms. In the adjusting delay procedure, the artificial organisms’ estimated discounting parameters aligned with the theoretical predictions of the equations used to describe the delay discounting of live organisms. This was also true for the adjusting amount procedure. These results further support the evolutionary theory of behavior dynamics and suggest that hyperbolic delay discounting is an emergent property of the dynamics of selection by consequences. Additional work inspired by the theory can advance our understanding of delay discounting.
 
Application of the Evolutionary Theory of Behavior Dynamics to Clinical Phenomena: Insights and Future Directions
(Applied Research)
JOHN FALLIGANT (Department of Behavioral Psychology, Kennedy Krieger Institute, Johns Hopkins School of Medicine), Louis P. Hagopian (Department of Behavioral Psychology, Kennedy Krieger Institute, Johns Hopkins School of Medicine )
Abstract: The evolutionary theory of behavior dynamics is a genetic algorithm that applies the Darwinian principles of evolutionary biology to model how behavior changes dynamically via selection by contingencies of reinforcement. The evolutionary theory of behavioral dynamics is a complexity theory in which low-level rules of selection, reproduction, and mutation operate iteratively to animate artificial organisms that generate emergent outcomes. Numerous studies have demonstrated the theory can accurately model behavior of live animals in the laboratory, and it has been applied recently to model various classes of challenging behavior and clinical procedures. In this presentation we will summarize recent work in this territory, and discuss some conceptual and methodological considerations on the application of the theory to clinical phenomena in future research. Although work is underway seeking to leverage the theory to understand the phenomenology of severe challenging behavior, its potential and limitations are not fully known. Additional efforts to refine extant models of clinical phenomena will likely lead to further refinement of the theory itself and its application by way of the same selectionist processes it models algorithmically.
 

Do Individualized Reinforcers Exacerbate Inflexibility? Applying the Evolutionary Theory of Behavior Dynamics to Understand a Replication Failure

(Applied Research)
SAMUEL L MORRIS (Louisiana State University), Celeste Tevis (Louisiana State University ), Pierce Taylor (Louisiana State University), Alva Elizabeth Allen (Department of Psychology, Louisiana State University )
Abstract:

Autism spectrum disorder is characterized by the occurrence of rigid and repetitive patterns of behavior. One way this has been evaluated is using reversal learning tasks in which the behavior of individuals with autism has been found to adapt less quickly to unpredictable changes in the contingencies of reinforcement. We attempted to replicate and extend research in this area by incorporating preferred, individualized reinforcers. We failed to replicate the findings of previous research: our clinical evaluation yielded much more extreme inflexibility than has been previously documented. We applied the evolutionary theory of behavior dynamics to investigate potential causes of this replication failure. First, we developed several models and artificial organisms animated according to each model were exposed to a reversal learning task. Second, we identified models that most closely corresponded to the findings of previous research as well as our clinical evaluation. Third, we exposed selected models to reversal learning tasks with varied reinforcer values to model the effects of including generic versus individualized, preferred consequences. Results indicate two distinct patterns of responding during reversal learning tasks, both of which are negatively impacted by the use of higher value reinforcers. Implications for future research and practice are discussed.

 

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