Association for Behavior Analysis International

The rapid growth in computer technology means that nearly anything imaginable is either possible or will soon become possible. Behavior analysts, as specialists in learning and behavior, are uniquely trained to become strong collaborators on multidisciplinary teams focusing on projects to advance machine learning, simulation-based experiences, and much more. In this tutorial, I will discuss how we currently leverage such technology in my lab and integrate robotics, virtual reality (VR), and artificial intelligence (AI) in our behavior analytic research. I will share the outcomes of some of our current research projects as well as my collaborative efforts on the Federal Emergency Management Agency (FEMA) and National Aeronautics and Space Administration (NASA) grants.

Board certified behavior analysts; licensed psychologists; graduate students.

Dr. Kazemi is a Professor at California State University, Northridge (CSUN) where she has developed and teaches undergraduate and graduate coursework in behavior analysis for the past 10 years. She founded the Masters of Science Program in Applied Behavior Analysis in 2010 and has collaborated with the CSUN community to provide graduate students high quality supervision experiences. She currently has two different lines of research. Her applied research interests involve identification of efficient, effective strategies for practical training, supervision, and leadership. Her laboratory research involves leveraging technology (e.g., robotics, virtual or augmented reality) for efficient training and feedback using simulations. She is currently working on several nationwide large projects (e.g., with FEMA and NASA) with a focus on effective training and behavioral outcomes. She has received several mentorship awards including the ABAI Best Mentor Award, the Outstanding Faculty Award, the Outstanding Teaching Award, and the Outstanding Service Award. She has published articles and book chapters on a variety of topics including training, staff turnover, and the use of technology in behavior analysis. She is the leading author of a handbook written for both supervisors and supervisees that is titled, Supervision and Practicum in Behavior Analysis: A Handbook for Supervisees.

Human behavioral pharmacology methods have been used to rigorously evaluate the effects of a range of centrally acting drugs in human beings under controlled conditions. Methods like drug self-administration and drug-discrimination have been adapted from non-human laboratory animal models. Because humans have the capacity to communicate verbally, self-report methods are also commonly used to understand drug effects. This presentation will provide an overview of these traditional human behavioral pharmacology methods, as well as more novel measures that have been introduced to the field. Representative data will be shared and the benefits, challenges and translational relevance of each method will be discussed. This session will cover guiding principles in the design of human behavioral pharmacology studies (e.g., using placebo controls, testing multiple doses) along with ethical (e.g., avoiding enrollment of individuals seeking treatment, determining capacity to consent) and safety (e.g., dose selection, pre-screening of participants for exclusionary health problems) that must be addressed when conducting these types of studies.

Board certified behavior analysts; licensed psychologists; graduate students.

The evolutionary theory of behavior dynamics (ETBD) is a complexity 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 tutorial is an introduction to the ETBD, and will illustrate how the theory is used to animate artificial organisms that behave freely, and continuously, in any desired experimental environment. Extensive research has shown that the behavior of artificial organisms animated by the theory successfully reproduces the behavior of live organisms, in qualitative and quantitative detail, in a wide variety of experimental environments, including concurrent ratio schedules with equal and unequal ratios in the components, and concurrent interval schedules with and without punishment superimposed on one or both alternatives. An overview and summary of the research testing the ETBD will be provided. The material interpretation of the theory as an instance of supervenient realism will also be discussed. Finally, possible future directions will be considered with an eye toward identifying the most valuable path or paths for future development.

Behavior analysts interested in the basic science; individuals interested in computational theories of behavior or machine learning; individuals interested in modeling clinically significant human behavior.

J. J McDowell received an A.B. from Yale University in 1972 and a Ph.D. from the State University of New York at Stony Brook in 1979. After completing his clinical internship, he joined the faculty of Emory University, where he is currently a professor in the Department of Psychology. Dr. McDowell is also a licensed clinical psychologist, and maintains a private practice of behavior therapy in Atlanta. Dr. McDowell's research has focused on the quantitative analysis of behavior. He has conducted tests of matching theory in experiments with humans, rats, and pigeons, has made formal mathematical contributions to the matching theory literature, and has proposed a computational theory of behavior dynamics. He has also written on the relevance of mathematical and computational accounts of behavior for the treatment of clinical problems. Dr. McDowell's current research is focused on his computational theory of selection by consequences, including studies of behavior generated by the theory's genetic algorithm, and possible implementations of the theory in neural circuitry. His work, including collaborations with students and former students, has been funded by NIMH, NSF, and NIDA. Dr. McDowell is a Fellow of the Association for Behavior Analysis International.

Functional communication training (FCT) has strong empirical support for its use when treating socially reinforced problem behavior. However, treatment effects often deteriorate when FCT procedures are challenged, leading to the recurrence of problem behavior, decreased use of the functional communication response (FCR), or both. Researchers have accordingly described a number of strategies to improve the long-term effectiveness of differential-reinforcement procedures (e.g., FCT). For example, Wacker et al. (2011) assessed the maintenance of FCT-treatment effects by periodically exposing the FCR to periods of extinction and found that additional exposure to FCT helped guard against the disruptive impact of later periods of extinction. Basic researchers have described similar modifications to behavior-reduction procedures based on quantitative theories of behavior (e.g., Behavioral Momentum Theory and Resurgence as Choice) that also should help mitigate treatment relapse. Our research team has recently begun investigating

BCBAs, applied and basic researchers