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Hagopian LP, Falligant JM. Application of the evolutionary theory of behavior dynamics to severe challenging behavior. J Appl Behav Anal 2023; 56:729-744. [PMID: 37614037 PMCID: PMC10591871 DOI: 10.1002/jaba.1018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
The evolutionary theory of behavior dynamics (ETBD) 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 ETBD is a complexity theory where low-level rules of selection, reproduction, and mutation operate iteratively to animate "artificial organisms" that generate emergent outcomes. Numerous studies have demonstrated the ETBD can accurately model behavior of live animals in the laboratory, and it has been applied recently to model automatically maintained self-injury. The purpose of the current series of studies was to further extend the application of the ETBD to model additional functional classes of challenging behavior and clinical procedures. Outcomes obtained with artificial organisms generally corresponded well with outcomes observed with clinical cases sourced from consecutive controlled case series studies. Conceptual and methodological considerations on the application of the ETBD to model challenging behavior are discussed.
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Affiliation(s)
- Louis P. Hagopian
- Department of Behavioral Psychology, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - John Michael Falligant
- Department of Behavioral Psychology, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
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Klapes B, Falligant JM, Hagopian LP. Modeling and quantifying resurgence in the Evolutionary Theory of Behavior Dynamics. Behav Processes 2023; 208:104860. [PMID: 36967093 DOI: 10.1016/j.beproc.2023.104860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/22/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
McDowell's Evolutionary Theory of Behavior Dynamics (ETBD) has been shown to model a wide range of live organism behavior with excellent descriptive accuracy. Recently, artificial organisms (AOs) animated by the ETBD were shown to replicate the resurgence of a target response following downshifts in the density of reinforcement for an alternative response and across repeated iterations of the traditional three-phase resurgence paradigm in a manner commensurate with nonhuman subjects. In the current investigation, we successfully replicated an additional study that used this traditional three-phase resurgence paradigm with human participants. We fitted two models based on the Resurgence as Choice (RaC) theory to the data generated by the AOs. Because the models had varying numbers of free parameters, we used an information-theoretic approach to compare the models against one another. We found that a version of the Resurgence as Choice in Context model that incorporates aspects of Davison and colleague's Contingency Discriminability Model provided the best description of the resurgence data emitted by the AOs when accounting for the models' complexity. Last, we discuss considerations when developing and testing new quantitative models of resurgence that account for the ever-growing literature of resurgence.
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Affiliation(s)
- Bryan Klapes
- Department of Biomedical Sciences, Philadelphia College of Osteopathic Medicine - Georgia Campus, USA.
| | - John M Falligant
- Department of Behavioral Psychology, Kennedy Krieger Institute, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, USA
| | - Louis P Hagopian
- Department of Behavioral Psychology, Kennedy Krieger Institute, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, USA
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Morris SL, Lucia SM. Applying the evolutionary theory of behavior dynamics to model the subtypes of automatically reinforced self-injurious behavior. J Appl Behav Anal 2023; 56:442-457. [PMID: 36922699 DOI: 10.1002/jaba.982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/19/2023] [Indexed: 03/18/2023]
Abstract
The delineation of the subtypes of automatically reinforced self-injurious behavior improved the utility of functional analysis results in predicting treatment efficacy. However, the mechanisms underlying subtype differences remain unclear and difficult to study in clinical populations. Morris and McDowell (2021) attempted to elucidate subtype differences by developing and evaluating models of the subtypes within the evolutionary theory of behavior dynamics. In the current study, we applied techniques from precision medicine to further evaluate the models developed by Morris and McDowell. This evaluation highlighted shortcomings of the existing models and suggested ways they could be improved. Thus, we conducted more extended modeling within the framework of precision medicine to identify models that were more quantitatively similar to available clinical data. Improved models that more closely approximate clinical data were identified. The implications of these models for research, practice, and further applications of the evolutionary theory of behavior dynamics are discussed.
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Affiliation(s)
- Samuel L Morris
- Department of Psychology, Louisiana State University, Baton Rouge, LA, United States
| | - Sarah M Lucia
- Department of Psychology, Southeastern Louisiana University, Hammond, LA, United States
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McDowell JJ. Creating and Studying the Behavior of Artificial Organisms Animated by an Evolutionary Theory of Behavior Dynamics. Perspect Behav Sci 2023; 46:119-136. [PMID: 37006601 PMCID: PMC10050662 DOI: 10.1007/s40614-023-00366-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 03/06/2023] Open
Abstract
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 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. Finally, possible future directions are discussed, such as the extension of the theory to behavior in a two-dimensional grid world.
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Affiliation(s)
- J J McDowell
- Department of Psychology, Emory University, 36 Eagle Row, Atlanta, GA 30322 USA
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McDowell JJ, Klapes B, Arashanapalli S. A test of the evolutionary theory's account of punishment superimposed on single-alternative schedules. J Exp Anal Behav 2023; 119:117-128. [PMID: 36416717 DOI: 10.1002/jeab.811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022]
Abstract
A test of the evolutionary theory was conducted by replicating Bradshaw et al.'s (1977, 1978, 1979) experiments in which human participants worked on single-alternative variable-interval (VI) schedules of reinforcement under three punishment conditions: no punishment, superimposed VI punishment, and superimposed variable-ratio (VR) punishment. Artificial organisms (AOs) animated by the theory worked in the same environments. Four principal findings were reported for the human participants: (1) their behavior was well described by an hyperbola in all conditions, (2) the asymptote of the hyperbola under VI punishment was equal to the asymptote in the absence of punishment, but the asymptote under VR punishment was lower than the asymptote in the absence of punishment, (3) the parameter in the denominator of the hyperbola was larger under both VI and VR punishment than in the absence of punishment, and (4) response suppression under punishment was greater at lower than at higher reinforcement frequencies. These four outcomes were also observed in the behavior of the AOs working in the same environments, thereby confirming the theory's first-order predictions about the effects of punishment on single-alternative responding.
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Affiliation(s)
- J J McDowell
- Department of Psychology, Emory University, Atlanta
| | - Bryan Klapes
- Department of Biomedical Sciences, Philadelphia College of Osteopathic Medicine - Georgia
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Examining Resurgence and Repetition with the Evolutionary Theory of Behavior Dynamics. Behav Processes 2022; 203:104776. [DOI: 10.1016/j.beproc.2022.104776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/16/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
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Modeling resurgence with an evolutionary theory of behavior dynamics. Behav Processes 2022; 197:104623. [PMID: 35318109 DOI: 10.1016/j.beproc.2022.104623] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022]
Abstract
McDowell's (2004) Evolutionary Theory of Behavior Dynamics (ETBD) is a computational theory that has reproduced a wide variety of behavioral phenomena observed in material reality. Here, we extended the generality of the ETBD by successfully replicating laboratory studies of resurgence with live animals using artificial organisms (AOs) animated by the theory. We ran AOs on concurrent random-interval random-interval (conc RI RI) schedules of reinforcement wherein one alternative (i.e., a target behavior) was reinforced while the other alternative (i.e., an alternative behavior) was not reinforced. Then, we placed the target behavior on extinction and reinforced the alternative response, producing a shift in allocation of responding from the target behavior to the alternative response. Finally, schedule thinning of the alternative response (i.e., downshifts) resulted in resurgence of target behavior. Our findings indicated that resurgence increased as a function of the relative downshift in reinforcement rate and magnitude, replicating findings from previous studies with live animals. These results further illustrate the utility of the ETBD for generating dynamic behavioral data and serve as a proof-of-concept for a novel computational approach for studying and understanding resurgence in future studies.
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McDowell JJ, Arashanapalli S. Evolutionary theory prediction: Response rate as a joint function of reinforcement rate and reinforcer magnitude. J Exp Anal Behav 2021; 116:225-242. [PMID: 34383960 DOI: 10.1002/jeab.710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/08/2021] [Accepted: 07/19/2021] [Indexed: 11/07/2022]
Abstract
Artificial organisms (AOs) animated by an evolutionary theory of behavior dynamics (ETBD) worked on concurrent interval schedules with a standard reinforcer magnitude on 1 alternative and a range of reinforcer magnitudes on the other. The reinforcer magnitudes on the second alternative were hedonically scaled using the generalized matching law. The AOs then worked on single interval schedules that arranged various combinations of the scaled reinforcer magnitudes and a range of nominal schedule values. This produced bivariate response rate data to which 5 candidate equations were fitted. One equation was found to provide the best description of the bivariate data in terms of percentage of variance accounted for, information criterion value, and residual profile. This equation consisted of 2 factors, 1 entailing the scaled magnitude, 1 entailing the obtained reinforcement rate, and both expressed in the form of exponentiated hyperbolas. The theory's prediction of the bivariate equation, along with additional predictions of the theory, were tested on data from an experiment in which rats pressed levers for various concentrations of sucrose pellets. The bivariate equation predicted by the theory was confirmed, as were all the additional predictions of the theory that could be tested on this data set.
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Affiliation(s)
- J J McDowell
- Department of Psychology, Emory University, Atlanta
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Morris SL, McDowell JJ. Modeling Subtypes of Automatically Reinforced Self-Injurious Behavior with the Evolutionary Theory of Behavior Dynamics. Perspect Behav Sci 2021; 44:581-603. [DOI: 10.1007/s40614-021-00297-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/03/2021] [Indexed: 10/21/2022] Open
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Klapes B, Calvin OL, McDowell JJ. A discriminated rapid‐acquisition laboratory procedure for human continuous choice. J Exp Anal Behav 2020; 114:142-159. [DOI: 10.1002/jeab.612] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/02/2020] [Accepted: 05/16/2020] [Indexed: 11/05/2022]
Affiliation(s)
| | - Olivia L. Calvin
- Department of PsychologyEmory University
- Department of PsychiatryUniversity of Minnesota
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McDowell JJ. On the current status of the evolutionary theory of behavior dynamics. J Exp Anal Behav 2019; 111:130-145. [DOI: 10.1002/jeab.495] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/02/2018] [Accepted: 12/15/2018] [Indexed: 11/11/2022]
Affiliation(s)
- J. J McDowell
- Department of Psychology; Emory University; Atlanta USA
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Hantula DA. Editorial: Reductionism and Holism in Behavior Science and Art. Perspect Behav Sci 2018; 41:325-333. [PMID: 31976397 PMCID: PMC6701733 DOI: 10.1007/s40614-018-00184-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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McDowell JJ, Klapes B. An evolutionary theory of behavior dynamics applied to concurrent ratio schedules. J Exp Anal Behav 2018; 110:323-335. [DOI: 10.1002/jeab.468] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 08/11/2018] [Indexed: 11/12/2022]
Affiliation(s)
- J. J McDowell
- Department of Psychology; Emory University; Atlanta USA
| | - Bryan Klapes
- Department of Psychology; Emory University; Atlanta USA
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