On the current status of the evolutionary theory of behavior dynamics

Model-based estimates for operant selection

We present a new methodology to partition different sources of behavior change within a selectionist framework based on the Price equation-the multilevel model of behavioral selection. The multilevel model of behavioral selection provides a theoretical background to describe behavior change in terms of operant selection. Operant selection is formally captured by the covariance-based law of effect and accounts for all changes in individual behavior that involve a covariance between behavior and predictors of evolutionary fitness (e.g., food). In this article, we show how the covariance-based law of effect may be applied to different components of operant behavior (e.g., allocation, speed, and accuracy of responding), thereby providing quantitative estimates for various selection effects affecting behavior change using data from a published learning experiment with pigeons.

Application of the evolutionary theory of behavior dynamics to severe challenging behavior

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.

Modeling and quantifying resurgence in the Evolutionary Theory of Behavior Dynamics

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.

Applying the evolutionary theory of behavior dynamics to model the subtypes of automatically reinforced self-injurious behavior

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.

Creating and Studying the Behavior of Artificial Organisms Animated by an Evolutionary Theory of Behavior Dynamics

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.

A test of the evolutionary theory's account of punishment superimposed on single-alternative schedules

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.

A Theory of the Extinction Burst

A preliminary theory of a temporary increase in the rate of an operant response with the transition to extinction (i.e., the extinction burst) is proposed. The theory assumes reinforcers are events permitting access to some valuable activity, and that such activity can compete for allocation with the target response under some conditions (e.g., very high reinforcement rates). With the transition to extinction, elimination of this competition for allocation can produce an increase in the the target response, but the increase is transient because the value of the target response decreases with exposure to extinction. The theory provides a way to understand why the extinction burst is not ubiquitous, seems more common following very small ratio schedules, occurs for a short period of time following the transition to extinction, and may be eliminated with the availability of alternative reinforcement. It appears to provide a reasonable starting point for a theory of the extinction burst that does not necessarily require inclusion of invigorating effects of frustration, and it is closely aligned with Resurgence as Choice theory. Additional research on factors modulating reinforcement-related activities and how they affect the extinction burst could help to further evaluate the theory.

Potential Contributions of Behavior Analysis to Research on Pro-environmental Behavior

Large parts of contemporary research on pro-environmental behavior focus on mechanistic explanations and mental constructs. Exclusive reliance on this approach may hinder the search for novel solutions to conceptual problems, more powerful methods, and innovative behavior change interventions. Theoretical diversity, on the other hand, can render a field adaptive in its responses to crises and impasses. Against this background, we describe the complementary approach of behavior analysis and its potential contributions to problems of contemporary research on pro-environmental behavior. Behavior analysis (1) provides a consistent account of phenomena that are difficult to reconcile with the mechanistic perspective, (2) redirects the spotlight to context, (3) provides a framework and methodology for assessing behavior with actual environmental impact, and (4) could inspire the development of new intervention techniques. Based on these contributions, we conclude that behavior analysis could substantially enrich research on pro-environmental behavior.

Modeling resurgence with an evolutionary theory of behavior dynamics

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.

Some Characteristics and Arguments in Favor of a Science of Machine Behavior Analysis

Researchers and practitioners recognize four domains of behavior analysis: radical behaviorism, the experimental analysis of behavior, applied behavior analysis, and the practice of behavior analysis. Given the omnipresence of technology in every sphere of our lives, the purpose of this conceptual article is to describe and argue in favor of a fifth domain: machine behavior analysis. Machine behavior analysis is a science that examines how machines interact with and produce relevant changes in their external environment by relying on replicability, behavioral terminology, and the philosophical assumptions of behavior analysis (e.g., selectionism, determinism, parsimony) to study artificial behavior. Arguments in favor of a science of machine behavior include the omnipresence and impact of machines on human behavior, the inability of engineering alone to explain and control machine behavior, and the need to organize a verbal community of scientists around this common issue. Regardless of whether behavior analysts agree or disagree with this proposal, I argue that the field needs a debate on the topic. As such, the current article aims to encourage and contribute to this debate.

Evolutionary theory prediction: Response rate as a joint function of reinforcement rate and reinforcer magnitude

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.

Further application of the generalized matching law to multialternative sports contexts

Cox et al. (2017) successfully applied the multialternative version of the generalized matching law (GML) to pitch selection among a sample of Major League Baseball (MLB) pitchers. The purpose of the present study was to replicate and extend these findings by fitting the multialternative GML to pitch data among a sample of MLB pitchers with varying levels of success in the major leagues. We also examined how matching parameters changed as a function of novel antecedent game contexts such as the infield shift, game location, and number of times the pitcher faced the batters in the batting order. These results replicate the findings from Cox et al. and suggest the multialternative GML is a robust descriptor of pitch selection among MLB pitchers. Together, these findings further extend the generality of the multialternative GML to naturalistic, nonlaboratory environments.