This article presents a model of consumer behavior that incorporates anchoring and price effects in describing purchase demand. The model, called F-Cap, for Finite Consumption Anchored to Price, offers an alternative to traditional microeconomic models of demand. This model is based on recent findings in psychology and behavioral economics and connects concepts from behavioral and traditional economics to the language and findings of behavior analysis. In particular, the model incorporates the idea of maximum consumption and reinforcement power developed in the exponential and exponentiated models of demand, and adds the possibility to estimate reference prices using a new, simpler estimation method. These elements are organized in a model based on the sigmoid function. A function estimation algorithm is proposed. This algorithm linearizes the function and estimates the parameters using ordinary least squares regressions. A core feature of the algorithm is that it allows the identification of reference prices, which is not possible in prior models. First, this article illustrates how the parameters of F-Cap modify the maximum level of consumption, the anchor point, and the decrease in consumption after that point, as proposed in the model. Next, using simulated data, the article shows that the algorithm estimates these parameters correctly both in standard and in mixed models. Third, the article presents evidence that F-Cap describes the behavior of human subjects in the hypothetical purchase task with less unexplained variance than alternative demand models. This function correctly estimates the parameters associated with the good's contribution to utility, which in behavior analysis language is equivalent to reinforcing power. It also estimates the response to reference prices, which can be interpreted as behavior governed by rules in the tradition of behavior analysis. The F-Cap model overall helps connecting the findings of operant behavioral economics with the practices of mainstream economics.
Behavior is dynamic because it results from the interactions between organisms and their environment. Reinforcement is the primary mechanism for explaining behavior, and it has evolved in various ways, allowing for the explanation of different aspects of behavior acquisition and maintenance. The adequacy of reinforcement in explaining behavior acquisition has mostly been tested on target behaviors. However, a broader understanding of behavior requires accounting not only for target behaviors but for all behaviors in a given situation. This article presents several experiments showcasing schedule-induced behaviors to analyze the variables that determine which behaviors are acquired and how they are organized. First, the effects of both physical and contingency-based constraints on the organization of behavior are examined. Second, the role of competition and collaboration between behaviors in determining their distribution is discussed. Third, a dual effect of reinforcers on behavioral patterns is proposed. It is concluded that behaviors interact with one another and with environmental stimuli, and behavioral patterns are continuously induced, updated, and reinforced. Data in this article highlight the need to focus on the moment-to-moment updating of behavioral patterns to fully understand behavioral dynamics.
In this article we investigate the teleological properties (functions and goals) of behavioral patterns, with emphasis on operant behaviors, including those associated with creativity and behavioral novelty. We integrate the etiological theory of teleology (as developed by Larry Wright, Ruth G. Millikan, James Garson, and others) with key concepts from behavior analysis. Although the language of functions and goals has traditionally faced resistance within behavior analysis, mainly due to concerns about causal confusion, we argue that such language is conceptually valuable when situated within the framework of selection by consequences. In the first part of the article, we disentangle teleological discourse from common misconceptions, particularly worries about reverse causation and the obstruction of causal explanation, drawing on insights from Larry Wright and others. In doing so, we set out what teleological concepts do not imply, while also identifying their core semantic features, such as the contrast between functions or purposes and mere accidents. In the second part, we develop an etiological interpretation of the teleological properties of behavioral patterns which, besides harmonious with the semantic core of teleological concepts, has theoretical synergies with behavior-analytic understanding of operant behavior, thus avoiding mentalistic aspects of some previous etiological readings of complex action. Our approach integrates Skinner's interpretation of selection processes with recent advancements in behavior analysis, including theories of operant generativity, behavioral variability, and relational frames. Finally, we conclude by setting our approach in contrast with two influential theories of teleology, in a way that brings into view its potential advantages.
Stagnation in the development of novel strategies for the management of major depression and other neuropsychiatric disorders has left many patients with unmet treatment needs. This state of affairs has encouraged critical appraisal of the very relationship between preclinical findings and their clinical applications in psychiatric practice. One consequence of such reflection has been a growing emphasis on reverse translation in preclinical research. Traditional preclinical approaches with laboratory animals have most often used a forward translational approach designed to identify classes of organized animal behavior that serve to predict outcomes in humans. On the other hand, reverse translational approaches identify patterns of human behavior revealed by task performance to develop assays with maximal formal similarity in laboratory animals. Presumably, such correspondence will evoke functionally similar behavioral outcomes across species, allowing for rigorous assessment of innovative, sometimes invasive, behavioral and pharmacological treatment strategies impossible to examine in human subjects without substantial preclinical evidence of safety and efficacy. Following validation and optimization, a reverse translational framework can be used for coordinated bidirectional pursuits across species to accelerate the drug discovery process. To aid appraisals of emerging reverse translational techniques, the present review outlines five considerations based on their longstanding association with rigorous assessments in behavioral science and informed by behaviorist traditions. Emphases on behavior, pharmacology, environmental determinants, levels of analysis, and cross-species continuity are discussed, with an emphasis on the Research Domain Criteria (RDoC) framework to advance innovative therapeutic strategies for treatment-resistant neuropsychiatric illness.
Theory of Mind (ToM) is typically used as an umbrella term to refer to and interpret a collection of responses that involve humans' ability to explain and predict others' behavior based on an understanding of their mental states, such as beliefs and desires. Not only is the ToM construct widely accepted in psychology, but it has also come to represent a broader theoretical system invoked to explain a range of social and cognitive processes in both typical and autistic development, with false-belief tasks serving as its litmus test. This article offers a behavioral account of ToM as verbal behavior-in particular, that it involves the speaker-listener first tacting the environmental variables that are exerting control over another person's behavior and then, engaging in conditional discriminations under multiple verbal control with respect to the difference between those variables and the ones influencing one's own behavior. The article is organized into four sections: (1) an overview of ToM within mainstream psychology; (2) the requirements for a behavioral account and summary of current approaches; (3) an alternative analysis rooted in verbal behavior; and (4) a potential instructional sequence for establishing ToM-related repertoires in children for whom they are currently absent or incomplete. Contrary to claims that ToM may exist in nonverbal or implicit form, the present analysis argues that it is necessarily verbal-requiring speaker-listener behavior shaped by social reinforcement. It attempts to redefine ToM in functional terms-as verbal behavior (Skinner, 1957) shaped through differential contact with the contingencies governing one's own and others' actions.
Frustration is an intrinsic feature of molecular complexes, arising when individual constituents must distort from their optimal isolated geometries to achieve collective stabilization. Although energetic frustration can be defined as the average distortion energy associated with complex formation, its quantitative origin and its connection to other molecular descriptors remain insufficiently understood. In this work, we systematically investigate frustration in four representative molecular complexes-two homogeneous clusters, (H2O)n and (HF)n, and two charged clusters, H3O+(H2O)n and F-(H2O)n (n = 1-20)-using three complementary density-based frameworks: (i) total-energy decomposition, (ii) global conceptual DFT (CDFT) descriptors, and (iii) information-theoretic approach (ITA) quantities. Strong linear correlations between the total frustration energy and most energy components, as well as CDFT indices, are revealed, enabling a quantitative interpretation of frustration from energetic and electronic-structure perspectives. Among ITA measures, only a subset, including Shannon entropy, Ghosh-Berkowitz-Parr entropy, Rényi entropy, and the relative Fisher information, exhibits robust and consistent correlations with frustration across all systems, indicating their suitability as ITA-based frustration descriptors. Particularly, the (HF)n clusters show uniformly excellent correlations for all descriptors due to their structurally simple and homogeneous hydrogen-bonding environment. Overall, this work provides a comprehensive density-based understanding of frustration and clarifies which descriptors reliably track its behavior. These insights establish a foundation for applying ITA and CDFT analyses to frustrated phenomena in broader chemical contexts, which could be applied to other systems, including molecular recognition, conformational dynamics, and catalysis.
The recent resurgence of Kantorian interbehavioral psychology in the context of relational frame theory (RFT) has prompted a reevaluation of RFT's core concepts through an interbehavioral lens. Although RFT acknowledges its Kantorian roots, recent works have called for a more serious consideration of interbehaviorism in the context of developing the theory towards a more complete analysis of the complexity of human language and cognition. In particular, the current article aims to explore the alignment between the RFT concept of the relational frame and the interbehavioral interpretation of psychological happenings. To this end, the relational frame is dissected to clarify (mentalistic) misconceptions of RFT, and is then compared with interbehavioral constructs such as stimulus and response functions, substitute stimulation, and interbehavioral history. The integration of these perspectives suggests that RFT may benefit from a field-based approach to experimental and applied research. We argue that by applying the interbehavioral concept of stimulus substitution for stimuli that differ arbitrarily in multiple ways (i.e., multiple stimulus relations), the door may be opened for the entire RFT research program to yield (at least potentially) to interbehavioral field-based analyses.
The concepts of reinforcement and punishment arose in two disparate scientific domains of psychology and artificial intelligence (AI). Behavior scientists study how biological organisms do behave as a function of their environment, whereas AI focuses on how artificial agents should behave to maximize reward or minimize punishment. This article describes the broad characteristics of AI-based reinforcement learning (RL), how those differ from operant research, and how combining insights from each might advance research in both domains. To demonstrate this mutual utility, 12 artificial organisms (AOs) were built for six participants to predict the next response they emitted. Each AO used one of six combinations of feature sets informed by operant research, with or without punishing incorrect predictions. A 13th predictive approach, termed "human choice modeled by Q-learning," uses the mechanism of Q-learning to update context-response-outcome values following each response and to choose the next response. This approach achieved the highest average predictive accuracy of 95% (range 90%-99%). The next highest accuracy, averaging 89% (range: 85%-93%), required molecular and molar information and punishment contingencies. Predictions based only on molar or molecular information and with punishment contingencies averaged 71%-72% accuracy. Without punishment, prediction accuracy dropped to 47%-54%, regardless of the feature set. This work highlights how AI-based RL techniques, combined with operant and respondent domain knowledge, can enhance behavior scientists' ability to predict the behavior of organisms. These techniques also allow researchers to address theoretical questions about important topics such as multiscale models of behavior and the role of punishment in learning.
Mass shootings affect both local and national communities and prompt extensive efforts to understand and prevent future events. Current approaches typically focus on profiling and typologizing mass shooters. Although these efforts are useful towards prediction of mass shootings, they do not tell us how to directly influence a shooter's behavior. Thus, our understanding of mass shootings remains incomplete. Given that behavior analysis is a systematic natural science approach to understanding all behavior, we believe it is poised to address this issue. This article focuses on fame-seeking shooters, which are a subset of all mass shooters. We first describe important behavior patterns and contextual events that have been associated with this subset. We propose that these behaviors are members of a larger response class which includes a mass shooting. We then provide a conceptualization of the selection process involved in the emergence of mass shooting behavior and its precursors. We close by describing several interventions aimed at disrupting the contingencies identified by the conceptual analysis. The goal of this article is to illustrate how behavior analysis may utilize and extend the currently existing and predominantly non behavior-analytic research (e.g., profiling and typologizing based on the form of behavior) to better enable the prediction and influence of mass shootings.
In behavior analysis, replication is one of the most fundamental strategies used to establish generality of results. However, replication is not restricted to just repeating an experiment, whether directly or systematically. Replication is also a defining component of many procedures used in individual experiments in behavior analysis. For example, some methods, such as single-stimulus discrimination procedures, exhibit direct control over behavior with a series of mini-AB designs (trial and intertrial periods) repeated multiple times within a single session. Once stimulus control is acquired, replication is demonstrated each time stimulus presentation is followed by the appropriate response. Conditional discrimination methods have the same structure with more trial types or stimuli that control response selections. So, replication is built in not only across experiments but also in within-session experimental designs. This will be illustrated by examples showing fine-grained data analysis. The illustrations will confirm Pennypacker's emphasis that moment-to-moment analyses of behavior are essential to successful replication.
Significant advancements in science occur when previously unobservable or immeasurable things critical to theory become observable and measurable. The "verbal community" is a case in point; it plays a critical role in the analysis of verbal behavior, but has primarily been described theoretically, as observing, measuring, or directly analyzing verbal communities has historically been difficult. In this article, we review recent technological advances in data collection and computational modeling that allow researchers to directly observe and measure verbal communities in real-time as they evolve. Because data are often collected at the individual level, researchers can directly observe, measure, and model the influence of a verbal community on the behavior of individual speakers and listeners. This approach is demonstrated through two examples in which two distinct verbal communities were directly observed, measured, described, and modeled. In doing so, previously vague theoretical descriptions and novel, nuanced questions about verbal communities and their influence on the behavior of speakers and listeners can be addressed and answered with empirical data. It should be noted that the approaches discussed herein rely on structural analyses of textual stimuli. Though uncommon in behavior analysis, future research demonstrating how integrating structural and functional approaches to the analysis of verbal behavior may lead to novel advances in our understanding of verbal behavior. The online version contains supplementary material available at 10.1007/s40614-025-00484-y.
The stimulus equivalence (SE) paradigm has become a central explanatory framework for language and complex symbolic behavior within behavior analysis. Its explanatory power rests on three core assumptions: (1) human symbolic behavior is grounded in the semantic relation between words and their referents; (2) this relation is one of equivalence; and in consequence (3) there is a transfer of stimulus functions between words and their referents. These assumptions are also endorsed by relational frame theory (RFT), although considering equivalence as a consequence of a relation of sameness within a relational frame of coordination. However, this article shows that the referential relation is neither reflexive, symmetrical, nor transitive, and therefore cannot be characterized as one of equivalence or sameness, invalidating (2) and (3). It is also shown that other attempts to support (2) or (3), based on the Fields-Place principle or contextual control, fail to achieve their aim. It is argued that between the behavior of the speaker and the listener, there is a functional asymmetry that grounds the asymmetry of the referential relation, and typical SE and RFT experimental paradigms cannot capture it. Finally, some consequences for the study of the SE phenomenon and the study of symbolic behavior from the perspective of behavior analysis are discussed.
Starting in the early 1970s, Henry S. Pennypacker and collaborators developed and validated a technology for assessing and training breast self-exam (BSE) skills that was eventually commercialized and widely disseminated. This article provides a brief synopsis of Pennypacker's research and highlights the connections among BSE, stimulus discrimination training, and signal detection theory. It also describes the role played by breast simulation models as a research tool that contributed to the identification and validation of effective BSE search strategies and eventually to the dissemination of a behaviorally based BSE training program to women and health-care workers. Commentary is provided on the impact of this research on the early detection and treatment of breast cancer. Finally, the focus on early detection skills is placed in the context of a larger body of research on the role of behavior and the application of behavior analytic interventions in improving health.
Feelings have long run high between many autistic advocates and behavior analysts. The former often experience and perceive ABA as harmful and traumatic in its methods, and prejudicial and stigmatizing in its objectives, with some of the latter retorting that criticisms reflect misunderstandings of the science rather than areas of true concern. The result? A deep and contentious conceptual divide, leaving little room for dialogue or progress. Recent months, though, have seen a tentative shift. Alongside recognition that behavioral interventions are so deeply entrenched that they are here to stay, some critical autism scholars are gingerly initiating public conversations with behavioral practitioners in a spirit of taking a pragmatic approach to meaningful reform. Further, a new generation of behavior analysts-including some autistic practitioners-is emerging, recognizing problems in their field, and considering how to address them. Interest in such developments is spreading and signals an opportunity for behavior analysts to follow other academic and advocate communities that recognize the importance of interdisciplinarity and critical self-reflection to evolve as a field. We-an interdisciplinary team of critical autism, neurodiversity, and behavior analysis scholars-feel that formalizing a broad field for scholars and practitioners sharing these ambitions holds potential. This field-let's call it Critical Behavioral Studies-would favor profound social, cultural, and historical understanding, a commitment to extend the scope of training to better contextualize practice in relation to the group served, and the self-examination that would bring meaningful change to the field.
Many recent studies have investigated rats' choice between drug and nondrug reinforcers to model variables influencing drug taking in humans. As research using this model accumulates, the complexity of factors affecting drug choice has become increasingly apparent. This review applies a behavioral economic perspective to research that has used this model. The focus is on experiments that have manipulated behavioral economic variables in studies of rats' choice between drugs like cocaine or heroin and nondrug reinforcers like saccharin or social interaction. Price effects, reinforcer interactions (i.e., as substitutes or complements), economy type, and income effects are described. Results of experiments testing the impact of these variables on rats' choice are presented and analyzed. Although rats' behavior in this model often conforms well with behavioral economic principles, there have also been instances where further explanation is required. By appreciating the behavioral economic context in which rats' choice between drug and nondrug reinforcers occurs, and by recognizing that both consequences and antecedents can play important roles in this behavior, our understanding of the complexity of factors involved in drug choice can be increased.
B. F. Skinner described countercontrol as a response to socially mediated aversive consequences that is primarily reinforced through negative reinforcement (i.e., removal or weakening of aversive stimuli) and may be strengthened further through positive reinforcement (e.g., peer approval or other attention). Skinner considered the empirical analysis of the phenomenon to be essential for a complete understanding of human behavior and recognized countercontrol as a necessary but complex aspect of treatment in vulnerable populations. Residential treatment settings are inherently restrictive, potentially aversive to consumers, and thus may evoke countercontrol by clients, especially when assent/consent is withheld or provided by someone other than the individual receiving treatment (e.g., guardian, conservator, or substituted judgement). We identify treatment challenges presented by countercontrol and considerations associated with: (1) setting events; (2) conditioned aversive stimuli; (3) topographies and other dimensions of behavior; (4) competing contingencies of reinforcement; and (5) functional behavior assessments. We conclude with a call to action for the long overdue experimental analysis of countercontrol in residential treatment settings and society at large.
Explicit memory dysfunction, such as in Alzheimer's dementia, impairs learning and daily functioning, requiring effective rehabilitation strategies to promote functional independence. Relational learning paradigms such as stimulus equivalence learning (SEL) imply the formation of networks of relations in which trained relations give rise to emergent relations, potentially providing a novel approach to addressing deficits in remembering and stimulus control. We evaluated the scope and nature of research on the application of relational learning paradigms for memory rehabilitation. In particular, we outline the evidence for the efficacy of identity matching and SEL in specific disorders, the associated effective strategies, and challenges to guide future research. A systematic search following the PRISMA-ScR guidelines identified 23 reports categorized into identity matching, arbitrary matching, and differential outcome procedure (DOP) paradigms. Findings were mixed regarding the success of training procedures. Studies indicate particularly positive outcomes under the DOP and overall efficacy seemed to depend on impairment severity. However, current evidence on the efficacy of relational learning paradigms in individuals with explicit memory dysfunction remains inconclusive due to uncontrolled designs and methodological weaknesses in statistical analysis and patient reporting. Nevertheless, insights from the reviewed studies can inform more rigorous future research. The focus should be on identifying the necessary and sufficient conditions for training stimulus equivalence relations in this population, within meaningful and well-controlled experimental designs to validate the preliminary findings and assess SEL's potential as a cognitive intervention.
The present article focuses on presentations from the second topical cluster from the 2024 Theory and Philosophy Conference held by the ABAI-Cultural Systems. The cultural systems cluster was comprised of two primary talks-"Unhinging Design from Darwinian and Skinnerian Selection" (Wasserman, 2024), and a second, co-authored by Sigrid Glenn and Maria Malott (and delivered by Sigrid Glenn), entitled "Behavior and Cumulative Cultural Evolution." We will begin by briefly summarizing some of the main points of each talk, and then discussing some of the implications of the arguments developed in each. The approach taken to link these two seemingly different primary talks will be interdisciplinary. I will seek to illustrate how dynamic patterns of systemic interactions within systems of physical energy parallel the dynamic patterns of behavioral systems and enable us to "reconstruct" some of the main principles emphasized in the primary talks, while also seeking to develop an understanding of how various processes of selection by consequences (natural selection, operant selection, and selection of cultures) emerge from systemic interactions.
Delay discounting is a behavioral phenomenon in which the subjective value of a reinforcer decreases as the reinforcer becomes more delayed. Two procedures are commonly used to assess how the value of a reinforcer changes as a function of delay: adjusting-delay and adjusting-amount. The evolutionary theory of behavior dynamics (ETBD) is a complex systems theory that uses an algorithm based on Darwinian principles of natural selection to animate artificial organisms. The behavior of artificial organisms animated by the theory are its predictions, and the theory has been shown to make accurate predictions about how living organisms behave in a variety of experimental arrangements. In the present article, we generated predictions with the ETBD for adjusting-delay and adjusting-amount procedures and evaluated whether these predictions align with live-organism delay discounting. The predictions were generated using modified procedures that could be conducted with continuous choice arrangements rather than discrete trials; however, despite these procedural differences, the ETBD's predictions were generally consistent with equations known to describe live-organism delay discounting well. This suggests that the ETBD might be used to generate other predictions that could expand our understanding of delay discounting.
This article discusses three contributions to the special issue from the Symbolic Processes cluster in the second Theory and Philosophy conference. Each of the articles advances a naturalistic, learning-based account of complex linguistic and social phenomena. The article by Barnes-Holmes and colleagues suggests a refinement of relational frame theory (RFT) informed by findings emerging from research conducted with the IRAP protocol for the past decade. Degli-Espinosa offers a behavioral reinterpretation of the development of theory of mind with implications for understanding its absence in certain populations and the potential for remediation where necessary. Palmer presents a thorough-going behavioral account of word order in novel utterances and, by way of example, offers a blueprint for the analysis of syntactic organization in spoken languages more generally. This paper discusses the articles' conceptual innovations, empirical grounding, and the implications of the analyses for future research. The discussion ends with an appreciation of the shared philosophical and methodological commitments reflected in these articles.