Results of quality behavioral intervention can be life-altering for our consumers. For our consumers to reach their fullest potential it requires that our interventionists are well-trained and that they are implementing behavioral intervention with a high degree of quality and fidelity. Practitioner training has evolved over the years and some of the standards appear to be improving. However, there are still numerous concerns with the training of behavior analysts. The purpose of this article is to describe some concerns with practitioner training and offer suggestions about how we can improve training within the field of applied behavior analysis.

Although applied behavior analysis (ABA) is regarded as providing the gold standard for interventions designed to meet the needs of autistic individuals in the United States, elsewhere this is not the case. In Northern Ireland, for example, successive governments have portrayed ABA simply as one of a number of commercially available interventions for autism. In this article, I argue that this view arises directly from the practice of behavior analysts who have courted the development of branded versions of ABA at the expense of promoting ABA directly. Because clinicians who advise government ministers are not trained in ABA, it is understandable that a discrimination issue arises whereby ministers are then encouraged not to invest in only "one of the commercially available interventions." To address this problem, the article ends with a suggestion in how a specially designed ethical code of practice might hold behavior analysts accountable for the discrimination problems that could arise as a consequence of their actions in countries struggling to promote the uptake of ABA.

Behavior analysis and applied behavior analysis (ABA) have had many opponents since their inception. These opponents present challenges to the understanding and acceptance of our perspective and our professional practice. Asserting that behavior is a product of environmental circumstances is in opposition to our everyday language understanding of the cause of a person's behavior in that their behavior is determined not autonomous. From a clinical perspective this leads to finding environmental causes and then engineering procedures to produce socially meaningful change in behavior. This piece discusses some challenges from internal critics who place their brand of service above ABA.

In recent years, public concerns about applied behavior analysis (ABA) have intensified. This article argues that foundational principles of ABA require behavior analysts to take seriously these concerns and actively work to improve our practices. We provide an overview of ongoing reform efforts and examine how these efforts have led to the emergence of distinct brands within the field. Although these reformist efforts signal a commitment to ethical progress, they also raise concerns about the proliferation of competing credentials, conferences, and professional affiliations, which could increase confusion among clients, practitioners, and policymakers. We argue that although branding within ABA may be an expected and ultimately benign response to shifting ethical and practical norms, it also carries the potential to faction reformist efforts and further divide the field, rather than promote our collective advancement toward ever-better practices. Thus, our goals are to: (1) raise awareness around the potentialities of siloing advancements under competing labels, and (2) suggest that an integrated approach synthesizing reformist insights into a cohesive framework may have the greatest impact on advancing the whole field's understanding of best practice.

Ethics is fundamentally concerned with claims of "right," "wrong," "good," "bad," and how we might know those claims are accurate. Artificial intelligence (AI) is a term that represents a suite of tools where nonbiological systems process data and information to generate an output considered "intelligent." As with any suite of technologies, choosing the right tool requires the tool user to critically evaluate which tool is best for the task (if one exists). Harnessing the power of AI systems to maximize benefit and minimize harm requires basic AI literacy. AI literacy requires a basic understanding of how mathematical models function. All future behavior analysts will need to be AI literate. This will require changes to education and training programs to ensure students have a basic understanding of model building, especially as we wait for the scholarship and research to unfold that outlines AI literacy skills specific to behavior analysts.

For behavior analysts, social validity should be a standard and evolving component of our own self-evaluation process. Our field has known that for decades, but we have not always been good about implementing such self-evaluations. Recent criticism of our field should be viewed as setting the occasion for self-reflection and improvement of our practices. To accomplish those aims (self-relection and improvement), we need to identify specific practices to extinguish, modify, or (at least) explain better. We will address this challenge in three contexts. First, we provide discussion on what is "meant" by "ABA." Second, we distinguish between ABA as a discipline and ABA as an intervention (we contend it is the former and not the latter). Third, we acknowledge and identify nuances of social validation, often by drawing parallels and comparisons to medical fields.

Client well-being remains a central goal of research on the assessment and treatment of problem behavior. Although methodologies have evolved over the decades, the use of isolated- and synthesized-contingency functional analyses as a starting point remains debated. This article explores several factors potentially inhibiting consensus, the harm caused by division, and ways to shift the discourse regarding isolation and synthesis toward collaboration.

Trauma-informed care (TIC) refers to the guiding principles that inform how organizations or individuals arrange services with respect to acknowledging both the prevalence and potential effects of trauma on the people they support. Discussions about TIC have become increasingly prevalent in applied behavior analysis in recent years, suggesting that the topic is relevant to both our science and practice. However, research evaluating the degree to which TIC values are embedded in applied behavior-analytic work, and the relative benefits and potential costs of doing so, has been lacking. The purpose of this article is to provide some suggestions about how we might begin to fill that gap.

This commentary critically appraises attacks on applied behavior analysis (ABA) from outside and-increasingly-within the field. Commonly repeated attacks are that ABA is coercive and suppresses individual identity, aligns with the medical model, causes trauma, and, in more extreme cases, constitutes abuse. We illustrate how these claims are based on unfounded criticism and longstanding myths about ABA and stand in direct contrast to the empirical foundations of behavior analysis. We also highlight how such criticism conflicts with over half a century of evidence that ABA supports autonomy and enhances wellbeing of people with autism and developmental disabilities. We call for self-reflection among well-meaning behavior analysts who repeat such criticisms and greater attention to evidence-based practice.

Recent attacks on applied behavior analysis (ABA) by neurodiversity advocates share a common theme with opposition to other supports, such as subminimum wage vocational programs and congregate residential settings: the intact mind assumption, which maintains that even profoundly autistic people have typical intelligence, even if they present as severely cognitively impaired. This article examines the history of the intact mind assumption, which was largely shaped by psychoanalytic theory in the mid-20 century, as well as its impact on contemporary disability policy and practice.

There are many pressure points in society that induce strong reactions and intransigent verbal behavior. In this article, I argue the current biggest challenge in culturally responsive applied behavior analysis lies in avoiding getting dragged into the current culture wars while continuing to engage in evidence-based practice. Although there is no simple solution, I refer to what might seem like simple ways to address this challenge: (1) behave like a scientist-practitioner; (2) conduct research to expand evidence-based practices; and (3) monitor your own behavior to ensure practices align with the relevant ethics code. Do not let the simplicity of the suggestions fool you, because enacting them can be challenging and requires diligent practice.

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 -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.

Within a popular seven-dimension framework, applied behavior analysis (ABA) interventions have historically been characterized by a focus on socially important . We propose that an intervention does not truly count as applied until it is being widely used and the world has improved in some meaningful way as a result. This perspective places active dissemination of ABA, with the goal of promoting adoption, on equal footing with other defining features, making it functionally ABA's eighth dimension. According to this perspective, much of the work from ABA first 6 decades qualifies as pilot or developmental, with widespread adoption yet to be achieved. Successful dissemination will require some changes to ABA's .

The development of applied behavior analysis (ABA) around the world can be conceptualized in various ways. One of these ways is how the field of ABA is developing towards a professional industry distinct from other professions, such as psychology. The international ABA community is a broad landscape of hundreds of diverse and culturally distinct national and regional communities with professional development in ABA progressing in unique ways. Some current examples highlighting various successes in the development of professional ABA will be discussed.

In recent years, the question has been raised as to whether teaching eye contact to autistic children is an ethically defensible educational objective. In the present article, I suggest that this question may be best answered by first defining contact with the eyes not as behavior, but as a consequence for the behavior of looking. Looking at people's faces, and in particular the eyes, provides information regarding the discriminative functions and reinforcing value of social stimuli, of people, of what they do, what they say, and what they feel, and is a critical part of all social behavior. Following a brief review of the available behavioral and developmental evidence on eye-looking behavior, its development from birth, and the role it plays in the context of social and verbal learning in early childhood, I suggest that on the topic of eye contact, the question is not simply whether we should or should not teach it. Rather, the question is whether we should seek to establish social interaction as a reinforcer for eye-looking behavior as an educational target for autistic children.

A neurodiversity movement (NDM) has gained momentum, mainly driven by autistic self-advocates. The main argument of the NDM is that neurodivergent people experience discrimination that is on par with the historical discrimination of other minority groups. In this article, we propose a behavior analyst's perspective on the NDM. We first explore the history and emergence of the concept of neurodiversity and its neurological as well as psychological basis. We consider its potential for generating what some consider a zero-sum game, in which one group makes all the gains potentially at the expense of another group. We finish with the suggestion that a win-win situation is possible if the focus shifts proactively to advocacy for all persons with autism, including those with very high support needs who often are not able to advocate actively for themselves and who tend to benefit greatly from evidence-based behavior-analytic interventions.

Private equity (PE) refers to a financial investment model that involves investment firms pooling funds from multiple investors to acquire, manage, and resell companies for profit. PE firms have identified applied behavior analysis (ABA) service providers as a lucrative investment opportunity, leading to a surge of involvement within the industry. Although concern has been expressed about PE involvement in ABA, there are few resources available to behavior analysts interested in understanding its potential impacts. In this article, I will provide an overview of the risks and appeals of PE ownership and offer my reckoning of what may come of PE involvement in ABA.

Researchers have identified questionable research practices that compromise replicability and validity of conclusions. However, this concept of questionable research practices has not been widely applied to single-case experimental designs (SCED). Moreover, to date researchers have focused little attention on improved research practices as alternatives to questionable practices. This article describes initial steps toward identifying questionable and improved research practices in SCED. Participants were 63 SCED researcher experts with varying backgrounds and expertise. They attended a 1-day virtual microconference with focus groups to solicit examples of questionable and improved research practices at different stages of the research process. A qualitative analysis of over 2,000 notes from the participants yielded shared perspectives, resulting in 64 pairs of questionable and improved research practices in SCED. Our results highlight the need for further evaluation and efforts to disseminate improved research practices as alternatives to questionable practices.

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.

Although associative learning research has been conducted for more than a century, little is known about learning processes when subjects are not alone, but in a group-a phenomenon termed . In collective learning situations, the behavior of conspecifics may serve as an associative cue for learning, like any other stimulus during individual learning. Two experiments investigated how individual versus collective training affects associative learning. Experiment 1 utilized a simultaneous discrimination task, whereas Experiment 2 implemented a serial go/no-go discrimination task. In both experiments, rats were trained either individually or collectively, exposing them to two distinct stimuli with only one of them signaling the availability of food reinforcement. Following acquisition training, all rats were tested both individually and collectively. Contingencies were then reversed: the previously nonreinforced stimulus now signaled the availability of food, and the previously reinforced stimulus now signaled the absence of food. Following reversal training, the rats were again tested individually and collectively. Results from both experiments suggest that the training condition (individual or collective) had little effect on learning the cue-outcome association. However, individual training negatively affected test performance in a collective context. These results suggest that collective training may have a facilitative effect on learning and points out key methodological considerations for more in-depth examination of this effect.

Research on the role of time in associative learning has changed our understanding of what an association is. It is a measurable fact about the distribution of events in time, not an altered activation-conducting connection in a mind, brain or net. Associative learning is the process of perceiving temporal associations and deciding to act on them. Informativeness- the ratio of a conditional rate to an unconditional rate-is the essential empirical variable, not the probability of reinforcement. The communicated information between temporally associated behavioral and reinforcing events is the log of informativeness. Because the time units in the rate estimates cancel, associative-learning is time-scale invariant: Perceivably associated events may be arbitrarily widely separated. There are no windows of associability nor decaying eligibility traces. The learning rate-operationally defined as the reciprocal of reinforcements prior to the appearance of a conditioned response-is an almost scalar function of relative temporal separation, as measured by informativeness. The central role of informativeness unites our understanding of Pavlovian and operant/instrumental phenomena, revealing unexpected quantitative and conceptual communalities.

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.

Some of the most basic phenomena in behavior analysis are the differences between performance on interval and ratio schedules. They have long been known and long puzzled over. Previous attempts to explain the performances have foundered either because they lacked a mechanism or because they adopted a molecular view of behavior based on discrete responses and contiguity. The molar view of behavior offers the sought-for explanation of differences in activity rate and the inability of ratio schedules to maintain activity at low food rates. The present account relies on induction by phylogenetically important events (PIE) according to power functions, molar feedback functions, and the framework of matching theory. A model described by a feedback system with all parameters the same predicts the relations between activity rate and PIE rate. The difference in overall activity rate arises from a difference in units of activities selected by ratio and interval schedules. The results demonstrate the greater explanatory power of the molar view of behavior.

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.

Pigeons () have played a central role as subjects in the experimental analysis of behavior since the 1940s. This review considers the use of pigeons by humans across several domains: (1) their early use as a domesticated species and in early psychology laboratory experiments; (2) their rise, and recent decline relative to the use of other species, as a subject in behavior-analytic research published in the ; and (3) their influence in research extending beyond behavior analysis. In addition, in the latter two sections, quantitative data are presented to document the frequency of use of laboratory pigeons and their impact outside of the lab, respectively. The review concludes with observations on both the past and future of the pigeon as a subject for the experimental analysis of behavior.

The concepts of reinforcement and punishment arose in two disparate scientific domains of psychology and artificial intelligence (AI). Behavior scientists study how biological organisms behave as a function of their environment, whereas AI focuses on how artificial agents 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 13 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.

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.

This study examined how starlings () adapt to a serial learning task with a predictable reversal in the reinforcement contingencies at midsession. The birds learned a simultaneous discrimination between two options, S1 and S2 (red and green key light colors). Choices of S1 were rewarded during the first 40 trials and choices of S2 were rewarded during the last 40 trials, with variable exponentially distributed ITIs separating the trials. Then, to test the hypothesis that starlings anticipate the midsession reversal based on time into the session, we changed the average of the ITIs during a test session. The hypothesis predicted that with ITIs twice as short during testing, preference would shift from S1 to S2 twice as many trials later than in training, and with ITIs twice as long during testing, preference would shift twice as many trials earlier than in training. Results showed that preference shifted in the predicted direction, but the shifts were smaller in magnitude than predicted. Cumulative difference records plotting choices across time- or trial-into-the-session revealed a variety of adjusting strategies, some consistent with the use of temporal cues, others consistent with the use of local or numerical cues. The variability of strategies occurred both between and within subjects and suggests that multiple cues combine to control behavior in the midsession reversal task.