Application Of Behavior Analysis Research Articles

Low-intensity ABA intervention for young children with ASD in Italy

ABSTRACT This study investigated the effects of a 12-hr per week education package for 7 children with autism spectrum disorders (ASD) within the context of the Italian public health system. Different from the US, the Italian public health system traditionally offers 1 to 3 hrs per week of speech, occupational, or physical therapy, and/or ABA-based activity for children with ASD. Hence, it is imperative to assess the criteria for effective ABA interventions to enable comparison with various models of care for children with ASD. We employed a treatment package based on the Comprehensive Application of Behavior Analysis to Schooling (CABAS®) model. Each participant received interventions for 4 hours a day, 3 days a week for one year. The results revealed a significant difference in the total scores and each sub-test score of both the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) and the Childhood Autism Rating Scale-Second Edition (CARS-2) before and after one year of instruction. Our low-intensity intervention appears to effectively improve the participants’ various academic, verbal, and self-management skills, positively impacting their overall development. Our findings provide insights into testing the effectiveness of an alternative model of ABA intervention while also expanding the application of advanced ABA practices to enhance the developmental trajectory of young children.

A Survey on AI-Driven Mouse Behavior Analysis Applications and Solutions

The physiological similarities between mice and humans make them vital animal models in biological and medical research. This paper explores the application of artificial intelligence (AI) in analyzing mice behavior, emphasizing AI’s potential to identify and classify these behaviors. Traditional methods struggle to capture subtle behavioral features, whereas AI can automatically extract quantitative features from large datasets. Consequently, this study aims to leverage AI to enhance the efficiency and accuracy of mice behavior analysis. The paper reviews various applications of mice behavior analysis, categorizes deep learning tasks based on an AI pyramid, and summarizes AI methods for addressing these tasks. The findings indicate that AI technologies are increasingly applied in mice behavior analysis, including disease detection, assessment of external stimuli effects, social behavior analysis, and neurobehavioral assessment. The selection of AI methods is crucial and must align with specific applications. Despite AI’s promising potential in mice behavior analysis, challenges such as insufficient datasets and benchmarks remain. Furthermore, there is a need for a more integrated AI platform, along with standardized datasets and benchmarks, to support these analyses and further advance AI-driven mice behavior analysis.

The Proper Calculation of Risk Ratios: How and Why

AbstractA recent article (Joslyn, P. R., & Morris, S. L. in Perspectives on Behavior Science, 47(1), 167–196, 2024) advocates the use of risk ratios, or relative risk, in behavior analysis. The authors present a strong case for the use of risk ratios and how they might improve the science and application of behavior analysis. Unfortunately, their computation of the risk ratio is incorrect and their examples gloss over important nuances in how risk ratios should be used. The present article corrects the calculations, describes how to determine whether a particular risk ratio differs from a reference group, comments on the importance of stability of the data entering the calculation, and demonstrates approaches to presenting them visually, such as Forest plots.

Promoting Safety While Addressing Dangerous Behavior via Telehealth: A Clinical Case Investigation Serving the Family of an Autistic Adolescent Living in India

When clinicians address dangerous behavior via remote telehealth consultation, it may be especially important to ensure remote implementers can apply behavioral interventions in a manner that keeps recipients and implementers safe while still achieving effective outcomes. We present the case of a 13-year-old autistic adolescent with limited communication skills, living in South India, whose dangerous behavior was escalating and becoming more pervasive during the pandemic quarantine, putting himself and his family at risk. In this study, we evaluated the effects of an enhanced choice model of skill-based treatment—informed by a practical functional assessment of dangerous behavior. We systematically replicated and extended procedures that have been shown to address dangerous behavior while avoiding difficult situations that place the individual and others at risk. The intervention yielded elimination of dangerous and associated non-dangerous behavior and socially valid acquisition of multiple alternative behaviors. We report multiple strategies to overcome barriers unique to remotely addressing dangerous behavior and discuss implications for the safe, telehealth application of behavior analysis in research and practice.

Dennis H. Reid and Mary Rosswurm: Promoting Desired Lifestyles Among Adults with Severe Autism and Intellectual Disabilities: Person-centered Applications of Behavior Analysis. Elsevier, 2023, 322 pp

Dennis H. Reid and Mary Rosswurm: Promoting Desired Lifestyles Among Adults with Severe Autism and Intellectual Disabilities: Person-centered Applications of Behavior Analysis. Elsevier, 2023, 322 pp

Recommendations Regarding Use of the Term "Ignore" in Applied Behavior Analysis.

Ignore is a common term used in behavioral assessment, behavior intervention plans, textbooks, and research articles. In the present article, we recommend against the typical usage of the term in most applications of behavior analysis. First, we briefly outline some history of the use of the term in behavior analysis. Then, we describe six main concerns about ignore and the implications for its continued use. Finally, we address each of these concerns with proposed solutions, such as alternatives to the use of ignore.

Driver Maneuver Identification with Multi-Representation Learning and Meta Model Update Designs

Driver maneuver identification (DMI), i.e., the task of predicting the driver maneuver classes given sensor measurements (such as IMU sensors from mobile devices), can serve as a key enabler for many ubiquitous driver behavior analysis applications. Despite the prior studies, two major technical challenges remain before an effective DMI system can be deployed: (i) latent complex maneuver behavior feature relations for accuracy enhancement, and (ii) inconsistency and uncertainty in model adaptation given dynamic data collection settings (e.g., given different urban environments, drivers, and mobile devices). To address the aforementioned challenges, we propose MetaDMI, a novel and adaptive driver maneuver identification framework based on multi-representation learning and meta model update, with the case study on the inertial measurement unit (IMU) sensor measurements (i.e., accelerometer and gyroscope). Specifically, we first extract multiple feature representations for each driver maneuver record in the forms of graph, spectral, and time sequence. Next, MetaDMI processes them with a novel multi-representation learning network, extracting complex patterns and feature relations from the driver maneuvers. Finally, to further enhance the adaptivity of our DMI model to external impacts with dynamic data collection, we have designed a regularized meta learning-based training method to regularize the knowledge transfers across the source and target datasets (e.g., across cities/devices, and few-shot initialization) for consistent and robust identification performance. We have conducted extensive experimental studies upon our MetaDMI prototype based on two datasets (one is collected on our own) and shown that our approach outperforms other baseline approaches for DMI in terms of accuracy and adaptivity.

Toward trauma-informed applications of behavior analysis.

Despite a growing acknowledgement of the importance of understanding the impacts of trauma on therapeutic approaches across human service disciplines, discussions of trauma have been relatively infrequent in the behavior analytic literature. In this paper, we delineate some of the barriers to discussing and investigating trauma in applied behavior analysis (ABA) and describe how the core commitments of trauma-informed care could be applied to behavior analysis. We then provide some examples of how trauma-informed care might be incorporated into ABA practice. We conclude by suggesting opportunities to approach trauma as a viable avenue for behavior analytic research and argue that omitting trauma-informed care from ABA could be detrimental not only to the public perception of ABA, but to the effectiveness of our assessment and treatment procedures.

USING EVOLUTIONARY GAMES THEORY FOR PLAYERS BEHAVIOR ANALYSIS IN COMPETITIVE SHOOTING GAMES: A LITERATURE REVIEW

The paper is dedicated to the overview of current state of the evolutionary games approach to the building of environments to analyze players behavior. The evolutionary game theory applications differ from the orthodox game theory. Initially, it was used to address problems in evolutionary biology and later was suited for broader range of problems.We will oversee the development of the evolutionary games theory in finance and its applications in behavior analysis in competitive gaming. The paper is focused on replicator dynamics, learning model based on it and its possible application to behavior analysis based on fuzzy algorithms and approaches used in economics to be applied to the new emerging field of cybersports.

An analysis of a system under pandemic conditions.

Recent events have emphasized the need for behavior-analytic service delivery models that can remain clinically consistent and produce high-quality outcomes while adapting to environmental change. This paper reports outcomes of an organization that adapted to changes in its service delivery model during a worldwide pandemic. The organization utilized components from the Comprehensive Application of Behavior Analysis to Schooling (CABAS®) model. The components were interconnected within the system and linked by specific interlocking contingencies. Performance data were used to make decisions within the organization on both individual and organizational levels during the transition to telehealth for clients and staff. Results are discussed in terms of how the interconnectedness of components within the organization contributed to the reliable, positive outcomes of the system during the transition to telehealth.

Building High Performance Explainable Machine Learning Models for Social Media-based Substance Use Prediction

Social media contain rich information that can be used to help understand human mind and behavior. Social media data, however, are mostly unstructured (e.g., text and image) and a large number of features may be needed to represent them (e.g., we may need millions of unigrams to represent social media texts). Moreover, accurately assessing human behavior is often difficult (e.g., assessing addiction may require medical diagnosis). As a result, the ground truth data needed to train a supervised human behavior model are often difficult to obtain at a large scale. To avoid overfitting, many state-of-the-art behavior models employ sophisticated unsupervised or self-supervised machine learning methods to leverage a large amount of unsupervised data for both feature learning and dimension reduction. Unfortunately, despite their high performance, these advanced machine learning models often rely on latent features that are hard to explain. Since understanding the knowledge captured in these models is important to behavior scientists and public health providers, we explore new methods to build machine learning models that are not only accurate but also interpretable. We evaluate the effectiveness of the proposed methods in predicting Substance Use Disorders (SUD). We believe the methods we proposed are general and applicable to a wide range of data-driven human trait and behavior analysis applications.

James G. Holland: A Análise do Comportamento como Prática Política

Resumo James G. Holland foi uma voz dissonante, não apenas na análise do comportamento, mas no contexto mais amplo do pensamento político estadunidense. Este artigo tem como objetivo apresentar as principais críticas e sugestões, direcionadas por Holland, à aplicação da análise do comportamento e avaliar sua relevância no cenário atual. Conclui-se que tais críticas e sugestões são úteis para todos os analistas do comportamento, pois contribuem para que mantenhamos uma cultura de avaliação interna sobre as consequências sociais de nossas práticas e para que analisemos de modo amplo o entrelaçamento de contingências presentes em situações de intervenção e sua influência sobre a formulação de nossos objetivos.

The Special Issue on the Education of Behavior Analysts: Common Themes.

The goal of Behavior Analysis in Practice's special issue on the education of behavior analysts is to highlight a variety of works on the topic of teaching and mentoring students in behavior analysis. The special issue is composed of empirical studies that evaluated teaching procedures aimed at the effective training of behavior analysts; surveys focusing on the content of training programs, including common readings and other components; and commentaries on topics pertaining to teaching and mentoring behavior analysts. Several themes emerged across the issue, including (a) a focus on knowledge of basic principles of behavior analysis, (b) the direct application of operant-based principles in the teaching and mentorship of behavior analysis students, and (c) a significant emphasis on the meaningful application of behavior analysis.

Student-Directed Behavioral Community Interventions in Social Work Education

Behavioral social work is the application of behavior analysis to the field of social work. There are behavioral social work interventions for individuals, groups, and communities. Nevertheless, behavioral social work is far from a widely adopted approach among social work practitioners. A reason for the underuse might be seen in the fact that most interventions in behavioral social work aim at individual clients and groups. Social work could further benefit from the application of methods taken from behavioral community interventions. Behavioral community interventions modify the behavior of a larger group of people (e.g., pedestrians using a crosswalk, cafeteria patrons, students using a university building) with antecedent- and consequence-focused interventions. The article describes a training program for social work students in behavioral community interventions. Results of two projects, undergraduate students designed and performed, are presented. Behavioral community interventions are recommended as a valuable part of the education of social workers.

The roles of the analogy with natural selection in B.F. Skinner’s philosophy

Beginning in the 1950s, B.F. Skinner made increasing reference to an analogy between operant conditioning and natural selection. This analogy is the basis of an argument that, in contrast to Skinner’s other critiques of cognitive science, is neither epistemological nor pragmatic. Instead, it is based on the claim that ontogenetic adaptation is due to a special mode of causation he called “selection by consequences.” He argued that this mode of causation conflicts with explanations that attribute action to an autonomous agent with reasons for acting. This argument dismisses ordinary explanations of action, and has implications not only for cognitive science but also for morals. Skinner cited the latter implications to counter objections to the application of behavior analysis to the reform of society and its institutions.Skinner’s critique, however, rests upon empirical assumptions that have been criticized by other behavior analysts. Although for Skinner the major role of the analogy was to propose an empirical thesis, it also can play a metaphysical role—namely, to demonstrate the possibility of ontogenetic adaptation without reference to agents who have reasons for acting. These two roles, empirical and metaphysical, are the mirror image of the empirical and metaphysical roles of the computer analogy for cognitive science. That analogy also can be (and has been) interpreted as an empirical thesis. Its empirical implications, however, have been difficult to confirm. It also, however, has played a metaphysical role—namely, to demonstrate the possibility that a physical process could perform logical operations on states having propositional content. Neither analogy provides a well-confirmed, general answer to the question of how to explain the process of ontogenetic adaptation. But together they show there are two metaphysically coherent, but conflicting, answers to this question. Depending upon one’s epistemology, the analogy with natural selection may provide a useful point of departure for a strategy of research. Such a pragmatic grounding for a research strategy does not, however, provide sufficient reason to abandon for purposes of ethics the concept of persons as autonomous agents.

The History and Current Status of Behavior and Social Issues: 1978–2016

We discuss the inception and history of Behavior and Social Issues (BSI) and its predecessor journals, Behaviorists for Social Action Journal and Behavior Analysis and Social Action , as the journals approaches 40 years of publication. We offer an analysis of the past volumes of the journals to provide a status update on BSI . Data on BSI article impact, content, and frequent contributors indicate that the journal has served as a valuable outlet to discuss applications of behavior analysis to a range of social issues.

WHERE WOULD ABA BE WITHOUT EAB? AN EXAMPLE OF TRANSLATIONAL RESEARCH ON RECURRENCE OF OPERANT BEHAVIOR AND TREATMENT RELAPSE

Treatment relapse may be defined as the recurrence of previously eliminated behavior or the failure of desired behavior to recur when the conditions of treatment change. Therefore, producing treatment effects that are durable through time and generalized across contexts is a critically important goal in applications of behavior analysis. However, traditional approaches within applied behavior analysis (ABA) in which technological development is divorced from basic findings in the experimental analysis of behavior (EAB) have yielded only modest progress towards this goal. Alternatively, translational research connecting basic science on the recurrence of operant behavior to applied problems of treatment relapse have led to improved understanding of the behavioral processes involved in relapse and technological innovations to reduce its probability and magnitude. This paper briefly contrasts the technology–driven and the translational approaches and then reviews the translational literature on recurrence and treatment relapse.

Maintaining Professional Relationships in an Interdisciplinary Setting: Strategies for Navigating Nonbehavioral Treatment Recommendations for Individuals with Autism.

Due to an increase in research and clinical application of behavior analysis with individuals with autism spectrum disorder (ASD), one setting a Board Certified Behavior Analyst (BCBA) may work within is an interdisciplinary setting, where multiple disciplines collaborate to improve the outcomes of individuals with ASD. In some cases, nonbehavioral colleagues could recommend nonbehavioral treatments, setting the occasion for the BCBA to offer an alternative treatment to or question the nonbehavioral treatment. However, excessive questioning or critiques of nonbehavioral treatments by the BCBA may unintentionally erode professional relationships between the BCBA and their nonbehavioral colleagues. Because an erosion of professional relationships may occur when a BCBA questions a nonbehavioral treatment, a decision-making model for determining whether or not the proposed nonbehavioral treatment is worth addressing may be useful. The purpose of this paper is to outline such a decision-making model in order to assist the BCBA in assessing nonbehavioral treatments while maintaining an ethical balance between professional relationships and the well-being and safety of the individual with ASD. Such a model could assist the BCBA in becoming familiar with the proposed treatment, understanding the perspective of the nonbehavioral colleague and assessing the negative impacts the treatment could have on the individual with ASD. With this information, the BCBA will be in a better position to decide whether or not addressing the nonbehavioral treatment is worth the possibility of eroding a professional relationship.

REMEMBERING NATHAN H. AZRIN

Short of his mentor and the discipline´s founder, B.F. Skinner, it is difficult to think of anyone who has a broader impact on both the science and application of behavior analysis than Nathan H. Azrin, whose long career spanning seven decades ended with his death on MAy 18, 2013. Nate´s contributions to both experimental and applied behavior analysis, and the discipline´s acknowledgements of these accomplishiments, are a matter of record.

The science of values: The Moral Landscape by Sam Harris

The science of values: The Moral Landscape by Sam Harris