An Introduction To Behavior Analysis

AN INTRODUCTION TO BEHAVIOR ANALYSIS Explore a fascinating introductory treatment of the principles of behavior analysis written by three leading voices in the field An Introduction to Behavior Analysis delivers an engaging and comprehensive introduction to the concepts and applications for graduate students of behavior analysis. Written from the ground up to capture and hold student interest, the book keeps its focus on practical issues. The book offers readers sound analyses of Pavlovian and operant learning, reinforcement and punishment, motivation and stimulus control, language and rule-following, decision-making and clinical behavior analysis. With fully up to date empirical research references and theoretical content, An Introduction to Behavior Analysis thoroughly justifies every principle it describes with empirical support and explicitly points out where more data are required. The text encourages students to analyze their own experiences and some foundational findings in the field in a way that minimizes jargon and maximizes engagement. Readers will also benefit from the inclusion of: A clear articulation and defense of the philosophical assumptions and overarching goals of behavior analysis. A thorough description of objective data collection, experimental methods, and data analysis in the context of psychology An exploration of the core principles of behavior analysis, presented at a level comprehensible to an introductory audience A broad array of principles that cover issues as varied as language, substance-use disorders, and common psychological disorders Perfect for students taking their first course in behavior analysis or behavior modification, An Introduction to Behavior Analysis will also earn a place in the libraries of students pursuing certification through the Behavior Analysis Certification Board or taking courses in the applied psychological sciences.

Albert J. Kearney explains the kinds of learning and reinforcement processes that form the basis of ABA programs. He then describes how the science of behavior analysis can be applied to real life problems. He looks at how behavior is assessed and various intervention techniques that are often employed with children who have special needs.

Behavior Analysis and Learning, Fifth Edition is an essential textbook covering the basic principles in the field of behavior analysis and learned behaviors, as pioneered by B. F. Skinner. The textbook provides an advanced introduction to operant conditioning from a very consistent Skinnerian perspective. It covers a range of principles from basic respondent and operant conditioning through applied behavior analysis into cultural design. Elaborating on Darwinian components and biological connections with behavior, the book treats the topic from a consistent worldview of selectionism. The functional relations between the organism and the environment are described, and their application in accounting for old behavior and generating new behavior is illustrated. Expanding on concepts of past editions, the fifth edition provides updated coverage of recent literature and the latest findings. There is increased inclusion of biological and neuroscience material, as well as more data correlating behavior with neurological and genetic factors. The chapter on verbal behavior is expanded to include new research on stimulus equivalence and naming; there is also a more detailed and updated analysis of learning by imitation and its possible links to mirror neurons. In the chapter on applied behavior analysis (ABA), new emphasis is given to contingency management of addiction, applications to education, ABA and autism, and prevention and treatment of health-related problems. The material presented in this book provides the reader with the best available foundation in behavior science and is a valuable resource for advanced undergraduate and graduate students in psychology or other behavior-based disciplines. In addition, a website of supplemental resources for instructors and students makes this new edition even more accessible and student-friendly (www.psypress.com/u/pierce).

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

The long-awaited second edition of the classic textbook, Applied Behavior Analysis, provides a comprehensive, in-depth discussion of the field, providing a complete description of the principles and procedures needed to systematically change socially significant behavior and to understand the reasons for that change. The authors' goal in revising this best-selling text was to introduce students to ABA in as complete, technically accurate, and contemporary manner as possible. As a result, the book's scope, treatment of various principles, procedures, and issues suggest that it is intended for concentrated and serious study.Readers of the new second edition will appreciate the inclusion of: more than 1,400 citations to primary-source literature, including both classic and contemporary studies; a glossary of more than 400 technical terms and concepts; more than 100 graphs displaying original data from peer-reviewed research, with detailed descriptions of the procedures used to collect the data represented; five new chapters written by leading scholars in the field of behavior analysis; and the addition of The Behavior Analyst Certification Board(r) BCBA(r) and BCABA(r) Behavior Analyst Task List, Third Edition. First published in 1987, Applied Behavior Analysis remains the top-choice primary text for appropriate courses at universities in the United States and abroad with leading programs in behavior analysis. This comprehensive text, best-suited for all upper-level courses in basic principles, applications, and behavioral research methods, helps students, educators, and practitioners appreciate and begin to acquire the conceptual and technical skills necessary to foster socially adaptive behavior in diverse individuals.

Applied Behavior Analysis in Early Childhood Education provides a basic introduction to applied behavior analysis and the highly beneficial role that it can play in early childhood education for both typically developing children and those with special needs. The objective is to provide future and current early childhood professionals with the tools that they need to positively impact the lives of young children. Specifically, the book will describe and provide useful examples related to the following: Implementing effective techniques for changing behavior; Strategies for every day challenges both in the classroom and at home; Strategies for addressing less frequent issues; Suggestions for how to consult and correspond with parents and caretakers. Applied Behavior Analysis in Early Childhood Education is written for professionals preparing for—or those already in—careers in child development, behavior analysis, early childhood education, developmental therapy, counseling, special education, and other helping professions. A Companion Website featuring additional information and resources for students and instructors can be accessed at www.routledge.com/cw/casey.

Modern classrooms face an increasing population of special needs students and 'regular' students who have behavioural problems. The mission of this book is to show teachers and other human service professionals working in school settings how to employ non-aversive, behaviour analysis principles in classrooms and other school settings.

MATLAB is a powerful data analysis program, but many behavioral science researchers find it too daunting to learn and use. An Introduction to MATLAB for Behavioral Researchers by Christopher R. Madan is an easy-to-understand, hands-on guide for behavioral researchers who have no prior programming experience. Written in a conversational and non-intimidating style, the author walks students—step by step—through analyzing real experimental data. Topics covered include the basics of programming, the implementation of simple behavioral analyses, and how to make publication-ready figures. More advanced topics such as pseudo-randomization of trial sequences to meet specified criteria and working with psycholinguistic data are also covered. Interesting behavioral science examples and datasets from published studies, such as visualizing fixation patterns in eye-tracking studies and animal search behavior in two-dimensional space, help develop an intuition for data analysis, which is essential and can only be developed when working with real research problems and real data.