The Classic Edition of this key text highlights seminal work done in the subject of learning by modeling and offers an extensive review of the major theories, edited by one of the most influential psychologists of his generation. In his introductory essay, Bandura identifies the most important controversial issues in the field of observational learning and reviews a large body of research findings, before carefully chosen articles, written by a team of expert contributors, tackle a range of key debates in the field. Topics explored include the role of reinforcement play in observational learning, the scope of modeling influences, the types of people most susceptible to modeling influences, and the relative effectiveness of models presented in live action, in pictorial presentations, or through verbal description. Written in a lively and engaging manner, this book will be of interest to all psychology students interested in psychological modeling, as well as educators and professionals working with children.

The phenomenon of learning has always been of fundamental interest to psychologists. Although much of the research in this area approaches the process of learning as a consequence of direct experience, this volume is principally concerned with learning by example. A widening interest in modeling and vicarious processes of learning has been apparent in recent years. Psychological Modeling highlights the most important work done in the subject and offers an extensive review of the major theories of learning by modeling. In his introductory essay, the editor identifies the most important controversial issues in the field of observational learning and reviews a large body of research findings. Among the questions debated in this volume are: How do observers form an internal model of the outside world to guide their actions? What role does reinforcement play in observational learning? What is the relative effectiveness of models presented in live action, in pictorial presentations, or through verbal description? What is the scope of modeling influences? What factors determine whether people will learn what they have observed? What types of people are most susceptible to modeling influences, and what types of models are most influential in modifying the behavior of others? This volume deals with an important problem area in a lively fashion. Its special organization makes it a stimulating adjunct to all courses in psychology - undergraduate and graduate - in which psychological modeling is discussed. It also provides a readable introduction for educators and other professionals seeking reliable information on the state of knowledge in this area. Albert Bandura has been Professor of Psychology at Stanford University since 1953. In 1969-70 he was Fellow of the Center for Advanced Study in the Behavioral Sciences. Professor Bandura served on the editorial boards of several professional journals including the Journal of Personality and Social Psychology, the Journal of Experimental Child Psychology, and the Journal of Experimental Psychology. He now serves on the editorial board of Applied Psychology as well as on the advisory board of European Journal of School Psychology. He is author or editor of over a dozen books. His articles appear in source books in many areas of the discipline of psychology, and he is a frequent contributor to academic and professional symposia and journals.

This concise reference serves as a companion to traditional research texts by focusing on such essentials as model construction, robust methodologies and defending a compelling hypothesis. Designed to wean Master's and doctorate-level students as well as new researchers from their comfort zones, the book challenges readers to engage in multi-method approaches to answering multidisciplinary questions. The result is a step-by-step framework for producing well-organized, credible papers based on rigorous, error-free data. The text begins with a brief grounding in the intellectual attitude and logical stance that underlie good research and how they relate to steps such as refining a topic, creating workable models and building the right amount of complexity. Accessible examples from psychology and business help readers grasp the fine points of observations, interviewing, simulations, interpreting and finalizing data and presenting results. Fleshed out with figures, tables, key terms, tips, and questions, this book acts as both a friendly lecturer and a multilevel reality check.

Face-to-face conversation between two or more people is a universal form, and perhaps the basic form, of social interaction. It is the primary site of social interaction in all cultures and the place where social and cultural meaning takes shape. Face-to-face conversation between children and parents can also be an important context for social and cognitive development. Given the universality, frequency and importance of conversation in social life, a psychological model of conversation is required for an understanding of the central issues in social and developmental psychology. This book provides such a model. Language in Action presents a critical examination of four models of conversation: the Code model based on Chomsky's linguistic views; the Speech Act model of Austin and Searle; the Inferential model of Grice, and the Conversation Analytic model of Sacks and Schegloff. It also considers the Brown and Levinson model of politeness in conversation. Using many examples from natural talk and drawing on the positive aspects of the reviewed models, Turnbull proposes a new Social Pragmatic model of conversation as social interaction. He also describes the research paradigm of Social Pragmatics that experimental psychologists can use to study conversation. This book will be invaluable for advanced students in psychology, sociology, language and linguistics and communication. It will also make fascinating and lively reading for anyone wanting a greater understanding of this fundamental form of social interaction.

This book explores the subject of artificial psychology and how the field must adapt human neuro-psychological testing techniques to provide adequate cognitive testing of advanced artificial intelligence systems. It shows how classical testing methods will reveal nothing about the cognitive nature of the systems and whether they are learning, reasoning, and evolving correctly; for these systems, the authors outline how testing techniques similar to/adapted from human psychological testing must be adopted, particularly in understanding how the system reacts to failure or relearning something it has learned incorrectly or inferred incorrectly. The authors provide insights into future architectures/capabilities that artificial cognitive systems will possess and how we can evaluate how well they are functioning. It discusses at length the notion of human/AI communication and collaboration and explores such topics as knowledge development, knowledge modeling and ambiguity management, artificial cognition and self-evolution of learning, artificial brain components and cognitive architecture, and artificial psychological modeling. Explores the concepts of Artificial Psychology and Artificial Neuroscience as applied to advanced artificially cognitive systems; Provides insight into the world of cognitive architectures and biologically-based computing designs which will mimic human brain functionality in artificial intelligent systems of the future; Provides description and design of artificial psychological modeling to provide insight into how advanced artificial intelligent systems are learning and evolving; Explores artificial reasoning and inference architectures and the types of modeling and testing that will be required to "trust" an autonomous artificial intelligent systems.

The emerging interdisciplinary field of cognitive choice models integrates theory and recent research findings from both decision process and choice behavior. Cognitive decision processes provide the interface between the environment and brain, enabling choice behavior, and the basic cognitive mechanisms underlying decision processes are fundamental to all fields of human activity. Yet cognitive processes and choice processes are often studied separately, whether by decision theorists, consumer researchers, or social scientists. In Cognitive Choice Modeling, Zheng Joyce Wang and Jerome R. Busemeyer introduce a new cognitive modeling approach to the study of human choice behavior. Integrating recent research findings from both cognitive science and choice behavior, they lay the groundwork for the emerging interdisciplinary field of cognitive choice modeling.

Mice are used as model organisms across a wide range of fields in science today—but it is far from obvious how studying a mouse in a maze can help us understand human problems like alcoholism or anxiety. How do scientists convince funders, fellow scientists, the general public, and even themselves that animal experiments are a good way of producing knowledge about the genetics of human behavior? In Model Behavior, Nicole C. Nelson takes us inside an animal behavior genetics laboratory to examine how scientists create and manage the foundational knowledge of their field. Behavior genetics is a particularly challenging field for making a clear-cut case that mouse experiments work, because researchers believe that both the phenomena they are studying and the animal models they are using are complex. These assumptions of complexity change the nature of what laboratory work produces. Whereas historical and ethnographic studies traditionally portray the laboratory as a place where scientists control, simplify, and stabilize nature in the service of producing durable facts, the laboratory that emerges from Nelson’s extensive interviews and fieldwork is a place where stable findings are always just out of reach. The ongoing work of managing precarious experimental systems means that researchers learn as much—if not more—about the impact of the environment on behavior as they do about genetics. Model Behavior offers a compelling portrait of life in a twenty-first-century laboratory, where partial, provisional answers to complex scientific questions are increasingly the norm.

Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results. User-Friendly Features *Real, worked-through longitudinal data examples serving as illustrations in each chapter. *Script boxes that provide code for fitting the models to example data and facilitate application to the reader's own data. *"Important Considerations" sections offering caveats, warnings, and recommendations for the use of specific models. *Companion website supplying datasets and syntax for the book's examples, along with additional code in SAS/R for linear mixed-effects modeling.