Interest in the field of neurodevelopmental disorders has grown exponentially in recent years across a range of disciplines, including psychology, psychiatry, education and neuroscience. The research itself has become more sophisticated, using multidisciplinary methods to probe interdisciplinary questions. Neurodevelopmental Disorders: Research Challenges and Solutions provides a thorough overview of the key issues involved in researching neurodevelopmental disorders. The volume includes 14 chapters, arranged over three sections. Chapters in the first section address general research challenges for the study of neurodevelopmental disorders. The second section draws upon specific disorders (such as Williams syndrome, Autism Spectrum Disorders, Down Syndrome, Fragile X Syndrome, ADHD, and Language Disorders) to consider the syndrome-specific issues or challenges that may be crucial to advancing our understanding of aspects of cognition and behavior associated with them. The final section considers how research evidence may be translated into practice to begin making an impact upon the lives of individuals who have neurodevelopmental disorders and their families. Each chapter in the book also includes 'practical tips' for either conducting research with individuals who have neurodevelopmental disorders or considering wider practical issues. The book will be indispensable reading for advanced students, researchers, and practitioners in the fields of developmental psychology, developmental psychopathology, special needs education, neuropsychology, and neurodevelopmental disorders.

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

Understanding how our brains and bodies actually work is a powerful tool in mitigating the anxiety generated by unpleasant physical and emotional symptoms that we all may experience from time to time. Here, Robert Scaer unravels the complexities of the brain-body connection, equipping all those who are in distress with a plausible explanation for how they feel. Making the science accessible, he outlines the core neurobiological concepts underlying the brain-body interface and explains why physical and emotional symptoms of stress and trauma occur. He explains why "feelings" represent physical sensations that inform us about the nature of our brain-body conflicts. He also offers practical, easy-to-implement strategies for strengthening motor skills, learning to listen to our gut to gauge our feelings, attuning to the present, and restoring personal boundaries to relieve symptoms and navigate a path to recovery.

Statistical Concepts-A First Course presents the first 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for first and lower-level statistics courses, this book communicates a conceptual, intuitive understanding of statistics that does not assume extensive or recent training in mathematics and only requires a rudimentary knowledge of algebra. Covering the most basic statistical concepts, this book is designed to help readers really understand statistical concepts, in what situations they can be applied, and how to apply them to data. Specifically, the text covers basic descriptive statistics, including ways of representing data graphically, statistical measures that describe a set of data, the normal distribution and other types of standard scores, and an introduction to probability and sampling. The remainder of the text covers various inferential tests, including those involving tests of means (e.g., t tests), proportions, variances, and correlations. Providing accessible and comprehensive coverage of topics suitable for an undergraduate or graduate course in statistics, this book is an invaluable resource for students undertaking an introductory course in statistics in any number of social science and behavioral science disciplines.

Electronic Inspection Copy available for instructors here Test Yourself: Research Methods and Design in Psychology provides essential learning and practice through assessment for psychology students. It enables year 1 and 2 undergraduates to assess their confidence and competence and tackle the types of questions they will face in their formal university assessments.

The book includes over 200 multiple-choice and extended multiple-choice questions, designed to assess depth of knowledge. At the end of each chapter sample essay questions are provided, plus further guidance, to complement the multiple-choice questions and further test understanding. Information is provided to help students make sense of their results and identify strengths and weaknesses.

This textbook offers a refreshingly clear and digestible introduction to statistical analysis for psychology using the user-friendly jamovi software. The authors provide a concise, practical guide that takes students from the early stages of research design, with a jargon-free explanation of terminology, and walks them through key analyses such as the t-test, ANOVA, correlation, chi-square, and linear regression. The book features written interpretations to help learners identify relevant statistics along the way. With fascinating examples from psychological research, as well as screenshots and activities from jamovi, this text is sure to encourage even the most reluctant statistics student. The comprehensive companion website provides an extra helping hand, with practice datasets and a full suite of tutorial videos to help consolidate understanding. This is essential reading for psychology students using jamovi for their courses in Research Methods and Statistics or Data Analysis.

When people are victimised by violent crime, the general public assumes that the victim could have been spared if the perpetrator had been identified as potentially dangerous by mental health agents. Yet prediction of dangerousness is an inexact science and depends upon many complex factors. This book provides a thorough and clear description of research findings in order to help clinicians make sound decisions concerning their clients' dangerousness. The book covers a broad spectrum of violent behaviour as well as crucial issues such as biological factors, domestic violence, and the influence of alcohol in violent behaviour. The book is divided into the following sections: Basic Issues in Violence Research, Mental Health Issues and Dangerousness, Family Issues and Dangerousness, Individual Characteristics and Dangerousness. It will serve as an important reference book that not only covers scientific literature but provides views on future directions for research and practice in this valuable field.

This is the first book of its kind to include the personal accounts of people who have survived injury to the brain, along with professional therapists' reports of their progress through rehabilitation. The paintings and stories of survivors combine with experts' discussions of the theory and practice of brain injury rehabilitation to illustrate the ups and downs that survivors encounter in their journey from pre-injury status to insult and post-injury rehabilitation. Wilson, Winegardner and Ashworth's focus on the survivors' perspective shows how rehabilitation is an interactive process between people with brain injury, health care staff, and others, and gives the survivors the chance to tell their own stories of life before their injury, the nature of the insult, their early treatment, and subsequent rehabilitation. Presenting practical approaches to help survivors of brain injury achieve functionally relevant and meaningful goals, Life After Brain Injury: Survivors' Stories will help all those working in rehabilitation understand the principles involved in holistic brain injury rehabilitation and how these principles, combined with theory and models, translate into clinical practice. This book will be of great interest to anyone who wishes to extend their knowledge of the latest theories and practices involved in making life more manageable for people who have suffered damage to the brain. Life After Brain Injury: Survivors' Stories will also be essential for clinical psychologists, neuropsychologists, and anybody dealing with acquired brain injury whether they be a survivor of a brain injury themselves, a relative, a friend or a carer.

Outcome Research and the Future of Psychoanalysis explores the connection between outcome studies and important and complex questions of clinical practices, research methodologies, epistemology, and sociological considerations. Presenting the ideas and voices of leading experts in clinical and extra-clinical research in psychoanalysis, the book provides an overview of the state of the art of outcome research, its results and implications. Furthermore, its contributions discuss the basic premises and ideas of outcome research and in which way the contemporary Zeitgeist might shape the future of psychoanalysis. Divided into three parts, the book begins by discussing the scientific basis of psychoanalysis and advances in psychoanalytic thinking as well as the state of the art of psychoanalytic outcome research, critically analyzing so-called evidence-based therapies. Part II of the book contains exemplary research projects that are discussed from a clinical perspective, illustrating the dialogue between researchers and clinicians. Lastly, in Part III, several psychoanalysts review the importance of critical thinking and research in psychoanalytical education. Thought-provoking and expertly written and researched, this book is a useful resource for academics, researchers and postgraduate students in the fields of mental health, psychotherapy, and psychoanalysis.

How does mindfulness promote psychological well-being? What are its core mechanisms? What value do contemplative practices add to approaches that are already effective? From leading meditation teacher Christina Feldman and distinguished psychologist Willem Kuyken, this book provides a uniquely integrative perspective on mindfulness and its applications. The authors explore mindfulness from its roots in Buddhist psychology to its role in contemporary psychological science. In-depth case examples illustrate how and why mindfulness training can help people move from distress and suffering to resilience and flourishing. Readers are guided to consider mindfulness not only conceptually, but also experientially, through their own journey of mindfulness practice.

The encouraging book that has guided thousands of students step by step through crafting a strong dissertation proposal is now in a thoroughly revised second edition. It includes new guidance for developing methodology-specific problem statements, an expanded discussion of the literature review, coverage of the four-chapter dissertation model, and more. Terrell demonstrates how to write each chapter of the proposal, including the problem statement, purpose statement, and research questions and hypotheses; literature review; and detailed plans for data collection and analysis. "Let's Start Writing" exercises serve as building blocks for drafting a complete proposal. Other user-friendly features include case-study examples from diverse disciplines, "Do You Understand?" checklists, and end-of-chapter practice tests with answers. Appendices present an exemplary proposal written three ways to demonstrate quantitative, qualitative, and mixed methods approaches, and discuss how to structure a four-chapter dissertation. New to This Edition *Introduction offering a concise overview of the entire proposal-writing process and the doctoral experience. *Additional help with tailoring problem and purpose statements for quantitative, qualitative, and mixed-methods studies. *Expanded discussion of the review of literature, including a criterion for judging the quality of primary versus secondary sources. *Many new examples from different disciplines, such as studies of depression treatments, approaches to reducing offender recidivism, health effects of irradiated crops, strength training in college football, and remote teaching and learning during COVID-19. *Focus on the five-chapter model is broadened to include specific guidance for four-chapter dissertations. *Broader, more detailed reference list and glossary.

This book fills a gap in international literature by providing critical reviews on variables of organizational behavior and the main psychological instruments developed to measure them. Measuring instruments developed with theoretical and methodological rigor in the field of Organizational and Work Psychology can contribute to the development of diagnostic analyses to enable organizations to implement the evidence-based changes required for their survival. These changes demand diagnoses based on precise assessments of organizational and individual variables, but many times the professionals responsible for conducting these assessments are not sure of what is the best measuring instrument available. This book is intended to serve as a guide to these professionals. The volume is divided in two parts. The first part brings together chapters dedicated to the following micro-organizational variables: Job Crafting, reactions to organizational change, Psychological Wellbeing at Work, Bridge Employment Assessment in the Work-Retirement Transition, Resilience at Work, and Leadership in Organizations. The second part presents the state-of-the-art of research on the following macro-organizational constructs: Quality of Life at Work, Organizational Climate for Creativity, Values and Organizations, Assessments of Organizational Support, and Contributions by Social Networks Analysis and Organizational Effectiveness. The last chapter presents a critical discussion about the nature and future of organization behavior measuring. Assessing Organizational Behaviors: A Critical Analysis of Measuring Instruments is intended to help market professionals select the diagnostic instruments that best fit into their organizational reality in order to correctly assess organizational behavior. The book will also be of interest to researchers and students in the field of Organizational and Work Psychology as it provides comprehensive overviews of a wide range of instruments developed to measure different variables of organizational behavior.

Shame remains at the core of much psychological distress and can eventuate as physical symptoms, yet experiential approaches to healing shame are sparse. Links between shame and art making have been felt, intuited, and examined, but have not been sufficiently documented by depth psychologists. Shame and the Making of Art addresses this lacuna by surveying depth psychological conceptions of shame, art, and the role of creativity in healing, contemporary and historical shame ideologies, as well as recent psychobiological studies on shame. Drawing on research conducted with participants in three different countries, the book includes candid discussions of shame experiences. These experiences are accompanied by Cluff's heuristic inquiry into shame with an interpretative phenomenological analysis that focuses on how participants negotiate the relationship between shame and the making of art. Cluff's movement through archetypal dimensions, especially Dionysian, is developed and discussed throughout the book. The results of the research are further explicated in terms of comparative studies, wherein the psychological processes and impacts observed by other researchers and effects on self-conscious maladaptive emotions are described. Shame and the Making of Art should be essential reading for academics, researchers, and postgraduate students engaged in the study of psychology and the arts. It will be of particular interest to psychologists, Jungian psychotherapists, psychiatrists, social workers, creativity researchers, and anyone interested in understanding the dynamics of this shame and self-expression.

Featuring contributions from some of the leading researchers in the field of SEM, most chapters are written by the author(s) who originally proposed the technique and/or contributed substantially to its development. Content highlights include latent variable mixture modeling, multilevel modeling, interaction modeling, models for dealing with nonstandard and noncompliance samples, the latest on the analysis of growth curve and longitudinal data, specification searches, item parceling, and equivalent models. This volume will appeal to educators, psychologists, biologists, business professionals, medical researchers, and other social and health scientists. It is assumed that the reader has mastered the equivalent of a graduate-level multivariate statistics course that included coverage of introductory SEM techniques.

Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates.

Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling.

The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied.

Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys.

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568.

The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced.

Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors' highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

A Handbook of Process Tracing Methods demonstrates how to better understand decision outcomes by studying decision processes, through the introduction of a number of exciting techniques. Decades of research have identified numerous idiosyncrasies in human decision behavior, but some of the most recent advances in the scientific study of decision making involve the development of sophisticated methods for understanding decision process-known as process tracing. In this volume, leading experts discuss the application of these methods and focus on the best practices for using some of the more popular techniques, discussing how to incorporate them into formal decision models. This edition has been expanded and thoroughly updated throughout, and now includes new chapters on mouse tracking, protocol analysis, neurocognitive methods, the measurement of valuation, as well as an overview of important software packages. The volume not only surveys cutting-edge research to illustrate the great variety in process tracing techniques, but also serves as a tutorial for how the novice researcher might implement these methods. A Handbook of Process Tracing Methods will be an essential read for all students and researchers of decision making.

This book introduces a new data analysis technique that addresses long standing criticisms of the current standard statistics. Observation Oriented Modelling presents the mathematics and techniques underlying the new method, discussing causality, modelling, and logical hypothesis testing. Examples of how to approach and interpret data using OOM are presented throughout the book, including analysis of several classic studies in psychology. These analyses are conducted using comprehensive software for the Windows operating system that has been written to accompany the book and will be provided free to book buyers on an accompanying website.

The software has a user-friendly interface, similar to SPSS and SAS, which are the two most commonly used software analysis packages, and the analysis options are flexible enough to replace numerous traditional techniques such as t-tests, ANOVA, correlation, multiple regression, mediation analysis, chi-square tests, factor analysis, and inter-rater reliability. The output and graphs generated by the software are also easy to interpret, and all effect sizes are presented in a common metric; namely, the number of observations correctly classified by the algorithm. The software is designed so that undergraduate students in psychology will have no difficulty learning how to use the software and interpreting the results of the analyses.

* Describes the problems that statistics are meant to answer, why popularly used statistics often fail to fully answer the question, and how OOM overcomes these obstacles * Chapters include examples of statistical analysis using OOM * Software for OOM comes free with the book * Accompanying websiteinclude svideo instruction on OOM use "

The advent of "Big Data" has brought with it a rapid diversification of data sources, requiring analysis that accounts for the fact that these data have often been generated and recorded for different reasons. Data integration involves combining data residing in different sources to enable statistical inference, or to generate new statistical data for purposes that cannot be served by each source on its own. This can yield significant gains for scientific as well as commercial investigations. However, valid analysis of such data should allow for the additional uncertainty due to entity ambiguity, whenever it is not possible to state with certainty that the integrated source is the target population of interest. Analysis of Integrated Data aims to provide a solid theoretical basis for this statistical analysis in three generic settings of entity ambiguity: statistical analysis of linked datasets that may contain linkage errors; datasets created by a data fusion process, where joint statistical information is simulated using the information in marginal data from non-overlapping sources; and estimation of target population size when target units are either partially or erroneously covered in each source. Covers a range of topics under an overarching perspective of data integration. Focuses on statistical uncertainty and inference issues arising from entity ambiguity. Features state of the art methods for analysis of integrated data. Identifies the important themes that will define future research and teaching in the statistical analysis of integrated data. Analysis of Integrated Data is aimed primarily at researchers and methodologists interested in statistical methods for data from multiple sources, with a focus on data analysts in the social sciences, and in the public and private sectors.

Longitudinal research is an essential element in the investigation of human development over time, with considerable advantages over more widely used cross-sectional research designs. This book examines the scope for longitudinal studies in a range of developmental fields, emphasizing the advantages of this approach for the investigation of causal mechanisms and processes and the dynamics of development over the lifespan. It also discusses methodological issues and some of the practical and ethical problems that longitudinal research may present. The distinguished contributors review normal and disordered development in the emotional, cognitive and social domains, including valuable discussions of gene-environment interactions, the maturation of the human brain, and issues relating to ageing.

Nowadays, event history analysis can draw on a well-established set of statistical tools for the description and causal analysis of event history data. The second edition of Event History Analysis with Stata provides an updated introduction to event history modeling, along with many instructive Stata examples. Using the latest Stata software, each of these practical examples develops a research question, refers to useful substantive background information, gives a short exposition of the underlying statistical concepts, describes the organization of the input data and the application of the statistical Stata procedures, and assists the reader in performing a substantive interpretation of the obtained results. Emphasising the strengths and limitations of event history model techniques in each field of application, this book demonstrates that event history models provide a useful approach with which to uncover causal relationships or to map out a system of causal relations. It demonstrates how long-term processes can be studied and how changing context information on the micro, meso, and macro levels can be integrated easily into a dynamic analysis of longitudinal data. Event History Analysis with Stata is an invaluable resource for both novice students and researchers who need an introductory textbook and experienced researchers (from sociology, economics, political science, pedagogy, psychology, or demography) who are looking for a practical handbook for their research.

This comprehensive Handbook is the first to provide a practical, interdisciplinary review of ethical issues as they relate to quantitative methodology including how to present evidence for reliability and validity, what comprises an adequate tested population, and what constitutes scientific knowledge for eliminating biases. The book uses an ethical framework that emphasizes the human cost of quantitative decision making to help researchers understand the specific implications of their choices. The order of the Handbook chapters parallels the chronology of the research process: determining the research design and data collection; data analysis; and communicating findings. Each chapter: Explores the ethics of a particular topic Identifies prevailing methodological issues Reviews strategies and approaches for handling such issues and their ethical implications Provides one or more case examples Outlines plausible approaches to the issue including best-practice solutions. Part 1 presents ethical frameworks that cross-cut design, analysis, and modeling in the behavioral sciences. Part 2 focuses on ideas for disseminating ethical training in statistics courses. Part 3 considers the ethical aspects of selecting measurement instruments and sample size planning and explores issues related to high stakes testing, the defensibility of experimental vs. quasi-experimental research designs, and ethics in program evaluation. Decision points that shape a researchers' approach to data analysis are examined in Part 4 - when and why analysts need to account for how the sample was selected, how to evaluate tradeoffs of hypothesis-testing vs. estimation, and how to handle missing data. Ethical issues that arise when using techniques such as factor analysis or multilevel modeling and when making causal inferences are also explored. The book concludes with ethical aspects of reporting meta-analyses, of cross-disciplinary statistical reform, and of the publication process. This Handbook appeals to researchers and practitioners in psychology, human development, family studies, health, education, sociology, social work, political science, and business/marketing. This book is also a valuable supplement for quantitative methods courses required of all graduate students in these fields.

This volume, Statistical Methods in Psychiatry Research and SPSS, now going into its second edition, has been helping psychiatrists expand their knowledge of statistical methods and fills the gaps in their applications as well as introduces data analysis software. It addresses the statistical needs of physicians and presents a simplified approach. The book emphasizes the classification of fundamental statistical methods in psychiatry research that are precise and simple. Professionals in the field of mental health and allied subjects without any mathematical background will easily understand all the relevant statistical methods and carry out the analysis and interpret the results in their respective field without consulting any statistician. This new volume has over 100 pages of new material, including several new appendixes. The sequence of the chapters, the sections within the chapters, the subsections within the sections, and the points within the subsections have all been arranged to help professionals in classification refine their knowledge in statistical methods and fills the gaps.

This book is a practical resource designed for clinicians, researchers, and advanced students who wish to learn about single-case research designs. It covers the theoretical and methodological underpinnings of single-case designs, as well as their practical application in the clinical and research neurorehabilitation setting. The book briefly traces the history of single-case experimental designs (SCEDs); outlines important considerations in understanding and planning a scientifically rigorous single-case study, including internal and external validity; describes prototypical single-case designs (withdrawal-reversal designs and the medical N-of-1 trial, multiple-baseline designs, alternating-treatments designs, and changing-criterion designs) and required features to meet evidence standards, threats to internal validity, and strategies to address them; addresses data evaluation, covering visual analysis of graphed data, statistical techniques, and clinical significance; and provides a practical ten-step procedure for implementing single-case methods. Each chapter includes detailed illustrative examples from the neurorehabilitation literature. Novel features include: A focus on the neurorehabilitation setting, which is particularly suitable for single-case designs because of the complex and often unique presentation of many patients/clients. A practical approach to the planning, implementation, data analysis, and reporting of single-case designs. An appendix providing a detailed summary of many recently published SCEDs in representative domains in the neurorehabilitation field, covering basic and instrumental activities of daily living, challenging behaviours, disorders of communication and cognition, mood and emotional functions, and motor-sensory disabilities. It is valuable reading for clinicians and researchers in several disciplines working in rehabilitation, including clinical and neuropsychology, education, language and speech pathology, occupational therapy, and physical therapy. It is also an essential resource for advanced students in these fields who need a textbook for specialised courses on research methodology and use of single-case design in applied clinical and research settings.

An up-to-date book about art through the lens of neuroscience and psychology. In Creativity & Art, Andreas and Barbara Steck use an interdisciplinary approach to discuss creative processes from a neuroscientific and psychoanalytic perspective. By referring to the current knowledge of brain sciences, the authors explore the understanding of the neural bases innate to the creation of art. Beginning with historical aspects of aesthetic experience and creation in ancient and modern times, the authors go on to present numerous artists in various fields, with an emphasis on how their subjective lived experiences are expressed and reflected in their artwork. Finally, the Stecks describe the building blocks of creativity in early childhood development discuss the psychoanalytic understanding of human aesthetic experience and creativity. Insightful and thought-provoking, Creativity & Art examines art through the lens of neuroscience and psychology.