As the second decade of the twenty-first century draws to a close, the cultural, social, and economic effects of artificial intelligence are becoming ever more apparent. Despite their long-intertwined histories, the fields of neuroscience and artificial intelligence research are notoriously divided. In Cognitive Code Johannes Bruder argues that seemingly incompatible scales of intelligence – the brain and the planet – are now intimately linked through neuroscience-inspired AI and computational cognitive neuroscience. Building on ethnographic fieldwork in brain imaging labs in the United Kingdom and Switzerland, alongside analyses of historical and contemporary literature, Cognitive Code examines how contemporary research on the brain makes routine use of engineering epistemologies and practices. Bruder elaborates on how the question of mimicking human cognition and thought on the scale of computer chips and circuits has gradually evolved into a comprehensive restructuring of the world through "smart" infrastructures. The brain, traditionally treated as a discrete object that thinks, is becoming part of the larger thinking network we now know as "the Cloud." The author traces a recent shift in the goals of brain imaging to show that the introduction of novel statistical and computational techniques has upset traditional paradigms and disentangled cognition from its biological substrate. Investigating understandings of intelligence from the micro to the macro, Cognitive Code explains how the future of human psychology is increasingly determined by engineering and design.

Although psychoanalytic concepts underlie most forms of psychotherapy practiced today, the basic Freudian theory of mind the metapsychology does not mesh with current scientific views in psychology and related fields. As a result, despite its many strengths, psychoanalysis has been relegated to the periphery by clinicians and researchers alike. Filling a significant void, this book from cognitive scientist and psychoanalytic researcher Wilma Bucci proposes a new model of psychological organization that integrates psychoanalytic theory with the investigation of mental processes. Solidly rooted in current cognitive science, multiple code theory recognizes the focus on meanings and motives that is intrinsic to psychoanalytic clinical work. The theory points to parallel functions underlying free association and dreams, as well as conceptual development in children and creative work in sciences and the arts, and provides a strong foundation for empirical research on the psychoanalytic treatment process.

Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.

Part I: Basic Concepts (16 cap.) + Part II: Models of Grammar (3 cap.) + Part III: Situating Cognitive Linguistics (3 cap.) + Part IV: Linguistic Structure and Language Use (13 cap.) + Part V: Linguistic Variation and Change (7 cap.) + Part VI: Applied and Interdisciplinary Perspectives.

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

"A great book with deep insights into the bridge between programming and the human mind." - Mike Taylor, CGI Your brain responds in a predictable way when it encounters new or difficult tasks. This unique book teaches you concrete techniques rooted in cognitive science that will improve the way you learn and think about code. In The Programmer’s Brain: What every programmer needs to know about cognition you will learn: Fast and effective ways to master new programming languages Speed reading skills to quickly comprehend new code Techniques to unravel the meaning of complex code Ways to learn new syntax and keep it memorized Writing code that is easy for others to read Picking the right names for your variables Making your codebase more understandable to newcomers Onboarding new developers to your team Learn how to optimize your brain’s natural cognitive processes to read code more easily, write code faster, and pick up new languages in much less time. This book will help you through the confusion you feel when faced with strange and complex code, and explain a codebase in ways that can make a new team member productive in days! Foreword by Jon Skeet. About the technology Take advantage of your brain’s natural processes to be a better programmer. Techniques based in cognitive science make it possible to learn new languages faster, improve productivity, reduce the need for code rewrites, and more. This unique book will help you achieve these gains. About the book The Programmer’s Brain unlocks the way we think about code. It offers scientifically sound techniques that can radically improve the way you master new technology, comprehend code, and memorize syntax. You’ll learn how to benefit from productive struggle and turn confusion into a learning tool. Along the way, you’ll discover how to create study resources as you become an expert at teaching yourself and bringing new colleagues up to speed. What's inside Understand how your brain sees code Speed reading skills to learn code quickly Techniques to unravel complex code Tips for making codebases understandable About the reader For programmers who have experience working in more than one language. About the author Dr. Felienne Hermans is an associate professor at Leiden University in the Netherlands. She has spent the last decade researching programming, how to learn and how to teach it. Table of Contents PART 1 ON READING CODE BETTER 1 Decoding your confusion while coding 2 Speed reading for code 3 How to learn programming syntax quickly 4 How to read complex code PART 2 ON THINKING ABOUT CODE 5 Reaching a deeper understanding of code 6 Getting better at solving programming problems 7 Misconceptions: Bugs in thinking PART 3 ON WRITING BETTER CODE 8 How to get better at naming things 9 Avoiding bad code and cognitive load: Two frameworks 10 Getting better at solving complex problems PART 4 ON COLLABORATING ON CODE 11 The act of writing code 12 Designing and improving larger systems 13 How to onboard new developers

This book shows cognitive scientists in training how mathematics, computer science and science can be usefully and seamlessly intertwined. It is a follow-up to the first two volumes on mathematics for cognitive scientists, and includes the mathematics and computational tools needed to understand how to compute the terms in the Fourier series expansions that solve the cable equation. The latter is derived from first principles by going back to cellular biology and the relevant biophysics. A detailed discussion of ion movement through cellular membranes, and an explanation of how the equations that govern such ion movement leading to the standard transient cable equation are included. There are also solutions for the cable model using separation of variables, as well an explanation of why Fourier series converge and a description of the implementation of MatLab tools to compute the solutions. Finally, the standard Hodgkin - Huxley model is developed for an excitable neuron and is solved using MatLab.