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Contributors to this book argue that we should study the brain basis of language as used in our daily lives.
How do infants learn a language? Why and how do languages evolve? How do we understand a sentence? This book explores these questions using recent computational models that shed new light on issues related to language and cognition. The chapters in this collection propose original analyses of specific problems and develop computational models that have been tested and evaluated on real data. Featuring contributions from a diverse group of experts, this interdisciplinary book bridges the gap between natural language processing and cognitive sciences. It is divided into three sections, focusing respectively on models of neural and cognitive processing, data driven methods, and social issues in language evolution. This book will be useful to any researcher and advanced student interested in the analysis of the links between the brain and the language faculty.
III. Language & Thought: Sharon Thompson-Schill (Volume Editor) (Topics covered include embodied cognition; discourse and dialogue; reading; creativity; speech production; concepts and categorization; culture and cognition; reasoning; sentence processing; bilingualism; speech perception; spatial cognition; word processing; semantic memory; moral reasoning.)
This book offers an introduction to the bilingual brain. It is a useful resource for researchers and students, bringing together various theories and research approaches in the cognitive neuroscience of bilingualism and a state-of-the-art overview of empirical findings on this topic from various perspectives.
How do infants learn a language? Why and how do languages evolve? How do we understand a sentence? This book explores these questions using recent computational models that shed new light on issues related to language and cognition. The chapters in this collection propose original analyses of specific problems and develop computational models that have been tested and evaluated on real data. Featuring contributions from a diverse group of experts, this interdisciplinary book bridges the gap between natural language processing and cognitive sciences. It is divided into three sections, focusing respectively on models of neural and cognitive processing, data driven methods, and social issues in language evolution. This book will be useful to any researcher and advanced student interested in the analysis of the links between the brain and the language faculty.
Don't Think of An Elephant is the antidote to decades of conservative strategising and the right's stranglehold on political dialogue. More specifically, it is the definitive handbook for understanding and communicating effectively about key social and political issues. George Lakoff explains in detail exactly how the right has managed to co-opt traditional values in order to popularise its political agenda. He also provides examples of how the centre-left can address the community's core values and re-frame political debate to establish a civil discourse that reinforces progressive positions. Don't Think of An Elephant provides a compelling linguistic analysis of political campaigning. But, more importantly, it demonstrates that real political values and ideas must provide the foundation for political progress by the centre-left.
An essential reference for the new discipline of evolutionary cognitive neuroscience that defines the field's approach of applying evolutionary theory to guide brain-behavior investigations. Since Darwin we have known that evolution has shaped all organisms and that biological organs—including the brain and the highly crafted animal nervous system—are subject to the pressures of natural and sexual selection. It is only relatively recently, however, that the cognitive neurosciences have begun to apply evolutionary theory and methods to the study of brain and behavior. This landmark reference documents and defines the emerging field of evolutionary cognitive neuroscience. Chapters by leading researchers demonstrate the power of the evolutionary perspective to yield new data, theory, and insights on the evolution and functional modularity of the brain. Evolutionary cognitive neuroscience covers all areas of cognitive neuroscience, from nonhuman brain-behavior relationships to human cognition and consciousness, and each section of Evolutionary Cognitive Neuroscience addresses a different adaptive problem. After an introductory section that outlines the basic tenets of both theory and methodology of an evolutionarily informed cognitive neuroscience, the book treats neuroanatomy from ontogenetic and phylogenetic perspectives and explores reproduction and kin recognition, spatial cognition and language, and self-awareness and social cognition. Notable findings include a theory to explain the extended ontogenetic and brain development periods of big-brained organisms, fMRI research on the neural correlates of romantic attraction, an evolutionary view of sex differences in spatial cognition, a theory of language evolution that draws on recent research on mirror neurons, and evidence for a rudimentary theory of mind in nonhuman primates. A final section discusses the ethical implications of evolutionary cognitive neuroscience and the future of the field. Contributors: C. Davison Ankney, Simon Baron-Cohen, S. Marc Breedlove, William Christiana, Michael Corballis, Robin I. M. Dunbar, Russell Fernald, Helen Fisher, Jonathan Flombaum, Farah Focquaert, Steven J.C. Gaulin, Aaron Goetz, Kevin Guise, Ruben C. Gur, William D. Hopkins, Farzin Irani, Julian Paul Keenan, Michael Kimberly, Stephen Kosslyn, Sarah L. Levin, Lori Marino, David Newlin, Ivan S. Panyavin, Shilpa Patel, Webb Phillips, Steven M. Platek, David Andrew Puts, Katie Rodak, J. Philippe Rushton, Laurie Santos, Todd K. Shackelford, Kyra Singh, Sean T. Stevens, Valerie Stone, Jaime W. Thomson, Gina Volshteyn, Paul Root Wolpe
Neuroscience Databases: A Practical Guide is the first book providing a comprehensive overview of these increasingly important databases. This volume makes the results of the Human Genome Project and other recent large-scale initiatives in the neurosciences available to a wider community. It extends the scope of bioinformatics from the molecular to the cellular, microcircuitry and systems levels, dealing for the first time with complex neuroscientific issues and leading the way to a new culture of data sharing and data mining necessary to successfully tackle neuroscience questions. Aimed at the novice user who wants to access the data, it provides clear and concise instructions on how to download the available data sets and how to use the software with a minimum of technical detail with most chapters written by the database creators themselves. Key databases and topics include: -Neuroinformatics for C. Elegans; -Gene Expression Patterns; -Functional Analyses of Olfactory Receptors -Protein-Protein Interactions; -Web-Based Neuronal Archives; -Neuronal and Network Modeling; -Storage and Retrieval of Experimental Data for Biophysically Realistic Modeling; -Analysis of Spike Trains; -Neural Connectivity Patterns; -Software Tools for Neuroimaging; -Data Management, Inspection and Sharing.
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
This esteemed reference work and professional resource, now substantially revised, integrates classic and cutting-edge research on how children and adolescents make meaning from text. The comprehension tasks and challenges facing students at different grade levels are explored, with attention to multiple text types and reading purposes. Preeminent researchers offer a range of perspectives--cognitive, neuroscientific, sociocultural, pedagogical, and technological--on key aspects of comprehension. Effective approaches to assessment, instruction, and intervention are reviewed. The volume also addresses issues in teaching specific populations, including struggling readers and English language learners. New to This Edition *A decade's worth of significant research advances are reflected in 10 entirely new chapters. *Revised throughout to incorporate new studies and timely topics: the expanding role of technology, changing school populations, the Common Core standards, international research, and more. *Chapters on graphic, scientific, and multiple digital texts. *Chapters on fluency, professional learning, and literacy coaching.