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Books in Cognitive science

    • Cognitive Big Data Intelligence with a Metaheuristic Approach

      • 1st Edition
      • November 9, 2021
      • Sushruta Mishra + 4 more
      • English
      • Paperback
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      • eBook
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      Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity. This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks.
    • Intelligent Systems and Learning Data Analytics in Online Education

      • 1st Edition
      • June 15, 2021
      • Santi Caballé + 4 more
      • English
      • Paperback
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      • eBook
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      Intelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning. This book addresses key problems such as attrition and lack of engagement in MOOCs and online learning in general. This book explores the state of the art of artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the various challenges of current e-learning in general and MOOC education. In particular, Intelligent Systems and Learning Data Analytics in Online Education shares stimulating theoretical and practical research from leading international experts. This publication provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners to evaluate and apply.
    • Human-Machine Shared Contexts

      • 1st Edition
      • June 9, 2020
      • William Lawless + 2 more
      • English
      • Paperback
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      • eBook
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      Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of “shared contexts” between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines. This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these "machines" may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers "think through this change in human terms," the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines. This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers.
    • Cognitive Ergonomics

      • 1st Edition
      • September 3, 2015
      • Pierre Falzon
      • English
      • eBook
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      This reference work covers the breadth of cognitive ergonomics in human*b1computer interaction (HCI). Covering models for design, learning procedures, and planning and understanding, this book is specifically concerned with the cognitive ergonomics of human*b1computer interaction--from analogical thinking to spreadsheet calculation, office organization to process control. It provides an overview of HCI issues from the cognitive perspective.
    • The Adaptive Brain II

      • 1st Edition
      • October 22, 2013
      • Stephen Grossberg
      • English
      • Paperback
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      • eBook
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      The Adaptive Brain, II: Vision, Speech, Language, and Motor Control focuses on a unified theoretical analysis and predictions of important psychological and neurological data that illustrate the development of a true theory of mind and brain. The publication first elaborates on the quantized geometry of visual space and neural dynamics of form perception. Discussions focus on reflectance rivalry and spatial frequency detection, figure-ground separation by filling-in barriers, and disinhibitory propagation of functional scaling from boundaries to interiors. The text then takes a look at neural dynamics of perceptual grouping and brightness perception. Topics include simulation of a parametric binocular brightness study, smoothly varying luminance contours versus steps of luminance change, macrocircuit of processing stages, paradoxical percepts as probes of adaptive processes, and analysis of the Beck theory of textural segmentation. The book examines the neural dynamics of speech and language coding and word recognition and recall, including automatic activation and limited-capacity attention, a macrocircuit for the self-organization of recognition and recall, role of intra-list restructuring arid contextual associations, and temporal order information across item representations. The manuscript is a vital source of data for scientists and researchers interested in the development of a true theory of mind and brain.
    • Attention and Memory

      • 1st Edition
      • October 22, 2013
      • G. Underwood
      • English
      • Paperback
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      • eBook
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      Written specifically for students of experimental psychology, this book focuses on attention and memory, and attempts to inegrate these two closely related phenomena. In addition to the concepts of short term and long term memory there has been added the system of immediate or sensory memory. In the description of the representation of knowledge by human memory the author has necessarily drawn conclusions about optimal presentation and retrieval procedures, which should be transferable to non-laboratory situations where information processing is presently inadequate. The present approach attempts to keep in perspective the functions of attention and memory that the proponents of model building techniques have tended to overlook in their investigations. A new and fresh contribution to a growing area of research and teaching interest
    • Science & Consciousness

      • 1st Edition
      • October 22, 2013
      • M. Cazenave
      • English
      • eBook
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      This book explores the concept of consciousness when defined in the terms mind, spirit, soul and awareness. It consists of the edited proceedings of a colloquium held in Cordoba, at which experts in physics, neuro- and psycho-physiology, analytical psychology, philosophy and religious knowledge discussed aspects of their work related to this main theme. The following areas are covered: quantum mechanics and the role of consciousness, neurophysiology and states of consciousness, the manifestation of the psyche in consciousness, the odyssey of consciousness, and science and consciousness. The discussions which follow give a multi-disciplinary perspective on the questions involved.
    • MATLAB for Neuroscientists

      • 1st Edition
      • October 29, 2008
      • Pascal Wallisch + 5 more
      • English
      • eBook
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      MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neurosciences and in Psychology. MATLAB is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as "black boxes". Virtually all computational approaches in the book are covered by using genuine experimental data that are either collected as part of the lab project or were collected in the labs of the authors, providing the casual student with the look and feel of real data. In some cases, published data from classical papers are used to illustrate important concepts, giving students a computational understanding of critically important research.
    • Philosophy of Psychology and Cognitive Science

      • 1st Edition
      • October 23, 2006
      • English
      • Paperback
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      • Hardback
        9 7 8 0 4 4 4 5 1 5 4 0 7
      • eBook
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      Psychology is the study of thinking, and cognitive science is the interdisciplinary investigation of mind and intelligence that also includes philosophy, artificial intelligence, neuroscience, linguistics, and anthropology. In these investigations, many philosophical issues arise concerning methods and central concepts. The Handbook of Philosophy of Psychology and Cognitive Science contains 16 essays by leading philosophers of science that illuminate the nature of the theories and explanations used in the investigation of minds. Topics discussed include representation, mechanisms, reduction, perception, consciousness, language, emotions, neuroscience, and evolutionary psychology.
    • Neuromimetic Semantics

      • 1st Edition
      • May 8, 2004
      • Harry Howard
      • English
      • Hardback
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      • eBook
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      This book attempts to marry truth-conditional semantics with cognitive linguistics in the church of computational neuroscience. To this end, it examines the truth-conditional meanings of coordinators, quantifiers, and collective predicates as neurophysiological phenomena that are amenable to a neurocomputational analysis. Drawing inspiration from work on visual processing, and especially the simple/complex cell distinction in early vision (V1), we claim that a similar two-layer architecture is sufficient to learn the truth-conditional meanings of the logical coordinators and logical quantifiers. As a prerequisite, much discussion is given over to what a neurologically plausible representation of the meanings of these items would look like. We eventually settle on a representation in terms of correlation, so that, for instance, the semantic input to the universal operators (e.g. and, all)is represented as maximally correlated, while the semantic input to the universal negative operators (e.g. nor, no)is represented as maximally anticorrelated. On the basis this representation, the hypothesis can be offered that the function of the logical operators is to extract an invariant feature from natural situations, that of degree of correlation between parts of the situation. This result sets up an elegant formal analogy to recent models of visual processing, which argue that the function of early vision is to reduce the redundancy inherent in natural images. Computational simulations are designed in which the logical operators are learned by associating their phonological form with some degree of correlation in the inputs, so that the overall function of the system is as a simple kind of pattern recognition. Several learning rules are assayed, especially those of the Hebbian sort, which are the ones with the most neurological support. Learning vector quantization (LVQ) is shown to be a perspicuous and efficient means of learning the patterns that are of interest. We draw a formal parallelism between the initial, competitive layer of LVQ and the simple cell layer in V1, and between the final, linear layer of LVQ and the complex cell layer in V1, in that the initial layers are both selective, while the final layers both generalize. It is also shown how the representations argued for can be used to draw the traditionally-recogn... inferences arising from coordination and quantification, and why the inference of subalternacy breaks down for collective predicates. Finally, the analogies between early vision and the logical operators allow us to advance the claim of cognitive linguistics that language is not processed by proprietary algorithms, but rather by algorithms that are general to the entire brain. Thus in the debate between objectivist and experiential metaphysics, this book falls squarely into the camp of the latter. Yet it does so by means of a rigorous formal, mathematical, and neurological exposition – in contradiction of the experiential claim that formal analysis has no place in the understanding of cognition. To make our own counter-claim as explicit as possible, we present a sketch of the LVQ structure in terms of mereotopology, in which the initial layer of the network performs topological operations, while the final layer performs mereological operations. The book is meant to be self-contained, in the sense that it does not assume any prior knowledge of any of the many areas that are touched upon. It therefore contains mini-summaries of biological visual processing, especially the retinocortical and ventral /what?/ parvocellular pathways; computational models of neural signaling, and in particular the reduction of the Hodgkin-Huxley equations to the connectionist and integrate-and-fire neurons; Hebbian learning rules and the elaboration of learning vector quantization; the linguistic pathway in the left hemisphere; memory and the hippocampus; truth-conditional vs. image-schematic semantics; objectivist vs. experiential metaphysics; and mereotopology. All of the simulations are implemented in MATLAB, and the code is available from the book’s website.