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Books on the topic 'Computational neuroimaging, cognitive neuroscience'

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Published: 6 September 2023

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1

Zhao, Qi, ed. Computational and Cognitive Neuroscience of Vision. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-0213-7.

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2

Gallistel, C. R. Memory and the computational brain: Why cognitive science will transform neuroscience. Chichester, West Sussex, UK: Wiley-Blackwell, 2009.

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3

Frank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.

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4

Frank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.

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5

G, Hillary Frank, and DeLuca John 1956-, eds. Functional neuroimaging in clinical populations. New York: Guilford Press, 2007.

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6

Brain-inspired Cognitive Systems Conference (2010 : Madrid, Spain). From brains to systems: Brain-inspired cognitive systems 2010. Edited by Hernández Carlos. New York: Springer, 2011.

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7

Roberto, Cabeza, and Kingstone Alan, eds. Handbook of functional neuroimaging of cognition. 2nd ed. Cambridge, MA: MIT Press, 2005.

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8

International Conference on Intelligent Computing (3rd 2007 Qingdao, China). Advanced intelligent computing theories and applications: With aspects of artifical intelligence ; third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007 ; proceedings. Berlin: Springer, 2007.

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9

W, Cottrell Garrison, ed. Proceedings of the eighteenth annual conference of the Cognitive Science Society: July 12-15, 1996, University of California, San Diego. Mahwah, N.J: Lawrence Erlbaum Associates, 1996.

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10

De-Shuang, Huang, Li Kang, and Irwin G. W. 1950-, eds. International Conference on Intelligent Computing: ICIC 2006, Kunming, China, August 16-19, 2006 : proceedings. Berlin: Springer, 2006.

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11

De-Shuang, Huang, Heutte Laurent, and Loog Marco, eds. Advanced intelligent computing theories and applications: With aspects of theoretical and methodological issues ; third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007 ; proceedings. Berlin: Springer, 2007.

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12

International Conference on Intelligent Computing (3rd 2007 Qingdao, China). Advanced intelligent computing theories and applications: With aspects of contemporary intelligent computing techniques : Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007 : proceedings. Berlin: Springer, 2007.

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13

Spain) Neural Computation and Psychology Workshop (13th 2012 San Sebastián. Computational models of cognitive processes: Proceedings of the 13th Neural Computation and Psychology Workshop, San Sebastian, Spain, 12-14 July 2012. Edited by Mayor, Julien, editor of compilation and Gomez, Pablo (Pablo Alegria), editor of compilation. Hackensack,] New Jersey: World Scientific, 2014.

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14

International Conference: Brain Inspired Cognitive Systems (3rd : 2008 : São Luís do Maranhão, Brazil), ed. Brain inspired cognitive systems 2008. New York: Springer, 2010.

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15

Xiang, Zhang, Reyes García Carlos Alberto, Zhang Lei, and SpringerLink (Online service), eds. Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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16

Konrad, Kording, ed. Sensory cue integration. New York: Oxford University Press, 2011.

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17

Lee, Gary. Advances in Intelligent Systems: Selected papers from 2012 International Conference on Control Systems (ICCS 2012), March 1-2, Hong Kong. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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18

Huang, De-Shuang. Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: 4th International Conference on Intelligent Computing, ICIC 2008 Shanghai, China, September 15-18, 2008 Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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19

Operskalski, Joachim T., and Aron K. Barbey. Cognitive Neuroscience of Causal Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.16.

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The era of functional neuroimaging promised to shed light on dark corners of the brain’s inner workings, breathing new life into subfields of psychology beset by controversy. Although revelations from neuroscience provide the foundation for current views on many aspects of human cognition, there continue to be areas of study in which a mismatch between the questions asked by psychologists and neuroscientists renders the implications of neuroscience research unclear. Causal reasoning is one such topic, for which decades of cognitive neuroscience findings have revealed a heterogeneity of participating brain regions and networks across different experimental paradigms. This chapter discusses (i) three cognitive and computational models of causal reasoning (mental models, causal models, and force composition theory), (ii) experimental findings on causal judgment and reasoning using cognitive neuroscience methods, and (iii) the need for a multidisciplinary approach to understanding the nature and mechanisms of causal reasoning.
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20

Adams, Reginald B., Daniel N. Albohn, and Kestutis Kveraga. A Social Vision Account of Facial Expression Perception. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0017.

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In this chapter, we discuss prospects for a future computational neuropsychology. Computerized approaches to assessment, the ability to implement life-like scenarios in a controlled virtual environment, and teleneuropsychology offer promise for expanding available approaches to cognitive remediation and self-monitoring. Computational models are also available increasingly for integrating neuroimaging into the assessment process. Neuropsychologists can use neuroimaging to develop new frameworks for neuropsychological testing that are rooted in the current evidence base on large-scale brain system interactions. This will allow for traditional assessment of discrete areas of neurocognitive functioning to be brought in line with recent findings that highly nuanced relations exist among brain networks. Furthermore, the new findings from systems neuroscience may allow for the development of neuropsychological assessments with greater accuracy and increased targeted testing. Neuroinfomatic approaches offer computational neuropsychology an approach to knowledge sharing via well-defined neuropsychological ontologies and collaborative knowledgebases.
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21

Bright, Peter, ed. Neuroimaging - Cognitive and Clinical Neuroscience. InTech, 2012. http://dx.doi.org/10.5772/1815.

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22

Keenan, Julian P. Methods of Neuroimaging. Psychology Press, 2009.

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23

Cohen Kadosh, Roi, and Ann Dowker, eds. The Oxford Handbook of Numerical Cognition. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.001.0001.

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This book provides a comprehensive overview of numerical cognition by bringing together writing by leading researchers in psychology, neuroscience, and education, covering work using different methodological approaches in humans and animals. During the last decade there had been an explosion of studies and new findings with theoretical and translational implications. This progress has been made thanks to technological advances enabling sophisticated human neuroimaging techniques and neurophysiological studies of monkeys, and to advances in more traditional psychological and educational research. This has resulted in an enormous advance in our understanding of the neural and cognitive mechanisms of numerical cognition. In addition, there has recently been increasing interest and concern about pupils' mathematical achievement, resulting in attempts to use research to guide mathematics instruction in schools, and to develop interventions for children with mathematical difficulties. This book aims to provide a broad and extensive review of the field of numerical cognition, bringing together work from varied areas. The book covers research on important aspects of numerical cognition, involving findings from the areas of developmental psychology, cognitive psychology, human and animal neuroscience, computational modeling, neuropsychology and rehabilitation, learning disabilities education and individual differences, cross-cultural and cross-linguistic studies, and philosophy. It also includes an overview 'navigator' chapter for each section to provide a brief up-to-date review of the current literature, and to introduce and integrate the topics of the chapters in the section.
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24

Shaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.

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Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.
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25

Munakata, Yuko, and Randall C. O'Reilly. Computational Explorations in Cognitive Neuroscience : Understanding. Mit Pr, 2000.

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26

Zhao, Qi. Computational and Cognitive Neuroscience of Vision. Springer Singapore Pte. Limited, 2016.

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27

Zhao, Qi. Computational and Cognitive Neuroscience of Vision. Springer, 2016.

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28

Zhao, Qi. Computational and Cognitive Neuroscience of Vision. Springer London, Limited, 2016.

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29

Zhao, Qi. Computational and Cognitive Neuroscience of Vision. Springer, 2018.

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30

(Editor), Roberto Cabeza, and Alan Kingstone (Editor), eds. Handbook of Functional Neuroimaging of Cognition, 2nd Edition (Cognitive Neuroscience). 2nd ed. The MIT Press, 2006.

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31

Arbib, Michael A., Joseph Ayers, and James J. Bonaiuto. From Neuron to Cognition Via Computational Neuroscience. MIT Press, 2016.

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32

From Neuron to Cognition Via Computational Neuroscience. MIT Press, 2016.

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33

(Editor), Randolph W. Parks, Daniel S. Levine (Editor), and Debra L. Long (Editor), eds. Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience). The MIT Press, 1998.

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34

Hosseini, Seyyed Abed, ed. Cognitive and Computational Neuroscience - Principles, Algorithms and Applications. InTech, 2018. http://dx.doi.org/10.5772/intechopen.68377.

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35

Caramazza, Alfonso, and Gary S. Dell. Computational Modelling: A Special Issue of Cognitive Neuropsychology. Taylor & Francis Group, 2015.

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36

Explaining The Computational Mind. MIT Press Ltd, 2013.

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37

Domhoff, G. William. The Cognitive Neuroscience of Dreaming. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190673420.003.0006.

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The fifth chapter presents the evidence for the novel idea that the neural substrate that enables dreaming is based in subsystems within the waking default network. The evidence for this hypothesis includes neuroimaging studies of representative samples of children and adults as well as studies of neurological patients who report they have experienced alterations in their dreaming, or even lost the ability to dream, due to their injury or illness. Based on these relatively new findings from many research settings, some as recent as 2015 and 2016, both the developmental trajectory of dreaming and the nature of dream content can be explained by the neurocognitive theory of dreams.
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38

Caramazza, Alfonso, and Àlex Martín. Organisation of Conceptual Knowledge in the Brain: Neuropsychological and Neuroimaging Perspectives. Taylor & Francis Group, 2003.

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39

Uttal, William R. Macroneural Theories in Cognitive Neuroscience. Taylor & Francis Group, 2015.

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40

Uttal, William R. Macroneural Theories in Cognitive Neuroscience. Taylor & Francis Group, 2015.

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41

Uttal, William R. Macroneural Theories in Cognitive Neuroscience. Taylor & Francis Group, 2015.

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42

Macroneural Theories in Cognitive Neuroscience. Taylor & Francis Group, 2015.

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43

Uttal, William R. Macroneural Theories in Cognitive Neuroscience. Taylor & Francis Group, 2015.

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44

Milkowski, Marcin. Explaining the Computational Mind. MIT Press, 2013.

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45

Milkowski, Marcin. Explaining the Computational Mind. MIT Press, 2013.

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46

The Organisation of Conceptual Knowledge in the Brain: Neuropsychological and Neuroimaging Perspectives. Routledge, 2015.

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47

Poppe, Andrew, and Angus W. MacDonald III. Cognitive Neuroscience Approaches to Personality Disorders. Edited by Christian Schmahl, K. Luan Phan, Robert O. Friedel, and Larry J. Siever. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199362318.003.0004.

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This chapter describes a cognitive neuroscience approach to understanding the psychological and neural processes that underlie personality and behavior. It explicates the utility of the cognitive neuroscience approach and the fundamental principles of the methods and how to interpret the findings. The chapter reviews the different neuroimaging tools and approaches that can be used to investigate brain structure and function. In doing so, it provides detailed information about what each method measures and how issues to consider when evaluating these measurements and their functional significance. The chapter provides the reader an appreciation of how understanding brain structure and function in vivo can serve as a bridge between molecular/genetic and symptom-based data to enrich the pathophysiology of personality disorders.
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48

Computational Modelling A Special Issue Of Cognitive Neuropsychology. Psychology Press, 2009.

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49

Mitchell, Karen J. The Cognitive Neuroscience of Source Monitoring. Edited by John Dunlosky and Sarah (Uma) K. Tauber. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199336746.013.2.

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Source monitoring is a metamemory function that includes processes for encoding and organizing the content of memories, and processes that selectively revive, cumulate, and evaluate that content in the service of making attributions about the origin of the information (e.g., perception vs imagination). Neuroimaging techniques, especially functional magnetic resonance imaging (fMRI), are encouraging rapid developments in understanding the neural mechanisms supporting source monitoring. This chapter reviews current findings, placing them in historical context. It highlights key issues of particular relevance, including: neural reinstatement—the match between brain activity at encoding and later remembering; the role of lateral parietal cortex in cumulating multiple features and attending to information during remembering; functional specificity of the prefrontal cortex with respect to cognitive control; and identifying functional networks that support source monitoring. Suggestions are made for clarifying the big picture and increasing the specificity of our understanding of source monitoring and its neural architecture.
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50

Willems, Roel M. Cognitive Neuroscience of Natural Language Use. Cambridge University Press, 2015.

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