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Religions --- Emotions --- Theologians --- Philosophers --- Religious aspects --- Handbooks, manuals, etc --- Christian theologians --- Religieus gevoel. Godsdienstig gevoel: vrees; eerbied; liefde; vertrouwen; onderdanigheid --- 291.12 Religieus gevoel. Godsdienstig gevoel: vrees; eerbied; liefde; vertrouwen; onderdanigheid --- 21*02 --- 291.12 --- 21*02 Filosofie en religieuze ervaring. Religious experience --- Filosofie en religieuze ervaring. Religious experience --- Scholars --- Feelings --- Human emotions --- Passions --- Psychology --- Affect (Psychology) --- Affective neuroscience --- Apathy --- Pathognomy --- Religious studies --- Religions - Handbooks, manuals, etc. --- Emotions - Religious aspects - Handbooks, manuals, etc. --- Theologians - Handbooks, manuals, etc. --- Philosophers - Handbooks, manuals, etc --- study of religion --- religion and emotion --- world religions --- religious life --- religious tradition --- emotional states --- new religious movements
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One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot.
Information technology industries --- text mining --- big data --- analytics --- review --- self-organization --- computational philosophy --- brain --- synaptic learning --- adaptation --- functional plasticity --- activity-dependent resonance states --- circular causality --- somatosensory representation --- prehensile synergies --- robotics --- COVID-19 --- social media --- hashtag networks --- emotional profiling --- cognitive science --- network science --- sentiment analysis --- computational social science --- Twitter --- VADER scoring --- correlation --- semantic network analysis --- intellectual disability --- adolescents --- EEG --- emotional states --- working memory --- depression --- anxiety --- graph theory --- classification --- machine learning --- neural networks --- phonotactic probability --- neighborhood density --- sub-lexical representations --- lexical representations --- phonemes --- biphones --- cognitive network --- smart assistants --- knowledge generation --- intelligent systems --- web components --- deep learning --- web-based interaction --- cognitive network science --- text analysis --- natural language processing --- artificial intelligence --- emotional recall --- cognitive data --- AI --- pharmacological text corpus --- automatic relation extraction --- gender stereotypes --- story tropes --- movie plots --- network analysis --- word co-occurrence network --- n/a
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One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot.
text mining --- big data --- analytics --- review --- self-organization --- computational philosophy --- brain --- synaptic learning --- adaptation --- functional plasticity --- activity-dependent resonance states --- circular causality --- somatosensory representation --- prehensile synergies --- robotics --- COVID-19 --- social media --- hashtag networks --- emotional profiling --- cognitive science --- network science --- sentiment analysis --- computational social science --- Twitter --- VADER scoring --- correlation --- semantic network analysis --- intellectual disability --- adolescents --- EEG --- emotional states --- working memory --- depression --- anxiety --- graph theory --- classification --- machine learning --- neural networks --- phonotactic probability --- neighborhood density --- sub-lexical representations --- lexical representations --- phonemes --- biphones --- cognitive network --- smart assistants --- knowledge generation --- intelligent systems --- web components --- deep learning --- web-based interaction --- cognitive network science --- text analysis --- natural language processing --- artificial intelligence --- emotional recall --- cognitive data --- AI --- pharmacological text corpus --- automatic relation extraction --- gender stereotypes --- story tropes --- movie plots --- network analysis --- word co-occurrence network --- n/a
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One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot.
Information technology industries --- text mining --- big data --- analytics --- review --- self-organization --- computational philosophy --- brain --- synaptic learning --- adaptation --- functional plasticity --- activity-dependent resonance states --- circular causality --- somatosensory representation --- prehensile synergies --- robotics --- COVID-19 --- social media --- hashtag networks --- emotional profiling --- cognitive science --- network science --- sentiment analysis --- computational social science --- Twitter --- VADER scoring --- correlation --- semantic network analysis --- intellectual disability --- adolescents --- EEG --- emotional states --- working memory --- depression --- anxiety --- graph theory --- classification --- machine learning --- neural networks --- phonotactic probability --- neighborhood density --- sub-lexical representations --- lexical representations --- phonemes --- biphones --- cognitive network --- smart assistants --- knowledge generation --- intelligent systems --- web components --- deep learning --- web-based interaction --- cognitive network science --- text analysis --- natural language processing --- artificial intelligence --- emotional recall --- cognitive data --- AI --- pharmacological text corpus --- automatic relation extraction --- gender stereotypes --- story tropes --- movie plots --- network analysis --- word co-occurrence network
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epigenetic healing --- gene research --- therapy --- genes and neurons --- genetic determinism --- dogma --- the inner and outer environment --- genetic expression --- magic --- science --- epigenetic medicine --- DNA --- epigenetic change --- childhood stress --- adult disease --- beliefs and biochemistry --- psychology --- physiology --- prayer --- spirituality --- meditation --- epigenetic visualizations --- attitude --- engineering cells --- the malleable genome --- gene chips --- immediate early genes --- behavioral state-related genes --- experience-dependent genes --- trauma in muscles --- memory for healing --- designer brain --- happiness --- the body piezoelectric --- electrical medicine in Western science --- pulsed electromagnetic field therapy --- electrical medicine in direct observation --- DNA regeneration --- cellular communication through coherent light --- energy medicine --- frequencies of healing --- the connective semiconducting crystal --- connective tissue system and collagen --- semiconduction in connective tissues --- rapid cellular signaling --- DNA change and intention --- the fight-or-flight response --- subatomic alchemy --- interactive fields --- nonlocal perception --- the half-second delay --- miracles --- energy psychology --- emotional freedom techniques --- energy cures for physical traumas --- genes and emotional states --- telomeres and life span --- soul medicine --- conventional medicine --- energy medicine in hospitals --- intention as quantum conversation --- energy leverage points --- Iraq vets --- lime juice --- posttraumatic stress disorder --- fibromyalgia and chronic fatigue --- medicine for the body politic --- social change --- wars between nations --- the epigenetic social cycle --- emotional peace --- principles of epigenetic medicine --- self-healing powers --- death --- attachment doctoring --- five minute energy routine --- the cross crawl --- the Wayne Cook posture --- the crown pull --- the zip up --- the hook up --- Soul Medicine Institute --- the three thumps --- stress-induced genetic changes --- practices of epigentic medicine --- neurolymphatic massage
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