Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A recent wave of brain research has advanced our understanding of the neural mechanisms of conscious states, contents and functions. A host of questions remain to be explored, as shown by lively debates between models of higher vs. lower-order aspects of consciousness, as well as global vs. local models. (Baars 2007; Block, 2009; Dennett and Cohen, 2011; Lau and Rosenthal, 2011). Over some twenty-five centuries the contemplative traditions have also developed explicit descriptions and taxonomies of the mind, to interpret experiences that are often reported in contemplative practices (Radhakrishnan & Moore, 1967; Rinbochay & Naper, 1981). These traditional descriptions sometimes converge on current scientific debates, such as the question of conceptual vs. non-conceptual consciousness; reflexivity or “self-knowing” associated with consciousness; the sense of self and consciousness; and aspects of consciousness that are said to continue during sleep. These real or claimed aspects of consciousness have not been fully integrated into scientific models so far. This Research Topic in Consciousness Research aims to provide a forum for theoretical proposals, new empirical findings, integrative literature reviews, and methodological improvements inspired by meditation-based models. We include a broad array of topics, including but not limited to: replicable findings from a variety of systematic mental practices; changes in brain functioning and organization that can be attributed to such practices; their effects on adaptation and neural plasticity; measurable effects on perception, cognition, affect and self-referential processes. We include contributions that address the question of causal attribution. Many published studies are correlational in nature, because of the inherent difficulty of conducting longitudinal experiments based on a major lifestyle decision, such as the decision to commit to a mental practice over a period of years. We also feature clinical and case studies, integrative syntheses and significant opinion articles.

contemplative practice --- structural plasticity --- functional plasticity --- Meditation --- mindfulness --- neural correlates of consciousness --- Cultural issues

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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 --- 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