Knowledge Modelling and Learning through Cognitive Networks

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Information technology industries; activity-dependent resonance states; adaptation; adolescents; AI; analytics; anxiety; artificial intelligence; automatic relation extraction; big data; biphones; brain; circular causality; classification; cognitive data; cognitive network; cognitive network science; cognitive science; computational philosophy; computational social science; correlation; COVID-19; deep learning; depression; EEG; emotional profiling; emotional recall; emotional states; functional plasticity; gender stereotypes; graph theory; hashtag networks; intellectual disability; intelligent systems; knowledge generation; lexical representations; machine learning; movie plots; n/a; natural language processing; neighborhood density; network analysis; network science; neural networks; pharmacological text corpus; phonemes; phonotactic probability; prehensile synergies; review; robotics; self-organization; semantic network analysis; sentiment analysis; smart assistants; social media; somatosensory representation; story tropes; sub-lexical representations; synaptic learning; text analysis; text mining; Twitter; VADER scoring; web components; web-based interaction; word co-occurrence network; working memory
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (240 p.)
• ISBN: 9783036543451; 9783036543468; books978-3-0365-4346-8
• Access: Open Access

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.