LEADER 04985nam 2201153z- 450 001 9910580215503321 005 20220706 035 $a(CKB)5690000000011932 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/87462 035 $a(oapen)doab87462 035 $a(EXLCZ)995690000000011932 100 $a20202207d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aKnowledge Modelling and Learning through Cognitive Networks 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (240 p.) 311 08$a3-0365-4345-7 311 08$a3-0365-4346-5 330 $aOne 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. 606 $aInformation technology industries$2bicssc 610 $aactivity-dependent resonance states 610 $aadaptation 610 $aadolescents 610 $aAI 610 $aanalytics 610 $aanxiety 610 $aartificial intelligence 610 $aautomatic relation extraction 610 $abig data 610 $abiphones 610 $abrain 610 $acircular causality 610 $aclassification 610 $acognitive data 610 $acognitive network 610 $acognitive network science 610 $acognitive science 610 $acomputational philosophy 610 $acomputational social science 610 $acorrelation 610 $aCOVID-19 610 $adeep learning 610 $adepression 610 $aEEG 610 $aemotional profiling 610 $aemotional recall 610 $aemotional states 610 $afunctional plasticity 610 $agender stereotypes 610 $agraph theory 610 $ahashtag networks 610 $aintellectual disability 610 $aintelligent systems 610 $aknowledge generation 610 $alexical representations 610 $amachine learning 610 $amovie plots 610 $an/a 610 $anatural language processing 610 $aneighborhood density 610 $anetwork analysis 610 $anetwork science 610 $aneural networks 610 $apharmacological text corpus 610 $aphonemes 610 $aphonotactic probability 610 $aprehensile synergies 610 $areview 610 $arobotics 610 $aself-organization 610 $asemantic network analysis 610 $asentiment analysis 610 $asmart assistants 610 $asocial media 610 $asomatosensory representation 610 $astory tropes 610 $asub-lexical representations 610 $asynaptic learning 610 $atext analysis 610 $atext mining 610 $aTwitter 610 $aVADER scoring 610 $aweb components 610 $aweb-based interaction 610 $aword co-occurrence network 610 $aworking memory 615 7$aInformation technology industries 700 $aStella$b Massimo$4edt$01318504 702 $aKenett$b Yoed N$4edt 702 $aStella$b Massimo$4oth 702 $aKenett$b Yoed N$4oth 906 $aBOOK 912 $a9910580215503321 996 $aKnowledge Modelling and Learning through Cognitive Networks$93033339 997 $aUNINA