Knowledge Modelling and Learning through Cognitive Networks

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 133175

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Special Issue Editors


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Guest Editor
Department of Psychology and Cognitive Science, University of Trento, Corso Bettini 33, 38068 Rovereto, Italy
Interests: cognitive data science; complex networks; knowledge modelling; multiplex networks; natural language processing
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Guest Editor
Faculty of Industrial Engineering and Management, Technion - Israel institute of Technology, Haifa 3200003, Israel
Interests: cognitive network science; creativity; cognitive search; memory retrieval; clinical cognitive networks

Special Issue Information

Dear Colleagues,

Cognitive network science is a rapidly growing research area investigating a wide range of mental phenomena through complex network representations of cognitive systems and supported by the increasing availability of cognitive Big Data. The researchers developing this innovative research area come from a variety of fields, including psychology, cognitive science, computer science, linguistics, physics, social science, and mathematics.

Cognitive networks represent a powerful approach for investigating cognitive phenomena where the networked, associative organization of this phenomenon influences cognitive processes operating over it. For example, investigating the structure of semantic memory via semantic networks has illuminated how its structure constrains processes related to creativity; memory search; learning; and, more generally, knowledge acquisition, exploration, and exploitation.

This Special Issue aims at bringing together quantitative, innovative research that focuses on modeling knowledge through cognitive networks for gaining insights into mechanisms driving cognitive processes related to knowledge structuring, exploration and learning. We are open to a variety of publication types, including reviews and theoretical papers, empirical research, computational modeling, and Big Data analysis. Submissions to this Special Issue should demonstrate how the application of network science extends and broadens cognitive science and knowledge modeling in ways that traditional approaches cannot.

Potential topics include but are not limited to the following:

  • Network models of knowledge construction and representation;
  • Modeling exploration and exploitation processes over knowledge structure;
  • Complex system approaches to knowledge modeling;
  • Stance detection through psycholinguistics and network science;
  • Network visualization of knowledge representation;
  • Quantifying the impact of phonological, syntactic, and semantic knowledge for language processing;
  • Multiplex networks as knowledge representations for modeling multiple aspects of the mental lexicon;
  • Knowledge structure and cognitive footprints for psychopathologies;
  • Predictive models of cognitive decline based on the structure of semantic memory and knowledge representations;
  • Network models of multichannel (e.g., visual and semantic) knowledge acquisition and processing;
  • Machine learning approaches to knowledge extraction with applications from text analysis or social media platforms like Twitter;
  • Theoretical models of knowledge building and evolution;
  • Network-oriented game theoretic models for modeling knowledge dissemination and cultural evolution;
  • Creativity, mind-wandering, and mental search strategies on knowledge representations;
  • Networked models of knowledge building for quantifying expertise;
  • Network models and machine learning for quantifying the perception and impact of learning in education research;
  • Language learning in L2 learners;
  • Network-based models for early language learning;
  • Learning in socio-cognitive systems and social dilemmas;
  • Network models, conceptual maps and learning outcomes in education research;
  • Network science and classroom teaching/learning dynamics.

Dr. Massimo Stella
Dr. Yoed N. Kenett
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cognitive network science
  • Machine learning and networks
  • Complex systems models for cognitive processing or learning
  • Complex networks and knowledge models
  • Complex networks and learning
  • Knowledge extraction
  • Network representations of semantic, syntactic, and phonological knowledge
  • Creativity
  • Curiosity
  • Personality traits and knowledge
  • Social learning and evolution
  • Knowledge evolution
  • Quantitative models
  • Transdisciplinary approaches to knowledge quantification
  • Memory, perception and learning
  • Language networks
  • Stance detection
  • Discourse analysis in social media
  • Knowledge representation in texts and social media
  • Content diffusion and learning in online networked systems
  • Multiplex and multilayer networks

Published Papers (12 papers)

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Editorial

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3 pages, 186 KiB  
Editorial
Knowledge Modelling and Learning through Cognitive Networks
by Massimo Stella and Yoed N. Kenett
Big Data Cogn. Comput. 2022, 6(2), 53; https://doi.org/10.3390/bdcc6020053 - 13 May 2022
Cited by 1 | Viewed by 2373
Abstract
Knowledge modelling is a growing field at the fringe of computer science, psychology and network science [...] Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)

Research

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32 pages, 5511 KiB  
Article
Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis
by Arjun M. Kumar, Jasmine Y. Q. Goh, Tiffany H. H. Tan and Cynthia S. Q. Siew
Big Data Cogn. Comput. 2022, 6(2), 50; https://doi.org/10.3390/bdcc6020050 - 06 May 2022
Cited by 3 | Viewed by 42152
Abstract
The present analysis of more than 180,000 sentences from movie plots across the period from 1940 to 2019 emphasizes how gender stereotypes are expressed through the cultural products of society. By applying a network analysis to the word co-occurrence networks of movie plots [...] Read more.
The present analysis of more than 180,000 sentences from movie plots across the period from 1940 to 2019 emphasizes how gender stereotypes are expressed through the cultural products of society. By applying a network analysis to the word co-occurrence networks of movie plots and using a novel method of identifying story tropes, we demonstrate that gender stereotypes exist in Hollywood movies. An analysis of specific paths in the network and the words reflecting various domains show the dynamic changes in some of these stereotypical associations. Our results suggest that gender stereotypes are complex and dynamic in nature. Specifically, whereas male characters appear to be associated with a diversity of themes in movies, female characters seem predominantly associated with the theme of romance. Although associations of female characters to physical beauty and marriage are declining over time, associations of female characters to sexual relationships and weddings are increasing. Our results demonstrate how the application of cognitive network science methods can enable a more nuanced investigation of gender stereotypes in textual data. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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16 pages, 407 KiB  
Article
Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications
by Alexander Sboev, Anton Selivanov, Ivan Moloshnikov, Roman Rybka, Artem Gryaznov, Sanna Sboeva and Gleb Rylkov
Big Data Cogn. Comput. 2022, 6(1), 10; https://doi.org/10.3390/bdcc6010010 - 17 Jan 2022
Cited by 5 | Viewed by 3436
Abstract
Nowadays, the analysis of digital media aimed at prediction of the society’s reaction to particular events and processes is a task of a great significance. Internet sources contain a large amount of meaningful information for a set of domains, such as marketing, author [...] Read more.
Nowadays, the analysis of digital media aimed at prediction of the society’s reaction to particular events and processes is a task of a great significance. Internet sources contain a large amount of meaningful information for a set of domains, such as marketing, author profiling, social situation analysis, healthcare, etc. In the case of healthcare, this information is useful for the pharmacovigilance purposes, including re-profiling of medications. The analysis of the mentioned sources requires the development of automatic natural language processing methods. These methods, in turn, require text datasets with complex annotation including information about named entities and relations between them. As the relevant literature analysis shows, there is a scarcity of datasets in the Russian language with annotated entity relations, and none have existed so far in the medical domain. This paper presents the first Russian-language textual corpus where entities have labels of different contexts within a single text, so that related entities share a common context. therefore this corpus is suitable for the task of belonging to the medical domain. Our second contribution is a method for the automated extraction of entity relations in Russian-language texts using the XLM-RoBERTa language model preliminarily trained on Russian drug review texts. A comparison with other machine learning methods is performed to estimate the efficiency of the proposed method. The method yields state-of-the-art accuracy of extracting the following relationship types: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown on the presented subcorpus from the Russian Drug Review Corpus, the method developed achieves a mean F1-score of 80.4% (estimated with cross-validation, averaged over the four relationship types). This result is 3.6% higher compared to the existing language model RuBERT, and 21.77% higher compared to basic ML classifiers. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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18 pages, 546 KiB  
Article
DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning
by Asra Fatima, Ying Li, Thomas Trenholm Hills and Massimo Stella
Big Data Cogn. Comput. 2021, 5(4), 77; https://doi.org/10.3390/bdcc5040077 - 13 Dec 2021
Cited by 5 | Viewed by 5887
Abstract
Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to [...] Read more.
Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad–happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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19 pages, 693 KiB  
Article
A Semantic Web Framework for Automated Smart Assistants: A Case Study for Public Health
by Yusuf Sermet and Ibrahim Demir
Big Data Cogn. Comput. 2021, 5(4), 57; https://doi.org/10.3390/bdcc5040057 - 18 Oct 2021
Cited by 12 | Viewed by 5291
Abstract
The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual [...] Read more.
The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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17 pages, 1362 KiB  
Article
Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks
by Michael S. Vitevitch, Leo Niehorster-Cook and Sasha Niehorster-Cook
Big Data Cogn. Comput. 2021, 5(4), 47; https://doi.org/10.3390/bdcc5040047 - 23 Sep 2021
Cited by 8 | Viewed by 3449
Abstract
In Linguistics and Psycholinguistics, phonotactics refers to the constraints on individual sounds in a given language that restrict how those sounds can be ordered to form words in that language. Previous empirical work in Psycholinguistics demonstrated that phonotactic knowledge influenced how quickly and [...] Read more.
In Linguistics and Psycholinguistics, phonotactics refers to the constraints on individual sounds in a given language that restrict how those sounds can be ordered to form words in that language. Previous empirical work in Psycholinguistics demonstrated that phonotactic knowledge influenced how quickly and accurately listeners retrieved words from that part of memory known as the mental lexicon. In the present study, we used three computer simulations to explore how three different cognitive network architectures could account for the previously observed effects of phonotactics on processing. The results of Simulation 1 showed that some—but not all—effects of phonotactics could be accounted for in a network where nodes represent words and edges connect words that are phonologically related to each other. In Simulation 2, a different network architecture was used to again account for some—but not all—effects of phonotactics and phonological neighborhood density. A bipartite network was used in Simulation 3 to account for many of the previously observed effects of phonotactic knowledge on spoken word recognition. The value of using computer simulations to explore different network architectures is discussed. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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16 pages, 1130 KiB  
Article
A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study
by Bhargav Prakash, Gautam Kumar Baboo and Veeky Baths
Big Data Cogn. Comput. 2021, 5(3), 39; https://doi.org/10.3390/bdcc5030039 - 28 Aug 2021
Cited by 4 | Viewed by 5315
Abstract
Brain connectivity is studied as a functionally connected network using statistical methods such as measuring correlation or covariance. The non-invasive neuroimaging techniques such as Electroencephalography (EEG) signals are converted to networks by transforming the signals into a Correlation Matrix and analyzing the resulting [...] Read more.
Brain connectivity is studied as a functionally connected network using statistical methods such as measuring correlation or covariance. The non-invasive neuroimaging techniques such as Electroencephalography (EEG) signals are converted to networks by transforming the signals into a Correlation Matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks (RNN), are implemented on two different types of correlation matrices: Correlation Matrix (static connectivity) and Time-resolved Correlation Matrix (dynamic connectivity), to classify them either on their psychometric assessment or the effect of therapy. These correlation matrices are different from traditional learning techniques in the sense that they incorporate theory-based graph features into the learning models, thus providing novelty to this study. The EEG data used in this study is trail-based/event-related from five different experimental paradigms, of which can be broadly classified as working memory tasks and assessment of emotional states (depression, anxiety, and stress). The classifications based on RNN provided higher accuracy (74–88%) than the other three models (50–78%). Instead of using individual graph features, a Correlation Matrix provides an initial test of the data. When compared with the Time-resolved Correlation Matrix, it offered a 4–5% higher accuracy. The Time-resolved Correlation Matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the Correlation Matrix, a static feature. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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12 pages, 1420 KiB  
Article
Structural Differences of the Semantic Network in Adolescents with Intellectual Disability
by Karin Nilsson, Lisa Palmqvist, Magnus Ivarsson, Anna Levén, Henrik Danielsson, Marie Annell, Daniel Schöld and Michaela Socher
Big Data Cogn. Comput. 2021, 5(2), 25; https://doi.org/10.3390/bdcc5020025 - 01 Jun 2021
Cited by 2 | Viewed by 6146
Abstract
The semantic network structure is a core aspect of the mental lexicon and is, therefore, a key to understanding language development processes. This study investigated the structure of the semantic network of adolescents with intellectual disability (ID) and children with typical development (TD) [...] Read more.
The semantic network structure is a core aspect of the mental lexicon and is, therefore, a key to understanding language development processes. This study investigated the structure of the semantic network of adolescents with intellectual disability (ID) and children with typical development (TD) using network analysis. The semantic networks of the participants (nID = 66; nTD = 49) were estimated from the semantic verbal fluency task with the pathfinder method. The groups were matched on the number of produced words. The average shortest path length (ASPL), the clustering coefficient (CC), and the network’s modularity (Q) of the two groups were compared. A significantly smaller ASPL and Q and a significantly higher CC were found for the adolescents with ID in comparison with the children with TD. Reasons for this might be differences in the language environment and differences in cognitive skills. The quality and quantity of the language input might differ for adolescents with ID due to differences in school curricula and because persons with ID tend to engage in different out-of-school activities compared to TD peers. Future studies should investigate the influence of different language environments on the language development of persons with ID. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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17 pages, 11783 KiB  
Article
A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19
by Toni Pano and Rasha Kashef
Big Data Cogn. Comput. 2020, 4(4), 33; https://doi.org/10.3390/bdcc4040033 - 09 Nov 2020
Cited by 93 | Viewed by 15777
Abstract
During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. [...] Read more.
During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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23 pages, 3397 KiB  
Article
#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19
by Massimo Stella, Valerio Restocchi and Simon De Deyne
Big Data Cogn. Comput. 2020, 4(2), 14; https://doi.org/10.3390/bdcc4020014 - 16 Jun 2020
Cited by 44 | Viewed by 8960
Abstract
The COVID-19 pandemic forced countries all over the world to take unprecedented measures, like nationwide lockdowns. To adequately understand the emotional and social repercussions, a large-scale reconstruction of how people perceived these unexpected events is necessary but currently missing. We address this gap [...] Read more.
The COVID-19 pandemic forced countries all over the world to take unprecedented measures, like nationwide lockdowns. To adequately understand the emotional and social repercussions, a large-scale reconstruction of how people perceived these unexpected events is necessary but currently missing. We address this gap through social media by introducing MERCURIAL (Multi-layer Co-occurrence Networks for Emotional Profiling), a framework which exploits linguistic networks of words and hashtags to reconstruct social discourse describing real-world events. We use MERCURIAL to analyse 101,767 tweets from Italy, the first country to react to the COVID-19 threat with a nationwide lockdown. The data were collected between the 11th and 17th March, immediately after the announcement of the Italian lockdown and the WHO declaring COVID-19 a pandemic. Our analysis provides unique insights into the psychological burden of this crisis, focussing on—(i) the Italian official campaign for self-quarantine (#iorestoacasa), (ii) national lockdown (#italylockdown), and (iii) social denounce (#sciacalli). Our exploration unveils the emergence of complex emotional profiles, where anger and fear (towards political debates and socio-economic repercussions) coexisted with trust, solidarity, and hope (related to the institutions and local communities). We discuss our findings in relation to mental well-being issues and coping mechanisms, like instigation to violence, grieving, and solidarity. We argue that our framework represents an innovative thermometer of emotional status, a powerful tool for policy makers to quickly gauge feelings in massive audiences and devise appropriate responses based on cognitive data. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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34 pages, 637 KiB  
Article
Text Mining in Big Data Analytics
by Hossein Hassani, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani and Mohammad Reza Yeganegi
Big Data Cogn. Comput. 2020, 4(1), 1; https://doi.org/10.3390/bdcc4010001 - 16 Jan 2020
Cited by 133 | Viewed by 24105
Abstract
Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine [...] Read more.
Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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Other

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17 pages, 2294 KiB  
Concept Paper
Seven Properties of Self-Organization in the Human Brain
by Birgitta Dresp-Langley
Big Data Cogn. Comput. 2020, 4(2), 10; https://doi.org/10.3390/bdcc4020010 - 10 May 2020
Cited by 16 | Viewed by 7494
Abstract
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their [...] Read more.
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: (1) modular connectivity, (2) unsupervised learning, (3) adaptive ability, (4) functional resiliency, (5) functional plasticity, (6) from-local-to-global functional organization, and (7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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