Knowledge Modelling and Learning through Cognitive Networks

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 250 words) can be sent to the Editorial Office for assessment.

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.