Special Issue "Computational Models of Cognition and Learning"

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

Deadline for manuscript submissions: closed (30 April 2019)

Special Issue Editors

Guest Editor
Prof. Dr. George D. Magoulas

Department of Computer Science and Information Systems, Birkbeck College, University of London, London WC1E 7HX, UK
Website | E-Mail
Phone: +44-20-7631-6717
Interests: computational models of learning and cognition; machine learning and evolution algorithms; neural networks and deep learning; nature-inspired computing; learner modelling and intelligent learning environments
Guest Editor
Dr. Maitrei Kohli

Developmental Neurocognition Lab, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK
Website | E-Mail
Interests: neural and evolutionary computation; cognitive computing; behavioural genetics; AI; computational modelling focused on learning and cognition; transfer learning
Guest Editor
Prof. Dr. Michael Thomas

Department of Psychological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK
Website | E-Mail
Phone: +44 (0)20 7631 6386
Interests: cognitive modelling of developmental change; cognitive variability and developmental disorders; language development; autism and Downs syndrome

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and cognitive science, psychology, and neuroscience have a long and entwined relationship with many examples of AI and Machine Learning (ML) research intricately coupled with research in cognitive science, psychology, neuroscience, and biology. In this context, computational modelling has been a powerful tool, eminently suitable to address the research goals of these fields. Computational models can help in enhancing both our fundamental and high-level understanding of human learning and cognitive processes, and at the same time provide both inspiration and validation for AI methods and ML techniques to accelerate and steer AI and ML research.

This Special Issue will explore the vital importance of this collaboration, disseminating research findings in cognitive science, psychology, and neuroscience that could aid in generating novel methods, models, and frameworks in the fields of AI and ML, and vice versa. It will present the state-of-the-art in:

  • Computational modelling using AI/ML methods and techniques for enhanced understanding of human learning and cognitive processing;
  • design and development of novel cognitive science-, psychology-, or neuroscience-inspired AI and ML methods;
  • models, tools, and methods that assist or supplement human reasoning;
  • computational approaches to develop human-level intelligence and human-like computing abilities;
  • systems capable of learning several tasks or skills from their environment;
  • Intelligent, human-like systems informed by biological or psychological models;
  • methods for human-like reasoning, transfer learning;
  • methods for interpretable ML and explainable AI.

Prof. Dr. George D. Magoulas
Dr. Maitrei Kohli
Prof. Dr. Michael Thomas
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 papers will be 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 quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • Computational models of cognitive processes
  • Human-like computing
  • Human-level intelligence
  • Intelligent systems with human-like reasoning and learning abilities
  • Nature-inspired models for cognitive processing or learning
  • Probabilities models of cognition
  • Cognitively-inspired approaches for knowledge representation and reasoning
  • Neural networks and deep learning
  • Human-interpretability of machine learning and artificial intelligence methods
  • Cognitive architecture
  • Neural networks
  • Deep learning
  • Evolution
  • Bayesian models
  • Analogical reasoning
  • Case-based reasoning
  • Memory and perception
  • Language models

Published Papers (1 paper)

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Research

Open AccessArticle
Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning
Big Data Cogn. Comput. 2019, 3(1), 10; https://doi.org/10.3390/bdcc3010010
Received: 22 December 2018 / Revised: 10 January 2019 / Accepted: 19 January 2019 / Published: 24 January 2019
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Abstract
Early language acquisition is a complex cognitive task. Recent data-informed approaches showed that children do not learn words uniformly at random but rather follow specific strategies based on the associative representation of words in the mental lexicon, a conceptual system enabling human cognitive [...] Read more.
Early language acquisition is a complex cognitive task. Recent data-informed approaches showed that children do not learn words uniformly at random but rather follow specific strategies based on the associative representation of words in the mental lexicon, a conceptual system enabling human cognitive computing. Building on this evidence, the current investigation introduces a combination of machine learning techniques, psycholinguistic features (i.e., frequency, length, polysemy and class) and multiplex lexical networks, representing the semantics and phonology of the mental lexicon, with the aim of predicting normative acquisition of 529 English words by toddlers between 22 and 26 months. Classifications using logistic regression and based on four psycholinguistic features achieve the best baseline cross-validated accuracy of 61.7% when half of the words have been acquired. Adding network information through multiplex closeness centrality enhances accuracy (up to 67.7%) more than adding multiplex neighbourhood density/degree (62.4%) or multiplex PageRank versatility (63.0%) or the best single-layer network metric, i.e., free association degree (65.2%), instead. Multiplex closeness operationalises the structural relevance of words for semantic and phonological information flow. These results indicate that the whole, global, multi-level flow of information and structure of the mental lexicon influence word acquisition more than single-layer or local network features of words when considered in conjunction with language norms. The highlighted synergy of multiplex lexical structure and psycholinguistic norms opens new ways for understanding human cognition and language processing through powerful and data-parsimonious cognitive computing approaches. Full article
(This article belongs to the Special Issue Computational Models of Cognition and Learning)
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