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

Prof. Dr. George D. Magoulas
E-Mail Website
Guest Editor
Department of Computer Science and Information Systems, Birkbeck College, University of London, London WC1E 7HX, UK
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
Special Issues, Collections and Topics in MDPI journals
Dr. Maitrei Kohli
E-Mail Website
Guest Editor
Developmental Neurocognition Lab, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK
Interests: neural and evolutionary computation; cognitive computing; behavioural genetics; AI; computational modelling focused on learning and cognition; transfer learning
Prof. Dr. Michael Thomas
E-Mail Website
Guest Editor
Department of Psychological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK
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) for publication in this open access journal is 1400 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

  • 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 (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Optimal Number of Choices in Rating Contexts
Big Data Cogn. Comput. 2019, 3(3), 48; https://doi.org/10.3390/bdcc3030048 - 27 Aug 2019
Cited by 1 | Viewed by 2391
Abstract
In many settings, people must give numerical scores to entities from a small discrete set—for instance, rating physical attractiveness from 1–5 on dating sites, or papers from 1–10 for conference reviewing. We study the problem of understanding when using a different number of [...] Read more.
In many settings, people must give numerical scores to entities from a small discrete set—for instance, rating physical attractiveness from 1–5 on dating sites, or papers from 1–10 for conference reviewing. We study the problem of understanding when using a different number of options is optimal. We consider the case when scores are uniform random and Gaussian. We study computationally when using 2, 3, 4, 5, and 10 options out of a total of 100 is optimal in these models (though our theoretical analysis is for a more general setting with k choices from n total options as well as a continuous underlying space). One may expect that using more options would always improve performance in this model, but we show that this is not necessarily the case, and that using fewer choices—even just two—can surprisingly be optimal in certain situations. While in theory for this setting it would be optimal to use all 100 options, in practice, this is prohibitive, and it is preferable to utilize a smaller number of options due to humans’ limited computational resources. Our results could have many potential applications, as settings requiring entities to be ranked by humans are ubiquitous. There could also be applications to other fields such as signal or image processing where input values from a large set must be mapped to output values in a smaller set. Full article
(This article belongs to the Special Issue Computational Models of Cognition and Learning)
Show Figures

Figure 1

Article
Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects
Big Data Cogn. Comput. 2019, 3(3), 41; https://doi.org/10.3390/bdcc3030041 - 12 Jul 2019
Cited by 2 | Viewed by 2488
Abstract
In this work, we build a computer aided diagnosis (CAD) system of breast cancer for high risk patients considering the breast imaging reporting and data system (BIRADS), mapping main expert concepts and rules. Therefore, a bag of words is built based on the [...] Read more.
In this work, we build a computer aided diagnosis (CAD) system of breast cancer for high risk patients considering the breast imaging reporting and data system (BIRADS), mapping main expert concepts and rules. Therefore, a bag of words is built based on the ontology of breast cancer analysis. For a more reliable characterization of the lesion, a feature selection based on Choquet integral is applied aiming at discarding the irrelevant descriptors. Then, a set of well-known machine learning tools are used for semantic annotation to fill the gap between low level knowledge and expert concepts involved in the BIRADS classification. Indeed, expert rules are implicitly modeled using a set of classifiers for severity diagnosis. As a result, the feature selection gives a a better assessment of the lesion and the semantic analysis context offers an attractive frame to include external factors and meta-knowledge, as well as exploiting more than one modality. Accordingly, our CAD system is intended for diagnosis of breast cancer for high risk patients. It has been then validated based on two complementary modalities, MRI and dual energy contrast enhancement mammography (DECEDM), the proposed system leads a correct classification rate of 99%. Full article
(This article belongs to the Special Issue Computational Models of Cognition and Learning)
Show Figures

Figure 1

Article
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 - 24 Jan 2019
Cited by 14 | Viewed by 2540
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)
Show Figures

Figure 1

Back to TopTop