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) | Viewed by 11691
Interests: computational models of learning and cognition; artificial neural networks and deep learning; evolutionary computing; learning technologies; bio-inspired machine learning; software engineering for AI and machine learning systems
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Special Issue in Informatics: Editorial Board Members' Collection Series: Bioinformatics and Medical Informatics
Interests: neural and evolutionary computation; cognitive computing; behavioural genetics; AI; computational modelling focused on learning and cognition; transfer learning
Interests: cognitive modelling of developmental change; cognitive variability and developmental disorders; language development; autism and Downs syndrome
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
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 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 1600 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.
- 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
- Bayesian models
- Analogical reasoning
- Case-based reasoning
- Memory and perception
- Language models