Journal Information
Cognitive Systems Research
Impact Factor:

Call For Papers
Cognitive Systems Research covers all topics in the study of cognitive processes, in both natural and artificial systems. The journal seeks top-quality contributions and encourages, in particular, articles that transcend traditional disciplinary boundaries (in terms of implications or in terms of approaches).

(1) The journal emphasizes the integration/synthesis of ideas, concepts, constructs, theories, and techniques from multiple paradigms, perspectives, and disciplines, in the analysis, understanding, and design of cognitive and intelligent systems.
(2) Contributions describing results obtained within the traditional disciplines (e.g., psychology, artificial intelligence) using well-established paradigms are also sought (especially if such work has broader implications and relevance).

The journal seeks to foster and promote the discussion of novel approaches in studying cognitive and intelligent systems. It also encourages cross-fertilization of disciplines. This is to be achieved by soliciting and publishing high-quality contributions in all of the areas of study in cognitive science, including artificial intelligence, linguistics, psychology, psychiatry, philosophy, system and control theory, anthropology, sociology, biological sciences, and neuroscience. The scope of the journal includes the study of a variety of different cognitive systems, at different levels, ranging from social/cultural cognition, to individual cognitive agents, to components of cognitive systems. Of particular interest are theoretical, experimental, computational, and integrative studies of cognitive systems at different levels of detail, and from different perspectives.
Last updated by Xin Yao in 2017-11-12
Special Issues
Special Issue on Innovative Dynamical Approaches to Cognitive Systems
Submission Date: 2018-02-28

During the past thirty years, dynamical theories and methods have been increasingly imported into investigations of cognitive systems. From the Haken-Kelso-Bunz model to the work of ecological psychologists to explain control and coordination in terms of dissipative structures, early emphases on the dynamics of cognition focused on macroscale whole-animal and animal-environment systems. More recent research has begun to emphasize microscale dynamics of neural networks and single neuron activity. Dynamical approaches have often supplemented more widespread frameworks such as connectionist and computational-representational theories of cognition. Conversely, some have argued that dynamics-focused frameworks can replace more traditional computational and representational approaches. In this way, dynamic-centered frameworks have guided innovative new ways of thinking about cognition, including embodied, extended, distributed, and other non-brain-centric conceptions of cognition. There is no doubt that dynamical theories and methods have proven their utility across scales of investigation into cognitive systems. This special issue of Cognitive Systems Research will feature recent work in dynamical approaches to cognitive systems. In particular, this issue aims to showcase recent theoretical and methodological innovations in research on various aspects of cognitive systems and across a range of spatial and temporal scales. Possible topics to be explored by the papers in this special issue may include innovative ways dynamical approaches are being applied to explaining and investigating cognitive systems in the following areas: - Bifurcation theory and applications - Boundaries of cognition - Chaotic dynamics - Complexity - Control and coordination - Data analyses, e.g., fractal, recurrence, sample entropy, etc. - Decision making and reasoning - Development and learning - Dynamical explanations vs. non-dynamical explanations (e.g., mechanistic) - Group dynamics and social cognition - Intersection of Bayesian and dynamical methods - Modeling practices - Multiscale spatial and temporal modeling and analyses - Networks - Neurodynamics - Perception-action - Robotics - Sensory substitution and augmentation - Synergetics
Last updated by Dou Sun in 2017-10-16
Special Issue on Deep Learning approaches for Cognitive Systems
Submission Date: 2018-03-31

Over a past few decades, deep learning has shown remarkable performance in many application domains such as speech recognition, natural language processing (NLP), and information retrieval. It has wider range of applications due to its capability in solving many complex tasks. Deep learning simulates and augment human thinking to make decisions. The academia and industry have been in a race to deploy it as systems of intelligence to derive meaning from information and apply judgment. Deep learning, along with many other disciplines within the field of artificial intelligence and cognitive systems, is gaining popularity, and it has colossal impact on the software industry. The discipline of deep learning also incorporates other data analysis disciplines, ranging from predictive analytics and data mining to pattern recognition. The editors expected to gather a set of recent advances in the related topics, to offer a platform for scientist and researchers to exchange their innovative ideas. This Virtual Special Issue (VSI) intends to bring together for theoreticians and practitioners from academic fields and industries worldwide working in the broad range of topics relevant to machine learning techniques in the field of multimodal data processing and analysis, including cross modal search and retrieval, cross modal hashing, pattern recognition and fusion. Application areas can cover but are not limited to Robotics, Human-Computer Interaction, Image processing, Social Media, Video Surveillance and Computer Vision. Recommended topics include (but are not limited to) the following: - Cognitive Computing For Smart Machine - Decision Making And Reasoning - Robotics - Modeling Practices Using Deep Learning - Social Networks And Social Contagion On Networks - Human – Computer Interaction - Agent Based Modeling - Human-Machine Interactive Systems - Cognitive Architectures - Intelligent Data Analysis System - Cognitive Recommender Systems - Spatial Problem Solving - Social Learning And Culture - Big data Analytics
Last updated by Dou Sun in 2017-12-16
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