Special Issue on Cybernetics and Cognitive Informatics

The contemporary wonder of sciences and engineering has recently refocused on the starting point of them: How the brain processes internal and external information autonomously and cognitively rather than imperatively as those of conventional computers do? This leads to the advances in the field of Cognitive Informatics (CI) as a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science. CI investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC'11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world's renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

Cognitive Informatics (CI) is a transdisciplinary enquiry of the internal information processing mechanisms and processes of the brain and natural intelligence shared by almost all science and engineering disciplines. This article presents an intensive review of the new field of CI. The structure of the theoretical framework of CI is described encompassing the Layered Reference Model of the Brain (LRMB), the OAR model of information representation, Natural Intelligence (NI) vs. Artificial Intelligence (AI), Autonomic Computing (AC) vs. imperative computing, CI laws of software, the mechanism of human perception processes, the cognitive processes of formal inferences, and the formal knowledge system. Three types of new structures of mathematics, Concept Algebra (CA), Real-Time Process Algebra (RTPA), and System Algebra (SA), are created to enable rigorous treatment of cognitive processes of the brain as well as knowledge representation and manipulation in a formal and coherent framework. A wide range of applications of CI in cognitive psychology, computing, knowledge engineering, and software engineering has been identified and discussed.

Background: This article explores the connections between the concept of anviksiki in Hindu philosophy and theories in cognitive science, and how these principles can be applied in modern education. Anviksiki, which focuses on critical thinking and deep reflection, is considered to have strong relevance to cognitive science concepts such as information processing, perception, and decision-making. Methods: The method used in this research is literature study and comparative analysis, by reviewing various literatures that discuss Hindu philosophy and cognitive science in depth. The analysis shows that anviksiki can provide an additional perspective in understanding how humans think and process information, especially in the context of moral and ethical reflection. Finding: In education, the principle of anviksiki that emphasises self-reflection and critical thinking can enrich students' learning process. This concept supports active learning methods that allow students to construct their own understanding, in a way that is in line with Piaget's theory of constructivism. In addition, cognitive science provides practical insights into information processing and memory, which can be used to design more effective teaching strategies. Conclusion: The conclusion of this study confirms that anviksiki can be integrated with cognitive science to create a holistic approach to education, which enriches intellectual, ethical and reflective aspects. By combining Hindu wisdom and cognitive science insights, education can support the development of balanced individuals, who are able to think critically and understand the moral consequences of their decisions. This research reveals that Anviksiki, as a philosophy of critical thinking in the Hindu tradition, has strong relevance to concepts in modern cognitive science, particularly in analytic approaches, logic, and deductive methods. Thus, the heritage of Hindu thought not only demonstrates traditional wisdom but also presents a philosophical foundation that supports contemporary cognitive exploration. Novelty/Originality of this article: This research offers a new perspective by bridging the ancient philosophy of Anviksiki with modern cognitive science, demonstrating the continuity of concepts in the critical thinking process involving logical analysis and deductive methods. It provides new insights in understanding the contribution of the Hindu tradition of thought to the development of cognitive theory today.

This study is concerned with processes for discovering new theories in science. It considers a computational approach to scientific discovery, as applied to the discovery of theories in cognitive science. The approach combines two ideas. First, a process-based scientific theory can be represented as a computer program. Second, an evolutionary computational method, genetic programming, allows computer programs to be improved through a process of computational trial-and-error. Putting these two ideas together leads to a system that can automatically generate and improve scientific theories. The application of this method to the discovery of theories in cognitive science is examined. Theories are built up from primitive operators. These are contained in a theory language that defines the space of possible theories. An example of a theory generated by this method is described. These results support the idea that scientific discovery can be achieved through a heuristic search process, even for theories involving a sequence of steps. However, this computational approach to scientific discovery does not eliminate the need for human input. Human judgment is needed to make reasonable prior assumptions about the characteristics of operators used in the theory generation process, and to interpret and provide context for the computationally generated theories.

In this chapter we survey Australasian philosophy of mind and cognition since 1980. In some of the fields we address, the boundaries between philosophy and related disciplines blur, with scientists participating actively in philosophical debates and philosophers in turn working in independent research groups in automated reasoning, Artificial Intelligence, cognitive science or cognitive neuropsychiatry. We seek to cite a large enough array of primary sources by Australasian philosophers to give readers significant initial guidance in each area. First we look in some detail at the mainstream debates about consciousness and physicalism which arose directly out of the earlier history of Australian materialism. We then work through the driving theories in cognitive science from its outset, through classical and connectionist versions of the computational theory of mind, and on to ideas about dynamical and extended cognition. Finally, and more briefly, we address a number of key issues or special topics with tight links to that history of foundational theories in cognitive science, looking at folk psychology and theory of mind, delusions and philosophy of psychiatry, and then discrete topics such as emotion, perception and memory.

Systems are the most complicated entities and phenomena in abstract, physical, information, and social worlds across all science and engineering disciplines. System algebra is an abstract mathematical structure for the formal treatment of abstract and general systems as well as their algebraic relations, operations, and associative rules for composing and manipulating complex systems. This article presents a mathematical theory of system algebra and its applications in cognitive informatics, system engineering, software engineering, and cognitive informatics. A rigorous treatment of abstract systems is described, and the algebraic relations and compositional operations of abstract systems are analyzed. System algebra provides a denotational mathematical means that can be used to model, specify, and manipulate generic “to be” and “to have” type problems, particularly system architectures and high-level system designs, in computing, software engineering, system engineering, and cognitive informatics.

Systems are the most complicated entities and phenomena in abstract, physical, information, and social worlds across all science and engineering disciplines. System algebra is an abstract mathematical structure for the formal treatment of abstract and general systems as well as their algebraic relations, operations, and associative rules for composing and manipulating complex systems. This article presents a mathematical theory of system algebra and its applications in cognitive informatics, system engineering, software engineering, and cognitive informatics. A rigorous treatment of abstract systems is described, and the algebraic relations and compositional operations of abstract systems are analyzed. System algebra provides a denotational mathematical means that can be used to model, specify, and manipulate generic “to be” and “to have” type problems, particularly system architectures and high-level system designs, in computing, software engineering, system engineering, and cognitive informatics.

The 12 experimental papers presented in this special issue brilliantly illustrate the creative and exciting ways in which the exchange of theoretical concepts and experimental methodologies, between cognitive and clinical science, is serving both to deepen our understanding of psychopathology, and to identify new ways of potentially remediating such dysfunction. These papers are not only individually excellent, but collectively they communicate some of the major themes that are presently driving new developments within this rapidly evolving field. In this brief commentary, I reflect upon the contributions made by the current studies to these important themes. I also share some suggestions concerning how future work might profitably build upon this present research, to further enhance theoretical progress and enrich therapeutic innovation. Copyright © 2010 John Wiley & Sons, Ltd.

Teaching false theories goes against the general pedagogical and philosophical belief that we must only teach and learn what is true. In general, the goal of pedagogy is taken to be epistemic: to gain knowledge and avoid ignorance. In this article, I argue that for realists and antirealists alike, epistemological and pedagogical goals have to come apart. I argue that the falsity of a theory does not automatically make it unfit for being taught. There are several good reasons for teaching false theories in school science. These are (a) false theories can bring about genuine (non-factive) understanding of the world; (b) teaching some false theories from the history of science that line up with children’s ideas can provide students “intellectual empathy” and also aid in better grasp of concepts; (c) teaching false theories from the history of science can sharpen students’ understanding of the nature of science; (d) scientists routinely use false theories and models in their practice and it is good sense for science education to mirror scientific practice; and (e) learning about patently non-scientific and antiscientific ideas will prepare students to face and respond to them. In making arguments for the foregoing five points, I draw upon the work of a variety of philosophers and historians of science, cognitive scientists, science education scholars, and scientists. My goal here is not only to justify theories considered false already being taught, but also to actively endorse the teaching of some theories considered false that are by and large not currently taught.

The present paper examines the historical choice points the led twentieth-century cognitive science to its current commitment to correspondence approaches to reality and truth. Such a “correspondence”-driven approach to reality and truth stands in contrast to coherence-driven approaches, which were prominent in the 1800s and early 1900s. Coherence approaches refused to begin the conversation regarding reality with the assumption that the important thing about it was its independence of observers because the reality-observer split inherent in correspondence-driven views often led to objective-subjective divides, which, within scientific theorizing, tended to render the latter causally unnecessary and in need of ontological justification. The present paper fleshes out the differences between coherence- and correspondence-driven approaches to reality and truth, proposes an explanation of why cognitive science came to favor correspondence approaches, describes problems that have arisen in cognitive science because of its commitment to correspondence theorizing, and proposes an alternative framework (i.e., Wild Systems Theory— WST ) that is inspired by a coherence approach to reality and truth, yet is entirely consistent with science.

Cognitive informatics in image semantics description, identification and automatic pattern understanding

Cognitive infocommunications (CogInfoCom) investigates connections between the cognitive sciences and different areas of infocommunications. CogInfoCom also focuses on engineering application fields integrating related scientific areas and results. Cognitive infocommunications systems involve hardware and software components that collect and store information and enable users to interact with this information. Besides communication security, considerations include the amount of stored information, which may be huge. This means that there is a need for algorithms and solutions that store and process data effectively. The CogInfoCom field presents a number of motivational challenges requiring active deep research, implementation, integration and measurement. This special issue focuses on the Cognitive Mobile Applications and Services track of the 3rd IEEE International Conference on Cognitive Infocommunications (CogInfoComf12). Mobile phones and tablets are now everyday tools that enable users to easily connect to the Internet, download social content, find interesting places, etc. Mobile technology has become one of the most important fields in the IT industry just as Web technology was 10-15 years ago. Mobile phones and tablets use sensors and interfaces like accelerometers, cameras, GPS, and thermometers that monitor and enable easier interaction with the real world. Agents hosted by mobile devices that learn from sensor-originated information also support individual applications and complex systems. This special issue focuses on the cognitive capabilities of mobile phones, the various agents that mobile devices host, and how they can be applied efficiently in applications and services including social aspects of mobile solutions. Papers from the conference cover mobile controlled environments, mobile-supported learning, augmented reality, energy efficiency and communication techniques. We thank the authors for submitting their papers to CogInfoCom as a venue for presenting their findings. We are also grateful to program committee members and reviewers for their efforts in making the conference and the special issue possible. We feel that these papers will provide readers with interesting and valuable results from the field of cognitive infocommunications.

The Seventh International Symposium on Neural Networks (ISNN 2010) was held on June 6–9, 2010 in Shanghai, China. The ISNN 2010 was a great success and provided a high-level international forum for scientists and engineers to present the latest research in neural networks, computational intelligence, cognitive systems, and related fields. To highlight the success of this conference, we edited this special issue for Cognitive Computation. We chose nine papers from over 591 papers submitted to ISNN 2010, which reflect the high quality of the presentations at the conference while capturing the spirit of our theme, ‘‘Advances in Computational Intelligence and Applications,’’ for this special issue. Our goal for this special issue is to present the latest research developments in computational intelligence with a focus on cognitive computation and their applications across a wide range of domains. Over the past decades, we have witnessed tremendous interest and developments in all aspects of computational intelligence research, ranging from both theoretical foundations, principles, to practical applications in different domains. To reflect a flavor of recent research activities in the community, we carefully selected these nine papers for this special issue. The selected papers can be organized into the following four coherent sections. The first section is directly related to neural network models and applications. The paper by Deng presents a new control strategy for the chaotic neural network, in which the refractoriness is tuned by using feedback control based on online averaging of network states. Simulation results demonstrate that the proposed model can achieve favorable performance in handling noisy, incomplete, and composite patterns, while also achieving either enhanced or comparable memory capacity compared with the classic Hopfield net and other chaotic neural network models. In the other paper, Gluge et al. discuss recurrent networks for implicit learning of temporal sequences, a very important area to understand high-level cognitive intelligence. The authors propose a recurrent network with backpropagation training algorithm that is adapted to the reinforcement learning scheme. The simulation results fit quantitatively as well as qualitatively to the behavioral results, suggesting the role of temporal context in associative learning scenarios. The second section is related to language processing and understanding, an important topic in cognitive science and computational intelligence. The paper by Murata et al. investigates the maximum entropy (ME) approach for natural language processing (NLP) problems including machine translation and information extraction. Comparative studies of the proposed approach with the existing techniques are illustrated in this paper. In related work to this field, the paper by Kaznatcheev investigates connectionist models on the interplay of nouns and pronouns in personal pronoun acquisition, a very interesting and important problem in psychology. For instance, the author discusses the learning in the shifting reference situation for children. It is reported that learning of two different noun-and-pronoun addressee patterns is consistent with Z. Zeng (&) Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China e-mail: zgzeng@gmail.com

Radical views on cognition are generally defined by a cluster of features including non-representationalism and vehicle-externalism. In this paper, I concentrate on the way radical views on cognition define themselves as revolutionary theories in cognitive science. These theories often use the Kuhnian concepts of “paradigm” and “paradigm shift” for describing their ambitions and the current situation in cognitive science. I examine whether the use of Kuhn’s theory of science is appropriate here. There might be good reasons to think that cognitive science is in a situation of foundational crisis, but that does not entail that the classical paradigm (computationalism) is currently displaced to the benefit of a new paradigm. Larry Laudan’s theory of research traditions is more enlightening than Kuhn’s theory for describing the scope and ambitions of radical views on cognition, and their relations with an anti-intellectualist tradition in philosophy.

Teaching and Learning Guide for: Pragmatics: From Theory to Experiment and Back Again