A novel standard for graphical representation of mental models and processes in cognitive sciences

Cognitive Science has positioned itself to be a common ground in which models of mental processes from multiple disciplines merge, situating itself as a common field for new learning theories, or for formalizing existing ones. However, the authors have identified a need for updating the existing graphical representations by incorporating more accessible understanding for teachers and researchers in cross- multidisciplinary fields. In this regard, the present investigation attempts to generate a standard graphical language to represent complex mental processes by the introduction of functional principles, schemes and models that have been successfully used in technical areas such as adaptive control systems, algorithm flow charts, and artificial intelligence. This graphical representation, entitled “Cognitive Functional Representation” (CFR), is further shown to be efficacious in incorporating the essence of complex cognitive theories

Technology of mental functional representations as a first stage of conceptualization and implementation of complex scientific knowledge in innovation processes

In innovation processes, it is common to deal with highly cross-multidisciplinary topics. For example, an innovation process may integrate psychological, neuroscientific, biological and engineering disciplines, among many others dealing with bio-cybernetic systems. One specific type of those theories is related to cognitive processes, knowledge representation, and self-learning systems. Therefore, there is a need to easily and rapidly understand, as well as apply and share knowledge of complex theories by innovation managers, engineers, scholars, training practitioners, computational modelers, managers, and stakeholders, among others. In this regard, the present article provides with a graphical tool to represent complex cross- multidisciplinary theories, concepts and processes in a simple, concise, and logical manner, by using functional principles and graphical representations that have been successfully used in engineering and technology areas such as adaptive control systems, algorithmic flow charts, and computational cognitive neuroscience. Once described the models that have been typically used to represent and model knowledge and cognition, functional cognitive modeling is introduced, and then applied to represent and model complex cognitive theories from psychology and neuroscience such as Jean Piaget’s Theory of Intellectual Growth, Antonio Damasio’s Somatic Marker Hypothesis, and Dante Dorantes’ Soft Skills Model

Abstract intelligence: Embodying and enabling cognitive systems by mathematical engineering

Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC’16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world’s renowned scholars in the transdisciplinary field of CI and CC

Detecting and Modelling Stress Levels in E-Learning Environment Users

A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions