Cognitive Informatics, Computer Modelling, and Cognitive Science

Volume 1: Theory, Case Studies, and Applications

G. R. Sinha, Jasjit Suri, Jasjit S. Suri

• Publisher: Academic Press
• Year: 2020
• Print ISBN:
  9780128194430
• eBook ISBN:
  9780128194447
• Topic: Technology & Engineering
• Subtopic: Chemical & Biochemical Engineering
• Index: Technology & Engineering

Cognitive Informatics, Computer Modelling, and Cognitive Science: Theory, Case Studies, and Applications presents the theoretical background and history of cognitive science to help readers understand its foundations, philosophical and psychological aspects, and applications in a wide range of engineering and computer science case studies. Cognitive science, a cognitive model of the brain, knowledge representation, and information processing in the human brain are discussed, as is the theory of consciousness, neuroscience, intelligence, decision-making, mind and behavior analysis, and the various ways cognitive computing is used for information manipulation, processing and decision-making. Mathematical and computational models, structures and processes of the human brain are also covered, along with advances in machine learning, artificial intelligence, cognitive knowledge base, deep learning, cognitive image processing and suitable data analytics. - Identifies how foundational theories and concepts in cognitive science are applicable in other fields - Includes a comprehensive review of cognitive science applications in multiple domains, applying it to neural engineering, robotics, computer science and STEM - Includes models of brain processing, consciousness, decision-making, and more - Provides in-depth technical coverage of cognitive informatics and computing, including coverage of cognitive knowledge base, information theory, cognitive machine learning and intelligence

Chapter 1

Introduction to cognitive science, informatics, and modeling

G.R. Sinha¹ and Jasjit S. Suri², ¹Myanmar Institute of Information Technology, Mandalay, Myanmar, ²Stroke Monitoring Division, AtheroPoint™, Roseville, CA, United States

Abstract

This chapter introduces the use of cognitive science, cognitive informatics, and computer modeling for cognitive applications. Cognitive science due to its broad scope is used in a variety of applications of science, technology, engineering, and mathematics. For each of the application areas the overview of cognitive science, necessary mathematical background, and a basic knowledge of modeling concept are required, and the chapter attempts to present this in addition to the psychological and philosophical background of the cognition process. Cognition is closely associated with brain, memory, and organization of information using suitable learning mechanism, and thus the concept of learning be introduced and detailed description of learning, various types of learning and their application will be discussed in subsequent chapters.

Keywords

Cognitive science; cognitive ability; human brain; memory; cognitive network; philosophy; psychology

1.1 Introduction and history of cognitive science

The evolution of cognitive science started since ancient time when Plato and Aristotle used to interpret the nature of human knowledge. Now, there have been a huge number of studies and research on cognitive science, which suggest that it is actually a systematic and scientific study of brain, mind, and intelligence. The concept of brain, mind, and intelligence does not only limit to human beings but to all other living beings in the world. This emerging science as an interdisciplinary field covers philosophy, psychology, computer science, neuroscience, linguistics, etc. Modern digital computers; robots; fighter pilot application; decision-making in medical science; IoT devices; environmental monitoring and surveillance-related applications; and many more employ the cognitive science theory in their automatic and programmable operations. Artificial intelligence (AI) makes computers and robots behave as human being and function like human brain. Human brain involves perception, understanding, decision-making, reasoning, emotion, and language as important processes, and all these need to be comprehended properly so that modeling of human brain could be achieved more precisely. The science that has been ruling most of the modern world, the cognitive science, and therefore its intricacies, theory, and applications need to be highlighted and elaborated that could help numerous researchers, scientist, psychologist, philosophers, neuroscientists, and others working in the field of human brain and exploiting its cognitive ability.

This chapter presents theoretical background and history of cognitive science to understand its background, followed by its philosophical and psychological aspects. Representation of cognitive science, cognitive model of brain, knowledge representation, and information processing of human brain are discussed, although cognitive science includes modeling and imitation of brains of all living beings. The theory of consciousness, neuroscience, intelligence, decision-making, and mind and behavior analysis are described with case studies supported by strong background. The computing part is another important factor that helps in decision-making, and thus cognitive computing presents different ways of information manipulation, processing, and finally decision-making. Neuroscience aims at developing mathematical and computational models, structures, and processes of human brains and other animals. AI is considered as a core part of cognitive science and advances began in the field of AI since the 1950s. The role of machine learning, AI, cognitive knowledge base, deep learning, cognitive image processing, and suitable data analytics are useful for cognitive science.

Evaluation of various cognitive science tasks and computation results has to be done using some statistics. Probability theory is discussed to address the uncertainty and interpret the cognitive scenario, which includes probability distribution concepts, maximum likelihood estimator, and other similar distribution functions. Bayesian statistics is presented that play an important role in establishing a hypothesis from evidences.

The modeling of human brain and assessment of cognitive ability would greatly help the researchers, neuroscientists, and psychologists working in the field of understanding human brain and its functions. Mind is always considered as different from brain; brain as physical and mind as mental entities and therefore consciousness of human brain and mind control are emerging applications in the field of cognitive psychology and philosophical understanding of brain. We discuss here the current status of emerging research in the field of cognitive science as current as well as future trends. Cognitive language processing is discussed that paves the ways for developing numerous tools for helping physically challenged persons. We are now in the age of self-driving cars and autonomous driver-assistance system and therefore the insight, theory, and applications of cognitive science in these areas are also explained with suitable case studies. Cognitive systems are employed in many of the modern innovations and tools that operate on certain wireless network using smart sensors. So, security becomes an important issue in the field of cognitive science.

Background information of necessary imaging modality can be seen in an important book on Medical Image Processing [1] where MRI and other brain-imaging modalities are discussed in detail. One more book [2] discusses about biometrics and various types of multimodal and unimodal biometric techniques. We are discussing biometrics and medical imaging here because both medical imaging and biometrics are important aspects of study of human cognitive ability. Cognitive ability of human brain and its assessments has been attempted by a limited number of research contributions, few such [3,4] provided enough background and conceptualized the assessment of cognitive ability of human brain. The term “cognition” is closely associated with brain, and thus assessing its cognitive ability is an emerging area of research all across the globe. One such result (only a sample out of extensive results) of cognitive ability assessment can be seen in Table 1.1 where we can observe few wonderful conclusions on the ability of brain that actually varies from age to age, and also from men to women.

Table 1.1

Cognitive ability assessment.

Different age group	Those who correctly recognized (RC)		Those who replied as “cannot say anything”		Those who did not recognize (NR)		Time taken to recall as retention time (RNT) in seconds
	Male	Female	Male	Female	Male	Female	Male	Female
45–60	18	22	06	02	16	16	8.3	6.9
35–45	55	64	04	12	31	24	7.9	6.7
21–35	59	67	12	11	29	22	6.8	5.8
15–21	60	69	11	11	29	20	5.4	4.8
10–15	65	70	04	04	21	16	5.2	4.1

Table 1.1 highlights the comparison of cognitive ability for different age groups, beginning from 10 to 60 years involved in the study. It can be clearly seen that the ability to recognize correctly is always higher in female, which means that the number of women who can recognize the faces that were shown to them some time back is more than that of men. The decisiveness of whether they can recognize or not is also better in female candidates. Moreover, the time taken to recognize, which we call retention time, is also less for women. This small comparison provides a huge scope of research and more studied evaluating ability of human brain when it comes to cognitive ability. The ability was found better in kids as compared to young-age and old-age persons, and among young and old people, young candidates or participants are performing better while recognizing the faces.

1.1.1 Cognition, brain, and consc...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• List of contributors
• Editors’ biographies
• Authors biography
• Preface
• Acknowledgments
• Chapter 1. Introduction to cognitive science, informatics, and modeling
• Chapter 2. Machine consciousness: mind, machine, and society contributors
• Chapter 3. Brain–computer interface and neurocomputing
• Chapter 4. The impact on cognitive development of a self-contained exploratory and technology-rich course on the physics of light and sound
• Chapter 5. Identification of normal and abnormal brain hemorrhage on magnetic resonance images
• Chapter 6. Cognitive informatics, computer modeling and cognitive science assessment of knee osteoarthritis in radiographic images: a machine learning approach
• Chapter 7. Adaptive circadian rhythm a cognitive approach through dynamic light management
• Chapter 8. Cognitive and brain function analysis of sleeping stage electroencephalogram wave using parallelization
• Chapter 9. The future networks—a cognitive approach
• Chapter 10. Identification of face along with configuration beneath unobstructed ambiance via reflective deep cascaded neural networks
• Chapter 11. Setting up a neural machine translation system for English to Indian languages
• Chapter 12. An extreme learning-based adaptive control design for an autonomous underwater vehicle
• Chapter 13. Geometric total plaque area is an equally powerful phenotype compared with carotid intima–media thickness for stroke risk assessment: A deep learning approach
• Chapter 14. Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms
• Chapter 15. Identification of road signs using a novel convolutional neural network
• Chapter 16. Machine learning behind classification tasks in various engineering and science domains
• Index