Cognitive Psychology And Artificial Intelligence
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Cognitive Psychology and Artificial Intelligence
The nature of cognition is examined by the methods of experimental cognitive psychology and the theoretical models of computational psychology. First explained is the nature and objectives of artificial intelligence, symbolic and connectionist paradigms, the architecture of cognition, and characteristics of a general theory of intelligence. Wagman then examines theory and research in human reasoning and reasoning systems. Experimental research in deductive and inductive reasoning, the nature of artificial intelligence reasoning systems, nonmonotonic and common-sense reasoning, and general types of reasoning in artificial intelligence are examined. Next the author examines the nature of human problem solving and problem-solving systems. Problem representation methods and their duplication by artificial intelligence is discussed at length. Concepts and research in human learning and learning systems are also reviewed, as are the nature of human expertise and expert systems. Major characteristics of expertise including deep knowledge, reasoning strategies, and pattern recognition are described and exemplified in research concerned with medical expertise. The nature of intelligence and intelligence systems is examined, and the physical symbol system hypothesis and its results are analyzed. The author covers an artificial intelligence system that emulates the cognitive processes in scientific discovery and its implications for human creativity.
Readings in Cognitive Science
Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence brings together important studies that fall in the intersection between artificial intelligence and cognitive psychology. This book is composed of six chapters, and begins with the complex anatomy and physiology of the human brain. The next chapters deal with the components of cognitive science, such as the semantic memory, similarity and analogy, and learning. These chapters also consider the application of mental models, which represent the domain-specific knowledge needed to understand a dynamic system or natural physical phenomena. The remaining chapters discuss the concept of reasoning, problem solving, planning, vision, and imagery. This book is of value to psychologists, psychiatrists, neurologists, and researchers who are interested in cognition.
Artificial Psychology
This book explores the subject of artificial psychology and how the field must adapt human neuro-psychological testing techniques to provide adequate cognitive testing of advanced artificial intelligence systems. It shows how classical testing methods will reveal nothing about the cognitive nature of the systems and whether they are learning, reasoning, and evolving correctly; for these systems, the authors outline how testing techniques similar to/adapted from human psychological testing must be adopted, particularly in understanding how the system reacts to failure or relearning something it has learned incorrectly or inferred incorrectly. The authors provide insights into future architectures/capabilities that artificial cognitive systems will possess and how we can evaluate how well they are functioning. It discusses at length the notion of human/AI communication and collaboration and explores such topics as knowledge development, knowledge modeling and ambiguity management, artificial cognition and self-evolution of learning, artificial brain components and cognitive architecture, and artificial psychological modeling. Explores the concepts of Artificial Psychology and Artificial Neuroscience as applied to advanced artificially cognitive systems; Provides insight into the world of cognitive architectures and biologically-based computing designs which will mimic human brain functionality in artificial intelligent systems of the future; Provides description and design of artificial psychological modeling to provide insight into how advanced artificial intelligent systems are learning and evolving; Explores artificial reasoning and inference architectures and the types of modeling and testing that will be required to "trust" an autonomous artificial intelligent systems.