John K. Tsotsos

What is the human mind, and how does it work? These questions have occupied humanity since antiquity but have only recently received rigorous scientific investigation. Cognitive architectures are complex software programs whose goal is to approach human-like behavior on a wide variety of tasks. This is accomplished by employing human-like, or at least human-plausible, mechanisms within an integrated framework that is claimed representative of human cognitive, perceptual, and movement capabilities. By examining how close their behavior is to human, they help us understand how the human mind and brain work. They contribute to our understanding as computational models that can be tested and whose details in turn provide insights on new aspects of the human brain and mind. This field of cognitive architectures emerged at the intersection of artificial intelligence and cognitive science and in less than fifty years has spawned hundreds of projects. In The Computational Evolution of Cognitive Architectures, the authors trace the evolution of cognitive architectures, their abilities, and future prospects, from their early logic-based beginnings to their recent melding of classic methodologies with deep learning concepts. Analyzing over 3000 publications on more than eighty cognitive architectures and hundreds more surveys, research papers, and opinion pieces spanning philosophy, cognitive science, computer science, and robotics, the authors aggregate their findings into broad themes, such as common components of the architectures, their organization, interaction, and relation to human cognitive abilities. They discuss both theoretical elements of cognitive architectures and their performance before finally considering the future of cognitive architectures and their challenges.

The derivation, exposition, and justification of the Selective Tuning model of vision and attention. Although William James declared in 1890, "Everyone knows what attention is," today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation--rather than simply the language of mathematics--as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and color; additional color illustrations and movies are available on the book's Web site.