In Things That Make Us Smart, Donald A. Norman explores the complex interaction between human thought and the technology it creates, arguing for the development of machines that fit our minds, rather than minds that must conform to the machine.Humans have always worked with objects to extend our cognitive powers, from counting on our fingers to designing massive supercomputers. But advanced technology does more than merely assist with thought and memory—the machines we create begin to shape how we think and, (...) at times, even what we value. Norman, in exploring this complex relationship between humans and machines, gives us the first steps towards demanding a person-centered redesign of the machines that surround our lives. (shrink)

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

Synthesizing situated cognition, reinforcement learning, and hybrid connectionist modeling, a generic cognitive architecture focused on situated involvement and interaction with the world is developed in this book. The architecture notably incorporates the distinction of implicit and explicit processes.