networks (also called parallel distributed processing or PDP systems). Under the production system model, cognitive states are represented in terms of the activation of specific “production rules,” which are stated as condition-action pairs. Under the PDP model, cognitive states are represented as patterns of activation or inhibition in a network of neuronlike elements.
At a global level, these two models share some important common features and processes. Both rely on the association of contexts with actions or facts, and both treat long-term memory as the source of information that not only defines facts and procedures, but also indicates how to access them (see Klahr and MacWhinney, 1998, for a comparison of production and PDP systems). The production system model has the added advantage of being very useful for constructing “intelligent tutors” —computerized learning systems, described later in this chapter, that have promising applications to instruction and assessment in several domains.
Unlike working memory, long-term memory is, for all practical purposes, an effectively limitless store of information. It therefore makes sense to try to move the burden of problem solving from working to long-term memory. What matters most in learning situations is not the capacity of working memory—although that is a factor in speed of processing—but how well one can evoke the knowledge stored in long-term memory and use it to reason efficiently about information and problems in the present.
In addition to examining the information processing capacities of individuals, studies of human cognition have been broadened to include analysis of mind-brain relations. This topic has become of increasing interest to both scientists and the public, especially with the appearance of powerful new techniques for unobtrusively probing brain function such as positron-emission tomography (PET) scans and functional magnetic resonance imaging (fMRI). Research in cognitive neuroscience has been expanding rapidly and has led to the development and refinement of various brain-based theories of cognitive functioning. These theories deal with the relationships of brain structure and function to various aspects of the cognitive architecture and the processes of reasoning and learning. Brain-based research has convincingly demonstrated that experience can alter brain states, and it is highly likely that, conversely, brain states play an important role in the potential for learning (NRC, 1999).
Several discoveries in cognitive neuroscience are relevant to an understanding of learning, memory, and cognitive processing, and reinforce many of the conclusions about the nature of cognition and thinking derived from behavioral research. Some of the more important topics addressed by this research, such as hemispheric specialization and environmental effects on