The situation may not be hopeless, however, because several types of simplified representations sufficient for certain needs are currently being used. First, there are exemplar-based systems (described later in the section on learning). These systems generally assume that knowledge consists of the storage of multiple copies (or strengthened copies) of repeated events. The more sophisticated of these systems place these copies in a high-dimensional metric space, in which smaller distances between exemplars represent increased similarity (e.g., Logan and Bundesen, 1997; Hintzman, 1986; Nosofsky and Palmeri, 1997; Landauer and Dumais, 1997). For some purposes, such as modeling improvements with practice and the production of errors based on similarity, this approach has proved fruitful. Second, neural net-based learning systems develop limited structural representations through the process of modifying connections between nodes. Although the structure thus developed is usually not transparent to the external observer, typically it is limited to connections between a few layers of nodes, and may not have any relation to actual cognitive structure. The developed representations capture much of the statistical structure of the inputs that are presented to the nets during learning and have often proved useful (Plaut et al., 1996). Other models that capture the statistical structure of the environment have also proved extremely promising (such as the modeling of word meanings developed by Landauer and Dumais, 1997). Third, in a number of application domains, experts have attempted to build into their representations those aspects they feel are necessary for adequate performance; examples include rule-based systems such as the Soar system (see the next section) and certain applications within the ACTR system (see Anderson, 1993). Although none of these approaches could be considered adequate in general, separately or in combination they provide a useful starting point for the representation of knowledge.

A second problem just now surfacing in the field is the nature of retrieval from generic memory: To what degree are generic retrieval processes similar to those used in explicit retrieval (i.e., retrieval from episodic memory)? Even more generally, many operations of cognition require a continuing process of retrieval from long-term (episodic and generic) memory and processing of that information in short-term memory that can last for periods longer than those usually thought to represent short-term retention. These processes are sometimes called ''long-term" or "extended" working memory. Some recent neurophysiological evidence may suggest that different neural structures are active during explicit and generic retrieval, but even if true this observation says little about the similarity of the retrieval rules used to access the stored information. The models thus far have largely assumed that retrieval processes involved in explicit and generic retrieval are similar (e.g., Humphreys et al., 1989; Anderson, 1993; Shiffrin and Steyvers, 1997). This assumption is not yet well tested, but probably provides a useful starting point for model development.

With regard to models of human behavior of use to the military, there is little question that appropriate models of explicit and generic retrieval and their inter-

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