In contrast to expectation-based, predictive views of discourse comprehension, a model is developed in which the initial processing is strictly bottom-up. Word meanings are activated, propositions are formed, and inferences and elaborations are produced without regard to the discourse context. However, a network of interrelated items is created in this manner, which can be integrated into a coherent structure through a spreading activation process. Data concerning the time course of word identification in a discourse context are examined. A simulation of arithmetic word-problem under-standing provides a plausible account for some well-known phenomena in this area. Discourse comprehension, from the viewpoint of a computa-tional theory, involves constructing a representation of a dis-course upon which various computations can be performed, the outcomes of which are commonly taken as evidence for com-prehension. Thus, after comprehending a text, one might rea-sonably expect to be able to answer questions about it, recall or summarize it, verify statements about it, paraphrase it, and SO on.

This article describes learning with media as a complementary process within which representations are constructed and procedures performed, sometimes by the learner and sometimes by the medium. It reviews research on learning with books, television, computers, and multimedia environments. These media are distinguished by cognitively relevant characteristics of their technologies, symbol systems, and processing capabilities. Studies are examined that illustrate how these characteristics, and instructional designs that employ them, interact with learner and task characteristics to influence the structure of mental representations and cognitive processes. Of specific interest is the effect of media characteristics on the structure, formation, and modification of mental models. Implications for research and practice are discussed Do media influence learning? The research reviewed in this article suggests that capabilities of a particular medium, in conjunction with methods that take advantage of

this paper. In particular, the protocols we have collected often involve picturing different situations (e.g., a mental map of South America, images of savannas, or an advertisement showing Juan Valdez on his coffee plantation in Colombia). These im- ages can be taken as evidence for the manipulation of mental models in Johnson-Laird's terms. But overlaying this manipulation of mental models are the systematic patterns in which they are deployed to support one's con- clusions (cf. Rips, 1986). So while mental models may be part of the story of plausible reasoning, there is another critical part which the theory we pro- pose addresses. The theory does not address the issue of whether people make systematic errors in their reasoning, as the psychological literature on decision making (Kahneman, Slovic, & Tversky, 1982) attempts to document. This issue does not arise in the theory because we are developing a formalism for representing the kinds of inferences people make and the parameters that affect their certainty, rather than a theory about how people make particular inferences. People may systematically ignore some kinds of information or undervalue particular certainty parameters--we have not attempted to determine whether they do or not. Instead we have tried to represent all the kinds of reasoning patterns and the kinds of certainty parameters that appear in the protocols we have analyzed (Collins, 1978a, 1978b). In this regard it is worth pointing out that certain fallacles in logic, such as affirming the consequent (Havi- land, 1974), become plausible inference patterns in the theory.' The theory was developed to account for protocols where. a question drives the search fo relevant information; in Artificial Intelligence this is called backward inferencing. One qu...

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Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied "off-task" activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that "working" is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems.

In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Many existing models address this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. Such an approach has led to a number of difficulties, including a reliance on overly rigid sequencing mechanisms, an inability to account for context sensitivity in behavior, and a failure to address learning. We consider here an alternative framework, according to which the representation of temporal context is facilitated by recurrent connections within a network mapping from environmental inputs to actions. Applying this approach to a specific, and in many ways prototypical, everyday task (coffee-making), we examine its ability to account for several central characteristics of normal and impaired human performance. The model we consider learns to deal flexibly with a complex set of sequencing constraints, encoding contextual information at multiple time-scales within a single, distributed internal representation. Mildly degrading this context representation leads

This review proposes that implicit learning processes are the cognitive substrate of social intuition. This hypothesis is supported by (a) the conceptual correspondence between implicit learning and social intuition (nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data. It is concluded that the caudate and putamen, in the basal ganglia, are central components of both intuition and implicit learning, supporting the proposed relationship. Parallel, but distinct, processes of judgment and action are demonstrated at each of the social, cognitive, and neural levels of analysis. Additionally, explicit attempts to learn a sequence can interfere with implicit learning. The possible relevance of the computations of the basal ganglia to emotional appraisal, automatic evaluation, script processing, and decision making are discussed. These "feelings " have an efficiency of operation which it is impossi-ble for thought to match. Even our most highly intellectualized operations depend upon them as a "fringe " by which to guide our inferential movements. They give us our sense of rightness and wrongness, of what to select and emphasize and follow up, and what

A surprisingly large number of research disciplines have contributed towards the development of knowledge on lazy problem solving, which is characterized by its storage of ground cases and its demand driven response to queries. Case-based reasoning (CBR) is an alternative, increasingly popular approach for designing expert systems that implements this approach. This paper lists pointers to some contributions in some related disciplines that offer insights for CBR research. We then outline a small number of Navy applications based on this approach that demonstrate its breadth of applicability. Finally, we list a few successful and failed attempts to apply CBR, and list some predictions on the future roles of CBR in applications.

We describe an oncology protocol management system, named ONCOCIN, that is designed to assist physicians in the treatment of cancer patients. The system is actually a set of programs, one of which is a rule-based reasoner that encompasses the necessary knowledge of cancer chemotherapy. Representation and control techniques are discussed, and ONCOCIN is contrasted with systems that could be built using EMYCIN. Of particular interest is the need to provide ONCOCIN with an interface that will make the system acceptable to oncologists. I

We present interpretation-based processing—a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation-based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals. The theory has been implemented in ACT-R [Anderson and Lebiere, The