This article reviews research on the use of situation models in lnguage comprehension and memory retrieval over the past 15 years. Situation models are integrated mental representations of a described state of affairs. Significant progress has been made in the scientific understanding of how situation models are involved in language comprehension and memory retrieval. Much of this research focuses on establishing the existence of situation models, often by using tasks that assess one dimension of a situation model. However, the authors argue that the time has now come for researchers to begin to take the multidimensionality of situation models seriously. The authors offer a theoretical framework and some methodological observations that may help researchers to tackle this issue.

Two experiments, theoretically motivated by the construction-integration model of text comprehension ( W. Kintsch, 1988), investigated the role of text coherence in the comprehension of science texts. In Experiment 1, junior high school students' comprehension of one of three versions of a biology text was examined via free recall, written questions, and a key-word sorting task. This study demonstrates advantages for globally coherent text and for more explanatory text. In Experiment 2, interactions among local and global text coherence, readers' background knowledge, and levels of understanding were examined. Using the same methods as in Experiment 1, we examined students' comprehension of one of four versions of a text, orthogonally varying local and global coherence. We found that readers who know little about the domain of the text benefit from a coherent text, whereas high-knowledge readers benefit from a minimally coherent text. We argue that the poorly written text forces the knowledgeable readers to engage in compensatory processing to infer unstated relations in the text. These findings, however, depended on the level of understanding, text base or situational, being measured by the three comprehension tasks. Whereas the free-recall measure and text-based questions primarily tapped readers' superficial understanding of the text, the inference questions, problem-solving questions, and sorting task relied on a situational understanding of the text. This study provides evidence that the rewards to be gained from active processing are primarily at the level of the situation model rather than at the superficial level of text-base understanding.

We present a memory-based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory which consists of events, event schemata, episodes, causal heuristics, and causal hypotheses. The learning algorithms are based on applying causal heuristics to precedents of new information. The heuristics are derived from principles of causation, and, to a limited extent, from domain-related causal reasoning. Learning is defined as finding---and later augmenting---inter-episodal and intra-episodal causal connections. The learning algorithms enable inductive generalization of causal associations into AND/OR graphs. The methodology has been implemented and tested in the program NEXUS. Memory-based hypothesis Error! Unknown switch argument. INTRODUCTION In this paper, we examine the mechanisms by which causal relations expressed in natural language can be learned. Natural languages provide ample means to describe physical and mental events, marked relati...

A construction-integration analysis of the comprehension of causal text sequences was performed. According to the validation analysis, the comprehension of both consistent and inconsistent causal sequences requires that tentative bridging inferences be validated with reference to relevant knowledge. Consistent with this proposal, Singer (1993) reported that people needed less time to answer questions about the hypothesized validating facts alter consistent and inconsistent sequences than after control, temporal sequences. The construction-integration analysis of these effects focused particularly on a central configuration of causally related propositions. The simulations adopted the following assmnptions and parameters: A working memory buffer size of I was used. All link strengths had an absolute value of 1. The representations included text base and situation model elements, but no surface elements. The simulation was performed for six randomly selected texts from Singer's (1993) experiments. The results revealed a good qualitative fit between the construction-integration activation of the probe questions and their corresponding answer tines: In particular, high activation was associated with fast answer time. A point of general agreement in the study of text comprehension is that reading leads to the representation of the surface features and idea structure of a message, and of the situation to which the message refers (van Dijk & Kintsch, 1983). In the latter regard, there is growing evidence that the categories of ideas that may contribute to the situation model are essentially unlimited. The situation model nay capture the referential, temporal, and spatial relations underlying a text (Graesser & Zwaan, in press). It may also represent the state...

A knowledge digraph defines a set of semantic (or syntactic) associative relationships among propositions in a text (e.g., Graesser and Clark (1985) conceptual graph structures and the causal network analysis of Trabasso & van den Broek, 1985). This paper introduces the Knowledge Digraph Contribution (KDC) data analysis methodology for quantitatively measuring the degree to which a given knowledge digraph can account for the occurrence of specific sequences of propositions in recall, summarization, talkaloud, and question-answering protocol data. KDC data analysis provides statistical tests for selecting the knowledge digraph which "best-fits" a given data set. KDC data analysis also allows one to test hypotheses about the relative contributions of each member in a set of knowledge digraphs. The validity of specific knowledge digraph representational assumptions may be evaluated by comparing human protocol data with protocol data generated by sampling from the KDC distribution. Specifi...

O'Brien et al. (1988) reported that readers generated elaborative inferences only when a text contained characteristics (a strong biasing context or a demand sentence) that made it easy to predict the specific inference that a reader would draw and, virtually eliminated the possibility of the inference being disconfirmed. Garrod et al. (1990), however, offered two refinements to the conclusions. First, the two text characteristics manipulated may have produced different types of elaborative inferencing: a biasing context results in a passive form of elaborative inferencing, involving setting up a context of interpretation, whereas the presence of a demand sentence invites the reader to actively predict a subsequent expression. Secondly, clear evidence for either type of inference will be apparent only with truly anaphoric materials. This paper describes how a passive form of elaborative inferencing, reported by Garrod et al, may be implemented in a connectionist manner. We used the con...

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Acknowledgements: The authors are indebted to Elaine Mauldin, two anonymous reviewers, and participants at the 2007 AAA IS Section Midyear Conference for helpful comments on an earlier version of the simulation. Note to UWCISA participants: The teaching notes with a suggested solution and Access databases with queries are available from the first author. Auditing System Development: Constructing the Meaning of “Systematic and Rational ” in the Context of Legacy Code Migration for Vendor Incentives Abstract: This simulation affords an opportunity for learning to audit system development for an accounting application. The simulation responds to the growing emphasis on controlling system development for complying with the internal control assurance requirements of Section 404 of the Sarbanes-Oxley Act of 2002. Because of the lack of detailed accounting standards for vendor incentives, learners have to construct a working definition of “systematic and rational” allocation of incentives in order to develop audit objectives and procedures. In the simulation, learners (1) develop objectives for auditing the specific project of migration of legacy code for vendor incentives and the system development for a group of projects, (2) design audit procedures to achieve the audit objectives, (3) execute the audit procedures by querying the

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Bridging inferences contribute to text coherence by identifying the connections among ideas, whereas elaborative inferences simply specify sensible extrapolations from text. Prior studies have shown that bridging inferences are indistinguishable from explicit text ideas on numerous measures, suggesting similar long-term memory (LTM) representations for the two; whereas elaborative inferences are inferior. We evaluated the LTM representations of explicit and implicit text ideas using the extended process dissociation procedure (Buchner, Erdfelder, & Vaterrodt-Plunnecke, 1995). Three experiments used the three phases of the process dissociation experimental paradigm (Jacoby, 1991) to partition the controlled, recollective contributions to text retrieval from the automatic, familiarity-based contributions. The experiments showed that (a) explicit text ideas are more strongly supported by both controlled and automatic influences than are inferences, (b) support for the recognition of inferences is predominantly controlled, and (c) there may be a modest automatic contributions to the retrieval of bridging inferences but not elaborative inferences. These results diagnose informative differences between the LTM representation of explicit text ideas and text inferences. Retrieval of text inferences 3