Tenth Annual Meeting of the Society for Text and Discourse

Lyon, France
July 19 – 21, 2000

History Learning as Literacy: Cognitive Text Research and Instructional Applications. C. A. Perfetti

LSA: A Psychological Theory of Meaning and its Applications. W. Kintsch

Modeling Online Construction of a Multidimensional Situation Model in the Landscape Model of Comprehension. Y. Tzeng, P. van den Broek & R. Zwaan

Situation Model and Causal Contradictions: A Distance Effect for Narrative Characters' Properties. N. Campion & D. Martins

Updating of Spatial Changes in Situation Models. M. Rinck, K. Wolf & J. Hasebrook

The Influence of Focus on Updating a Mental Representation. H. van Oostendorp & C. van der Puil

Updating and the Reactivation of Situation-Model Information. R. A. Zwaan, C. J. Madden & R. A. Stanfield

The Effectiveness of Tutorial Dialog in an Automated Conversational Tutor. K. Link, V. Pomeroy, R. DiPaolo, S. Rajan, B. Klettke, L. Bautista, R. Kreuz, A. Graesser & The Tutoring Research Group

The Dialog Advancer Network: A Mechanism for Improving AutoTutor’s Conversational Skills. N. Person, A. C. Graesser, D. Harter & The Tutoring Research Group

Repetition in Discourse: A Linguistic Strategy Signifying Involvement in Children's Conversational Dialogues with a Literary Work. R. Horowitz & C. Cummings

Accessability, Duration, and Modeling the Listener in Dialogue.E. G. Bard & M. P. Aylett

Achieving Understanding in Multiparty Interactions. A. H. Anderson, J. Mullin, E. Katsavras, R. McEwan, E. Grattan & P. Brundell

Psychology, Discourse and Ideology. T. A. van Dijk

The Acquisition of Information Search Skills in 9 to 13 Year-Old Students. J-F. Rouet & C. Chollet

LSA in the Classroom: Automatic Feedback for Learning Summarizing Skills. E. Kintsch & D. Steinhart

The Use of Narrative in Argumentation. J. F. Voss & J. van Dyke

The Understanding Of Nominal Metaphor. C. Tijus, B. Pudelko, E. Hamilton, & D. Legros

The Costs and Benefits of Metaphor. I. A. Noveck & M. Bianco

Prosodic Correlates of Text Structure. H. J. N. den Ouden, L. G. M. Noordman & J. M. B. Terken

Sourcer's Apprentice: Facilitating Document-Supported History Instruction in the Classroom. J. van Dyke, C. A. Perfetti & M. A. Britt

Middle School Students’ Processing of Multiple Accounts of an Historical Event. M. Wolfe, S. Goldman, C. Mayfield, P. Meyerson & D. Bloome

Using Shared Physical Space to Ground Analogical Models. R. A. Engle

Characterizing Discourse Modes with Linguistic Tools. C. S. Smith

The Scientific Status of Rhetorical Structure Theory: Two Views. W. C. Mann & S. A. Thompson

Emotional Inferences‚ Course of Activation in the Landscape Model.S. Groen, A. Syssau, F. De la Haye & D. Brouillet

The Influence of Distance and Sufficiency on the Production and Maintain of Forward Inferences. M-P. Quintana, I. Tapiero & P. van den Broek

Causal Inferences in the Comprehension of Scientific Text: The Role of Causal Connectives. P. Maury & A. Teisserenc

Activation and Integration Processes Involved in Predictive Inferences Generation. N. Campion & J. P. Rossi

Inference Generation During Comprehending Written Directions : Example of a Posology. C. Kohler, C. Kékenbosch & J. C. Verstiggel

How Does Negation Affect Inferences During Reading? R. B. Lea & E. J. Mulligan

Predictive Inference and Text Characteristics : Can Different Types of Causality Lead to Different Degrees of Activation During Reading? A. Teisserenc & P. Maury

Genre of the Text and the Activation of Elaborative Inferences: A Cross-Cultural Study Based on Thinking-Aloud Tasks. J. A. Leon , I. Escudero & P. van den Broek

Individual Differences in Instrument Inference: Lexical Decision versus Picture Naming. S. Kim & H. J. Yoo

Hemisphere Differences in the Processing of High and Low Constraint Predictive Inferences. S. Virtue, T. Linderholm & P. van den Broek

Multidimensional Situation Model: Evidence for the Existence of Links Between Specific Situational Dimensions. N. Blanc & I. Tapiero

The Effects of Readers’ Prior Knowledge on the Monitoring of Spatial and Emotional Dimensions. S. Guéraud & I. Tapiero

Cognitive and Emotional Aspects of the Reader’s Response to a Story. M. C. Levorato & P. Boscolo

Updating a Mental Model: On-Line Accessibility of Backgrounded Information. J. D. Murray

Analysis of Descriptive and Narrative Texts Written by French and Dutch Children. L. Chanquoy & J. Schilperoord

Sources of Reading Comprehension Difficulties in Children: On-line Processing of Cohesion in Listening Comprehension. M-F. Ehrlich & H. Megherbi

Transitory Situation Model and Linear Procedural Model: Their Roles in Comprehension of Word Problems Statments at Primary School. D. Coquin-Viennot & S. Moreau

Metacognitive Knowledge in Text Comprehension: Some Issues in Development and Individual Differences. P-E. Eme & C. Haro

The Development of Analogical Processes in Learning to Read in French. I. Brun, J. Faure, J. Ecalle & A. Magnan

Development of Syntactic Connexion in French Children’s Narrative and Expositive Texts. A. Viguié

Phonological Recoding in Deaf Children. L. Paire-Ficout, S. Colin & A. Magnan

The Influence of the Phonological and Orthographic Characteristics of French During Reading Acquisition.J. B. Aimar & A. Magnan

Select-a-Kibitzer: A Multi-Agent Architecture for Giving Feedback on Student Compositions. P. Wiemer-Hastings & A. C. Graesser

ETAT: A Tool to Analyse Expository Text Coherence. E. Vidal-Abarca, H. Reyes, R. Gilabert, J. Calpe, E. Soria & A. C. Graesser

Insights into the Interactive Process of a Computerized Tutor:A Focus Group Study. R. DiPaolo & S. Rajan

Short Responses in Human and Intelligent Tutoring Systems S. Rajan, D. Harter & A. C. Graesser

Verb-based Classification of Abstract Concepts.K. Wiemer-Hastings & A. C. Graesser

AutoCoder: An Intelligent Assistant for Coding Protocol Data. J. Earwood, M. A. Durbin & R. M. Golden

Conceptual Change via Learning from a Text. A Connectionist Model. V. Sanjose, O. M. Padilla & E. Vidal-Abarca

The Effects of Information Presentation Style on Question Generation.S. D. Craig, B. Gholson, M. Ventura & The Tutoring Research Group

Advertising Discourse: A Critical Approach to Chinese Advertisements. V. T-H. Chang

Language-Related Causes of Communication Breakdown in Medical Practice. M. Ibba

More than a Stately Dance: Dialogue as a Reaction Time Experiment. E.G. Bard, M.P. Aylett & M. Bull

Literacy is More than Learning to Read: Information Retention from TV News. S. Gulgoz, F. Goksen & C. Kagitcibasi

Functions of Honorifics and Topic Manipulation in "Discourse Politeness" as Unmarked Politeness: From the Analyses of Japanese Conversations. M. Usami

The Chechnya War: Soviet Political Discourse Revisited. T. Tsyrendorjieva

Understanding the Ironic Language. C. Munch

Are you Trying to be Funny? The Importance of Exaggeration on Discourse Goal Clarity. C. O. Stewart & R. J. Kreuz

Interactions on the Web Between Reviewers and Authors: Towards Reviewing an Article for the e-Journal "JIME". A. Chevalier, N. Bonnardel & A. Piolat

The Impact of Verbal Information on the Aesthetic Experiences to Visual Art. K. Millis, K. Schaefer & B. Salmon

Conjoined Effects of Long-Term Working Memory Representation, Text Difficulty and Reading Purpose on Reading Strategies in Text Problem Comprehension.M. A. Schelstraete & I. Walschaerts

The Effect of Verbal Context on Picture Recognition: Initial Support for Perceptual Symbol Theory. R. A. Stanfield & R. A. Zwaan

Long-Term Working Memory: Some Empirical Results. C. Bellissens & G. Denhière

Perspective Effects on Fixation Times and Memory for Text. J. Kaakinen, J. Hyönä & J. M. Keenan

The Effects of Testing and Delay on Recall and Learning of Information from Texts. S. Gulgoz, M. E. Aktunc, D. Senol & H. A. Van

The Study of Locative Sentences from Corpora of Narratives. M. de Vega, M. J. Rodrigo, M. Ato, D. Dehn & B. Barquero

Processing Figurative Language in the Underspecification Model. S. Frisson & M. Pickering

Metaphor and the Space Structuring Model: Evidence from Event-Related Brain Potentials. S. Coulson

Figurative Language: Retention vs. Suppression. R. Giora

Convention, Form, and Figurative Language Processing. B. F. Bowdle & D. Gentner

Cognitive Evidence for a Parameterization of Cohesion and Coherence relations. M. Louwerse

Coherence and Evidence in Testimony Evaluation on Incest Narratives. B. Klettke & A. C. Graesser

Generating Inferences from Scientific Text. J. Wiley & J. L. Myers

Previous Knowledge as an Influential Factor in the Clinical Diagnosis Inferences Generation: An Expert and Novice study. J. A. Leon & O. Perez

Integration of Domain Knowledge from an Outline and a Target Text: Effects of Expertise, and Semantic Information. I. Tapiero & G. Molinari

Complex Goal Structures and Narrative Comprehension. M. Singer & E. Richards

Information Structure in the Processing of Sentences in Text. W. Vonk/, W. M. Mak & J. C. J. Hoeks

Gender Processing in French. S. Monnery, A. Seigneuric & D. Zagar

How to Succeed with Telephone Answering Machines: Leaving a Message from a Psycholinguistic Perspective. J. Grabowski

Genre Change in the HistoricalDevelopment of Sales Invitations. Y. Zhu 

Semantic Network(s) for Words and Pictures : Toward an Organization in Terms of Situation. M. Cornuejols & J-P. Rossi

Spatial Arrangement in Tables: The Influence on Reader’s Mental Representation. A. Pellegrin & M. Bétrancourt

What Determines Deep Comprehension for Illustrated Text? S. N. Whitten, S. Lu & A. C. Graesser

Cognitive Maps Constructed from Text and Virtual Navigation: Effects of Modality and Prior Knowledge in the Representation of Spatial Environments. A. Tan & C. R. Fletcher

Interpersonal Expectations in Comprehension of Visual Narratives. E. Gámez, H. Marrero & J. M. Díaz

Computerized Document Search by Young Learners: Effects of Conceptual Organization in Memory on Search Terms Production.J. Dinet, J-F. Rouet & J-M. Passerault

Referential Information and Composition Strategy: Differing Influences on Temporal Variables.C. Dansac

Unconscious Inhibitory and Conscious Facilitatory Effects in Semantic Priming. F. Herbelleau & S. Delord

Verbs and Mental Lexicon: A Case System in Mind. R. Bussone & J-P. Rossi

The Impact of Causal Markers on Expository Discourse Comprehension in L1 and L2. L. Degand & T. Sanders

Text Cohesiveness and Feeling of Comprehension. N. Lefèvre & G. Lories

Anaphoric Resolution and Discourse Focus : Referential Accessibility in question ... M. Fossard/, J-L. Nespoulous & D. Cardebat

Idiom Processing : Effect of Compositionality. S. Caillies , K. Butcher

Why Should We Use Connectives in Discourse? Overspecification of Referential Expressions in Instructive Discourse. A. Arts, A. Maes, L. G. M. Noordman & C. Jansen

Causal Factors that Influence Story Agents‚ Blame and Responsibility B.A. Olde & A. C. Graesser

Measuring Causal Force in Causal Chains. A. Guha & J-P. Rossi

Comparing the Contribution of the Visuo-Spatial Sketchpad and Phonological Loop in the Written Recall of a Story. T. Olive, V. Li Calzi & A. Piolat

Diagnosis of Cognitive Functioning During Text Reading and Comprehension by Adolescents. H. Thomas

Representation and Processing of Contrastive Information in discourse Comprehension. J. M. Lee, S. Choi, J. G. Lee, & K. H. Cho

Inter-Individual Differences in the Integration Processes of Text Comprehension: Do they Influence the Use of the Leading Edge Strategy and Comprehension? L. Demanet, M. A. Schelstraete & M. Hupet

Representation of Simple Arguments During Reading. A. Britt, J. F. Rouet & C. A. Perfetti K/p>

Semantic Macrostructure Effect in Accessing Relevant Prior Information during Text Reading. S. Montoya, G. Denhière & T. Baccino

Which Role the Position of Lexical Explanations Plays in Text Comprehension?. E. Jamet & O. Le Bohec

Textuality and the Rhyming Principle in Narrative Superstructure. H. Masuda

Building a Mental Model from Text and Pictures: The Role of the Visuo-Spatial Working Memory in the Memory of Pictures. V. Gyselinck & M. F Ehrlich

Story Comprehension in Patients with Damage to Prefrontal Cortex. T. Zalla/, M. Phipps, M. Colvin & J. Grafman

Functional Hemispheric Asymmetry in Voicing Feature Processing in Reading. N. Bedoin & H. Chavand

"My Recaller is on Vacation" Can memory books change discourse patterns in residents with dementia? K. Dijkstra, M. Bourgeois, G. Petrie, L. Burgio & R. Allen-Burge

The Effect of Context and Categorizability on Verbal Learning T. Guthke, E.C. Ferstl & A. Hauptmann

Non-aphasic Language Deficits After Brain Injury: A Comparison of Comprehension Skills of Patients with Different Discourse Production Profiles E. C. Ferstl, T. Guthke, F. Siebörger & D. Y. von Cramon

Discourse Coherence and Cohesion in Aphasia. T. Djurovic

"Aliens" and "Foreigners": Discourses of Exclusion : The Discourse Historical Approach. R. Wodak

History Learning as Literacy: Cognitive Text Research and Instructional Applications

C. A. Perfetti

University of Pittsburgh (U.S.A.)

perfetti+@pitt.edu

Amidst much published confusion about the nature of literacy, a simple distinction between basic literacy and extended literacy is useful. Basic literacy is fundamentally about the acquisition of knowledge of written words, their forms and meanings, and secondarily about the application of language abilities and general knowledge to the printed word. Extended literacy goes beyond these basics to engage students in literacy–based problem solving, writing and argumentation. In considering “the literacy problem”, it is important not to confuse the one with the other. Basic literacy is a prerequisite for extended literacy and different obstacles to competence arise at the two levels. Elsewhere (e.g. Perfetti, 1998), I have focused on the lexical foundations of basic literacy. Here the focus is extended literacy—demanding text environments that require reasoning and problem solving. What processes and skills are required by multiple-text problem-oriented reading? What kinds of learning environments can support the acquisition of these skills?

The study of history offers a distinctive, rich literacy environment. It centers on documents-letters, treaties, notes, official records, diaries--as well as textbooks. Thus, instruction that makes good use of this rich text environment has the potential to support broad-based literacy skills that may extend beyond history classrooms to other cases of text learning, reasoning, and writing. Indeed, in the United States, the National Standards for United States History include thinking skills that "enable students to evaluate evidence, develop comparative and causal analyses, interpret the historical record, and construct sound historical arguments and perspectives on which informed decisions in contemporary life can be based" (National Standards for United States History, 1996, p. 2). The problem is that history instruction in the United States may not do enough to promote these examples of extended literacy: “The typical history classroom is one in which they listen to the teacher explain the day's lesson, use the textbook, and take tests. Occasionally they watch a movie. Sometimes they memorize information or read stories about events and people. They seldom work with other students, use original documents, write term papers, or discuss the significance of what they are studying” (p. 194). This dramatic gap between the literacy potential of history study and the bleak picture of the actual practice of history study (at least in the U.S.) suggests an interesting problem for the application of cognitive science. We–my collaborators, J.F. Rouet, Peter Foltz, Julie Van Dyke, Gareth Gabrys, and especially M. Anne Britt and I–have focused on history learning as a case of extended literacy. In addition to the interesting theoretical issues for text learning that history affords, we hoped to address the gap between history’s potential and practice mentioned above. Our project includes theoretical, empirical, and instructional components, as summarized below: The Documents Model is a framework for describing how readers represent multiple documents that deal with a common problem space (Perfetti, Rouet & Britt, 1999). The framework includes a Documents Space where documents are represented and interlinked by Rhetorical Predicates to form the learner’s Intertext Model. They also are linked to a Situations Model, representing the learner’s understanding of one or more temporal-causal scenarios that are the “story” (or stories) revealed in the texts. Thus, the theoretical formulation represents both causal networks (Trabasso & van den Broek, 1985) and strict text representations, distinct from inferred situations (van Dijk & Kintsch, 1983). It allows a framework for student understanding. For example, many students achieve a well developed situation model with only weak links to one or more texts. A higher standard is seen in students who achieve an articulated intertext model as well as a situation model. Most sophisticated is the student who can entertain two or more situation models linked to specific texts. One assumption of the theory is that learning from multiple documents sharpens the distinction between text and situation for the reader. This implies that multiple documents can promote a more flexible representation of the situation, and, indeed students who write essays based on multiple texts transform the text information more than students with access to a single text (Wiley & Voss, in press).

Empirical component

Our studies have examined sustained learning over time with real texts, experimental manipulations of document characteristics, and other variables. They suggest, among other things, that both younger and college age readers readily acquire basic temporal-causal structures that comprise the narrative heart of many historical texts. And college age readers can construct flexible representations that reflect the integration of information across texts with contrasting perspectives (Perfetti, Britt, & Georgi, 1995). But students at high school and even college levels lack clear perceptions on the uses of documents and the evidential privileges they entail for arguments. We have observed a preference among regular-class American history students for textbooks, not only in use but in trustworthiness. We have observed differences among students, including discipline-background differences (Rouet, Favart, Britt, & Perfetti, 1998) in their appreciation of document privilege. Other studies have examined aspects of the Documents Theory, notably the trade-off between integration of information across documents and the separation of source information.

A Computer Document Tool

In light of our studies of students learning history in multiple text environments, we concluded that high school students could benefit from help in the use of multiple documents. We developed the Sourcer’s Apprentice (SA) to assist students in evidence seeking and evaluation skills and to foster an awareness of document type and document privilege (Britt, Perfetti, van Dyke & Gabrys, in press). The SA incorporates instructional design principles and reflects research on obstacles to high school students’ use of documents (Wineburg, 1991). For example, it engages the student directly in text-based problem solving, one of the goals of extended literacy. It displays a bookshelf with documents from which the student can select and take notes while addressing a problem. Students can use the tool as a tutor for direct instruction or as a problem-solving writing environment. Several studies on the effectiveness of the SA as a learning tool have produced positive results in usability and effectiveness. A recent study has suggested more causally coherent essays are written by high school students who have used the SA.

Conclusion

Have we learned something about how to promote a wider realization of the potential of history learning for extending literacy? In a project with many facets, there are many small conclusions: About the privilege of temporal-causal representations as mental models for students; about the obstacles and opportunities this preference for narrativity present to real document study; about the theoretical question of integrating content while separating sources; about the ability of a simple tool, the SA, to support student learning of document use; etc. At a more general level, we believe that history as extended literacy is attainable in a wide range of students who have achieved basic literacy. The special demands on problem-oriented multiple text learning, however, may require some direct and explicit instruction for many students. The Sourcer’s Apprentice is one example of how such instruction might occur.

LSA: A Psychological Theory of Meaning and its Applications

W. Kintsch

University of Colorado at Boulder (U.S.A.)

wkintsch@psych.colorado.edu

LSA is a mathematical technique that generates a high-dimensional semantic space from the analysis of a large corpus of written text. The technique was originally developed in the context of information retrieval (Deerwester, Dumais, Furnas, Landauer, & Harshman, 1990) and was adapted for psycholinguistic analyses by Landauer and his colleagues (Landauer & Dumais, 1997; Landauer, Foltz, & Laham, 1998; Landauer, 1999).

LSA must be trained with a large corpus of written text. The raw data LSA are meaningful passages and the set of words each contains. A matrix is constructed whose columns are words and whose rows are documents. The cells of the matrix are the frequencies with which each word occurred in each document. The data upon which the analyses reported below are based consist of a training corpus of about 11 million words (what a typical American school child would read from grade 3 through grade 14), yielding a co-occurrence matrix of more than 92,000 word types and more than 37,000 documents. Note that LSA considers only patterns of word usage; word order, syntax, or rhetorical structure are not taken into account.

Word usage patterns, however, are only the input to LSA which transforms these statistics into something new - a high-dimensional semantic space. LSA does this through dimension reduction. Much of the information in the original pattern of word usage is accidental and inessential. Why did an author choose a particular word in a specific place rather than some other alternative? Why was this particular document included in the corpus rather than some other one? LSA discards all of this excess information and focuses only upon the essential semantic information in the corpus. To tell what is essential and what is distracting information, LSA uses a standard mathematical technique called singular value decomposition, which allows it to select the most important dimensions underlying the original co-occurrence matrix, discarding the rest. The matrix is decomposed into components associated with its singular values, which are ordered according to their importance. The 300 most important components define the semantic space. The dimensionality of the space is chosen empirically: A (roughly) 300-dimensional space usually compares best with human performance.

LSA thus makes the strong psychological claim that word meanings can be represented as vectors in a semantic space of approximately 300 dimensions. But not only word meanings are represented as vectors in this space, documents are similarly represented as well. And new documents - sentences, paragraphs, essays, whole book chapters - can also be represented as vectors in this same space. This is what makes LSA so useful. It allows us to compare arbitrary word and sentence meanings, determine how related or unrelated they are, and what other words or sentences or documents are close to them in the semantic space. A word of caution is necessary here: LSA knows only what it has been taught. If words are used that did not appear in the training corpus, or which are used differently than in the training corpus, LSA, not unlike a person, does not recognize them correctly or at all.

The measure that is used to calculate semantic relatedness is the cosine between two vectors. As a first approximation, readers unfamiliar with this concept may think of cosines as analogous to correlation coefficients. The cosine varies from -1 to +1, +1 denoting identity and 0 denoting unrelatedness. Most cosines between words are positive, though small negative values are common (the average cosine for randomly chosen word pairs is .02, with a standard deviation of .06). The more closely two words are related semantically, the higher their cosine. For instance, the singular and plural forms of a sample of 100 common nouns had a mean cosine of .66, with a standard deviation of .15.

A second measure that is often useful is the length of a vector, which, like the cosine, is defined mathematically. Intuitively, the vector length tells us how much information LSA has about this vector. Thus, the length of sentence vectors is generally greater than the length of word vectors,

and the length of paragraph vectors is even greater. Words that LSA knows a lot about (because they appear frequently in the training corpus, in many different contexts) have greater vector lengths than words LSA does not know well. Thus, horse has a vector length of 2.49, while porch has a vector length of .59. Function words that are used frequently in many different contexts have low vector lengths (the and of have vector lengths of .03 and .06, respectively, and their cosine is .99 - LSA knows nothing about them and cannot tell them apart since they appear in all contexts).

All we can do, however, is compare one vector with another. Inspecting the 300 numbers that compose it tells us little, for the dimensions of the semantic space are not identifiable. The only way we can tell what a given vector means is to find out what other words or sentence vectors are close to it. Thus, we can ask LSA to list the words closest to a given vector in the semantic space. The semantic neighborhood of a word tells us a great deal about the word. Indeed, we shall make considerable use of semantic neighborhoods below.

Often we have some specific expectations about how a vector should be related to particular words or phrases. In such cases it is most informative to compute the cosine between the vector in question and the semantic landmark we have in mind. In most of the examples discussed below when we need to determine what a vector that has been computed really means, it will be compared to such landmarks. Suppose we compute the vectors for horse and porch. To test whether what has been computed is sensible or not, we might compare these vectors to landmarks for which we have clear-cut expectations. For instance, the word gallop should have higher cosine with horse than with porch (the cosines in fact are .75 and .10, respectively), but the word house should have a higher cosine with porch than with horse (the cosines are .08 for horse and .65 for porch). This is not a very powerful test, but it is intuitively compelling and simple. What the particular landmarks are is not terribly important, as long as we have clear shared semantic expectations. Someone else might have chosen race instead of gallop, or door instead of house, or many other similar word pairs, with qualitatively equivalent results.

Readers can make their own computations, or check the ones reported here, by using the web site of the Colorado LSA Research group: http://lsa.colorado.edu. First select the appropriate semantic space and dimensionality. The semantic space used here is the "General Reading through First Year of College" space with 300 dimensions and term-to-term comparisons. To find the semantic neighborhood of horse, one types "horse" into the Nearest-Neighbor-box and chooses "pseudodoc". To find the cosine between horse and gallop, one types "horse" and into one box and "gallop" into the other box of the One-to-Many-Comparison.

LSA has proved to be a powerful tool for the simulation of psycholinguistic phenomena as well as in a number of applications that depend on an effective representation of verbal meaning. Among the former are Landauer and Dumais (1997), who have discussed vocabulary acquisition as the construction of a semantic space, modeled by LSA; Laham's (1997) investigation of the emergence of natural categories from the LSA space; and Foltz, Kintsch and Landauer's (1998) work on textual coherence. To mention just three of the practical applications, there is first, the use of LSA to select instructional texts that are appropriate to a student's level of background knowledge (Wolfe, Schreiner, Rehder, Laham, Foltz, Landauer, & Kintsch, 1998). Second, LSA has been used to provide feedback about their writing to 6th-grade students summarizing science or social science texts (E. Kintsch, Steinhart, Stahl, Matthews, Lamb, & the LSA Research Group, in press). The application of LSA that has aroused the greatest interest is the use of LSA for essay grading. LSA grades the content of certain types of essays as well and as reliably as human professionals (Landauer, Laham, Rehder, & Schreiner, 1997). The human-like performance of LSA in these areas strongly suggests that the way meaning is represented in LSA is closely related to the way humans operate. The present paper describes an LSA-based computational model, which accounts for another aspect of language use, namely, how meaning can be modified contextually in predication. The model is discussed first and illustrated with some simple examples of predication. Then the model is used to simulate several more complex kinds of language processing.

Modeling Online Construction of a Multidimensional Situation Model in the Landscape Model of Comprehension

Y. Tzeng*, P. van den Broek** & R. Zwaan***

*National Chung Cheng University (Taiwan)

**University of Minnesota (U.S.A.)

***Florida State University (U.S.A.)

ttcytt@ccunix.ccu.edu.tw

Readers gain a true understanding of a text by building a situation model. The study of situation models thus is central to understand comprehension processes (see the seminal work by van Dijk & Kintsch, 1983). A new development in the study of situation models is the proposal of the Event-Indexing model (Zwaan, Langston, & Graesser, 1995; Zwaan, Magliano, & Graesser, 1995; Zwaan & Radvansky, 1998). This model hypothesizes that readers build situation models by monitoring at least five event dimensions: Protagonist, time, space, causation, and intentionality. By integrating information from these dimensions, readers gradually update their mental representations and build a richly interconnected coherent network or situation model.

Experimental data generally have supported the predictions of the event-indexing model (e.g., Zwaan, Langston, & Graesser, 1995; Zwaan, Magliano, & Graesser, 1995; Zwaan & Radvansky, 1998; Zwaan, Radvansky, Hilliard, & Curiel, 1998). However, converging evidence is necessary to test different aspects of this model. One important feature of a good scientific theory is the display of internal consistency. A rigorous way of validating a model's internal consistency is to examine whether it can be implemented in a computational architecture and still can reasonably account for human performance. Therefore, one purpose of the current study was to examine whether the event-indexing model is detailed and consistent enough to be implemented computationally. A second purpose of this study was to test whether a particular computational model of comprehension, the Landscape model, is able to model online processes of situation models.

The Landscape model is a connectionist model designed to capture the relations between online processes and offline representation of comprehension (Tzeng, 1999; van den Broek, Young, Tzeng, & Linderholm, 1999; van den Broek, Risden, Fletcher, & Thurlow, 1996). The model assumes that readers routinely make causal and referential inferences during the processes of reading and build a coherent representation through a delta learning rule. Furthermore, the Landscape model has a recurrent property, cohort activation, which allows the model to dynamically incorporates readers' activation state from the previous reading cycle into the current mental representation. A cohort is a group of reading elements that are related because they were co-activated at some time during reading. An important property of cohort is that if any part of a cohort is activated the rest of the cohort will be activated as well, to a less degree. The result of reading comprehension by the Landscape model is a representation that emerges from the landscape of fluctuating activations. The landscape itself reflects from various sources of activation. The model has been found to account for a high proportion of readers' recall and to do so across a wide range of texts (Tzeng, 1999; van den Broek, et. al., 1996). Moreover, the model shows potential improvements over the Construction-Integration model (Kintsch, 1988; see Tzeng, 1999).

Accounting for offline recall data only explains the products of reading. The question remains whether the model can predict online comprehension processes. Thus, modeling online reading time data predicted by a theory of situation model becomes a robust challenge for the Landscape model. The event-indexing model was implemented within the Landscape model architecture, with a focus on the idea of continuity between sentences. According to the event-indexing model, sentences vary in their degree of continuity, as determined by the number of dimensions in which consecutive sentences overlap. Specifically, sentence continuities vary along the five dimensions of situation models. The continuity of sentences among these five dimensions will affect readers’ processing speed and, hence, sentence reading times. The event-indexing model predicts that sentences with stronger continuity are easier to process than those with less continuity. As a result, a sentence sharing more dimensions with its previous sentence will have less reading time than another sentence sharing fewer dimensions with its previous one.

Sentence reading time data reported in the second experiment of Zwaan, Radvansky, Hilliard, and Curiel (1998) were adopted for modeling purpose. These reading times were adjusted for the number of syllables. The number of overlap along the five dimensions of situation models was determined for consecutive sentences and used as an indicator of continuity. A sentence can overlap with another by zero to five dimensions therefore the range of continuity was between 0 to 5. These continuity measures were transformed into an input matrix and submitted to the Landscape model. The amount of new activation was computed for each reading cycle by subtracting the total amount of activation of the earlier cycle from the total amount of activation of the current cycle. These activation differences, representing the continuity between sentences, were correlated with sentence reading times by human readers. The resulting correlation coefficients indicate to what extent the Landscape model can model these online reading times.

The observed correlation strengths between activation differences (sentence continuity) and reading times ranged from .46 to .75 with a mean of .57 (all ps < .05). This pattern of results suggests that the Landscape model aptly simulate the online processes predicted by a theory of situation models. It also indicates that the event-indexing model is well specified and it is logically consistent to be captured by a computational model. Thus, this study simultaneously has provided evidence for both the Landscape and the event-indexing theories.

Situation Model and Causal Contradictions:

A Distance Effect for Narrative Characters' Properties

N. Campion* & D. Martins**

*University of Paris XI (France)

**University of Paris X (France)

Campion@limsi.fr

We addressed how readers will detect and process a causal contradiction between the properties of a narrative character and the actions performed by that character. Albrecht and O'Brien (1993) already showed that the contradiction was detected when three sentences intervened between the description of the character's properties and the description of its actions. Thus, Albrecht and O'Brien concluded that readers construct and update a situation model that maps the causal properties of the described situation. Here we looked at what would happen if the contradictory actions were stated immediately after the character's description.

Method

Material. Eighteen experimental texts were written. After an introduction of two sentences, an elaboration part of the text was made of three sentences which expressed specific properties of a main character. These elaboration parts were followed by two sentences which expressed the critical actions performed by the main character. Three coherence conditions were created by varying the character's properties, although the critical actions remained the same. In the Coherence and Incoherence conditions, our knowledge allowed or not to establish a causal relation between the character's properties and the critical actions. In the Neutral condition, there were no causal relation between the properties and the critical actions. Distant and close conditions were also created by adding or not three filler sentences between the elaboration part and the critical actions. In the distant condition, the character's properties were considered as backgrounded by the reading of the three filler sentences and no more present in readers' working memory.

Procedure. Seventy-twelve students read the experimental texts which were randomly mixed with filler texts. The reading was made sentence by sentence on a computer screen and at a free speed. In the experimental design, a coherence factor (Coherent, Incoherent, Neutral) varied within subjects and a distance factor (Short, Long) varied between subjects.

Results

The reading times for the two critical action sentences are presented in Table 1. Globally, the reading times were significantly shorter for the first sentences than for the second sentences (at the critical threshold of p < .05 as for the following results). Separate analyses of variance (ANOVA) were performed for each of these sentences and showed exactly the same pattern. For the first or second sentences, there was a significant interaction between the factors Coherence and Distance. Planned comparisons confirmed that the factor Coherence had no significant effect in the Close condition, but had a significant effect in the Distant condition. In the Distant condition, the reading times were significantly shorter in the Coherent condition than in the Incoherent or Neutral conditions. Our results first confirmed that readers detect the causal discrepancy between the properties previously attributed to a narrative character and an action performed three sentences later by that character. This is attested by readers difficulties to process the contradictory actions and corroborate their use of a situation model to update characters properties. However, our results also showed that when the contradictory action was stated immediately after the character's description, readers had no more apparent difficulties to process the contradictory action. Thus it seems that a situation model first need to be backgrounded by the process of intervening material for a causal contradiction being perceived as such. A plausible explanation is that, when the situation is first introduced in a text and when its model is still activated in reader's working memory, the contradictory actions are interpreted as an interesting paradox (Schank, 1979) or as an initial outcome introduced by the author. Therefore the readers would notice the contradiction, but would adopt a "wait and see strategy", considering that further explanation will be stated in the following of the text. In the distant condition, however, the contradiction was perceived but was now considered as unacceptable. In such case, readers probably considered that a conventional rule of consistency would be violated by the introduction of the contradictory actions at that moment without previous explanation. Therefore readers try to solve themselves the contradiction. They may check their text base representation to find information that they might have neglected while building their situation model or more fully activate their situation model (O'Brien, Rizzella, Albrecht & Hallebran, 1998). They could also try to infer an elaboration that solves the contradiction. Finally, when a situation model has been backgrounded in a fixed state, because of filler information, that state would become a reference point for the reader and thus, without a previous explanation, the content of the model could be updated by coherent completion but not by radical change. On the contrary, the situation model could still be easily modified when it is initially activated in working memory and has not yet been backgrounded. It should however be noticed that this last conclusion is probably limited to characters properties that are described as a usual state. It has been showed to be invalid for unfocused spatial relation (O'Brien & Albrecht, 1992) and for stable knowledge about the world that are incongruent with a character goal ("Swim and sunbathe are practiced in Florida and not in Alaska", Huitema, Dopkins, Klin & Myers, 1993).

Table 1. Mean reading times (ms) for the two critical action sentences in the Coherence and Distance conditions

An example of text: Francoise was living close to many little shops. She preferred to do her shopping in those shops than in the supermarket.

Coherent: Francoise was the best client in a little butchery because she was found of red meat. She specially liked beef and here favorite steaks were the ribs, the sirloin and the roast. She always eat her meat very little cooked and was telling everybody to eat meat underdone.

Incoherent: Since many years Francoise had stopped to go to the butcher because she became a strict vegetarian. She would never eat any beef whatever the pieces. Only the idea of eating ribs, sirloins or roast beef was disgusting her very deeply.

Neutral: Francoise was probably the best client in a little bakery because she was found of their special bread. She was specially found of their bread made with three kinds of cereal: wheat, rye and bran. She always eat her bread as soon as possible when it was still warm.

Filler: Francoise was not living so far from downtown and needed to make a little bit of sport. Evry weekend, Francoise appreciated to walk down town when it was not too cold. It was taking exactly an hour to her to go from her door to the most central building of the town.

Critical 1: That night, Francoise joined a friend in a restaurant and ordered for herself an enormous beef steak.

Critical 2: She eat her meat with pleasure and found it particularly tasteful and juicy.

End: After the diner Francoise proposed a travel plan to her friend. She wanted to cross Africa by foot with her friend. The friend of Francoise couldn't imagine that Francoise was serious.

Updating of Spatial Changes in Situation Models

M. Rinck*, K. Wolf* & J. Hasebrook**

*Technical University of Dresden (Germany)

**Bankakademie Frankfurt (Germany)

rinck@Rcs1.urz.tu-dresden.de

Current theories of text comprehension assume that readers build situation models of the information described by texts. Situation models constitute the level of text representation associated with "deep" processing, and serve to integrate the information stated in a text with general information supplied by the readers' world knowledge. Many experiments investigated how readers update spatial aspects of their situation model during narrative comprehension, following a paradigm introduced by Morrow and his colleagues (e.g., Morrow, Greenspan, & Bower, 1989). In this paradigm, participants first memorize the spatial layout of a building including its rooms and a number of critical objects, and later read stories about characters moving through the building in pursuit of some goal. These experiments have shown that readers focus their attention on the protagonist of the story. A primary consequence of the readers' focus of attention is that known objects close within the model to the current focus become more activated in memory, so that readers can readily refer to them or retrieveinformation about them to answer questions. The term spatial distance effect was coined for this result: The closer an object is to the protagonist of the story, the more accessible it is in memory.

Despite many replications of the distance effect (e.g., Rinck & Bower, 1995; Rinck, Hdhnel, Bower, & Glowalla, 1997), the type of spatial updating investigated in these studies is actually very limited. Except for the location of the protagonist, all spatial aspects of the situation model remain constant. For instance, neither the rooms of the memorized building nor the objects in them change over the course of the experiment. Therefore, one goal of the two experiments described here was to find out if readers also update more drastic changes of the spatial scenario, namely reconstructions of the previously memorized building. These reconstructions involved the addition and deletion of walls separating rooms of the building. The second goal was to find out if these changes affect the spatial distance effect observed in previous studies. Presumably, the distance effect should be stronger if the protagonist and the probed object are located in different rooms, that is, if a separating wall exists between them (see Rinck et al., 1997). Third, the present experiments aimed at answering these questions with more naturalistic materials than had been previously used. Therefore, a naturalistic long text, similar to a soap opera involving a cast of main characters was read by the participants, and the layout learning procedure was part of reading the text.

Both experiments followed the same basic procedure while participants were reading the text sentence by sentence in a self-paced manner on the computer screen. In the first part of the text, the main characters and the spatial setting were introduced: A group of researchers who had to move into a building which had to be remodeled before it could be used as a research building. In this part, the original remodeling plan was described using both verbal and map information. In the second part of the narrative, participants read that two critical walls had been forgotten during remodeling. Again, this was described by both verbal statements and a map showing all existing and missing walls. After this, a set of episodes described how the protagonists worked and lived in the incomplete building. In the third part, the text described that the building had to be remodeled again to introduce the previously forgotten walls. However, the result of this second remodeling attempt was unsatisfactory again, yielding the four different experimental conditions of "wall status": One critical wall was correct in that it had been put in during the first remodeling and was still there after the second (always existent wall), one wall had been put in correctly first but was accidentally removed during the second remodeling (removed wall), one wall had been missing after the first remodeling and was added correctly during the second (added wall), and one wall was missing both after the first and the second remodeling (always missing wall). In the final part of the text, participants read another set of episodes describing events and actions of the protagonists in the still unsatisfactory building.

As part of these final episodes, three types of experimental sentences were used to explore readers' updating of the spatial changes involved in the two remodeling events. First, "watch sentences" described how the protagonist saw an event in the adjacent room, thereby following the inconsistency paradigm introduced by O'Brien and Albrecht (1992). These sentences made sense only when the wall between the protagonist's location room and the adjacent room was missing. In both experiments, reading times of these sentences were longer if they were inconsistent with the situation model, that is, if the wall existed. If the wall had been removed or had been missing all the time, reading times were significantly shorter, indicating that readers incorporated the existence of the walls into their situation model. Second, "motion sentences" stated that the protagonist walked from his current location room into the adjacent room. Earlier studies have shown that reading times of sentences such as these are correlated with the length of the protagonist's path in the situation model. Indeed, in both experiments described here, reading times were significantly shorter if there was no wall between the location room and the adjacent room, reflecting the fact that the protagonist could walk directly into the adjacent room rather than having to take a route via the hallway. Thus, reading times of these sentences indicate that readers used their knowledge about the existence of walls to represent the protagonist's movements. Third, "anaphoric target sentences" stated that the protagonist thought about or remembered an object located in the location room or in the adjacent room. Given the previously found spatial distance effect, reading times of these sentences should be shorter if they refer to the spatially close objects in the location room compared to the more distant objects in the adjacent room, reflecting the fact that the former are more accessible in memory than the latter. However, no such difference was found in Experiment 1. We reasoned that this might have occurred because - unlike previous experiments - the text did not refer repeatedly to the protagonist's location. Therefore, the text was slightly altered in Experiment 2 to mention the protagonist's location more often. This yielded a weak, but significant distance effect. Thus, it seems that the spatial distance effect might not be so very general after all, and that readers only pay close attention to the protagonist's spatial location if the text or the instructions make it particularly salient.

References

Morrow, D. G., Greenspan, S. C. & Bower, G H. (1987). Accessibility and situation models in narrative comprehension. Journal of Memory and Language, 26, 165-187.

O'Brien, E. J., & Albrecht, J. E. (1992). Comprehension strategies in the development of a mental model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 777-784.

Rinck, M. & Bower, G. H. (1995). Anaphora resolution and the focus of attention in situation models. Journal of Memory and Language, 34, 110-131.

Rinck, M., Hdhnel, A., Bower, G. & H., Glowalla, U. (1997). The metrics of spatial situation models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 622-637.

The Influence of Focus on Updating a Mental Representation

H. van Oostendorp & C. van der Puil

Utrecht University (The Netherlands)

H.vanOostendorp@FSS.UU.NL

An important process in text comprehension is the updating of mental representations during reading, that is, the process of transforming knowledge already represented in a readers' memory in the light of new, correcting information (van Oostendorp, 1996; Johnson & Seifert, 1999; van Oostendorp & Bonebakker, 1999). For instance, van Oostendorp (1996) had people read a news report about a military intervention in Somalia followed by a second related text. This second text contained transformations of facts mentioned in the first text. Performance on a later inference task showed that little updating occurred. Furthermore, it appeared that changes that were more important to the situation described were less updated. It was suggested that central parts of a situation model may be less easily updated than peripheral parts (cf. Chinn & Brewer, 1993). In this paper we studied whether the central part of a mental representation is less updated when this information is in focus. Focus was manipulated by letting readers compare one text with another along some dimension. The way we examined whether the representation has been updated is based on a method, recently introduced by Ferstl and Kintsch (1999), the Cued Association task. With this task subjects are presented with a word (cue) and they are asked to provide an association to it. The number of times a keyword is given as a response to another keyword is used as a measure of relatedness between these two words. Based on these data an asymmetric proximity matrix is calculated for each subject, which is formally equivalent to a network structure. The keywords represent the nodes and the strengths of links are determined by the response order. In these network structures it is possible to discern concepts that are highly related to each other, and concepts less strongly related. Useful notions for measures of centrality are here the in-degrees and out-degrees of concepts (the summation of all the links going into a node or out of a node, respectively; see Ferstl & Kintsch, 1999). The central part of the mental representation was here defined by the area containing concepts with the highest inner cohesion (that is, having the highest number of links between selected concepts). Subsequently, we examined the similarity of the central parts of the networks of the subjects in the focus group before and after reading the texts and compared that with the similarity in the non-focus group. In line with the preceding study (van Oostendorp, 1996) we hypothesized that the central part of the mental representation is less updated when that information is in focus compared to the updating of this information when it is not in focus. We predicted, thus, that the similarity of the central part of the network structures will be higher in the first case (when it is in focus) than in the latter case (when it is not in focus).

Method

Subjects (40 university students) received two related texts about the Spanish terrorist movement ETA. The second text contained a number of important changes or corrections of facts compared to the first ETA text. Each text could be a typical newspaper article (about 500 words each). Both texts contained two kinds of information. First, information about attacks and violence. Secondly, information about political standpoints and reactions. Focus was introduced by providing the focus group first a text about the Irish terrorist movement IRA, also containing political information. Subjects were requested to compare the political situation in Northern-Ireland and Spain. Subjects in the non-focus (control) group received a neutral text about Spain without any specific reading instruction. Before and after reading the second ETA text the Cued Association task was presented. The subjects read a list of 64 keywords twice and were instructed to write down next to each word the one, two or three words that came first to mind. The list contained 20 words that were important to the domain -based on a pilot study- but were not mentioned in the text, the other 44 keywords came from the text. The first answer to a cue (keyword) got a connection strength of 1, the second one mentioned 1/2, and the third 1/3. Answers that do not come from the list are ignored. We focused on the central parts of the representations, that is, the areas with the highest inner cohesion. The procedure to select these was based on (20) concepts with the highest in-degrees and out-degrees from and to other selected concepts respectively. To assess the amount of updating, that is the degree of change in the networks, we computed the similarity of the networks gathered before and after reading the second ETA text (see Ferstl & Kintsch, 1999). A high degree of similarity corresponds to a low degree of updating. In that case the network-before corresponds to a high degree with the network after reading the second ETA text. Also some textbase-oriented true/false recognition questions were presented at the end of the session.

Results

Because the size of the initial network structure of a reader -influenced by his/her background knowledge- could be a relevant variable to the extent of updating, next to the effect of focus on updating, or even interacting with it, we first determined the network size of each reader on the first cued association task. That is, the sum of the link strengths between concepts in the (first) matrix (see Ferstl & Kintsch, 1999). In other words, the more answers a reader produces consisting of keywords from the (first) list, the greater the network. Based on the mean score we distinguished subjects with an initial small and subjects with an initial large network size. A 2x2 analysis of variance (with focus and initial network size as factors) on the updating scores of the central parts of the networks only showed a significant interaction effect (F(1, 37) = 4.02, p < .05). For subjects with a small network there was less updating in the focus group compared to the non-focus group, while opposed to this, for subjects with a large network, focus showed a significant increase in updating compared to no focus. In a secondary analysis we examined what happened with the political and action concepts that were corrected in the text. We checked whether the embeddedness of political concepts (based on the in-degrees) in relation to the other, action concepts changed more in the focus group than in the non-focus group, since political concepts should be more in focus in the focus group. It appeared that for subjects with a small network the embeddedness of political concepts in other political concepts significantly decreased in the focus group compared to the non-focus group (F(1, 17) = 7.09, p < .05), while opposed to this, for subjects with a large network, focus leads to significantly less embedding of political concepts in action concepts compared to the non-focus group (F(1, 17) = 5.27, p < .05).There were no significant main or interaction effects of focus on the textbase-oriented questions.

Conclusions

Two conclusions can be drawn from these results. First, for subjects with small initial networks there is indeed some resistance to the updating of concepts relevant to the focus of reading. Subjects with initial small networks, reading with an unbiased reading goal, update to a higher degree and they embed the (correcting) political concepts to a greater extent. These results of a low degree of updating with a focus instruction correspond to what was mentioned in the introduction: Changes can be updated less easily with important information than with less important information, that is, information which is not in focus. Second, subjects with a initial large network, probably due to extensive background knowledge, update to a high degree when they read with focus. Furthermore, they show less embedding of political concepts in the action concepts. It seems like they construct two separate knowledge areas, one for political concepts and one for action concepts.

References

Chinn, C.A. & Brewer, W.F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1-49.

Ferstl, E.C. & Kintsch, W. (1999). Learning from text: Structural knowledge assessment in the study of discourse comprehension. In H. van Oostendorp & S.R. Goldman (Eds.), The Construction of Mental Representations During Reading. Mahwah, NJ: Erlbaum.

Johnson, H.M. & Seifert, C.M. (1999). Modifying mental representations: Comprehending corrections. In H. van Oostendorp & S.R. Goldman (Eds.), The Construction of Mental Representations During Reading. Mahwah, NJ: Erlbaum.

van Oostendorp, H. (1996). Updating situation models derived from newspaper articles. Medien psychologie. Zeitschrift fur Individual- and Massenkommunikation, 8, 21-33.

van Oostendorp, H. & Bonebakker, C. (1999). Difficulties in updating mental representations during reading news reports. In H. van Oostendorp & S.R. Goldman (Eds.), The Construction of Mental Representations During Reading. Mahwah, NJ: Erlbaum.

Updating and the Reactivation of Situation-Model Information

R. A. Zwaan, C. J. Madden & R. A. Stanfield

Florida State University (U.S.A.)

zwaan@darwin.psy.fsu.edu

O'Brien et al., (1998) proposed that comprehenders do not update their information according to the here-and-now of the situation, as is claimed by theories of situation-model construction (e.g., Zwaan & Radvansky, 1998). That is, if later information qualifies earlier information in a text, this qualification does not prevent reactivation of the qualified (earlier) information. To test this hypothesis, O'Brien et al., used a clever technique. Their experimental stories came in three versions. In all versions, the beginning of the story described a personality or physical trait of a protagonist (e.g., being a vegetarian or being a junk-food lover). In the Consistent version, a later sentence described an action that was consistent with the trait. For example, a junk-food lover orders a cheeseburger with fries or an athletic young man rescues a little boy. In the inconsistent version, however, the action contradicts what is known about the protagonist, for example, a vegetarian orders a cheeseburger with fries or an octogenarian runs across the street and carries a boy to safety. Finally, and most importantly, in the Qualification condition, the initial description of the protagonist's trait is qualified in a later clause. Thus, the vegetarian is said to occasionally like to eat junk food and the octogenarian is said to be still fit.

O'Brien et al., rightly ascribe the prediction to the updating view that updated information should no longer be re-activated, as it is not part of the current situation. On the other hand, the resonance view proposed by O'Brien et al., predicts that incoming information sends activation to all of long-term memory, such that even information that was supposedly updated can be reactivated. Both views predict that the Inconsistent condition should yield longer reading times than the consistent condition, given that inconsistencies are difficult to integrate with the evolving situation model. However, only the resonance view predicts that the previous information will get reactivated and might thus cause some comprehension problems. However, given that there is not really a contradiction in the Qualification condition, the increase in reading times, relative to the Consistent condition, should be smaller than that produced by the Inconsistent condition. The updating view, on the other hand would claim that the qualified information replaces the previous information in the situation model, so that the Qualified condition should yield a pattern identical to that of the Consistent condition. In five experiments, O'Brien et al., find patterns that appear to be consistent with the resonance view and not with the updating view. In this presentation, we have three goals. We will first argue that some characteristics of O'Brien et al.'s materials do not allow for a proper test of the updating hypothesis. We then report two experiments that support the updating hypothesis, using the same paradigm as O'Brien et al. used, but with different materials. Finally, we will propose an account of our results that integrates the resonance mechanism with our model of situation-model construction (Zwaan & Radvansky, 1998). There are three characteristics of O'Brien et al.'s materials that are likely to have contributed to longer reading times in the Qualified than in the Consistent condition. Therefore, the O'Brien et al., findings cannot be used to properly evaluate the updating hypothesis. The first characteristic has to do with the question of what updating is. We argue that the manipulation used by O'Brien et al., does not involve exhaustive updating, such that the qualified information is still relevant to interpret the current situation. The second characteristic involves a potential confound in the materials which might have been partially responsible for the difference in reading times between the Qualified and Consistent conditions. The third characteristic also introduces a confound in that there was no equal billing for the original information and the qualification. The former was elaborated in much greater detail than the latter, which may have rendered the qualification less effective. In the presentation, we will discuss these characteristics of O'Brien et al's stimulus materials in detail.

We tested the updating and the resonance hypotheses using texts that met the following criteria: (1) the updating and updated information are mutually exclusive, i.e., if one holds, the other does not, (2) the updating information is not inconsistent with world knowledge, (3) the materials are free of a priming bias favoring one of the hypotheses, and (4) the updating information receives equal billing with the updated information. Consider the text in the Appendix. The first sentence states that Bobby's hammer is available for use in the Enablement condition, but not in the Disablement condition. Consequently, the sentence "The hammer was heavy for his young arm." creates a contradiction in the Disablement condition, but not in the Enablement condition. In the Re-enablement condition, the first sentence was identical to that in the Disablement condition, but it was followed by a re-enablement sentence, which, in this case states that Bobby found his hammer. Thus, if readers update their situation models, there should be no contradiction when they read the sentences about Bobby using the hammer.

We conducted two experiments using these materials. In Experiment 1a, the Disablement and Re-enablement texts were identical except for the fact that a re-enablement sentence was inserted in the Re-enablement condition right after the disablement sentence. In Experiment 1b, we tried to control for surface distance by inserting a filler sentence after the first sentence in the Enablement and Disablement conditions. Table 1 shows the results. As can be seen, they clearly support the here-and-now hypothesis. The fact that the instrument had been previously disabled did not elevate the reading times of the Re-enablement condition above those of the Enablement condition, whereas the Disablement sentence did yield reliably longer RTs than the other two conditions. We will provide an integrative discussion of these and O'Brien et al.'s results.

Table 1. Reading times (ms) for the critical sentences in Experiments 1a and 1b (standard deviations in parentheses)

Sample story: Title A BIRD HOUSE

Bobby took out a saw, but then remembered that he lost his hammer (Disable) / Bobby took out a hammer, but then remembered that he lost his saw (Enable) / After some searching, he found it in his father's toolshed (Re-enable) / After some searching, he found his father's tape measure (Filler, Exp.1b) / He also collected the lumber and paint he had bought (S1) / He had already selected an oak tree as the site for the birdhouse (S2) / He marked the boards and cut them out (S3) / Then, Bobby began pounding the boards together (Action) / The hammer was heavy for his young arm (Target sent).

The Effectiveness of Tutorial Dialog in an Automated Conversational Tutor

K. Link, V. Pomeroy, R. DiPaolo, S. Rajan, B. Klettke, L. Bautista, R. Kreuz, A. Graesser & The Tutoring Research Group

The University of Memphis (U.S.A.)

klink@memphis.edu

Many studies have demonstrated the effectiveness of tutoring in student learning (Cohen, Kulik, & Kulik, 1982). There are many possible reasons for this benefit, but one of them is undoubtedly the conversational interaction between the tutor and student (Graesser, Person, & Magliano, 1995). Tutorial dialog may allow for the specific remediation of knowledge deficits on the part of the student. In addition, this format engages the student because it is inherently interactive, as opposed to merely reading a text or listening to a lecture.

We have created an intelligent tutoring system, referred to as AutoTutor, that responds to student’s natural language contributions in a manner that mimics the conversational strategies of a normal, unskilled human tutor (Graesser, Wiemer-Hastings, Wiemer-Hastings, Kreuz, & The Tutoring Research Group, 1999). AutoTutor’s contributions are delivered via synthesized speech through an animated talking head with appropriate intonation and facial expressions (Person, Klettke, Link, Kreuz, & the TRG, 1999). The system is designed to help college students learn topics from a course in computer literacy.

Several modules in AutoTutor are used to understand the students’ contributions. These include a word and punctuation segmenter, a part of speech tagger (Olde, Hoeffner, Chipman, Graesser, & the TRG, 1999), and a speech act classifier. In addition, latent semantic analysis (LSA; Landauer, Foltz, & Laham, 1998), a statistical technique used to represent world knowledge, is used by AutoTutor to determine the quality of the students’ contributions (Graesser, Wiemer-Hastings, Wiemer-Hastings, Harter, Person, & the TRG, in press). Included in AutoTutor is a curriculum script that contains primarily concepts, facts, and question-answer pairs related to topics in computer literacy. The dialog move generator component of AutoTutor is composed of 15 fuzzy production rules that determine what, from the curriculum script, AutoTutor will say next. Depending on various aspects of the student’s previous contribution and the preceding dialog in the tutoring session, AutoTutor will respond with one, or a combination of, the following: (1) pump, in which AutoTutor makes a request for the student to provide more information (e.g., "Anything else?"); (2) splice, in which AutoTutor inserts the correct answer to a question in response to a student’s incorrect answer; (3) prompt, in which AutoTutor elicits a specific piece of information from the student (e.g., "ROM is a kind of _____?"); (4) hint, in which AutoTutor presents a fact or leading question, or reformulates the original question; (5) elaboration, in which AutoTutor contributes an important, but overlooked, piece of information; (6) feedback, in which AutoTutor provides an immediate evaluation of the student’s last contribution that is either positive, negative, or neutral; or (7) summary, in which AutoTutor provides a brief synopsis of the answer or solution. For example, if the student’s contributions have been minimal and if little of the topic currently under discussion has been covered, AutoTutor will offer a prompt.

The pedagogical effectiveness of AutoTutor was evaluated in an experiment by having 48 participants interact with the system. The participants were enrolled in an undergraduate computer literacy course and participated for course credit or for pay. The content of AutoTutor’s curriculum script was developed from the textbook for this course. The topics on which participants were tutored in the experiment had been covered previously in their computer literacy course. Participants reread the chapter(s) on one topic and were tutored on a second topic. For a third topic, participants reread the chapter(s) and were also tutored. Participants were then tested on these three topics: Computer hardware, Operating systems, and the Internet.

Two versions of the test were created, each of which consisted of three components: 18 shallow knowledge multiple choice questions, 12 deep reasoning multiple choice questions, and 72 recall questions presented in 18 Cloze passages. The shallow questions were selected from the test bank included with the textbook used in the computer literacy course. The deep questions were designed by the experimenters to get at causal chains (antecedents, consequences), goals, and purposes of procedures (e.g., "How can you best find information on the World Wide Web?"). Finally, the Cloze passages were created by removing key words and phrases from ideal answers to each question covered by AutoTutor in each of the three topics. Each component of the test assessed all three topics covered in the experiment.

On average, the participants interacted with AutoTutor for 63 min (SD = 17), and took an average of 144 turns (SD = 11). The mean performance on the test was 44% (SD = 13), and did not differ according to version (45% vs. 43%). A repeated measures analysis of variance showed that there were significant differences between the conditions (F(2, 94) = 5.68, p < .01). The text only condition resulted in the lowest performance on the test (M = 39%, SD = 15%). In contrast, the test scores on topics for which participants received only tutoring were higher (M = 47%, SD = 17%). The combined effect of tutoring and reading, however, was no better than the tutor only condition (M = 46%, SD = 16%). Paired comparisons showed that performance in the text only condition was significantly lower than the tutor only and the tutor and text conditions.

These results suggest that AutoTutor was an effective pedagogical partner. Specifically, the conditions that included AutoTutor resulted in 8% higher test scores than in the text only condition. Since the content in AutoTutor is functionally the same as the information in the textbook, we can conclude that the interactive nature of tutoring is responsible for this difference. These results are encouraging and suggest that intelligent tutoring systems require careful attention to conversational interaction. Other measures, such as participants‚ computer literacy course grades and self-assessments of computer literacy, will also be analyzed to evaluate the performance of AutoTutor more thoroughly.

References

Cohen, P. A., Kulik, J. A., & Kulik, C. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal, 19, 237-248.

Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialog patterns in naturalistic one-on-one tutoring. Applied Cognitive Psychology, 9, 359-387.

Graesser, A. C., Wiemer-Hastings, P., Wiemer-Hastings, K., Harter, D., Person, N., & The Tutoring Research Group. (in press). Using latent semantic analysis to evaluate the contributions of students in AutoTutor. Interactive Learning Environments.

Graesser, A. C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R., and The Tutoring Research Group. (1999). AutoTutor: A simulation of a human tutor. Journal of Cognitive Systems Research, 1, 35-51.

Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25, 259-284.

Olde, B. A., Hoeffner, J., Chipman, P., Graesser, A. C., & the Tutoring Research Group (1999). A connectionist model for part of speech tagging. In Proceedings of the 12th International Florida Artificial Intelligence Research Society Conference (pp. 172-176). Menlo Park, CA: AAAI.

Person, N. K., Klettke, B., Link, K., Kreuz, R. J., & the Tutoring Research Group (1999). The integration of affective responses into AutoTutor. In Proceedings of the International Workshop on Affect in Interactions (pp. 167-178). Siena, Italy: Springer.

The Dialog Advancer Network: A Mechanism for Improving AutoTutor’s Conversational Skills

N. Person*, A. C. Graesser**, D. Harter** & The Tutoring Research Group**

*Rhodes College (U.S.A.)

**The University of Memphis (U.S.A.)

person@rhodes.edu

AutoTutor is an automated computer tutor that serves as a conversational partner with the student. AutoTutor is a working system that responds to Students’ natural language contributions by simulating the dialog moves of normal (not expert) human tutors. Descriptions of how AutoTutor works have been discussed rather extensively in previous publications, and will therefore, not be provided in this brief proposal (see Graesser, Franklin, Wiemer-Hastings, & the TRG, 1998; Graesser, Wiemer-Hastings, Wiemer-Hastings, Harter, Person, & the TRG, in press; Hu, Graesser, and the TRG, 1998; Landauer & Dumais, 1997; McCauley, Gholson, Hu, Graesser, & the TRG, 1998; Olde, Hoeffner, Chipman, Graesser, & the TRG, 1999; Person, Graesser, Kreuz, Pomeroy, & the TRG, 1999; Person, Klettke, Link, Kreuz, & the TRG, 1999; Wiemer-Hastings, Graesser, Harter, & the TRG, 1998; Wiemer-Hastings, Wiemer-Hastings, & Graesser, 1999). The creation of AutoTutor was inspired by numerous studies that have systematically analyzed the collaborative discourse that occurs between human tutors and students (Fox, 1993; Graesser & Person, 1994; Graesser, Person, & Magliano, 1995; Hume, Michael, Rovick, & Evens, 1996; McArthur, Stasz, & Zmuidzinas, 1990; Merrill, Reiser, Ranney, & Trafton, 1992; Moore, 1995; Person & Graesser, 1999; Person, Graesser, Magliano, & Kreuz, 1994; Person, Kreuz, Zwaan, & Graesser, 1995; Putnam, 1987). One reoccurring finding in several of these studies is that human tutors rarely adhere to “ ideal ” tutoring models that are often integrated into intelligent tutoring systems. Instead, human tutors tend to rely on pedagogically effective strategies that are embedded within the conversational turns of the tutorial dialog.

AutoTutor’s overall effectiveness as a tutor is contingent on him being an adequate conversational partner. Hence, many of our efforts have been directed toward implementing mechanisms that will enhance his conversational skills. One such mechanism is the Dialog Advancer Network (DAN). The DAN depicts AutoTutor’s entire dialog move option space for any given student contribution (e.g., Assertion, Yes/No Question, Short Response). The DAN makes AutoTutor a more effective conversational partner in that it enables AutoTutor to: (1) adapt each dialog move to the previous turn of the student, and (2) indicate when the student has the floor to contribute. Both of these DAN functions are elaborated below.

Adapt each dialog move to the previous turn of the student. Coherence emerges in human conversations because participants generally try to adapt the content of their turns so that they are relevant (or linked) to the preceding conversational turn (Clark & Schaefer, 1987; Grice, 1975; Hobbs, 1979; McLaughlin, 1984; Nofsinger, 1991; Sacks, Schegloff, & Jefferson, 1978). This “ turn-adaptation ” process is somewhat problematic for AutoTutor because the content of his dialog moves is predetermined. That is, AutoTutor doesn’t generate the content of his dialog moves on the fly but rather selects each dialog move from a scripted set of moves that is related to the tutoring topic being discussed. The DAN, however, enables AutoTutor to make quasi-adapted dialog moves that are relevant to the learner’s preceding turn. As the DAN illustrates, AutoTutor assigns learner contributions to different speech act categories (e.g., Assertion, WH-Question, and Short Response) and then generates discourse markers, dialog moves, and answers to learner questions, accordingly. Thus, AutoTutor is able to sustain mixed-initiative dialog by acknowledging student Questions (e.g., “ What is RAM? ”) and by responding to Short Responses (e.g., “ I’m lost ” or “ I don’t know ”).

Indicate when the student has the floor to contribute Turn taking is an integral feature of the conversational process. To facilitate the turn-taking process in human-to-human conversations, speakers signal to listeners that they are relinquishing the floor (i.e., it is the Listener’s turn to say something). However, human-to-computer conversations lack many of the subtle signals inherent to human conversations. When conversational agents like AutoTutor lack turn-taking signals, computer users (in our case, learners) often do not know when or if they are supposed to respond. For example, in previous versions of AutoTutor, learners were often confused after AutoTutor’s Hint, Elaboration, and Prompt Response dialog moves. That is, the tutorial conversation would abruptly stop, and students would often ask lab assistants, “ Am I supposed to say something now? ” or “ I’m not sure what to do ”. This is illustrated in the following tutoring excerpt from a session about the CPU. AutoTutor says (Prompt dialog move): “ See if you can get this, when the CPU executes programs, it uses RAM as . . . ? ” Student types: memory. AutoTutor says (Positive feedback + Complete Prompt Response): “ Good! As short-term memory storage. ” Student says to lab assistant: “ Am I supposed to type something in? ”

In earlier versions of AutoTutor, the DAN was instrumental in helping us identify potential dead spots in the conversation (e.g., after Hints, Elaborations, Prompt Responses). We addressed this problem by adding more informative discourse markers and by adjusting the content of some of AutoTutor’s dialog moves. As illustrated in the figure in the attachment, all pathways in AutoTutor’s dialog move option space ultimately lead to the learner’s next turn. In recent tutoring sessions with AutoTutor, learners appear to know when it is their turn to contribute. In the next version of AutoTutor, the DAN will include gestures and other paralinguistic signals (e.g., eye gaze) that facilitate the turn-taking process. In the presentation, we will provide additional excerpts from tutoring sessions with AutoTutor that illustrate how the DAN has enhanced AutoTutor’s overall conversational capacities. We will also present data from a number of evaluative cycles in which AutoTutor’s conversational skills were rated by knowledgeable judges. Some of the evaluative cycles occurred before the DAN was fully implemented.

References

Clark, H. H., & Schaefer, E. F. (1987). Collaborating on contributions to conversations. Language and Cognitive Processes, 2, 19-41.

Fox, B. (1993). The human tutorial dialog project. Hillsdale, NJ: Erlbaum.

Graesser, A. C., & Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31, 104-137.

Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialog patterns in naturalistic one-on-one tutoring. Applied Cognitive Psychology, 9, 359-387.

Graesser, A.C., Wiemer-Hastings, P., Wiemer-Hastings, K., Harter, D., Person, N., & the TRG (in press). Using latent semantic analysis to evaluate the contributions of students in AutoTutor. Interactive Learning Environments.

Hume, G. D., Michael, J. A., Rovick, A., & Evens, M. W. (1996). Hinting as a tactic in one-on-one tutoring. The Journal of the Learning Sciences, 5, 23-47.

Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review.

McArthur, D., Stasz, C., & Zmuidzinas, M. (1990). Tutoring techniques in algebra. Cognition and Instruction, 7, 197-244.

McLaughlin, M. L. (1984). Conversation: How talk is organized. Beverly Hills, CA: Sage.

Merrill, D. C., Reiser, B. J., Ranney, M., & Trafton, J. G. (1992). Effective tutoring techniques: A comparison of human tutors and intelligent tutoring systems. The Journal of the Learning Sciences, 2, 277-305.

Moore, J.D. (1995). Participating in explanatory dialogues. Cambridge, MA: MIT Press.

Person, N. K., & Graesser, A. C. (1999). Evolution of discourse in cross-age tutoring. In A.M. O’Donnell and A. King (Eds.), Cognitive perspectives on peer learning (pp. 69-86). Mahwah, NJ: Erlbaum.

Person, N. K., Graesser, A. C., Magliano, J. P., & Kreuz, R. J. (1994). Inferring what the student knows in one-to-one tutoring: The role of student questions and answers. Learning and Individual Differences, 6, 205-29.

Person, N. K., Kreuz, R. J., Zwaan, R., & Graesser, A. C. (1995). Pragmatics and pedagogy: Conversational rules and politeness strategies may inhibit effective tutoring. Cognition and Instruction, 13, 161-188.

Putnam, R. T. (1987). Structuring and adjusting content for students: A study of  live and simulated tutoring of addition. American Educational Research Journal, 24, 13-48.

Sacks, H., Schegloff, E. A., & Jefferson, G. (1978). A simplest systematics for the organization of turn taking for conversation. In J Schenkein (Ed.), Studies in the organization of conversational interaction. New York: Academic Press.

Wiemer-Hastings, P., Wiemer-Hastings, K., & Graesser, A. C. (1999). Improving an intelligent tutor's comprehension of students with Latent Semantic Analysis. Artificial Intelligence in Education (pp. 535-542). Amsterdam: IOS Press.

Repetition in Discourse: A Linguistic Strategy Signifying Involvement in Children's Conversational Dialogues with a Literary Work

R. Horowitz & C. Cummings

The University of Texas -- San Antonio (U.S.A.)

rhorowitz@utsa.edu

Often repetition in oral and written discourse has been viewed as ineffective communication. Teachers admonish students for repetitive and incomplete utterances, which are also viewed as a mark of ineffective thinking. Tannen (1989) introduced a number of features characteristic of high involvement in dinner talk among adults. Repetition was one such characteristic. Weber (1995) hypothesized that repetition in child conversations might signal involvement with text. We will demonstrate, in the research to be presented, how repetition may be an important device in the creation of sustained dialogue and ideas and a sign of cognitive and affective involvement with literary text.

Rationale: There is little understanding about how involvement emerges or how it can be sustained during conversations about literary works. Julie of the Wolves (George, 1972), a winner of the prestigious Newberry Medal, was selected for this research because of its attention to culture and assimilation. This novel was chosen for its potential appeal to individuals in transition--including Mexican-American students who are exploring their cultural identities. It is a novel that immigrant children would find challenging because of the attention it gives to a child's cultural conflicts -- choosing between one's past traditions and the culture(s) of their new world. It addresses issues related to human survival in a markedly different world -- that of the animal kingdom.

Questions posed in the present research were: (1). What are the linguistic features that characterize high involvement in classroom conversations about text? (2). How does repetition function as a linguistic strategy of high involvement in classroom conversations? (3). What forms of repetition do students use? (4). How does the use of repetition evolve and change across four conversations?

Method

Subjects. Mexican-American, 5th graders, 11 years of age, in an urban classroom in South Texas participated in the research. Students were selected on the basis of their school schedule and willingness to participate in the project. They had a mean score of 6.8 ranging from 4.8 to 8.1 on the reading portion of the Metropolitan Achievement Test.

Procedures. The data was collected over a 2-week period in a departmentalized language arts block that met for 2 and 1/2 hours each day for 4 days. Pre-reading sources and activities were designed to build prior knowledge for the text. The classroom teacher videotaped the 4 conversations during the regular class time. The videotapes were transcribed by two teachers, a researcher and a community liaison, with each turn numbered in sequence. Two teachers scored the repetitions. There was ninety percent agreement in the categorizing of repetitions.

Results

Data Analysis. Repetitions used by students were categorized as: 1) self-repetitions of exact word or phrases that occurred within the same turn, 2) self-repetitions that were repeated from what students had read (not from what students had said), 3) self-repetitions of words, phrases or ideas that occurred across turns.

Findings. The children who participated in the study used repetition to serve a number of purposes as they explained and negotiated the meaning of, Julie of the Wolves. To summarize, the fifth graders used repetition to verify, for emphasis, and to persist, in their beliefs and opinions about the text. Functions of Repetition (examples provided in presentation):

1. Verification Repetition is a function of self-repetition. The speaker recalls or uses the text to verify something they have said to capture the listener's interest and show mastery of the novel.

a.) Verification Text Language referred to the speaker's use of the author's language.

b.) Verification Text Search was used more frequently by speakers and refers to a speaker's reference to a particular part of the text, where the speaker stated a page number, location of an idea.

2. Emphasis Repetition occurs when speakers repeat their own words, phrases, or sentences within a single turn for purpose of emphasizing an idea or event. There are two types of emphasis repetition:

a.) Extension involves the adding of additional information-elaboration.

b.) Dramatization refers to the speaker's use of language explicitly for the purpose of involving and drawing the audience in a dramatic way. Involvement is building and reflected by dramatization in the 3rd conversation.

3. Persistence Repetition occurs when a speaker repeats a word, phrase, statement or idea over a series of turns. There are two types of persistence repetition:

a.) Turn Signal refers to a speaker's use of repetition to enter a conversation. It is a false start, which occurs during someone else's turn for the purpose of requesting the floor. This sets the audience up to expect something of importance to be said.

b.) Stance occurs when a speaker has strong beliefs, takes a stand about those beliefs and as the conversation moves along, persists at returning to their position.