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					Discourse, Cognitive Perspective

      The field of discourse processing investigates the structures,

patterns, mental representations, and processes that underlie written

and spoken discourse.   It is a multidisciplinary field that includes

psychology, rhetoric, sociolinguistics, conversation analysis,

education, sociology, anthropology, computational linguistics, and

computer science.

      Researchers in discourse processing have identified a number of

mechanisms that promote learning.    The practical mission is to improve

the comprehension and production of discourse in textbooks, tutoring

sessions, classrooms, computer-based training, and other learning

environments. This entry focuses primarily on cognitive mechanisms, but

it should be acknowledged that cognitive, social, emotional, and

cultural foundations are tightly intertwined in contemporary theories

of discourse processing (Clark 1996; Graesser et al. 2002).

Levels of Discourse Processing

      Discourse researchers have identified five levels of cognitive

representation that are constructed during comprehension (Graesser et

al. 1997; Kintsch 1998).   These include the surface code, the textbase,

the situation model, pragmatic communication, and the discourse genre.

In order to illustrate these five levels, suppose that a high school

student had a broken door lock and was reading the following excerpt

from the book The Way Things Work (Macaulay 1988, p. 17).

      Inserting the key raises the pins and frees the cylinder.     When

      the key is turned, the cylinder rotates, making the cam draw back

      the bolt against the spring.

The surface code is a record of the exact wording and syntax of the

sentences.    This surface code is preserved in memory for only a few

seconds when technical text is read.      The textbase contains explicit

propositions in the text in a stripped-down form that captures the

semantic meaning, but loses details of the surface code.         For example,

the following propositions are in the textbase of the first part of the

second sentence:     PROP1:(someone turns key), PROP2:(cylinder rotates),

and PROP3: (PROP1 when PROP2).     The textbase is preserved in memory for

several minutes or longer.     The situation model (or what is sometimes

called the mental model) is the referential mental world of what the

text is about.     The situation model would contain causal chains of

events that unfold as the key unlocks the door, a visual spatial image

of the parts of the lock, and the goals of the person who uses the

lock.    The construction of an adequate situation model requires a

sufficient amount of relevant world knowledge, such as general

knowledge about locks and mechanical equipment. Deep comprehension

consists of the construction of this referential situation model,

whereas shallow comprehension is limited to the surface code and

textbase.    The situation model is retained in memory much longer than

the textbase and the surface code, assuming that the comprehender has

adequate world knowledge to build a situation model.

        The pragmatic communication level refers to the information

exchange between speech participants.      In a two-party oral

conversation, the two speech participants take turns speaking while

pursuing conversational goals.     There may be additional participants in

a conversation, such as side participants in the circle of conversation

and bystanders who are outside of the circle.      Speech acts are crafted

in a fashion that is sensitive to the common ground (shared knowledge)

between speech participants; linguistic cues differentiate given (old)

information in the dialog history from new information (Clark 1996).

The cognitive representation of a spoken utterance can be quite complex

when there are several communication channels between multiple

participants (sometimes called agents) in a conversation.    When printed

text is comprehended, the pragmatic communication is somewhat

simplified, although there are vestiges of oral communication and

multiple communication channels in text (Nystrand 1986).    For example,

there is communication between the reader and writer, between the

narrator and audience, and between agents in embedded dialogues within

the text content.   Text comprehension improves when readers are

sensitive to the communication channel between author and reader (Beck

et al. 1997).

      Discourse genre is the type of discourse, such as narration

(stories), exposition, persuasion, and so on.    Discourse analysts have

proposed several different discourse classification schemes that are

organized in a multi-level hierarchical taxonomy or in a

multidimensional space (Biber 1988).    Our example cylinder lock excerpt

would be classified in the expository text genre. Narrative text is

normally much easier to comprehend than expository text because

narrative has a closer affinity to everyday experiences.

      Deep comprehenders construct rich representations at the levels

of the situation model, pragmatic communication, and discourse genre,

whereas the textbase and surface code have a secondary status.

Paradoxically, the examinations that students normally receive tap the

surface code and textbase rather than the deeper levels.    Teachers ask

students to recall explicit content or to answer multiple choice

questions that tap word recognition, definitions, or attributes of

concepts.   One way of promoting deep comprehension is to compose exams

with questions that emphasize the situation model, inferences,

reasoning, and other aspects of the deeper levels.     Since the late

1980’s, researchers have advocated a shift in assessment standards to

encourage deep comprehension.

Discourse Coherence

        Coherence is achieved both within and between the levels of

representation when comprehension is successful.     That is, there should

be no serious coherence gaps within a particular level and there should

be harmony between the levels of representation.     A coherence gap

occurs within the situation model, for example, when an incoming clause

in the text cannot be linked to the previous content on any conceptual

dimension, such causality, temporality, spatiality, or motives of

characters (Gernsbacher 1997; Zwaan & Radvansky 1998).     Simply put, a

coherence gap occurs when the incoming event is mentioned out of the

blue.    Similarly, there may be coherence gaps at the levels of the

surface code, textbase, pragmatic communication, and discourse genre.

Regarding coherence between levels, the elements of the representation

at one level need to be systematically related to the elements at

another level.    Comprehension suffers, for example, when there is a

clash between the textbase and situation model.    If the text stated

“The key is turned after the cylinder rotates”, there would be a

discrepancy between the order of events in the correct situation model

(the key is turned before the cylinder rotates) and the explicit

textbase (clause X after clause Y).

        Comprehension breaks down when there are deficits in world

knowledge or processing skills at particular levels of representation.

When there is a deficit at one level of representation, the problems

can propagate to other levels.    For example, nonnative speakers of

English may have trouble processing the words and syntax of English, so

they also have trouble constructing the deeper levels of

representation.   Readers have trouble comprehending technical texts on

arcane topics because they lack world knowledge about the topic. A

barrier in constructing the situation model ends up confining the

processing to the surface code and textbase levels, so the material

will soon be forgotten.

      McNamara et al. (1996) documented an intriguing interaction among

(a) the readers’ knowledge about a topic, (b) the coherence of the

textbase, and (c) the level of representation that was being tapped in

a test.   The readers varied in the amount of prior knowledge they had

about the topic covered in the text (which was the functioning of the

heart).   Half of the readers read a text with a coherent textbase.

That is, clauses were linked by appropriate connectives (therefore, so,

and); the topic sentences, headings, and subheadings were inserted at

appropriate locations.    The other half of the texts had low coherence

because there were violations in the insertion of connectives, topic

sentences, headers and subheaders.    The tests tapped either the

textbase level of representation (which included recall tests) or the

situation level (which included tests of inferences and answers to

deep-reasoning questions).

      The results of the McNamara study were not particularly

surprising for the low knowledge readers.    For these readers, texts

with high coherence consistently produced higher performance scores

than texts with low coherence.    The results were more complex for the

readers with a high amount of prior knowledge about the heart.      A

coherent textbase slightly enhanced recall, but actually lowered

performance on tasks that tapped the situation model.    The gaps in text

coherence forced the high knowledge reader to draw inferences,

construct rich elaborations, and compensate by allocating more

processing effort to the situation model.       In essence, deep

comprehension was a positive compensatory result of coherence gaps at

the shallow levels of representation.

Comprehension Calibration

      One counterintuitive finding in comprehension research is that

most children and adult readers have a poor ability to calibrate the

success of their comprehension (Hacker et al. 1998).       Comprehension

calibration can be measured by asking readers to rate how well they

comprehend a text and then correlating such ratings with comprehension

scores on objective tests.   These correlations are either low or modest

(r = .2 to .4), which suggests that college students have disappointing

comprehension calibration. Another method of calibrating comprehension

is to plant contradictions in text and to observe whether they are

detected by readers. Such contradictions are not detected by a

surprising number of adult readers.       Instead, there is a strong

tendency for readers to have an illusion of comprehension by pitching

their expectations at handling the surface code and textbase.          They

need to be trained to adjust their meta-cognitive expectations and

strategies to focus on the deeper levels.

      Classroom discourse is too often skewed to the shallow rather

than the deep end of the comprehension continuum. Teachers typically

follow a curriculum script that covers definitions, facts, concepts,

attributes of concepts, and examples.       This content is at the lower

levels of Bloom’s taxonomy of cognitive objectives (Bloom 1956).          Less

frequently do they attempt Bloom’s higher levels of inference,

synthesis, integration, and the application of knowledge to practical


Discourse Mechanisms that Promote Deep Comprehension

       This section identifies some methods of improving deep

comprehension and learning by invoking discourse processing mechanisms.

       Constructing explanations. Good comprehenders generate

explanations as they read text or listen to lectures (Chi et al. 1994;

Trabasso & Magliano 1996).    The explanations trace the causes and

consequences of events, the plans and goals of agents (humans, animals

or organizations), and the logical derivations of assertions.      The

questions that drive explanations are why, how, what-if, and what-if-

not.   For example, a deep comprehender might implicitly ask the

following questions while reading the cylinder lock text: Why would the

person turn the key to the right?,    How does the bolt move back?, What

causes the cam to rotate?, and What if the pins don’t rise?     Students

learn much more when they construct these explanations on their own

(which are called self-explanations) than when they merely read or

listen to explanations.

       Asking questions.   Students should be encouraged to ask and

answer deep-reasoning questions during comprehension because they help

construct explanations.    Unfortunately, students are not in the habit

of asking many questions and most of their questions are shallow.        A

typical student asks only .11 question per hour in a classroom and less

than 10% of student questions involve deep reasoning (Graesser &

Person, 1994).   When students are trained how to ask good questions

while reading or listening to lectures, their comprehension scores

increase on objective tests (King, 1994; Rosenshine et al. 1996).

Teachers rarely ask deep-reasoning questions in classroom settings, so

it would be prudent to train teachers to model good questioning skills.

       Challenging the learner’s beliefs and knowledge. One of the

easiest ways to get students to ask questions is to challenge one of

their entrenched beliefs and thereby put them in cognitive

disequilibrium.    Suppose, for example, that a teacher expresses the

claim that overpopulation is not a significant problem to worry about.

This will normally stimulate a large number of student questions and

counter arguments.    Research on question asking has revealed that

genuine information-seeking questions are inspired by contradictions,

anomalies, incompatibilities, obstacles to goals, salient contrasts,

uncertainty, and obvious gaps in knowledge (Graesser & McMahen, 1993).

So the secret to eliciting student questions is to first create

cognitive disequilibrium and then to provide useful information when

students ask questions.

      Tutoring. One-to-one human tutoring is superior to normal

learning experiences in traditional classroom environments (Graesser

and Person, 1994).    This advantage cannot entirely be attributed to the

possibility that tutors are more accomplished pedagogical experts than

teachers.   Peers often do an excellent job serving as tutors.    Normal

tutors rarely implement sophisticated pedagogical strategies, such as

the Socratic method, building on prerequisites, error diagnosis and

repair, or modeling-scaffolding-fading.    It is the discourse patterns

in normal tutoring that explain much of the advantages of tutoring over

the classroom.    The discourse in tutoring emphasizes collaborative

problem solving, question asking and answering, and explanation

building in the context of specific problems, cases, and examples.

There is a turn-by-turn collaborative exchange in tutoring that would

be impractical to implement in the classroom.

      In closing, research in discourse processing can help solve some

of the pressing challenges in education.    Discourse plays an important

role in helping the learner shift from shallow to the deep

comprehension, and from being a fact collector to becoming an

inquisitive explainer.

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By Arthur Graesser and Natalie Person


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