586 Text Comprehension and Discourse Processing
and Rodriguez (2000) found that the concreteness of a text
was a strong predictor of interest in a text. Manipulating a
text to use concrete descriptions may enhance interest in a
text and promote recall. However, not all texts or concepts
can be expressed in a concrete way and doing so may com-
promise abstract or complex relationships in certain texts.
For these types of texts, it is difficult to envision modifica-
tions that would increase text interest without sacrificing the
rigor of the text.
Certainly the factors previously discussed and other fac-
tors a reader brings to the text (e.g., emotion) are important
to comprehension performance, and the influence of such
factors should be included in a complete model of compre-
hension. We are confident that cognitive psychology will
continue to explore these issues and will be able to describe
the ways in which the individual interacts with a text during
comprehension. The current and future challenge for research
in text comprehension will be to continue to uncover individ-
ual factors and text variables that influence and support learn-
ing from texts and to integrate such knowledge into the
already complex picture of what factors predict what and
how much an individual will learn from a text.
MODELS OF COMPREHENSION
Schema-Based Models
Early comprehension models heavily emphasized the role of
top-down processes. Comprehension was thought to involve
(a) schema activation through key words or phrases in the
text, followed by (b) filling the slots of the schema with rele-
vant information from the text (Anderson & Pichert, 1978;
Rumelhart & Ortony, 1977; Schank & Abelson, 1977). An
extreme version of such a theory was the artificial intelli-
gence (AI) program FRUMP (DeJong, 1979), which actually
attempted to understand news reports in this way. It was
never meant as a psychological theory, but it illustrates nicely
both the strengths and weaknesses of a schema-based ap-
proach. FRUMP was equipped with a large number of
schemas relevant to news reports (e.g., a schema for acci-
dents). A schema could be activated by appropriate key
words in the text (e.g., crash). Once activated, it serves as a
guide for searching the text for schema-relevant information:
What sort of vehicle crashed? How many people? Killed?
Wounded? Causes of the crash?
The comprehension problem was thereby greatly simpli-
fied. One did not have to fully understand a text, but merely
find certain well-specified items of information. As an AI
program, FRUMP turned out to be fatally limited. The main
difficulty was that the schema often did not fit the facts of a
text. Even for something as well-structured as an accident re-
port, one needs to look for different information in stories
about a car crash, a plane crash, or a skier crashing into a tree.
It is simply not possible to predefine adequate schemas for
all (or even most) texts. Schank (1982) realized this and
modified his approach accordingly by introducing memory
organization packets—building blocks from which to con-
struct a schema. It was clear that a simple schema-based ap-
proach would not work, neither in AI nor as a psychological
model.
Nevertheless, schemas play a major role in comprehen-
sion, and every psychological model of comprehension uses
schemas in one way or another (Whitney, Budd, Bramucci, &
Crane, 1995). However, schemas are no longer regarded as
the sole or even the most important control structure in com-
prehension. Instead, prior knowledge and expectations—
some in the form of schemas—are top-down influences that
interact with a variety of bottom-up processes to yield what
we call comprehension.A Psychological Process ModelComprehension has many facets and there are many ways to
model comprehension: Rhetoric and linguistics represent an
ancient and important tradition, whereas artificial intelli-
gence programs are a recent innovation. Psychological
process models take a different approach yet. They build on
the constraints provided by our knowledge of the perceptual
and cognitive processes involved in comprehension: word
perception and recognition, attention, short- and long-term
memory, retrieval processes, sentence comprehension, knowl-
edge representation and activation, and the like. Of course,
psychological process models cannot neglect the constraints
imposed by the text to be comprehended, and indeed, it may be
the case that textual constraints dominate the comprehension
process, relegating cognitive aspects to a minor role—which is
the premise of purely linguistic or AI approaches. However,
the recent research on psychological models of comprehen-
sion suggests otherwise.
The attempt to analyze comprehension in psychological
terms began with the model of W. Kintsch and van Dijk
(1978; van Dijk & Kintsch, 1983). The model is based on the
assumption that the limitations of working memory force
readers (or listeners) to decode one sentence at a time.
Decodingconsists of translating the sentence from natural
language to a general and universal mental language—a
propositional representation. In spite of its name, this propo-
sitional structure is not a full semantic representation of the
meaning of a sentence or a text; rather, it is designed merely