592 Text Comprehension and Discourse Processing
there is first the use of LSA to select instructional texts that
are appropriate to a student’s level of background knowledge
(Wolfe et al., 1998). Second, LSA provides feedback about
their writing to 6th-grade students summarizing science or
social science texts (E. Kintsch et al., 2000). And last but not
least, LSA has been successfully employed for essay grading.
LSA grades the content of certain types of essays as well and
as reliably as human professionals (Landauer, Laham, &
Foltz, 2000). The humanlike performance of LSA in these
areas strongly suggests that the way meaning is represented
in LSA is closely related to the way humans operate.
Again, LSA does a very good job of representing seman-
tic meaning, but it does not represent all the components of
language that humans may use in comprehension. For one
thing, people use syntax in the construction of meaning,
whereas LSA does not. However, it might be possible to com-
bine LSA with other psychological process theories, thereby
expanding the scope of an LSA-based theory of meaning.
W. Kintsch (2001) has combined an LSA knowledge base
with a spreading activation model of comprehension, thereby
offering a solution to the problem of how word senses might
be generated in a discourse context—instead of being
prelisted, as in WordNet.
According to LSA, word meanings are vectors in a high-
dimensional semantic space. The meaning of a two-word
sentence in LSA is the centroid of the two-word vectors.
Thus, for The horse runsandThe color runs,we compute the
vectors {horse, runs} and {color, runs}. However, there is a
problem, for the meaning of runin the two contexts is some-
what different; two different senses of the verb runare in-
volved.
In the CI model of discourse comprehension (W. Kintsch,
1988, 1998), mental representations of a text are constructed
via a constraint satisfaction process, computationally realized
via a spreading-activation mechanism: The semantic rela-
tions among the concepts and propositions of a text are
strengthened if they fit into the overall context and deacti-
vated if they do not. This idea can be extended to the predi-
cation problem. Those aspects of the predicate (runin our
example) that are appropriate for its argument are strength-
ened and the others are de-emphasized. This is achieved by
means of a constraint satisfaction process in the manner of
the CI model, in which the argument is allowed to select
related relevant terms from the neighborhood of the predi-
cate, which are then used to modify the predicate vector
appropriately (W. Kintsch, 2001).
This turns out to be a powerful algorithm. It correctly
computes thatThe bridge collapsedis related to failure
and that The runner collapsedis related to race. It differ-
entiates appropriately between A pelican is a bird and
The bird is a pelican. It also correctly computes the meaning
of metaphors—for example, that My lawyer is a sharkis
more related to viciousnessthan to fish(W. Kintsch, 2000).
Furthermore, it computes that The student washed the table
is more related to The table is cleanthanThe student is
clean. And it mirrors many of the well-documented asymme-
tries and context effects in human similarity judgments
(W. Kintsch, 2001).
LSA by itself models the associative foundation of mean-
ing. Together with the spreading-activation mechanism of the
CI theory, it allows us to model a broad range of additional
phenomena, but we still fall short of a complete semantic the-
ory. We need to explore other psychological process theories
of human thought processes that can be combined with an
LSA knowledge base to further broaden the scope of an LSA-
based semantic theory. Research on LSA is still new, but one
can expect that it will have an increasingly large impact on
the way we think about comprehension and the way we do
research on language in the coming years.CONCLUSIONSOverall, cognitive psychology has made great strides in
understanding the factors that predict individual differences
in comprehension. We have learned about both factors
internal to the learner (such as background knowledge) and
external to the individual (such as text organization or con-
versational coherence) that determine comprehension. The
variables influencing comprehension performance interact in
quite complex ways; as discussed earlier, readers who are
knowledgeable about a subject learn better from a difficult
text, whereas readers with less prior knowledge about a topic
learn better from a more coherent, organized text. Thus, no
single factor can be shown to be sufficient to ensure adequate
comprehension by a learner, and no single prescription can be
recommended for all learners in all situations.
The practical applications of comprehension research are
obvious; with adequate understanding of the variables that
influence reading and listening comprehension, educators
can manipulate situations to maximize learning for an indi-
vidual in a set of particular circumstances. Even though cog-
nitive psychologists understand many of the variables that
influence learning, unfortunately we are far from developing
a complete model of comprehension. There currently is
no exact recipe for creating comprehension in a learner. We
know about some key ingredients of the comprehension
recipe and how they contribute to a successful performance,
but we do not fully understand the extent to which changes in
these factors exert a direct influence on comprehension and