Information Processing Theory
and Learning Disabilities:
A Commentary and Future Perspective
H. Lee Swanson
domain-specific processes related to
academic performance include recognizing word patterns, exercising decisions or rules for emitting math
responses, formulating heuristic hypotheses about how to spell an unknown word, and producing algorithms for problem solving in math,
reading, and spelling (e.g., Gerber &
Hall, 1987; Pellegrino & Goldman,
1987; Samuels, 1987). All of these mediating activities are largely under the
control of the learner.
COMMENTS AND
The present article reviews some assumptions related to the information processing
CRITIQUE
framework. The major implications ofprevious articles are reviewed, as well as some
conceptual ambiguities related to the role ofthe automaticity, prior knowledge, and ex-A number of important points reecutivefunctioning in explaining learning disabilities. The ecological validity ofthe inlated to the articles have been made in
formation processingframework,as well as the limitations of differential theories, the
is introduction of each special issue
discussed. Discussion is also given to: (a) identifying common denominations, (b) and
sub- will not be repeated here. Howgrouping, (c) isolating mental components underlying academic change, and (d) incorever, one uniformly positive point
porating neuropsychological data into future research practice.
across all articles is that attempts have
been made to understand learning disperusal of the literature suggests
As suggested by the articles in this abled children's functioning in terms
that differences between learning series, one emerging theoretical of mental processes that contribute to
disabled and nondisabled children on framework for describing learning performance. While traditional modcognitive tasks are virtually limitless, disabled children's performance on els of information processing have
and it has been possible to describe cognitive tasks has been information focused on speed of processing and
those differences in a variety of ways. processing theory. Within this frame- used simple tasks that control for
While this state of affairs may reflect work, learning disabled students, as isolated variables (e.g., Jensen, 1981),
the generalized deficits of learning well as their nondisabled counter- the present articles have been more indisabled children, it may also reflect parts, are perceived as learning terested in emphasizing aspects of task
an amorphous conceptual framework through various intervening stages of performance which include strategies
that underlies the discipline. As noted cognition such as encoding, organiz- and/or more complex forms of proby several commentators, a great deal ing, storing, retrieving, comparing, cessing, and have tended to discount
of research in learning disabilities is and generating (reconstructing) infor- speed of mental processing.
generated out of a "shotgun" approach mation. These stages also include
The articles also appropriately caprather than "a carefully planned body higher- and lower-order processes ture a broad continuum of instruction
of research guided by either practical (e.g., mechanisms that regulate and for LD students. At one end of the inor theoretical concerns" (Pressley, deploy relevant skills versus elemen- structional continuum, the teacher is
Heisel, McCormick & Nakamura, tary processing components). Greatly viewed as one who acts as a model and
1982, p. 139). Likewise, others suggest simplified, learning disabled chil- interrogator of the child's strategic
that LD research is plagued by enor- dren's task performance is seen as an thinking, as well as one who engineers
mous conceptual problems (e.g., interaction between executive rou- instructional activities that influence
Backman, Mamen, & Ferguson, 1984) tines, control processes, and their the child's strategic use of mental reand that it "has yielded little (if any) knowledge base. As suggested in the sources. As the learner's self-regulareliable information for psychology" various articles, the mechanisms in- tory control eventually becomes more
(Humphreys, Lynch, Revelle, & Hall, volved in this interaction may involve internalized, the teacher's level of par1983, p. 56). Thus, in considering the the organization and elaboration of ticipation diminishes (e.g., Baker &
myriad studies published in the area information (Pressley, Johnson, & Sy- Brown, 1984). It is assumed that cerof learning disabilities, some con- mons, in press), the activation of tain instructional activities influence
sideration must be given toward uni- hemispheric resources (Hiscock & the learner's executive control funcfying the diversity of isolated research Kinsbourne, this issue), conscious tions. For example, Palincsar and
in order to provide a theoretical awareness of cognitive processes (e.g., Brown (in press) and Pressley et al. (in
framework that helps us determine Palincsar & Brown, in press), and at- press) suggest the possibility of enwhich studies are important and tentional capacity constraints (e.g., hancing LD students' metacognitive
Samuels, 1987), to name a few. More knowledge about learning as a means
which ones are trivial.
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of further influencing executive control skills that monitor strategies
across various tasks. Metacognitive
knowledge (i.e., learners' awareness
and knowledge of their own learning
processes, as well as their abilities and
tendencies to control those processes
during learning) is viewed as providing mental input to the executive system, which in turn organizes and
mobilizes relevant information processing skills and subskills.
At the other end of the continum,
some articles place a focus on processing skills and subskills that must be
performed automatically. It is assumed that the ability to perform deliberate and effortful tasks, such as
reading, math, and spelling, requires
the automatic and rapid deployment
of relevant subskills. Within this context, instruction which includes computer-based drill and practice is
viewed as a possible medium for training subskills (e.g., Goldman & Pellegrino, this issue).
When combining both ends of the
continuum, instruction for the LD
child may be conceptualized as moving through a metacognitive training
phase in which the learning environment consciously directs, encourages,
or elicits learning strategies toward a
more automatic and less controlled
form of processing. Thus, while some
articles focus on raising the LD student's "metacognitive consciousness,"
others are directed toward reducing
the conscious level of activity to an
automatic state. This continuum from
highly effortful, conscious processing
to processing that occurs without
awareness, effort, or intention appropriately represents the continuum of
difficulties experienced by the LD student. In short, improvement in the
learning ability of LD children necessitates not only the deployment of
strategies, but also an executive mechanism that automatically accesses and
combines learning skills (i.e., information processing components) when
they are needed.
There is a need, however, to clarify
some of the cognitive operations discussed in the various articles, as well
as a need to make some comments
about the ecological validity of the in-
formation processing framework. To
begin with, some comments about the
role of automaticity, prior information
knowledge, and the executive function
in processing ability are in order.
AUTOMATICITY
We note from the literature that the
term automaticity has been used to
describe several sorts of mental operations. Sometimes the term represents
preattentive involuntary processing,
initiated by stimulus events (e.g, Neisser, 1967), and at other times it represents processes independent of capacity demands in the information
processing system (e.g., Hasher &
Zacks, 1979; LaBerge & Samuels,
1974). Shiffrin and Schneider (1977),
for example, characterize automaticity as involving rapid, involuntary,
parallel processing, as opposed to
slow, sequential, and
subjectcontrolled processing.
The majority of articles in this series
assume that cognitive processes place
space and/or time demands upon a
limited-central capacity system (see
Logan, 1978). Processing that becomes
more and more independent of this
capacity system, because of practice
and experience, is assumed to be automatic. For example, learning to read is
initially a laborious activity and proceeds on a letter-by-letter basis, requiring much attention on the part of the
child. After considerable practice and
experience, reading becomes faster,
letters are processed in parallel, and
words are activated with little attentional load (LaBerge & Samuels,
1974). This conceptualization, however, is in contrast to other accounts
which view automaticity as an unlearned process (e.g., Kahneman,
1973; Neisser, 1967). Further, some
authors (e.g., Cheng, 1985) have suggested that automatic processing may
not represent an activity that becomes
capacity free, but rather reflect an efficient restructuring, reordering, and/or
reorganizing of tasks. My point is that
only when a theoretical model can
demonstrate the various learnable and
nonlearnable characteristics of automaticity will one have an explanatory
sense of LD children's academic prob-
lems. Thus, articles (e.g., Gerber &
Hall, 1987; Kolligian & Sternberg,
1987; Samuels, 1987) describing the
various cognitive activities of learning
disabled children as reflecting poor
automaticity are further enhanced if
their description is embodied in a
theoretical framework that accounts
for many different sorts of data (see
Sternberg & Powell, 1982). From an instructional perspective, of course, the
issue may not be whether automatic
processing is learnable or capacityfree in learning disabled students, but
whether such children can efficiently
and quickly monitor and/or smoothly
coordinate mental processes on a particular task, such as reading.
KNOWLEDGE BASE
A second concern is that some articles place little emphasis on the interaction between previously stored
information and strategy utilization
(however, see Pressley et al., in press).
The majority of the articles in this
series have tended to focus on subjectcontrolled processing and ignore the
"information" part of information
processing theory. Some recent studies
(e.g., Baker, Ceci, & Herrmann, in
press; Swanson, 1986) have indicated
that the differences between LD and
nondisabled children reflect not only
strategic or control processing, but
also differences in the basic information stored in long-term memory. This
finding has important implications
for information processing models
(e.g., see Chi, 1978; Chi & Koeske,
1983; Keil, 1982). For example, Chi
(1978) demonstrated that very young
children proficient in chess or dinosaur classification can show adult-like
strategies, whereas adult novices show
child-like problem solving behavior.
The implication here is that dramatic
differences observed in the performance of nondisabled and LD students may often be consequences of
differences in underlying knowledge
bases (e.g., cognitive structures prior to
learning) rather than of basic differences in control processes. A child's
knowledge base places formal restrictions on the class of logically possible
strategies that can be used within a
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given academic domain (e.g., Chi,
1978). Thus, one may assume that LD
students bring to a learning task a set
structure that limits the class of strategies they are likely to use.
tualized as having, at best, an indirect
effect on executive functioning.
dards, there is still a reluctance to consider new "processing" paradigms.
From a practical standpoint, teachers
ECOLOGICAL VALIDITY
are not likely to become interested in
theories of the learning process when
Criticisms related to validating the LD children are going to be placed in
information processing model to the standardized programs anyway. That
EXECUTIVE FUNCTION
field of learning disabilities are also in is, when LD children's functioning is
A final concern is the issue over order. Two comments can be made conceptualized in terms of behavioral
whether the executive control function related to the articles. First, only objectives and/or performance outcan be trained (on this issue, see also minimal information was given on the comes, a specification of such chilGreeno & Simon, 1984; Sternberg, explicit basis for selecting a task in dren's "processing abilities" may be
1983). The research reviewed in this order to understand learning dis- seen as impractical and unnecessary.
series (e.g., Palincsar & Brown, in abilities. No doubt, the tasks selected Further, there are time constraints, as
press; Pressley et al., in press) suggests should clearly identify and isolate well as administrative pressures, that
that an instructional focus be placed mental components that correlate prevent practitioners from engaging in
upon enhancing LD students' meta- highly with performance parameters the luxury of clearly conceptualizing
cognitive knowledge about learning as in the real world. This is no trivial task the processing difficulties of children
a means for developing executive con- when one considers that information suspected of having learning distrol skills involved in the maintenance processing research to date has not abilities (e.g., Doris, 1986).
and transfer of learning "actics. A given much attention to the context in
theme that has emerged from some of which learning occurs. Thus, the ques- EMERGENCE OF
these articles is that executive skills tion of which repertoire of informa- INFORMATION
may not be trained easily, but must be tion processing tasks can better pre- PROCESSING
gradually developed and automated dict later performance over more
over time (see Pressley, Borkowski, & traditional psychometric measures
The idea that learning disabled stuO'Sullivan, 1984). Some authors (e.g., will remain unsettled (see Jensen, dents are deficient in information proGagne, 1980a, 1980b), however, argue 1981) until clear specification is given cessing is certainly not a new one. At
that executive functions cannot be to the cognitive processes required for the inception of information processdirectly taught. For example, while LD real-life activities. Further, even if ing model development, several austudents can be induced to rehearse, these processes are identified, it is un- thors (e.g., Senf, 1972) were acknowltake notes, outline a text, and so on, as certain whether these processes can be edging that learning disabled students
well as respond to explicit instructions easily reduced to stable information had restricted access to information
concerning the significance of strat- processing components. An obvious processing activities. Perhaps the reaegies and the range of their utility, ex- example of such instability is noted son why information processing has
ecutive function capability (e.g., the when parents and teachers report that not been viewed as a dramatic feature
ability to weigh and choose from sev- learning disabled children cannot at- of the learning disabled field up until
eral strategies) is much more difficult tend to instruction in the classroom, now is that the deficits being identified
to impart. Further, because the execu- but can watch television over an ex- were tolerable. Children are not likely
tive control function involves the com- tended period of time and remember to be placed in an LD classroom
bining of relevant prior knowledge, detailed information.
because they suffer strategy and metaand because these knowledge strucSecond, little discussion was given cognitive difficulties or because they
tures evolve slowly through years of to the resistance of practitioners, as suffer from problems in executive proschooling, such a mechanism is not well as countertheorists, to the infor- cessing (i.e., self-regulation of cogni(or is less) susceptible to direct train- mation processing framework (see tion). It is my opinion that one of the
ing. In addition, it may also be argued Farnham-Diggory, 1986, for further reasons for the recent upsurge of inthat a positive influence of the execu- discussion on this point). This resis- terest in information processing thetive function or routine occurs when tance can occur at many levels and ory is that, for three decades now,
the child can monitor or self-regulate from many perspectives. For example, research on LD children has not prolower-order mechanisms (e.g., phono- from a historical perspective one vided significant theoretical or
logical coding) that are stable (e.g., Bel- could argue that remedial programs practical directions toward a science
mont, Butterfield, & Ferretti, 1982; based upon earlier models of informa- of learning disabilities (see Kavale &
Brown & Campione, 1981). Unfortun- tion processing were largely unsuc- Forness, 1985b). Few, if any, substanately, instability of such mechanisms cessful. Although many models of tive scientific outcomes have been acis most likely a hallmark of being remediation were conceptualized in complished in terms of operationalizlearning disabled (see Swanson, the 40s and 50s and the theoretical ing, preventing, or ameliorating
1984a; Swanson, 1985). Thus, meta- models for such programs would be learning disabilities. To the researcher
cognitive training may be concep- considered incorrect by today's stan- interested in cognition, the term
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"learning disabilities" is still fluctuating and elusive. It is my opinion that
part of the reason for this elusiveness
has been due to the field's over-reliance on differential or psychometric
theories.
LIMITATIONS OF
DIFFERENTIAL THEORY
Differential theories are based upon
the assumption that latent sources of
individual differences are related to
mathematically derived factors. While
interpretations of factor analyses are
equivocal, the goal of such procedures
is to isolate global constellations of
LD children's functioning. In a practical sense, this approach assumes that
standardized tests assess individual
differences along some continuum,
and that test items reflect a particular
construct; otherwise the test will be of
little interest or value as a descriptive
and/or predictive instrument. Although a psychometric approach does
predict academic success fairly well
(e.g., Jensen, 1981), such an approach
has difficulty in (a) defining the mental processes necessary for effective
learning, (b) identifying subprocesses
underlying academic tasks as well as
global processes required for school
learning, and (c) providing a link between cognitive theory and educational practices (see Wagner & Sternberg,
1984, for a review). Further, it has been
suggested that standardized tests have
questionable ecological validity (e.g.,
Swanson, 1984a) and that clinical interpretations for popularly used tests
have poor experimental support (e.g.,
see Kavale & Forness, 1984).
Perhaps the most serious limitation
to a differential approach, at least in
the LD field, is that such an orientation obscures the specific learning disability we are trying to find. We know,
for example, that LD children perform
less well than nondisabled students on
a multitude of cognitive tasks. Recognizing this, most practitioners concerned with the cognitive problems of
the learning disabled focus on a differential deficit, that is, a greater deficit
in one (or more) subtest than in another. Unfortunately, poor subtest performance does not necessarily repre-
sent a differential deficit in ability. It
may instead reflect learning disabled
children's generalized performance
deficits, coupled with the fact that one
or some of the subset items measure
these generalized deficits better than
others do. Thus, low scores on various
test items cannot be interpreted as indicating a specific learning disability.
Stated differently, standardized tests
or measures may provide incomplete
representations of children's knowledge bases and possibly obscure our
understanding of the processes LD
children use to acquire knowledge.
In search of a better conceptualization of the cognitive deficits in learning disabled children, many researchers have turned to an information
processing approach. It is not my intent, however, to suggest that psychometric approaches be abandoned,
but rather put into perspective. Thus,
psychometric research programs (e.g.,
research emanating from the University of Minnesota Learning Disability
Institute) may be rather limited, at
least in a theoretical sense, in helping
the profession create a science of
learning disabilities (see McKinney,
1983). Therefore, some consideration
must be given to how an information
processing framework can contribute
to our understanding of learning disabilities. We will briefly consider
four possibilities.
COMMON DENOMINATOR
Information processing theory may
provide a possible framework for
identifying common processing patterns in all children identified as
learning disabled. This approach is in
contrast to current approaches which
search for unitary or specific deficits
(see Poplin, 1984, for a critique of this
approach); here, the focus is on specifying how the components at various
stages and levels—for example, the
relationships between micro-, macroand metacomponents (see Sternberg,
1983 for discussion)—interact with
learning conditions (e.g., Detterman,
1980). Not until we understand how
these components interact, and hence
become coordinated, will we have a
sense of atypical or inefficient patterns
of processing in LD children.
I assume that if a cogent theory and
instructional program for LD children
is to emerge, some common denominators or patterns of information processing must be identified in order to
conceptualize, unify, and predict such
children's performance. Thus, I assume that certain inefficient patterns
of processing are commonly shared
among children who have learning
disabilities. Of course, this assumption is in contrast to previous commentaries (e.g., Ysseldyke et al., 1983),
which have suggested that we abandon efforts at finding out "what learning disability really is" and get on with
the practical task of instructing LD
children. Unfortunately, it appears
that such an approach is unsatisfactory. The practical task of instruction
represents, in many cases, a standardized program in a school district not
motivated by guidelines related to
learning theory or cognitive training.
In addition, one of the major tenets of
the practical task of instruction—individualization—in many cases only
represents a partial approximation to
the concept (e.g., see commentary by
Doris, 1986, in which individualization unfortunately represents the
speed at which the LD child advances
through a fixed curriculum). Beyond
these contradictions, however, I would
suggest that if any science of the practical aspects of teaching learning disabilities is to exist, we must converge
on some unified descriptive explanation of the behaviors that such children exhibit so that meaningful instruction and/or intervention can
occur (see Sternberg, 1983; Sternberg,
Ketron, & Powell, 1982, for related
discussion).
As suggested earlier, one research
area that may yield valuable information in identifying common patterns
among LD children has to do with
how LD children access, execute, and
coordinate several mental components across various academic
tasks. It has traditionally been assumed that LD children are of normal
or above-normal intelligence but suffer from specific clusters of learning
problems revealed on intelligence
and/or academic tests. The assumpJournal of Learning Disabilities
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tion is that these specific, identifiable related to self-evaluation (e.g., predictclusters of abilities are involved in ing outcomes, organizing strategies,
learning impairment (e.g., reading, using various forms of trial and error),
math). Unfortunately, this assumption and certain subprocesses are relatively
is questionable (e.g., Wagner & Stern- familiar or automatized (see Spear &
berg, 1984), and it may be further Sternberg, 1986; Sternberg & Wagner,
argued that specific skill deficiencies 1982), training attempts are successful
do not represent difficulties in specific(e.g., Palincsar & Brown, 1981, 1984;
processes, but rather reflect difficulties Wong & Swatsky, 1984). In addition,
in the coordination of such processes these children suffer from deficits in
(see Swanson, 1984a). Likewise, others such mental operations as logically
(e.g., Palincsar & Brown, in press) have organizing and coordinating incomalso suggested this possibility, by sug- ing information and carrying out
gesting that the LD field focus on mental operations (e.g., Swanson,
decision-making mechanisms (i.e., 1982). Thus, the research clearly sugmetacognition) that influence the co- gests that a focus on isolated deficienordination of mental processes. Fur- cies (i.e., specific processing deficienther, several studies within the last few cies) fails to capture the regulative
years have supported the assumption nature of LD children's functioning.
that LD children have difficulty regu- That is, learning disabilities may be
lating (i.e., accessing and coordinating) the result of a unique regulation or
a number of mental activities. The coordination of mental processes rathresearch in this area may be sum- er than a specific type of processing
marized as follows: LD children ex- deficiency (see Swanson, 1985, for a
perience difficulty with such self- related discussion).
regulating mechanisms as checking,
planning, monitoring, testing, revis- SUBGROUP SELECTION
ing, and evaluating during an attempt
to learn or solve problems (e.g., Bauer,
Information processing theory may
in press; Barclay & Hagen, 1982; provide a tentative framework for the
Brown & Palincsar, 1982; Hallahan, selection of subgroups. Contrary to
Lloyd, Kosiewicz, Kaufman, & what was.called for in the previous disGraves, 1979; McKinney & Haskins, cussion (i.e., identification of common
1980; Scruggs, Mastropieri, & Levin, in information processing patterns),
press; Swanson, 1984b; Wong, in present evidence indicates that
press). Such children perform poorly school-labeled learning disabled chilon a variety of tasks that require the dren routinely contradict the assumpuse of general control processes or tion that some common denominator
strategies for solution (e.g., Bauer, underlies performance (e.g., Keogh,
1979, 1982; Bauer & Emhert, 1984; Major-Kingsley, Omori-Gordon, &
Brainerd, Kingman, & Howe, 1986; Reid, 1982). Given that more than half
Dallego & Moely, 1980; Howe, Brain- of the published research in learning
erd, & Kingma, 1985; Tarver, Halla- disabilities is based upon students
han, Kaufman, & Ball, 1976; also see receiving services from established
Bauer, in press; Worden, 1983, for public school programs for the learnreview). Under some conditions, well- ing disabled, it is reasonable to ask
designed strategy training improves what characterizes this population. In
performance (e.g., Torgesen & Houck, my home state, it was estimated that
1980; Torgesen, Murphy, & Ivey, 1979; over 50% of the students receiving serWong, 1978,1979,1982), while at other vices for the learning disabled did not
times general cognitive constraints match conventional definitions of
prevent effective use of control pro- learning disabilities (Shepard, Smith,
cesses (e.g., Shankweiler, Liberman, & Vojir, 1983). In a companion study,
Mark, Fowler, & Fischer, 1979; Swan- Shepard and Smith (1983) analyzed
son, 1984b; Torgesen & Houck, 1980; records of 1,000 pupils receiving serWong, Wong, & Foth, 1977). However, vices in programs designed for the
when training of information process- learning disabled; they reported that
ing components includes instructions only 28% of the students met defini-
tions of learning disabilities. More
than 50% of the students were better
described by other indicators (e.g.,
non-English dominant, minor behavior disorders, slow learners). Recent
attempts to remedy this state of affairs
have resulted in reports on learning
disability definitions (e.g., Hammill,
Leigh, McNutt, & Larsen, 1981; Smith
et al., 1984). Unfortunately, such reports have not converged on learning
paradigms in which to "test out" or
operationally refine such definitions.
Because LD students have high intersubject as well as intrasubject variability on most cognitive measures,
one strategy to control for some of the
subject variability has been to identify
subgroups within learning disabilities.
There are obvious advantages to subgrouping in our understanding of
learning disabilities: (a) Subgroups
reduce a large amount of data about
LD subjects to a manageable size and
(b) subgrouping forces researchers to
specify in a precise manner the important parameters of LD functioning.
Some of the recent subgrouping
schemes have included behavioral
characteristics (McKinney, 1984;
Speece, McKinney, & Appelbaum,
1985), memory performance (Torgesen
& Houck, 1980; Torgesen, Rashotte,
Greenstein, Houck, & Portes, in press),
language ability (Ceci, Lea, & Ringstrom, 1980), achievement ability (e.g.,
Siegel & Linder, 1984), lexical ability
(Swanson, in press), and neuropsychological profiles (e.g., Lyon &
Watson, 1981), to mention a few. If
subgrouping is successful, variability
should be less within one group and
greater from one subgroup to another.
There are, however, two major stumbling blocks to the subtyping enterprise.
First, a taxonomy of subtypes will
not be readily accepted by fellow researchers until the definitional issues
are resolved and some theoretical
framework is agreed upon. In terms of
definitional issues, one problem noted
in the literature is that the apparent
heterogeneity of the learning disabled
population may not be the result of
"real heterogeneity" but, rather, may
reflect definitions that are not specific
(see Farnham-Diggory, 1986; Keogh et
al., 1982). Thus, it appears logical to
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conclude that subgrouping studies on
poorly specified LD samples merely
obscure the commonalities that may
exist within the learning disability
population. In terms of theoretical
issues, it may be argued that the majority of subgrouping studies use psychometric measures or clinical instruments that do not reflect a
theoretical framework of learning or
cognition. Thus, tasks are chosen arbitrarily which in turn give rise to a
post-hoc theory of task performance.
An example of this post-hoc conceptualization is most likely to occur in
studies which have been based upon
empirically derived subgroups. This
occurs because, at present, there is no
satisfactory method for determining
the number of clusters (subgroups) for
any type of cluster analysis (Everitt,
1980). Thus, when definitions are nonspecific, when there is a lack of theoretical integration in the choice of
measures, and when the number of
subgroups must be decided upon
somewhat arbitrarily, Nimzovitch's
dictum comes into operation: When
there is no good move, a botch will
come along to fill the breach.
Second, the establishment of subtypes is based upon the assumption
that learning disabilities can be assigned to a category having some unitary causes and well-defined effects. A
review of the subtyping literature has
not yielded such conclusions (e.g.,
Siegel & Heaven, 1986). For example,
one of the major difficulties with attempts at subdividing LD children
into more homogeneous groups is that
low or high performance on the various standardized measures (e.g., reading tests) may be due to several underlying causes (e.g., see Siegel &
Heaven's [1986] discussion on the various causes of poor performance on
standardized reading tests). Perhaps
one solution to the problem of determining unitary causes is to study the
relationships between information
processing constructs that synthesize
several skills (e.g., metacognition, executive function, working memory) in
learning disabled populations as a
whole (see Jorm, 1983, for a discussion). Thus, a focus is placed on overlapping disabilities rather than on
separating the population in terms of
isolated or specific processing deficiencies or disabilities. This does not
mean that a subgrouping of overlapping disabilities on macrocomponents of information processing
should merely reflect graduations in
severity. Rather, particular subgroup
classifications are only meaningful
when it is discovered after intervention that one LD subgroup's performance differs in one direction and
another subgroup differs in the opposite direction form the NLD group.
MENTAL COMPONENTS
UNDERLYING ACADEMIC
CHANGE
The information processing framework may provide for a clarification of
the changes that occur in mental processing as a result of classroom learning. Farnham-Diggory (1986) has suggested that the issue of definition and
identification of learning disabilities
will subside once "the processes identified in the laboratory enter into the
performance of school tasks" (p. 134).
Therefore, research needs to be directed toward identifying the mental processes required on selected classroom
tasks. Likewise, complex models for
assessing mental processes must be
sensitive to changes that occur during
actual instruction on a day-to-day
basis. One step in this direction is to
use measures of processing activity
during classroom instruction. That is,
one can assume that, in order to adequately assess whether a particular intervention has facilitated a child's
learning, changes in the supporting
processes must be measured. It is assumed that, in reading, math, or spelling, certain processes mediate lowerlevel classroom competencies (rate of
work), as well as higher-level activities
(e.g., reading). To illustrate how this
can be done, let us consider a pilot
study recently completed in a LD
classroom (Swanson, Kosleski, & Stegink, in preparation). Two learning disabled adolescents, who had serious
memory and reading (i.e., comprehension) problems, were given two tasks.
One task required remembering critical details of various stories read
orally from a newspaper column on a
daily basis. The second task, which
assessed any possible generalization
of training effect from the primary
task, required the retrieval of critical
information on a social studies assignment. Intervention on the primary
task required the child to use a mnemonic strategy to organize main ideas
and to link the main ideas in terms of
an outline. Mental processes were assessed by asking the children to verbally report (see Ericsson & Simon,
1980, for a discussion related to the
validity of verbal protocol procedures)
the "ways" they were remembering the
information. Student responses during task performance were tape recorded and the verbal protocols were
coded and categorized in terms of the
information processing strategies
used.
As shown in Figure 1, the students
were using various processing strategies to sustain and/or direct their
prose recall. A majority of these strategies emerged over training sessions
(i.e., rehearsal), and in many cases,
were sustaining the imagery or visual
mnemonic. In addition, it appears that
the teacher's intervention strategy had
little impact on the social studies assignment, suggesting that the mental
processes activated for the primary
task were not directly relevant to the
secondary activity. Such information
is critical in our attempts to isolate
some of the mental mechanisms involved in successful and unsuccessful
classroom intervention (see ForrestPressley & Gillies, 1983, for a more
detailed discussion of the components
necessary for transfer).
There are several other, more direct,
ways to isolate information processing
components underlying classroom
performance. One obvious approach
is to determine whether invariant processing mechanisms—hardware—or
strategies and learning under the control of the performer—software—are
responsible for performance difficulties. This approach has been discussed elsewhere (e.g., Swanson &
Watson, 1982, chapter 6) and includes
assessing basic structures (i.e., structure describes the nature of information—sensory memory, short-term
160
Journal of Learning Disabilities
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components related to psychometric
instruments used to assess academic
change (e.g., tests used in a pre/post
fashion). For example, Carroll (1976)
has compiled a list of component processes that arbitrarily define academic and intellectual domains in terms of
memory, long-term memory) as well
as the operations and/or voluntary
strategies under control of the child,
such as rehearsal, organizing, or categorizing of information.
Another approach, the cognitivecomponent method, compiles a list of
GREG
100-j
Cognitive
Intervention
80-1
10
Strategy
11
12
13
14
13
14
Components
* a
a -e
8060-1
o a
40-1
20-1
7
8
9
Reading
10
11
12
Comprehension
10080
40
I
# | STRATEGY COMPONENTS |
20-J
A
10
11
12
13
14
JULIE
Cognitive
10
11
trategy
-I
1
1
1
1
1
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IMAGERY
O
*8S0CI*TI0HS/L0H0Tt*M M
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»D»»N«0 0.0.-.IE.
Intervention
12
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12
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Reading Comprehension
100-j
oS
•2
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°5«
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Figure 1. Prose recall, verbally reported strategies, and comprehension as a function of training
sessions.
information processing components.
In order to assess these components,
Carroll included psychometric tests
from French, Ekstrom, and Price's
(1963) Kit of Reference Tests for Cognitive Factors. This set includes 24 factors
of cognitive abilities which were further analyzed by Carroll (1976) in
terms of operations (i.e., strategies)
that must be performed in order for
the task to be mastered successfully.
These operations are of three kinds:
attentional, memorial, and executive.
Attentional operations relate to visual
searches (e.g., as measured by eye
movements); memorial operations relate to storage; searching and retrieving and executive operations relate to
judgments about stimulus attributes.
Another, although not final, way to
understand the influence and/or relationship between mental components and classroom learning activities is to use tasks from the
contemporary information processing
literature which are sensitive to individual differences. Gadow and
Swanson (1986) have listed some of
these tasks in hopes of facilitating the
inclusion of such measures in future
intervention studies. Before any task
can be selected, however, several
criteria must be kept in mind:
1. The task has to have a history in the
information processing literature
and have empirical support as a
device for isolating the sources of
individual differences.
2. There has to be a theoretical rationale for the task (e.g., construct validity), and that task must assess elementary processing mechanisms.
3. The task must be adaptable to
moderately language handicapped
children, and must lend itself to
paper and pencil format, computer
simulation, and/or group administration.
4. The task must be logically related
and interrelated to performance on
a number of academic tasks.
In general, it is suggested that these
tasks be described in a series of mental
operations or processes that the executive function operates upon. Tests of
these operations are provided below:
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161
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1. Encoding: The process by which
input information is initially analyzed. The child matches the input
against past learning. This process
also involves the extraction of different types of information from a
stimulus. For example, consider a
child who is presented a series of
words to be remembered for a spelling
test. The child may process the words
by their orthographic features, the
phonological features represented by
the printed word, and/or the semantic
features represented by the meaning of
the word.
2. Elaboration: The process by
which connections with the material
to be learned are made to other information previously stored. For example, when the child is presented spelling words to remember, he or she
forms associations to those words by
use of extra ways of mediating the information (visualizing the word "boy",
i.e., the process of imagery), proposing
or answering questions about the word
(e.g., "is a boy the same as a man?"),
and categorizing information (e.g., the
word "boy" represents one of the two
genders) or associating the word with a
context (e.g., the teacher uses the word
"boy" in several sentences).
3. Transformation: The process by
which rules, algorithms, or heuristics
are applied to the incoming information (e.g., the word "perceive"
follows the I before E except after C
rule). In contrast to encoding and elaboration, this process requires the
application of previously stored rules
about information processing.
4. Storage: The process by which input information is added to the existing information within the mental system. This process forms a memory
trace. Forgetting this memory trace
can be attributed to an interference
from other learning (e.g., the child has
difficulty remembering how to spell
the word "perceived" for the present
spelling test because the previous
week's spelling test included the word
"received", i.e., proactive interference).
The effects of interference can be assessed by the degree to which the child
has overlearned and reviewed the
material.
5. Retrieval: The process by which
information that was previously
stored can be made available. The process generally requires the reproduction of information with minimal aids
(i.e., free recall, serial recall). For example, the child is asked to spell a
word with all letters in their correct
order.
6. Searching: The process by which
information is accessed by determining the presence or absence of additional properties. For example, a
child is asked if any of the word
spellings that were forgotten rhyme
with the word "receive" (i.e., cued recall). The child may internally develop
an aid to remembering the word, or the
aid may be external (e.g., teacher
induced).
7. Comparing: The process by
which information is judged or recognized to be either old or new, same or
different, and so on, to information
that has been previously stored. For
example, a child is asked to pick out 10
words for a review spelling test from a
list of 20 words in which 10 words
are new.
8. Reconstruction: The process by
which the recalled information is
based on concepts that tie together
fragments or pieces of stored information. It is generally assumed that information recall is not a duplicate of the
information encoded (Neisser, 1981).
The important point of the previous
discussion is that if a meaningful link
is to be obtained between information
processing abilities and classroom
performance, a fine-grained analysis
of the learning process is necessary.
This fine-grained analysis should occur on school tasks which the learning
disabled child is having difficulties
mastering. Further, evaluation of instruction related to processing efficiency is not merely a matter of asking whether such instruction directly
influences the "macro-components"
that improve the oral reading, math
computation, etc., but whether such
instruction incorporates the information processing components that
influence and sustain instructional
performance that is appropriate for
learning on a day-to-day basis (see
Belmont et al., 1982; Campione &
Armbruster, 1985, for a related discussion).
HEMISPHERIC
PROCESSING
Information processing theory may
provide a means of conceptualizing
the neurological deficiencies noted in
learning disabled children. We must
expect that few theories of learning
disabilities will be validated without
recourse to neuropsychological data.
This notion is supported in recent
definitions of learning disabilities that
include the qualification that such disabilities are "intrinsic to the individual
and presumed to be due to central
nervous system dysfunction" (Hammill, Leigh, McNutt, & Larsen, 1981).
Further, a committee of the Association for Children and Adults with
Learning Disabilities has indicated
that the prevention of learning disabilities is related to our understanding of neuropsychology (ACLD Scientific Studies Committee, 1980). Unfortunately, findings of neuropsychologists have not been operationalized in
terms of constructs that are usable for
the cognitive psychologist and/or educator (see Hiscock & Kinsbourne, this
issue). It appears reasonable, however,
that trying to understand the mind of
the learning disabled child by focusing exclusively on neuropsychological
data would be like trying to understand a large time-sharing computer
by focusing on a lesion in one of the
terminals. Hopfield (1984) has captured this analogy by pointing out that
it is impossible to understand the processing functions of a computer by
focusing on a computer chip and ignoring its circuitry.
One of the ways information processing theory may enhance our understanding of learning disabilities is
by providing some unification to the
mixed results on hemispheric specialization (Hiscock & Kinsbourne,
this issue). Lateral hemispheric specialization of the brain has been employed as a physiological indicator of
learning disabled children's processing of information for some time (e.g.,
Hynd, Obrzut, & Bowen, in press;
Obrzut, Hynd, & Boliek, 1986, for a
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review). Some studies show that disabled and nondisabled children show
left hemispheric specialization (rightear advantage) for linguistic tasks;
however, disabled children are limited
in their performance relative to nondisabled children (e.g., Newell &
Rugel, 1981; Witelson, 1977). Others
suggest that the evidence is inconsistent on whether ability groups actually
differ in how they utilize the two
hemispheres (e.g., see Hiscock &
Kinsbourne, this issue; Young & Ellis,
1981, for a review). Coupled with these
mixed results, there have also been
many "levels of analyses" which attempt to associate the constructs of
hemispheric specialization with cognitive ability (see Hiscock & Kinsbourne, this issue). Thus, conclusions
related to disabled children's hemispheric processing and cognitive abilities are extremely mixed and quite
diverse (see Obrzut et al., 1986, for a
review).
Fortunately, information processing frameworks are emerging that account for individual differences on
various neuropsychological indices
(e.g., see Friedman & Poison, 1981).
For example, Friedman and Poison
view hemispheric resources as those
processes, strategies, or mechanisms
from two independent cerebral hemispheres that influence information
processing performance (Friedman &
Poison, 1981; Friedman, Poison,
Dafoe, & Gaskill, 1982; also see
Hellige & Wong, 1983). A focus on how
these two sources combine has been of
recent theoretical interest in learning
disabilities (e.g., Kershner, Henninger,
& Cooke, 1984; Swanson, 1986; Swanson & Mullen, 1983). The wide range
of ability group differences suggests
that information processing is characterized by different hemispheric resource combinations, which may or
may not require supplies primarily
from one or both cerebral hemispheres. For example, some information tasks may require a competition
for independent hemispheric resources, while others may be related to
the coordination of hemispheric resources (see Friedman et al., 1982).
Therefore, because the human system
develops a number of alternative
means for combining hemispheric resources, it appears necessary to use an
information processing model to capture ability group differences. Thus,
the poor association between cognitive and behavioral indices of hemispheric specialization, as noted by Hiscock and Kinsbourne (this issue), may
reflect individual variations in the use
of alternative information processing
routes.
There are additional neuropsychological indices that lend themselves to
an information processing perspective. For example, variations in processing styles may be related to patterns of spontaneous electroencephalographic (EEG) asymmetry (e.g.,
Ehrlichman & Wiener, 1980). For subjects performing activities that draw
resources primarily from the left hemisphere (resources required for verbal
analytic processing), there is an increase in alpha waves or idling rhythm
over the right hemisphere, while in individuals accessing resources that
draw primarily from the right hemisphere (e.g., resources required for spatial synthetic processing) there is usually an increase in alpha or idle
rhythm over the left hemisphere. The
implication is that some individuals
use a different means of processing information, and idling rhythm may be
used as one of the many indices of
such processing (e.g., Galin & Ornstein, 1972). Additional measures of
hemispheric processing differences
have been related to reflective eye
movements and pupillary responses
(e.g., Hardyck, Dronkers, Chiarello, &
Simpson, 1985; Schluroff et al., 1986).
For example, when subjects are asked
a question demanding a certain
amount of time to respond (pause
time), they avert their eyes before finally answering the question.
In sum, an LD student's information processing difficulties may be indirectly reflected in such behavioral indices as laterality, EEG, and eye
movement data. It is likely that the use
of sophisticated computer-aided techniques will make it possible to incorporate data on evoked potentials
(Kolc, Vrjver, & Bouma, 1985;
Thatcher & April, 1976). Such procedures would provide a concep-
tualization of cerebral activity during
learning that goes beyond simply
localizing certain processing activity
to an area of the brain. It is also possible
that
computer-based,
individualized, instructional programs
could be monitored by physiological
measures in order to better assess
automatic processing. Such procedures could further refine traditional assessment of learning disabled
children's performance. There is some
evidence that demonstrates the superior validity of psychophysiological
and mental indices over standardized
achievement measures for predicting
student performance (e.g., Ackerman,
Anhalt, Dykman, & Holcomb, 1986;
Lewis, Rimland, & Callaway, 1976).
Consequently, it is possible that
physiological correlates of learning,
conceptualized within an information
processing framework, will advance
our understanding and assessment of
learning disabilities.
CONCLUSIONS
If our profession is to fulfill the
promise of resolving the acute issues
of teaching learning disabled children, it will be necessary to converge
on a paradigm for the study of such
children. In the absence of such a paradigm, researchers, teachers, and diagnosticians are confronted with a
wide range of phenomena. These phenomena are described and interpreted
in many different ways. As a result,
fact-gathering appears to be random.
According to several authors (e.g.,
Kavale & Forness, 1985a, 1985b), the
LD field can be best characterized as
being in the very earliest stages of
scientific development. In view of this
state of affairs, adoption of a paradigm
is necessary as the first step en route to
a normal science (e.g., Kuhn, 1974). Although the field is far from a consensus on the most appropriate theoretical model, the articles in this series
suggest that there is an implicit adherence to a theoretical infrastructure,
namely, the language and methods of
information processing models of
memory and cognition. Within this infrastructure, there are many commonalities. In many cases, the instruc-
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tional implications are complementary (e.g., Goldman & Pellegrino, this
issue, vs. Pressley et al., in press), and
the characterizations of LD children's
performance remain fairly consistent
(e.g., metacognitive and automaticity
problems). Of course we do not know
what model might best characterize
learning disabilities. Rather, it is only
suggested that the articles represented
in this series provide a basic map or
route toward a normal science. It
should also be recognized that the
story of learning disabilities has been
one of traveling down some blind
alleys. Of course, it is not known
whether the information processing
model will prove to be a valuable
avenue. The research is fairly consistent, however, in suggesting that the efficient academic performance required of students requires them to
integrate several kinds of mental capabilities and that, unfortunately, LD
children do not efficiently incorporate
and/or coordinate mental components into final correct responses.
ABOUT THE AUTHOR
H. Lee Swanson received his PhDfrom the University of New Mexico. Current research interests include the assessment of learning disabled children's
intelligence, semantic memory, and working memory abilities. Address: H Lee Swanson, Department
of Educational Psychology, University of Northern
Colorado, Greeley, CO 80639.
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REACTION TO KOLLIGIAN
AND STERNBERG'S
TRIARCHIC SYNTHESIS
A
pplication of Sternberg's triarchic theory to the LD field provides a useful and timely illustration
of the value of such conceptual activity (Kolligian & Sternberg, 1987).
And while the specific implications
for "specific learning disabilities" are
not new, some are so widely ignored
throughout the field that another
statement of them is always welcome.
This is not to say that the theory itself
or this particular application is completely satisfactory. The presentation's
importance is as a stimulus for discussion, focused not on the conclusions
drawn but on the usefulness of the
theory itself for understanding learning disabilities. In this specific connection, the following brief reactions
are meant to balance the authors'
stress on the strengths of this theoretical application by underscoring a few
major deficiencies.
First, it is likely that Kolligian and
Sternberg would agree that efforts to
understand the nature of specific
learning disabilities require a broader
theoretical base than can be provided
by a theory of human intelligence
(Adelman & Taylor, 1983; Deci &
Chandler, 1986). Sternberg's theory
certainly goes beyond other cognitive
theories that have been applied in the
LD field in the way he includes environmental and motivational factors.
However, as with other applications of
cognitive theory, Kolligian and Sternberg pay too little systematic attention
to the role of affect and ongoing reciprocal transactions (e.g., see Neisser,
1976).
Second, since the discussion is
based on the authors' definition of
learning disabilities, one would as-
166
sume that the theoretical application
is specific to those individuals who fit
that definition, namely those having
"localized impairments of cognitive
functioning." However, this assumption is not supported by the indiscriminate way the term learning disabilities is used throughout the
presentation. The matter is confounded further because certain key concepts are not well defined and differentiated from each other; for
example, the discussion fails to clarify
whether impairment, the key term in
the authors' definition of LD, is comparable to deficit, a key term used in the
triarchic theory. Additional confusion
about this matter results from failure
to distinguish carefully among terms
used to indicate problems and pathology, for example, disorders, disabilities,
impairments, deficits.
Whatever the reason, the fact is that
the term specific learning disabilities is
used indiscriminately to describe inJournal of Learning Disabilities
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