Theoretical and Review Articles
Learning, Language, Memory, and Reading: The Role
of Language Automatization and Its Impact on
Complex Cognitive Activities
James M. Bebko
York University
Two related models of the role of developing and automatized
language skills in the cognitive processing of deaf and hearing
children are presented. One model focuses on explaining apparent delays in the emergence of a memory strategy (cumulative rehearsal) in children who are deaf, linking strategy use
with the child's emerging language skills and the automatization of those skills. The second model is larger in scope and
integrates this rehearsal model with added components relevant for higher-level cognitive activities such as reading. A
program of research is reviewed that provides support for
various components of the models with deaf children. Implications of the models for potential concurrent learning disabilities are discussed.
Memory skills are centrally involved in a wide variety
of cognitive activities, from simple daily skills, such as
remembering a list of groceries, to more complex cognitive activities, such as reading or mathematics. Research into the memory capabilities of deaf children
has a long history and has tended to parallel shifting
theoretical research paradigms into hearing children's
skills. Initial investigations tended to proceed from a
more psychometric tradition with an initial focus on
Para of this research were supported by research grants from the Faculty
of Arts, York University, ind from the minor grants program of the Social
Sciences and Humanities Research Council of Cimrh. I appreciate the
contributions of several individuals to some of the ideas and studies discussed here: Michelle Bell, Carol Kennedy, Martha Lacasse, Elaine
McKinnon, AHsa Metcalfe-Haggert, Anne-Siri Oyen, Christina Ricciuri,
and, particularly, Mark Greenberg and Philip Dak. However, I take sole
responsibility for the ideas discussed in this article. Correspondence
should be sent to James M. Bebko, Department of Psychology, York
University, 4700 Keete Street, North York, Ontario, Canada, M3J 1P3
(e-mail: [email protected]).
Copyright O 1998 Oxford University Press. CCC 1081-4159
how much is or is not recalled by deaf children in comparison with hearing children (e.g., Pintner & Paterson, 1917). A more recent focus has derived from a
cognitive information-processing theoretical framework, with a focus, for example, on the strategies used
by children and adults to enhance memory performance. The literature to be discussed and the models
presented in this article are informed by this information-processing approach, particularly from a child development perspective.
Memory strengths and limitations among the deaf
have been reviewed previously (e.g., Bebko, 1984;
Marschark, 1993) and will not be reviewed in detail
here. Overall, while memory for spatial or simultaneous information tends to be at least equivalent among
deaf and hearing samples, a greater challenge is noted
with sequential processing, tasks typically handled by
hearing individuals with verbal encoding. Of particular
note, some of the greatest discrepancies between deaf
and hearing learners tend to be on tasks that benefit
from the use of verbal rehearsal of sequenced information.
Spontaneous cumulative rehearsal is a linguistically based strategy that has been linked to increased
recall for sequentially ordered information. It is a relatively simple cognitive activity that involves the cumulative repetition of information in order to keep it available for later recall in the same order ("later" typically
meaning seconds or minutes as opposed to days). An
example would be to keep repeating "pen, ball, sock;
Language, Memory, and Cognition 5
pen, ball, sock" if the three objects had been shown in
that order. It is the common strategy used to remember
a phone number or a short shopping list.
The program of research to be reviewed here is organized mainly around the development of two models.
One of the models is focused on how and why cumulative rehearsal develops when it does in hearing children
and why its emergence is apparently delayed in children who are deaf. The second model is larger in scope
and links this rehearsal model with higher-level cognitive activities such as reading. In both models the role
of developing language skills is closely examined, since
both rehearsal and reading are highly dependent on
language processing. A central theme is the link between language mastery and its role in reducing the
mental effort of cognitive strategies used in the service
of a variety of learning behaviors, such as memory for
ordered events, reading, and problem solving. Overlearning, or automatization of these language processes, is seen as critical for the development of efficient
learning strategies.
Early Work on Rehearsal Strategy Use by
Deaf Children
It has long been observed in the hearing literature that
older children (i.e., those older than 8 or 9) tend to remember more information than do younger children
on a variety of memory tasks. Initially, it was thought
that perhaps older children had larger memory capacities than younger children—that is, that the hardware
was more advanced. However, from the mid-1960s on,
it became increasingly clear that what really differentiated older and younger hearing children on these
tasks was not so much their memory capacities, but
rather how they made use of those capacities—the
strategies (software) they used (e.g., Flavell, Beach, &
Chinsky, 1966; Flavell, 1970). When strategies were
used to assist memory performance, recall improved
regardless of age; however, older children were more
likely to use strategies than younger children (Bebko,
1984; Flavell et al., 1966).
A similar pattern of research emerged in the deafness literature. Initially, when well-controlled studies
were done with deaf children, differences were noted
on a variety of tasks compared to hearing children. Indeed, a pattern of poorer recall performance on some
tasks had been noted at least as early as the first quarter
of this century (Pintner & Paterson, 1917). However, it
became clear that basic memory capacity did not differ,
because there were clear strengths, and often superiorities, by the deaf students on many tasks that relied on
visual-spatial skills, as opposed to tasks that benefitted
from sequential, or language-based, strategies. The implication, then, was a need to examine the strategies
used by deaf children, and this has been the focus of a
research program spanning a number of years in my
research lab.
Only selected aspects of relevant studies in this
program of research will be presented here (for greater
detail, see Bebko & Metcalfe-Haggert, 1997). In each
of the studies to be discussed, the deaf samples were
comprised of children with a prelinguistic hearing loss
S:80 dB in the better ear, whose deafness was due to
hereditary or unknown causes, and with no identified
additional medical, behavioral or emotional difficulties.
In some of our early work on rehearsal strategies
(e.g., Bebko, 1984; Bebko & McKinnon, 1990; Bebko,
Lacasse, Turk, & Oyen, 1994), we found that children
who are deaf did engage in rehearsal strategies to help
recall information in which the order of the information was essential. The use of these strategies was generally as effective for deaf as for hearing children, but
the age at which the deaf samples began to use the
strategies was significantly delayed compared to the
hearing children.
However, the children in these studies were mostly
deaf children of hearing parents, whose systematic language experience (whether with spoken or signed language) had effectively not begun until after diagnosis,
typically two or three years after birth. When the delays in language exposure were taken into consideration, the delays in the use of strategies were essentially
eliminated. That is, the children's language experience
(years of training in their current modality of communication, whether spoken or signed) was a much
stronger predictor of rehearsal use than simple chronological age. Consistent with this view were the data
from deaf children of deaf parents who were involved
in the studies. Their language experience essentially
6
Journal of Deaf Studies and Deaf Education 3:1 Winter 1998
RUTOMRTIZED LfiNGURGE
PROCESSING
LRNGURGE PROFICIENCV
MENTRL EFFORT
LRNGURGE EKPERIENCE
RECRLL
Figure 1 Model of the relation between language proficiency and rehearsal strategy use.
paralleled that of our hearing samples (i.e., beginning
at birth), and they were observed to be using rehearsal
several years earlier than the other deaf children, at an
age comparable to that of the hearing sample.
The Language Proficiency-Rehearsal Use Model
The language experience results of the Bebko and
McKinnon (1990) study led us to hypothesize that a
degree of developing language proficiency was a necessary prerequisite for the emergence of spontaneous rehearsal. More recently, we have been investigating this
language proficiency hypothesis further, both in deaf
and in hearing children. Each of the studies to be discussed below focused on testing at least one component
of the model in Figure 1.
The model in Figure 1 is a representation of the
hypothesized complex role of developing language
skills as an underlying prerequisite to the development
of spontaneous rehearsal strategy use. In the lower two
triangles of the figure are represented the findings already summarized: first, that the often-cited link between age and memory recall performance on serial recall tasks is mediated by the development of rehersal
strategy use. That is, as discussed above, older children
tend to rehearse more, and it is the presence or absence
of rehearsal, not age itself, that is the key predictor of
recall. This distal contribution of age is represented by
the lighter line in the figure. The second-lowest triangle correspondingly represents Bebko & McKinnon's (1990) findings that the assumed language experience of deaf and hearing children can, in turn, predict
rehearsal use more accurately than can age. Hence, the
link between age and rehearsal strategy use is also
shown as a light line. The studies to be discussed will
evaluate similar mediating relationships in the remaining parts of the model.
One central feature of each study to be reviewed is
that the language proficiency of the participants was
directly evaluated, rather than simply being assumed
from the number of years of systematic language experience, as in Bebko and McKinnon's (1990) study.
However, one of the challenges was how to evaluate
language proficiency directly. For the deaf sample, who
used a variety of methods of communication (e.g.,
American Sign Language [ASL], signed English,
speech), the situation was most complex. The use of
any single measure, such as of ASL skills, or even a
combination of different independent measures of
single methods of communication, would run the risk
of not capturing the entirety of the students' language
proficiency. That is, the child's overall language competency is likely greater than the sum of its individually
measured parts.
Therefore, Bebko and McKinnon (1987) devised a
scale by adapting some very useful assessment materi-
Language, Memory, and Cognition 7
als that had been developed at the Kendall School in
Washington, D.C., by Francis (1980) and Francis, Garner, and Harvey (1978). Based on some of their work,
we derived a new assessment tool, the Language Proficiency Profile (LPP-1). The LPP is an individual rating scale of language development that provides a measure of the emerging language proficiency of the child:
that is, the child's use of language, independent of modality or method of communication. There have since
been some important psychometric examinations and
revisions of the LPP, and its reliability and validity
have been established on a limited basis (cf., Bebko,
Bell, Metcalfe-Haggert, & McKinnon, 1996; Bebko &
Metcalfe-Haggert, 1997; also, more detail is available
from the author).
Having earlier established that age was only a distal, indirect predictor of rehearsal use, in the first examination of the language proficiency model Bebko et
al. (1996) examined the triangle marked "a" in Figure
1. This triangle portrays the hypothesis that the contribution of the child's language experience to the emergence of rehearsal is an indirect one, mediated by the
child's actual language proficiency. That is, the child's
number of years of language experience is a distal predictor, affecting rehearsal only through the more proximal predictor of actual language proficiency. This is
not a trivial hypothesis, as two children with the same
number of years of language experience/training may
have quite different levels of language proficiency.
In addition, we hypothesized that at least one of the
important aspects of language proficiency related to rehearsal or other strategy use was the automatization of
language skills (triangle marked "b" in the model). At
the risk of oversimplifying, automaticity is the state
that, for example, skilled typists attain when they no
longer need to think about the individual letters or
words being typed. Rather, they execute the task of
typing in an automatic way, requiring less mental
effort, and can focus, if they wish, on editing or evaluating the content of the material being typed. We argued that it is this kind of automatization that leads
to reduced mental effort requirements for applying the
strategy. In particular, it is the automatization of the
language component of rehearsal that enables rehearsal
to be used as an efficient strategy. Prior to this automatization, the strategy requires too much mental ef-
fort—a level of mental effort that exceeds the processing resources available to the child—so it is not used.
Therefore, in Bebko et al. (1996) a series of tasks
was given to a sample of 7- to 15-year-old deaf children
who had been involved in a school program using a simultaneous or total communication method. These
students were given a serial recall memory task (Bebko,
1984; Bebko & McKinnon, 1990), the revised LPP to
assess their language skills, and a Rapid Automatized
Naming task. The RAN (Denckla & Rudel, 1974) is a
measure of how rapidly the child is able to name an
array of simple pictures. It has been used in a variety
of studies to evaluate automatized processes in reading,
such as rapid word retrieval. Here, the children were
asked to name the items in the array as quickly as possible, using any modality of expression they wished.
The resulting data were analyzed using hierarchical discriminant analysis and logistic regressions. The
goal was to evaluate the strengths of the various measures in predicting which children would be spontaneous rehearsal users. The results were consistent with
the model. When the language proficiency of the children was entered into the analysis first, there was no
significant variance left to be accounted for by age or
the number of years of language experience. This is reflected by the very light lines for both the age->
rehearsal use and language experience —»rehearsal use
relations in Figure 1. These two variables exert their
influence on rehearsal primarily through language proficiency. This indicates that the child's language proficiency is a strong and necessary prerequisite for rehearsal to be used spontaneously as a memory strategy.
Next, we evaluated the importance of the automaticity of language processes in the contribution of general language proficiency. This corresponds to a test of
the topmost triangle ("b") in the model. Indeed, when
the contribution of automatized language skills, as
measured by the RAN, was evaluated, it was found to
be a significant predictor of strategy use. However, it
only partially accounted for the larger contribution of
language proficiency. That is, the role of language proficiency in rehearsal use did involve the skills measured
by the automatized naming task, but language proficiency also involved significant additional skills beyond
those measured on the RAN.
In addition to having established the fundamental
8 Journal of Deaf Studies and Deaf Education 3:1 Winter 1998
consistency of the predictions of this model for children who are deaf, we concurrently examined the universality of it. Using the same basic procedures, the
model was examined with hearing children, and, more
recently, with a language-disordered population, children with autism. We replicated the central findings
with both populations, although the automaticity component was not tested with the group with autism
(Bebko, Kennedy, & Metcalfe-Haggert, 1992; Bebko &
Ricciuti, 1995).
In Bebko, Metcalfe-Haggert, and Hansen (1991),
the remaining component of the model was tested with
a group of hearing children. The hypothesis was that
automaticity is helpful in enabling a strategy like rehearsal to emerge because it helps to reduce the mental
effort of that rehearsal. The argument, similar to the
typing example given earlier, is that automatization of
language skills enables less mental effort to be directed
to the language process, and more to the task at hand.
The hypothesis was supported. Once again, however,
there was additional variance for language proficiency
by itself to account for, beyond the contribution of
mental effort. (For more statistical details, see Bebko &
Metcalfe-Haggert, 1997).
To sum up this model, language proficiency was
found to be a critical variable in the development of a
language-based cognitive strategy, rehearsal. Automaticity of stimulus naming is only one component of
those skills and is thus significantly, but more weakly,
linked to rehearsal use, as indicated by the lighter density of the line in the model. Finally, the impact of automaticity was found to operate through the reduction of
the mental effort involved in using the strategy.
An important follow-up question flows from these
data: If automatization is only one part of the contribution of language proficiency, what is or are the
other component skill(s) involved? Language proficiency may be represented by the development of
different, but typically associated, skill areas, including
syntactic, semantic, and pragmatic skills. While each of
these areas is linked with improving language skills, the
pragmatic side of language development, that is, the varying functions and uses that language comes to serve
for the child, would be most closely associated with advances in the use of language in the service of cognition. While the various syntactic, semantic, phono-
logical, and pragmatic components of language are
important in interpersonal communication of information and ideas, it is the expanded pragmatic functions
of language, from this initial interpersonal communication function to more abstract, internal, self-regulatory
uses, which is critical in accounting for both deaf and
hearing children's performance in language-related
tasks, both simple and complex.
A model of language development was proposed by
Cummins (1984, 1989) that outlined separate and sequentially developing levels of pragmatic proficiency.
Two levels were proposed in order to differentiate between the social/interpersonal skills children acquire
early, and the more advanced skills related to literacy
and cognitive or academic tasks acquired later. However, for the present context, the second level of pragmatic skills is further differentiated into those basic
reasoning skills involved in simpler cognitive activities
(here identified as level 2 skills), and more abstract,
higher-order skills, identified here as hypothesized
level 3 skills.
In Cummins's model, children first acquire skills in
the interpersonal domain. These level 1 skills derive
from contexts that offer considerable external support
for communication, with feedback from parents or
other partners, for example about the accuracy of their
vocabulary choices, or understandability of their syntactic structures. In this way, communication is negotiated through its context; in turn, the context helps the
learner refine linguistic skills. Skills at this level tend to
be more concrete in nature, useful for describing ongoing activities and maintaining conversation, as young
children may do with parents. For second-language
learners (e.g., ASL users learning English or vice
versa), these skills would correspond to more social, or
"street" language use. Clearly, without the feedback of
partners it is very difficult to achieve real language
competence.
With mastery and automatization of these primary
communicative skills, language learners come to develop level 2 skills, which correspond to less contextembedded, less interpersonal, and more internalized
uses of language, such as for academic or basic reasoning situations. For children who are first-language
learners, this level corresponds to an expansion in the
functions of their language, analogous to Vygotsky's
Language, Memory, and Cognition
Leuel 1
Language
Leuel 2
Language
Leuel 3
Language
1
Hutomatlztng
of Leuel 1 and
Leuel 2 ikillt
Figure 2 The contribution of automatization to the acquisition of higher-level language skills.
(1934/1962) interiorization of dialogue in the "ingrowth stage," resulting in "inner speech" for the
child. Vygotsky phrased his thinking in terms of spoken language, but the ideas are readily applicable to
other modalities of language. Thus, more generally,
language becomes "interiorized" to be usable as an
effective cognitive tool for the child. These interiorized
language skills require considerably more time to develop. In many developmental theories, they constitute
the central core of the changes that occur in the early
school years (Bruner, 1964; Vygotksy, 1934/1962), in
contrast to level 1 skills, which have typically reached
a sophisticated level by age 4.
For second-language learners, who have presumably already acquired these functions in their first language, second-level skills have been achieved when
thought or basic reasoning begins to occur in the newly
acquired language (e.g., English), without need to shift
into the first language. Cummins (1983) reported that
although communicative skills were largely mastered in
a new language by immigrant children within two
years, it typically took five to seven years for these children to develop proficiency in less context-embedded,
academic language skills. Thus, automatization of level
1 skills, such as accurate word retrieval, pronunciation,
and use of basic syntactic structures, enables the acquisition and mastery of secondary, more academic and internalized level 2 language skills. This process is summarized in Figure 2.
One plausible explanation for the additional developmental time needed for level 2 skills to emerge is
that, as new skills, they must compete for the same,
limited information-processing resources as level 1 language and other cognitive skills. Thus, their chances of
mastery and successful execution are aided greatly by
9
Table 1 Characteristics of three levels of language
proficiency
Level 1 skills
Interpersonal
Context-imbedded
Labeling
Basic syntax
Pronunciation/articulation
Level 2 skills
Self-regulatory
Less contextual
Level 3 skills
Higher-order thought
Most abstract
Basic reasoning
Verbal intelligence
Abstract reasoning
Strategic thinking
Comprehension monitoring, etc.
the automatization of early-acquired level 1 skills such
as pronunciation, word retrieval, and early syntactic
features. In Cummins's (1983) framework, such automatized early skills are described as "cognitively undemanding." For a child with less developed level 1 skills,
such as many deaf children, as discussed above, the acquisition of level 2 skills may be interfered with or delayed, both directly by a lack of automatization of level
1 skills and more indirectly as a result of more restricted language experience.
For both primary and second-language learners,
additional internal pragmatic language skills are acquired with further mastery and automatization of
both level 1 and level 2 skills as the learner gains experience with the language. These skills would be involved in more abstract uses of language, such as might
be used in reasoning about complex abstract systems,
or in sophisticated metalinguistic analyses. These skills
are grouped here as level 3 skills, although they may
themselves be more properly represented by multiple
levels. Characteristics of these three levels of language
proficiency are summarized in Table 1.
In terms of the language proficiency required for a
strategy such as rehearsal to be used spontaneously, we
have argued (Bebko, 1996; Bebko & Metcalfe-Haggert,
1997) that it is the acquisition of and fluency with level
2 skills that is critical. Thus, level 1 skills, such as are
measured by tasks like the RAN task (i.e., rapid word
identification and labeling), are found to be important
skills, but they account for only part of the language
proficiency contribution to rehearsal.
Some support for the significance of level 2 skills
in rehearsal came from a closer examination of the LPP
10 Journal of Deaf Studies and Deaf Education 3:1 Winter 1998
data in our previous studies. Questions that discriminated best between (deaf and hearing) rehearsers and
nonrehearsers appeared to be those that tapped into
some of the somewhat abstract and context-free uses of
language. Examples are describing "clearly and completely the details of complex systems or things that are
not present" (e.g., the operation of a 10-speed bike) or
giving enough background information on a topic that
has a lot of new information so that even a stranger
could understand it. These assumed level 2 skills contrast with earlier level 1 types of skills, such as "(using)
parts of the questions asked by someone else to build
his/her answer" or "(identifying) objects and actions
in pictures." Thus, performance on questions that appear to be associated with level 2 skills seems to predict
rehearsal strategy use most directly. Clearly these
findings are only tentative and in need of replication.
However, they provide at least initial support for the
view that language proficiency, in the context of rehearsal, involves both the automatization of primary
language skills and the development of secondary level
skills. In Figure 2, this would be represented by the addition of a "spontaneous rehearsal" box connected by
an arrow from the level 2 language box.
The Role of Language Proficiency and
Automatization in Reading
Our studies to date have focused in considerable detail
on the complex role of language in a relatively simple
cognitive activity such as rehearsal. But what of more
complex activities? Bebko, Dale, and Greenberg (unpublished manuscript, 1991) developed a model that
readily adapted components of the rehearsal model in
Figure 1 to the highly complex process of reading, particularly in addressing the reading skills of deaf students. Since reading involves linguistic encoding, automatized language processes, and short-term memory
skills, as well as other related component skills, elements of the previous two models were directly incorporated into the Bebko, Dale, and Greenberg reading
model presented in Figure 3. For example, the three
levels of language proficiency outlined in Figure 2 are
here explicitly represented, as well as the importance
of automatization of them. A number of studies have
identified the role played by simple automatized naming skills in reading proficiency (cf, Guttentag &
Haith, 1978; Stanovich, Cunningham, & West, 1981),
leading to the conclusion that it is one of the primary
factors associated with skilled reading from early stages
through to adulthood (Lovett, 1987).
To the extent that automatized naming skills reflect
a general underlying skill of accessing arbitrary linguistic symbols rapidly, then the applicability of the model
to the deaf population would seem to be straightforward. However, it is likely that rapid naming skills in a
particular language are linked more specifically to
reading skills in that language, implying a narrower interpretation of the model. One difference between
memory strategies and reading is that memory strategies are essentially self-regulatory cognitive activities
that can make use of the child's internal idiosyncratic
code system. Reading, however, involves interaction
with a standardized, external coding system that is
likely to be somewhat dissimilar to the inner codes of
deaf children, unless the primary mode of communication for the child has been the language in which the
text is written.
Mayer and Wells (1996), for example, have argued
that language skills in ASL are not predictive of reading abilities in English or in other languages with a
written form. The reason posited is that ASL and written English are derived from quite different roots. Vygotsky (1934/1962) argued that written language is a
highly abstracted form of inner speech (language-based
thinking), which itself occurs developmentally earlier.
In addition, spoken language is assumed to provide the
initial bridge between the written form of the language
and the individual's inner speech (Vygotsky, 1934/
1962). The written form of English, for example, is an
alphabet-based system, and hearing readers appear dependent on phonetic recoding for reading to be efficient (cf, Hanson, 1989). This recoding may initially
occur overtly and subsequently become more internal,
although still phonetically-based, as the child becomes
more skilled at reading.
However, for deaf children, inner speech appears to
be more visual-spatial in nature (Jamieson, 1995;
Mayer & Wells, 1996), often incorporating elements of
signs. Indeed, in some of the studies in our lab, deaf
students reported using visual and manual properties
of signs to encode and rehearse information for later
recall. Similar findings have been reported previously
by Klima and Bellugi (1979) and more recently by Wil-
Language, Memory, and Cognition
guaga
Exparianoa
\
x.
Lawall
leitguag*
11
DaVari^aSf^afit
Laval2
languaga
Laval3
languaga
\
'
EartyRaadlng
aUDa
Advanced
Riarftng
ProncJaiiclaa
Figure 3 A model of the contributors to different levels of reading skills (Bebko, Dale, &
Greenberg, unpublished manuscript, 1991).
son and Emmorey (in press). Evidence of a different
type can also be found in the presence of manual interference effects when deaf subjects have been given
manual tasks to do (e.g., finger tapping) during memory tasks (Marschark & Mayer, in press).
Given this fundamental difference in the nature of
the inner speech of children who are deaf, the isomorphic relationship between the written form of a
language such as English and the inner speech of the
individual is considerably compromised. If ASL had
a common written expression, the correspondence
might be restored, with manually expressed ASL serving as the bridge to inner speech. However, ASL, at
present, has no accepted written form. Therefore, facility with ASL as a first language may not, in itself, be
predictive of good reading skills in English.
Hanson (1989) summarized a series of studies that
examined the importance of phonological information
in English text for hearing good readers and deaf good
readers. The central finding of the studies reviewed
was that good readers, both hearing and deaf, accessed
rapidly, and made use of, the phonological information
in written text. This finding was true for readers of all
ages, and the participants in the studies were described
as being not generally from oral backgrounds. Akamatsu and Fischer (1991), wh^le not examining reading
directly, found that deaf students with better levels of
English fluency were able to take better advantage of
semantic and syntactic relations within sentences or
lists of words, compared with deaf students with lower
levels of English proficiency. Recognizing and taking
advantage of semantic and syntactic information are
skills that are obviously closely linked with fluid comprehension during reading. Thus, while the evidence is
not yet unequivocal, it would appear that facility with
English, rather than language skill generally, is more
directly related to reading skills in English. Therefore,
the language proficiency components of the model in
Figure 3 should be assumed to refer to English language skills, or another language in which the text is
written. Similarly, the automatization required of those
skills is assumed to be in the specific language of the
textual material.
Within this context, the automatizing of level 2
skills has not been examined in depth to date, but the
need for these skills has been clearly demonstrated,
both in the metacognitive literature, such as Baker's
(1982) work linking metacognitive skills with reading
development, and in the variety of studies that have
identified performance on verbal intelligence measures
as a significant predictor of reading achievement (e.g.,
Berninger, 1986). Berninger found that both individual
differences on a simple picture vocabulary measure
(level 1), and higher-level skills, such as phonemic analysis and verbal definition performance, were significantly related to early reading achievement.
In the deafness literature, Gibbs (1989) reported a
similar finding on the importance of level 2 languagebased metacognitive measures on reading achievement.
Deaf high school students' performance on measures
that assessed skills such as detecting knowledge violations in the text, nonsense words, and internal consistencies correlated highly with reading achievement.
The inclusion in the model of the different levels of
12 Journal of Deaf Studies and Deaf Education 3:1 Winter 1998
language skills and their mastery is meant to incorporate into it an interactive, dual processing view of reading, involving both bottom-up (text-elicited) processes,
such as rapid access to linguistic symbols, and topdown (reader-generated) processes, for example, metacognitive skills, as in comprehension monitoring.
Another component represented in the reading
model in Figure 3 as a distal predictor is the contribution of general cognitive development. This box refers
to nonlinguistic intellectual skills in particular, since
skills in the language-based intellectual domain would
be closely linked to the components already discussed
that are related to language proficiency. Nonlinguistic
(e.g., visual-spatial) cognitive skills have been found to
make independent contributions to reading achievement, and so are included here for completeness. For
example, for hearing children Berninger (1986) reported that visual pattern recognition skills were important predictors of variation in early reading achievement, and Palmer, Macleod, Hunt, and Davidson
(1985) reported similar results from a speed of identification of visual symbols task with adults. A similar
contribution from visual-spatial cognitive skills and
from visual memory skills has been found for hearing
impaired readers (Kusche & Greenberg, 1991; Spencer & Delk, 1989).
One important feature of our model is the differentiation of reading skills into basic and more advanced
skills. This analysis is meant to parallel the development of reading skills in deaf students. According
to the model, with automatization of levels 1 and 2
English-language skills, and the contribution of the
other component pieces represented in the model, basic reading skills are usually acquired. However, the
transition from basic reading to more advanced reading
is not necessarily a simple task.
In our model, the near predictors of advanced reading proficiency are early reading skills, level 3 language
skills, and additional automatizing of earlier language
skills. The impact of level 3 skills has been demonstrated through the variety of active strategies involved
in reading. For example, rehearsal and speech recoding
in short-term memory have been identified as important skills for integrating information within and across
sentences, both essential comprehension skills (Baddeley, 1979; King and Quigley, 1985; Levy, 1977).
Other important strategies involved in reading have
included the accessing of existing cognitive schemata
to provide a basis for organizing and chunking input
and the spontaneous use of inferences. In particular,
with more advanced reading materials, linguistic structures typically become more complex, and inferences
increasingly need to be made, thus placing a correspondingly heavier demand on these short-term memory and related processing strategies. Paris and Lindauer (1976) found that recall of sentences when an
inference was involved was poorer in young hearing
children (third grade) than for older children (fifth
grade), attributing the result to inappropriate strategy
utilization in the generation of inferences. This finding
is important because of the increasing need for inference in children's texts with grade, particularly beyond
the third or fourth grade level (King & Quigley, 1985).
Wilson, Karchmer, and Jensema (1978) found deaf students to have particular difficulties with inferencing
and hypothesized that this might be associated with
the frequent leveling out of reading skill acquisition
around the fourth grade.
The increased automatization of earlier skills has
also been identified as important in discriminating between proficient and less skilled readers. Ehri and
Wilce (1983) suggested that additional speed increases
after initial automatization of word recognition skills
are associated with the highest levels of reading
achievement. Others have found that as children progress from initial reading skills to more advanced skills,
a dominance by top-down processing gives way to a
balance with bottom-up processing, as automatization
of lower level skills occurs Quel, 1980; King & Quigley,
1985). Until that time, younger and poorer readers
appear to rely excessively on top-down skills, such
as prior knowledge and immediate contextual cues
for word identification and comprehension. In contrast, truly skilled readers use fast, automatized, welldeveloped text-driven skills in conjunction with their
top down processing.
The model, then, correspondingly incorporates the
implication that if language skills are not additionally
automatized beyond what is required for early reading
skills, then the total mental effort associated with the
increased linguistic loading, and the additional content
requirements of the reading, will exceed the individu-
Language, Memory, and Cognition
al's available processing resources, working against the
acquisition of more sophisticated reading skills. Those
skills associated with advanced reading are represented
in the Advanced Reading Proficiency box in the model.
The additional automatization required is represented
by the bottom-most horizontal line in the figure.
Therefore, to the extent that a deaf (or any other) student may have difficulty with this additional automatization of English language skills, or the acquisition of
higher-level English language skills, then a limitation
in English reading skills might be expected, corresponding to a ceiling at the more basic level referred to
in the model as Early Reading Skills.
Summary and Overview of the Model
Clearly, elements of the model represented in Figure 3
remain untested, such as the contribution of automatized level 2 skills to reading. Nonetheless, a number of
the elements have been tested in the context of research
associated with the models presented earlier (Figures 1
and 2) or have been supported in previous studies.
These have included the link between language experience and the development of early, presumably level 1
and level 2, language skills (e.g., Bebko & McKinnon,
1990; Bebko & Metcalfe-Haggert, 1997). Similarly, automatization of at least level 1 language skills has been
found to be associated with good early reading skills
through tasks involving the rapid naming of stimuli
(e.g., Guttentag & Haith, 1978; Lovett, 1987; Stanovich et al., 1981). Other studies have shown that in addition to language-based cognitive skills, nonlinguistic
cognitive development makes an independent contribution to at least early reading skills (e.g., Berninger,
1986; Kusche & Greenberg, 1991; Palmer, Macleod,
Hunt, & Davidson, 1985). Thus, while other elements
of the model may await further research to be corroborated, the role of developing language proficiency appears to be a robust one, of clear relevance to discussions of reading skills.
Broader Applications and Future Research Directions
The goal in presenting the present model was to enable
additional hypotheses to be generated, which might
serve to stimulate further research in the field. For ex-
13
ample, one area in which initial hypotheses have been
generated (Bebko, 1996) is the ability of the model to
account for the impact on reading achievement of potential learning disabilities among deaf children. Depending on the nature of the learning disabilities (for
example, problems with the automatization of language
skills, or differences in the organization of long-term
memory [Marschark, 1996]), an impact on reading
through various different components of the model
would be predicted. Research in these and similar directions would help to clarify the interrelation among
the major elements proposed in the model and the potential impact of processing differences among these elements on eventual reading skills. The fruits of such
investigations would in turn provide critical tests of the
theoretical relations represented in the model and
hopefully would lead to suggested avenues for specialized educational interventions.
References
Akamatsu, C T , & Fischer, S. D. (1991). Using immediate recall
to assess language proficiency in deaf students. American Annals of the Deaf, 136, 428-434.
Baddeley, A. (1979). Working memory and reading. In H. Bouma
(Ed.), Processing of visible language (vol. 1) (pp. 355-370).
New York: Plenum Press.
Baker, L. (1982). An evaluation of the role of metacognitive
deficits in learning disabilities. Topics in Learning and Learning Disabilities, 2, 27-35.
Bebko, J. M. (1984). Memory and rehearsal characteristics of
profoundly deaf children. Journal of Experimental Child Psychology, 38, 415-428.
Bebko, J. M. (1996). Learning, language and memory: Potential links with reading skills. Invited address: Conference on Learning Disability, Neuropsychology and Deaf
Youth: Theory, Research and Practice. Seattle, WA: April
1996.
Bebko, J. M., Bell, M., Metcalfe-Haggert, A., & McKinnon, E.
(19%). Language proficiency and the prediction of spontaneous rehearsal use in children who are deaf. Manuscript
submitted for publication. York University, North York,
Ontario, Canada.
Bebko, J. M., Kennedy, C , & Metcalfe-Haggert, A. (1992). Automatized language skills in hearing and deaf children: The
link with memory processing strategies. Paper presented at
the University of Waterloo Conference on Child Development; Waterloo, Ontario; May 1992.
Bebko, J. M., & McKinnon, E. E. (1987). The Language Proficiency Profile-I. York University, North York, Ontario,
Canada.
Bebko, J. M., & McKinnon, E. E. (1990). The language expert-
14
Journal of Deaf Studies and Deaf Education 3:1 Winter 1998
cnce of deaf children: Its relation to spontaneous rehearsal
in a memory task. Child Development, 61, 1744-1752.
Bebko, J. M., & Metcalfe-Haggert, A. (1997). Deafness, language skills and memory: A model for the development of
spontaneous rehearsal use. Journal of Deaf Studies and Deaf
Education, 2, 131-139.
Bebko, J. M., Metcalfe-Haggert, A., & Hansen, J. (1991). Does
the automatization of language reduce the mental effort of
cumulative rehearsal in children? Poster presented at the biennial meeting of the Society for Research in Child Development; Seattle, Washington; April 1991.
Bebko, J. M., & Ricciuti, C (1995). Memory strategy use in children with autism. Poster presented at the biennial meeting
of the Society for Research in Child Development; Indianapolis, Indiana; April 1995.
Berninger, V. W. (1986). Normal variation in reading acquisition.
Perceptual and Motor Skills, 62, 691-716.
Cummins, J. (1983). Language proficiency and academic
achievement. In J. W. Oiler, Jr. (Ed.), Issues in language testing research. Rowley, MA: Newbury House.
Cummins, J. (1984). Bilingualism and special education: Issues
in assessment and pedagogy. England: Multilingual Matters.
Cummins, J. (1989). Institutionalized racism and the assessment
of minority children: A comparison of policies and programs
in the United States and Canada. In R. J. Samuda, S. L.
Kong, J. Cummins, J. Pascual-Leone, & J. Lewis (Eds.), Assessment and placement of minority students (pp. 173-189). Toronto: C. J. Hogrefe.
Denckla, M., & Rudel, R. (1974). Rapid 'automatized' naming
of pictured objects, colors, letters and numbers by normal
children. Cortex, 10, 186-202.
Ehri, L. C , & Wilce, L. S. (1983). Development of word identification speed in skilled and less skilled beginning readers.
Journal of Educational Psychology, 75, 3—18.
Francis, J. (1980). The evaluation of language proficiency. Washington, D G Gallaudet University.
Francis, J., Garner, D , & Harvey, J. (1978). KDES language curriculum guide: A pragmatic approach to language for teachers of
deaf children. Washington, DC: Kendall Demonstration Elementary School.
Gibbs, K. W. (1989). Individual differences in cognitive skills related to reading ability in the deaf. American Annals of the
Deaf, 135, 214-218.
Guttentag, R. E., & Haith, M. M. (1978). Automatic processing
as a function of age and reading ability. Child Development,
49, 707-716.
Hanson, V. L. (1989). Phonology and reading: Evidence from
profoundly deaf readers. In D. Shankweiler, & I. Y. Liberman (Eds.), Phonology and reading disability: Solving the
reading puzzle. Ann Arbor, MI: University of Michigan
Press.
Jamieson, J. (1995). Visible thought: Deaf children's use of
signed and spoken private speech. Sign Language Studies,
86, 63-79.
Juel, C (1980). Comparison of word identification strategies
with varying context, word type, and reader skill. Reading
Research Quarterly, 3, 358-376.
King, C , & Quigley, S. (1985). Reading and deafness. San Diego:
College-Hill Press.
Klima, E. S., & Bellugi, U. (1979). The signs of language. Cambridge, MA: Harvard University Press.
Kusche, C A., & Greenberg, M. T. (1991). Cortical organization
and information processing in deaf children. In D. S. Martin
(Ed.), Advances in cognition, education and deafness. Washington, DC: Gallaudet University Press.
Levy, B. (1977). Reading: Speech and meaning processes. Journal of Verbal Learning and Verbal Behavior, 16, 623—638.
Lovett, M. W. (1987). A developmental approach to reading disability: Accuracy and speed criteria of normal and deficient
reading skill. Child Development, 58, 234-260.
Marschark, M. (1993). Psychological development ofdeafchildren.
New York: Oxford University Press.
Marschark, M. (1996) Mental representation and memory: Cognitive implications for deaf learners. Invited address: Conference on Learning Disability, Neuropsychology and Deaf
Youth: Theory, Research and Practice. Seattle, WA; April
1996.
Marschark M., & Mayer, T. S. (in press). Mental representation
and memory in deaf adults and children. In M. Marscahrk &
M. D. dark (Eds.), Psychological perspectives on deafness (vol.
2). Mahwah, NJ: Lawrence Erlbaum.
Mayer, C , & Wells, G. (1996). Can the linguistic interdependence theory support a bilingual-bicultural model of literacy
education for deaf students? Journal of Deaf Studies and Deaf
Education, 1, 93-107.
Paris, S. G., & Lindauer, B. K. (1976). The role of inference in
children's comprehension and memory for sentences. Cognitive Psychology, 8, 217-227.
Palmer, J., Macleod, C M., Hunt, E., & Davidson, J., E. (1985).
Information processing correlates of reading. Journal of
Memory and Language, 24, 59—88.
Pintner, R., & Paterson, D. G. (1917). A comparison of deaf and
hearing children in visual memory for digits. Journal of Experimental Psychology, 2, 76-88.
Spencer, P., & Delk, L. (1989). Hearing-impaired students' performance in tests of visual processing: Relationships with
reading performance. American Annals of the Deaf, 135,
333-337.
Stanovich, K. E., Cunningham, A. E., & West, R. F. (1981). A
longitudinal study of the development of automatic recognition skills in first graders. Journal of Educational Psychology,
73, 809-815.
Vygotsky, L. S. (1934/1962). Thought and language. Translated in
1962. Cambridge, MA: MIT Press.
Wilson, M., & Emmorey, K. (in press). A "phonological loop" in
visual-spatial working memory: Evidence for American Sign
Language. Memory and Cognition.
Wilson, K., Karchmer, M., & Jensema, C (1978). Literal vs. inferential item analysis of reading achievement in hearingimpaired students. In H. Reynolds & C Williams (Eds.),
Proceedings of the Gallaudet Conference on Reading in relation
to deafness (pp. 154-170). Washington, DC: Gallaudet
College.-