DEVELOPMENTAL NEUROPSYCHOLOGY, 29(1), 61–92
Copyright © 2006, Lawrence Erlbaum Associates, Inc.
Early Development of Language by
Hand: Composing, Reading, Listening,
and Speaking Connections;
Three Letter-Writing Modes;
and Fast Mapping in Spelling
Virginia W. Berninger, Robert D. Abbott, and Janine Jones
Department of Educational Psychology
University of Washington
Beverly J. Wolf
The Slingerland® Institute for Literacy
Renton, WA
Laura Gould, Marci Anderson-Youngstrom,
and Shirley Shimada
Department of Educational Psychology
University of Washington
Kenn Apel
Department of Communication Disorders
Florida State University
The first findings from a 5-year, overlapping-cohorts longitudinal study of typical
language development are reported for (a) the interrelationships among Language by
Ear (listening), Mouth (speaking), Eye (reading), and Hand (writing) in Cohort 1 in
1st and 3rd grade and Cohort 2 in 3rd and 5th grade; (b) the interrelationships among
three modes of Language by Hand (writing manuscript letters with pen and keyboard
and cursive letters with pen) in each cohort in the same grade levels as (a); and (c) the
Correspondence should be sent to Virginia W. Berninger, Department of Educational Psychology, University of Washington, 322 Miller, Box 353600, Seattle, WA 98195–3600. E-mail: vwb@
u.washington.edu
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ability of the 1st graders in Cohort 1 and the 3rd graders in Cohort 2 to apply fast
mapping in learning to spell pseudowords. Results showed that individual differences in Listening Comprehension, Oral Expression, Reading Comprehension, and
Written Expression are stable developmentally, but each functional language system
is only moderately correlated with the others. Likewise, manuscript writing, cursive
writing, and keyboarding are only moderately correlated, and each has a different set
of unique neuropsychological predictors depending on outcome measure and grade
level. Results support the use of the following neuropsychological measures in assessing handwriting modes: orthographic coding, rapid automatic naming, finger
succession (grapho-motor planning for sequential finger movements), inhibition, inhibition/switching, and phonemes skills (which may facilitate transfer of abstract letter identities across letter formats and modes of production). Both 1st and 3rd graders
showed evidence of fast mapping of novel spoken word forms onto written word
forms over 3 brief sessions (2 of which involved teaching) embedded in the assessment battery; and this fast mapping explained unique variance in their spelling
achievement over and beyond their orthographic and phonological coding abilities
and correlated significantly with current and next-year spelling achievement.
Language is not a unitary construct. A distinction is often made among language,
reading, and writing, but this distinction is not meaningful. One of the important
discoveries during the last 3 decades of the 20th century was that reading is not a
visual perceptual activity—rather, it is language (e.g., Mattingly, 1972; Vellutino,
1979). Although a distinction is also made between oral and written language, this
distinction is also not likely sufficient, and neither is another well-documented distinction between receptive language (processing language input) and expressive
language (producing language output; Semel, Wiig, & Secord, 2003). Liberman
(1999) provided the missing conceptual framework when he pointed out that because language has no end organs of its own it teams up with the sensory (auditory
and visual) and motor systems (mouth/oral-motor and hand/ grapho-motor) to
make contact with the environment. Hence, there is Language by Ear (listening to
aural language input), Language by Mouth (producing oral language output), Language by Eye (reading written language input), and Language by Hand (producing
written language output). Language by Ear, Language by Mouth, Language by
Eye, and Language by Hand are each complex functional systems that draw on
common as well as unique brain processes to achieve different goals (Luria, 1973).
Programmatic research at the University of Washington since 1989
(cross-sectional, longitudinal, and instructional studies) has shown that these functional systems are both separate and interacting systems that have their own developmental trajectories (Berninger, 2000), and the functional systems for reading
and writing are more interrelated within levels of language (text or word levels)
than across these levels (text in one and word in the other; Berninger, Abbott,
Abbott, Graham, & Richards, 2001). Initially, the focus of the research program
was on the development of the functional writing system. Figures 1, 2, and 3 sum-
LANGUAGE BY HAND
FIGURE 1
63
Handwriting development.
marize some of the findings about the development of writing as a functional language system from this programmatic research and other research laboratories (for
review, see Berninger, 1994, 1995; Berninger & Abbott, 2001). The first two
stages depicted in Figure 1 for handwriting development should be familiar to developmental neuropsychologists who assess infants and toddlers. The third stage
in Figure 1 covers tasks typically practiced and often mastered during the preschool years. The fourth stage covers activities typically mastered by the middle or
end of first grade. Note that whereas the first two stages rely greatly on perceptual
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FIGURE 2
Spelling development.
and motor skills and integrating them, the last two stages rely on coordinating language (names of letters) with the act of writing alphabet letter forms. By early elementary school, handwriting is neither solely a visual (Berninger et al., 1992) nor
solely a motor (Abbott & Berninger, 1993) process—it is an integration of orthographic codes (letter forms), phonological codes (names), and grapho-motor codes
(output). It is Language by Hand (Berninger, 2000).
As shown in Figure 2, early spelling development relies greatly on analysis of the
phonological word form (speech sounds in spoken words) and proceeds from a phonetic, to partially phonemic, to fully phonemic spelling in representing speech with
LANGUAGE BY HAND
FIGURE 3
65
Early composition development.
alphabet letters (see Ehri, 1992; Treiman, 1993; Treiman & Bourassa, 2000). Spelling is never purely visual—orthographic representations of words are created by
mapping them onto their spoken word counterparts (Berninger, 1994, 1995;
Varnhagen, Boechler, & Steffler, 1999). (For distinction between visual and orthographic processes, see Berninger & Richards, 2002.) Once mapping relationships
between phonology and orthography for words are learned, an autonomous orthographic lexicon begins to be constructed, in which orthographic word forms are represented independent of phonological word forms (Olson, Forsberg, & Wise, 1994),
linked to spoken words and meaning (Ehri, 1980a, 1980b), and retrieved automatically (Steffler, Varnhagen, Friesen, & Treiman, 1998). This orthographic stage of
rapid expansion of the autonomous orthographic lexicon is followed by a morphological stage in which children learn to spell the morphological variants of base
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words and the spelling rules for transforming letters at the end of the base word or beginning letters in suffixes (e.g., Bear, Invernizzi, Templeton, & Johnston, 2004;
Carlisle, 1994; Dixon & Englemann, 2001). Although spelling development appears to proceed from a primarily phonological to orthographic to morphological
stage (Bear & Templeton, 1998; Templeton, 2004), from the beginning children are
learning to coordinate phonological, orthographic, and morphological information
(e.g., Apel & Masterson, 2001; Berninger & Richards, 2002; Carlisle, 1994; Henry,
2003; Nagy, Osborn, Winsor, O’Flahaven, 1994; Venezky, 1970, 1999).
Likewise, as portrayed in Figure 3, written composition develops in predictable
stages with several phases preceding the emergence of genre-specific discourse
structures for narrative and expository writing. Just as oral expression proceeds
from a one-word stage to word combinations to more complex constructions with
syntactic and discourse structures, so does written composition, which continues
to develop from kindergarten to middle school in predictable ways (Berninger,
Fuller, & Whitaker, 1996).
The focus of our research has expanded beyond the domain of writing as a functional system to the studyof the development of the connections between writing and
other functional language systems. In this article, we report the first findings regarding these connections between writing and other language systems from an
in-progress 5-year longitudinal study that has an overlapping cohorts design and annually collects a half-day of assessment data from each participating child. One cohort began in first grade and will be studied until fifth grade. The second cohort began
in third grade and will be studied until seventh grade. The two cohorts overlap in the
third, fourth, and fifth grades. In this article, we focus on the connections among Language by Ear, Language by Mouth, Language by Eye, and Language by Hand when
the goal is high-level meaning primarily at the text level (although subword- and
word-level processes also contribute to the text processing or production): listening
comprehension, oral expression, reading comprehension, and written expression.
This meaning-driven language function is what educators at the end of the 20th centuryoften refer to as “authentic.” We began with the connections among the language
systems at this level of analysis because we have found in early intervention studies
for reading that not only phonological decoding but also oral comprehension and
reading comprehension played a role in explaining some reading disabilities and in
effectivelyteaching at-risk readers (Berninger et al., 2002; Berninger et al., 2003).
However, in keeping with the theoretical framework guiding the programmatic
research, we also examine other levels of language in the functional writing system
that prior research has shown are of fundamental importance early in schooling for
preventing writing problems later in schooling (for review of evidence, see
Berninger & Amtmann, 2003). These levels of language include subword-level
transcription (letter production) and word transcription (spelling).
Traditionally, letter production involved two formats children had to learn during schooling: manuscript letters and cursive letters. Cursive letter writing has be-
LANGUAGE BY HAND
67
come a standard component of multisensory and structured language treatment of
dyslexia (Birsch, in press; Clark & Uhry, 1995; Stempel-Mathey & Wolf, 1999).
Beverly J. Wolf proposed that if we understood better the relationship between
manuscript and cursive writing in typically developing students we might better
understand the mechanisms of how cursive writing may contribute to treating reading disabilities and possibly to normal reading development. However, manuscript
and cursive writing are not the only forms of letter production that school-age children must master: The rapid explosion of computer tools in home and schools is
leading to educational evolution as many preschoolers are becoming adept at using
a mouse to operate a computer keyboard and many school-age children use computers routinely at home as well as school to read and produce written text and access information on the Web (Berninger & Winn, in press). Although it is widely
assumed that children who struggle with handwriting can bypass this difficulty by
using a keyboard to produce written language, this assumption has received little
research attention. For all of these reasons, the research aims of the longitudinal
study include investigation of the normal developmental trajectories in producing
manuscript, cursive, and keyboard letters and the neuropsychological predictors of
these alternative modes of letter production.
Written spelling involves more than letter writing or memorizing visual forms
of words. Like handwriting, it is not merely a motor or visual process. Beginning
spelling requires learning to create connections between the spoken and written
forms of words (see Figure 2). A longitudinal study of at-risk spellers demonstrated that making second-grade children explicitly aware of alphabetic principle
(correspondences between phonemes and one- and two-letter units), onsets and
rimes, and lexical units (naming all the letters in a word and the corresponding spoken name) significantly improved their spelling (Berninger et al., 1998) and the
gains were maintained at the beginning and end of third grade (Berninger et al.,
2000); also, teaching syllable awareness (counting syllables in spoken words and
categorizing them on the basis of the syllable types in English) had an added advantage in helping the slower responders during third grade when they made additional gains (Berninger et al., 2000). However, not all children need this amount
and intensity of explicit spelling instruction to become aware of the orthographic
and phonological units and their connections in spelling written words. Ken Apel
posed the hypothesis that fast mapping, which has been shown to be a mechanism
for language learning in preschoolers (e.g., Dollaghan, 1987; Rice, Buhr, &
Nemeth, 1990; Storkel & Rodgers, 2000), might also play a role in how typically
developing childen learn to spell written words early in the school years.
Thus, three sets of research questions were addressed in this initial investigation
within the larger longitudinal study. First, when functional language systems tied
to specific senses (auditory for listening or visual for reading) or motor senses
(oral-motor for speaking or grapho-motor for writing) develop the high-levels
goals of understanding or communicating meaning, do they reflect (a) one under-
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lying language system that shares common resources, or (b) distinct language systems that share some common resources but also develop unique signatures as they
evolve in interactions with the social and physical environment? Moreover, to what
degree do relationships among the four functional language systems seem to be
fixed over time or flexible over time?
Second, do they create separate functional systems for Language by Hand that
depend on the modality of output—for example, for manuscript letters, cursive letters, and letters produced by keyboard press? Are there different neuropsychological correlates and unique predictors of these functional grapho-motor output
systems that provide a window on understanding the individual differences that influence how Language by Hand develops? We did not study every possible variable that might explain handwriting but rather a set of neuropsychological predictors validated in prior literacy research to evaluate which of these uniquely
contributed to each handwriting mode within and across grade levels.
Third, does fast mapping (see Apel, Wolter, & Masterson, this issue, who studied this phenomenon in preschoolers) play a role in how typically developing first
and third graders learn to spell novel words? By investigating how children learn to
spell a small set of taught unfamiliar pseudowords across repeated teaching trials
close in time, we may discover whether individual differences in learning to apply
fast-mapping procedures to link phonological word forms and orthographic word
forms may contribute uniquely to learning to spell over and beyond the phonological and orthographic awareness needed to learn to the alphabetic principle underlying American spelling (Venezky, 1970, 1999).
METHOD
Sample
This sample was recruited from the Seattle Public Schools and other local
schools. A letter announcing the opportunity to participate in a research study
was sent to all parents of children who would be entering first grade or third
grade in the fall. Interested parents contacted the research coordinator who explained the study and obtained informed consent from those parents who decided to enroll their child and bring their child to the university once a year for
the next 5 years to complete a comprehensive assessment of writing, reading,
and related processes. Of the 128 first graders (71 girls and 57 boys) who were
assessed in Year 1, 122 (68 girls and 54 boys) also participated when they were
in third grade (Cohort 1). Of the 113 third graders (57 girls and 56 boys) who
were assessed in Year 1, 106 (54 girls and 52 boys) also participated when they
were in fifth grade (Cohort 2). Overall, the attrition rate has been low and is
mainly due to families moving to another state.
LANGUAGE BY HAND
69
The participants reflected diversity in self-reported ethnicity and parents’ level
of education (one indicator of socioeconomic background). In Year 1, children of a
variety of ethnic backgrounds participated: Asian American (23.4%, Cohort 1;
21.2%, Cohort 2), African American (6.3%, Cohort 1; 9.7%, Cohort 2), European
American (64.8%, Cohort 1; 65.5%, Cohort 2), Hispanic (1.6%, Cohort 1; 0.9%,
Cohort 2), Native American (1.6%, Cohort 1 only), and other (2.3%, Cohort 1;
2.7%, Cohort 2). About 7% of the parents had less than a high school education or
graduated from high school (7% mothers and 12.5% fathers, Cohort 1; 7.1% mothers and 7.1% fathers, Cohort 2). About 11% of the parents had more than a high
school education but less than a college education (11.7% mothers and 7.8% fathers, Cohort 1; 11.5% mothers and 14.2% fathers, Cohort 2). About 40% of the
parents had an undergraduate education (45.3% mothers and 39.8% fathers, Cohort 1; 50.4% mothers and 36.3% fathers, Cohort 2). About 33% of the parents had
completed graduate degrees (33.6% mothers and 32.0% fathers, Cohort 1; 30.1%
mothers and 35.4% fathers, Cohort 2). Information on parental level of education
was missing for 2.4% of the mothers and 7.9% of the fathers in Cohort 1 and 0.9%
of the mothers and 7.2% of the fathers in Cohort 2.
Parent questionnaires documented that these children had prior experience with
computers and keyboards both at home and at school. This finding is not unexpected, given the recent widespread availability of computers in the majority of
homes in North America but does contrast markedly with what we found in our
first studies 15 years ago.
First Research Question
Tests. Four subtests of the Wechsler Individual Achievement Test, 2nd edition (WIAT II; The Psychological Corporation, 2001) were administered according to the manual: Listening Comprehension, Oral Expression, Reading Comprehension, and Written Expression. The Listening Comprehension test has receptive
and expressive vocabulary and sentence comprehension items. The student selects
a picture that portrays a word or sentence meaning or generates a word that
matches a picture or oral description. The Oral Expression test has sentence repetition (Grades 1 to 3 only), word fluency, visual passage retell, and giving directions
items. The student repeats a sentence spoken by the examiner, says examples of
specified categories with a brief time limit, orally explains stories depicted in a series of cartoon pictures, and gives oral directions for executing designated
real-world tasks that require multiple steps. The Reading Comprehension test involves reading passages in item sets for grade level. The student answers orally a
variety of kinds of questions including identifying factual information in the text
and inferential thinking (going beyond what is stated). The Written Expression test
has word fluency, sentence combining, and paragraph/essay writing (Grades 3 and
higher) items. The student writes examples of provided categories within a brief
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time limit, composes a new sentence containing each of two displayed sentences,
and writes a paragraph on one of two provided topics. The same topic was used at
each of the grade levels reported in this article for Years 1 and 3.
According to the table for age-based reliability coefficients in the examiner’s
manual for the WIAT, reliabilities for the ages of the children in the studies reported in this article (6–10) were as follows: Listening Comprehension (.78–.83),
Oral Expression (.83–.89), Reading Comprehension (.94–.97), and Written Expression (.81–.87). Responses were scored by school psychology graduate students who double-checked each others’ scoring, which had to meet final review by
the research coordinator or by Janine Jones, a licensed psychologist who has been
a trainer of school psychologists and investigator on this project.
Data analyses. Pearson product moment correlations were computed for
each of the four language measures for each cohort within and across the 1st and
3rd years of the longitudinal study.
Second Research Question
Tests. Three tasks were administered to assess writing alphabet letters in different modes. The task requirement was kept constant across the three tasks—produce each letter of the alphabet in lowercase format in alphabetic order as accurately and quickly as you can—except for mode of output. The mode of response
varied—to write manuscript letters with a pen on lined paper, to write cursive letters with a pen on lined paper, or to select letters on a computer keyboard and press
the key to produce a displayed letter on the monitor.
For the first two tasks, the written output was analyzed subsequently for accuracy. For the last task, the testers recorded the order and accuracy of the selected
key presses as the student produced them and saved the output and printed it at the
end of the session to use in checking the accuracy of their scoring while the child
was selecting and pressing keys. For all tasks, the testers recorded the last letter
produced at 15 sec (an index of automaticity) and total time.
The coding system for accuracy of manuscript and cursive letter writing required that, regardless of modality, the letters were produced in the correct order of
the alphabet. A point was awarded for each letter produced in the correct order; no
points were subtracted for omitted letters or letters in the wrong order. In addition,
regardless of modality, these criteria had to be met that (a) the letter could be accurately identified if it was alone outside word context, (b) relative proportionality of
component parts of the letter was maintained, and (c) the letter was not a reversal
or inversion of another letter. For both manuscript and cursive letters, models of
letter forms that would and would not be considered accurate were developed for
the coders. Additional criteria for assessing manuscript letter accuracy were the
same as used in prior research in this research group (e.g., Berninger et al., 1992).
LANGUAGE BY HAND
71
Beverly J. Wolf helped the team develop additional criteria for assessing the cursive letters: Stems should come close to the top line; tails should extend low
enough that they are clearly tails; each letter must contain a leader that could connect to a previous letter and a trailing part that could connect to the following letter;
and if letters are connected, they must connect correctly. Investigators who assessed the accuracy of manuscript and cursive writing achieved an interrater reliability of 98% agreement for manuscript letters (based on 10 samples at each grade
level) and 93.4% agreement for cursive letters (based on 25 samples at each grade
level) before scoring all protocols.
The following neuropsychological processes were assessed so that their potential contribution to these different modes of letter writing could be evaluated: phonological awareness, orthographic coding in temporary memory, and rapid automatized naming (RAN), because these are the three language phenotypes that were
found to be the best predictors of the word reading and spelling problems in the
University of Washington family genetics study of dyslexia and dysgraphia
(Berninger, Abbott, Thomson, & Raskind, 2001). In addition, we assessed
grapho-motor planning based on the finger succession task—dominant hand only
(which requires timed imitation of sequential finger–thumb touches without visual
cues), because this task had been found to be a reliable and unique predictor of
manuscript letter writing in previous studies of typically developing writers and
readers (Berninger & Rutberg, 1992; Berninger et al., 1992), and the executive
function of inhibiting irrelevant information while focusing on relevant information and inhibition/switching (inhibition while switching attention across categories or mental sets), because recent research shows these are additional important
phenotypes for dyslexia (Berninger & O’Donnell, 2004).
To assess the first four neuropsychological processes, we used subtests of the
PAL (Berninger, 2001), which are nationally normed versions of measures used in
our programmatic research: Phonemes (repeat word spoken by the examiner and
then repeat word and delete designated phoneme), Receptive (Orthographic)
Coding (briefly view a written word and then decide without the word present
whether another word matches it exactly, a single letter was in it, or a letter group
was in it), Rapid Automatic Naming of Letters (single letters in rows in one trial
and letter groups in rows in another trial), and Finger Succession (sequentially
touch finger and thumb five times in succession). For executive function, we used
Inhibition and Inhibition/Switching (across categories or set) of the Color Word
Form Test, which is a nationally normed version of the Stroop test (Delis Kaplan
Executive Function System; Delis, Kaplan, & Kramer, 2001), in children ages 8
and older for whom there are norms. All neuropsychological measures were
scored according to instructions in the test manuals.
Data analyses. To evaluate whether automaticity (number of correct letter
productions or selections in the first 15 sec), total accuracy, and total time were af-
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fected by the mode of letter writing, correlations were computed between each of
the modes of letter writing (two at a time) within Year 1 and within Year 3 for each
of the cohorts. Correlations and multiple regressions were then used to evaluate
whether the same neuropsychological processes were significantly correlated with
or uniquely predicted each of the letter writing modes within a grade level (first,
third, or fifth), across grade levels (first to third or third to fifth), and across samples (third grade when the two cohorts were overlapping at the same grade level).
Third Research Question
Teaching protocol. During Year 1 only, both cohorts participated in a
mini-instructional experiment. They were given two minilessons in spelling novel
(pseudowords) and a final spelling test in three sessions spaced at least 1 hr apart.
The pseudowords were constructed to reflect the degrees of predictability in
American spelling (Venezky, 1970, 1999), which was based on (a) the number of
options for spelling the phoneme, and (b) the degree to which correct spelling
could rely on phoneme–orthographic mapping only or orthographic memory only
and prior research results for primary grade spellers (e.g., Berninger et al., 1998).
Three pseudowords (het, lum, rab) reflected higher predictability, three (blea, voy,
tround) reflected moderate predictability, and three (quard, chelth, shing) reflected
lower predictability.
Procedures for each learning trial in which the tester took on the role of teacher
included the following. In Trial 1, teachers showed cards, each of which had one
pseudoword written on it, one at a time. They instructed the child to look at the
made-up word and listen while they pronounced it, and then they named each letter
in it in order. Then the child was asked to do the same. After imitating the teacher in
naming the word and its letters, the child was instructed to copy the word and name
it one more time. At all phases of this instruction, if the child could not imitate or
made an error, the teacher provided feedback and modeled a correct response.
Teachers used prompts like “name,” “spell,” and “copy” to cue the children for the
various steps in the procedure.
During Trial 2, the teacher pronounced the made-up word (but did not show the
card) and asked the child to spell the word in writing on the lines in the writing
booklet. If the child did not spell the word correctly, the teacher said “No, that is
not exactly right” and showed the child the card with the correct spelling; then the
teacher continued by naming the word and spelling the word by naming its letters
and instructing the child to write it. If the child did not write it correctly, the teacher
provided teaching feedback as in first trial. In Trial 3, the teacher pronounced each
word used and asked the child to spell each made-up word one more time on lines
provided in the writing booklet.
LANGUAGE BY HAND
73
Data analyses. For each trial of the mini-instructional experiment, children
received 1 point for each novel word that was correctly spelled. Partial credit was
not awarded, because the research question was whether they could learn fast mapping of phonological word forms onto orthographic word forms after only two
teaching trials. A two-way analysis of variance, with repeated measures across
three teaching trials and grade as a between-subjects variable, was used to analyze
whether significant improvement in spelling occurred across these brief exposures
to novel word forms and whether amount of learning varied with grade level of
speller. Scores on the third trial were entered into a multiple regression to evaluate
whether fast mapping contributed uniquely to spelling over and beyond phonological awareness, orthographic coding, and RAN.
RESULTS
Interrelationships Among Four Language Systems
Stability. Each of the four language systems assessed (listening comprehension, oral expression, reading comprehension, and written expression) was well
developed in Years 1 and 3 of this longitudinal study (see Table 1 for the means and
standard deviations for the total sample). For purposes of the analyses relevant to
this aims of our research, we identified children who had complete data on all four
language measures in both Years 1 and 3; results in Table 2 are based on these children. Each language system was significantly correlated between Grades 1 and 3
in Cohort 1 (range = .54–.60) and between Grades 3 and 5 in Cohort 2 (range =
.41–.64). (See Table 2.) Except for oral expression, which is typically less practiced in schools that emphasize listening to teachers and learning written language,
the correlations were slightly (not significantly) higher in the older children (Cohort 2) than the younger children (Cohort 1).
Intraindividual differences. When the relative level of development of two
language systems at a time was examined within the same individual (see Table 2),
the intercorrelations tended to be significant but in the low to moderate range, providing evidence that overall the four language systems of interest are not exactly
the same: a range of .26 to .53 in Grade 1 and .26 to .58 in Grade 3 for Cohort 1, and
a range of .13 to .57 in Grade 3 and .24 to .63 in Grade 5 for Cohort 2. The low to
moderate correlations cannot be attributed solely to the unreliability of the assessment instruments; these correlations were lower than the reported reliabilities for
the four language tests (see the Methods section).
For Cohort 1 in both Grades 1 and 3, the highest correlations were for (a) listening comprehension and reading comprehension and (b) reading comprehension
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TABLE 1
Means and Standard Deviations for WIAT II Listening Comprehension,
Oral Expression, Reading Comprehension, and Written Expression in a
Longitudinal Study of Typically Developing Language Learners
Year 1
1st Gradersa
Language Skill
Listening Comprehension
Oral Expression
Reading Comprehension
Written Expression
Year 3
3rd Gradersb
3rd Gradersc
5th Gradersd
M
SD
M
SD
M
SD
M
SD
109.5
108.5
106.6
98.8
12.1
12.5
16.2
13.9
113.7
113.9
114.2
111.7
13.6
13.1
14.0
15.1
112.3
113.3
115.8
111.6
10.7
12.2
11.4
13.4
115.8
118.5
116.3
111.5
12.5
14.1
9.3
14.5
Note. Standard scores for age (M = 100, SD = 15 for the total sample). WIAT II = Wechsler Individual Achievement Test, 2nd edition.
an = 128. bn = 122. cn = 113. dn = 106.
and written expression (see Table 2). Likewise, when Cohort 2 was in Grade 3, the
highest correlations were for (a) listening comprehension and reading comprehension and (b) reading comprehension and written expression. When Cohort 2 was in
fifth grade, the highest correlation was for listening comprehension and reading
comprehension. Still, at best, less than 40% (and typically at most 25%) of the variance in one language system ever accounted for the variance in another language
system in either cohort at any grade level (1, 3, or 5). Functional language systems
for receiving and for expressing oral language and for receiving and expressing
written language are distinct functional systems (evidenced by the unique variance) that also draw on some common processes (shared variance).
Three Letter-Writing Modes
Developmental differences. The following results are based on a constant
task (writing all alphabet letters from memory in alphabetic order) but variable
handwriting mode (manuscript or cursive writing with a pen or selecting letters on
a keyboard). For both cohorts, there was a significant effect related to grade: Older
children were more accurate, automatic, and faster than younger children. Likewise, for both cohorts, there was a significant effect related to task; in general, accuracy was higher for keyboarding than for manuscript writing or cursive writing.
However, for both cohorts, effects related to task speed interacted significantly
with grade level; in first grade, keyboarding was faster than manuscript writing,
but by third grade (in both cohorts), manuscript writing was faster. Then, by Grade
5, keyboarding was again faster than manuscript writing by pen. Consistently, cursive writing was less accurate and slower (see Table 3).
LANGUAGE BY HAND
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TABLE 2
Correlations Among WIAT II Listening Comprehension, Oral Expression,
Reading Comprehension, Written Expression Within Years and Correlation
of Same Measure Across Years
Nature of Correlation
Correlations within Grade 1
in Year 1 (Cohort 1)
Listening Comprehension
Oral Expression
Reading Comprehension
Correlations within Grade 3
in Year 3 (Cohort 1)
Listening Comprehension
Oral Expression
Reading Comprehension
Correlations within Grade 3
in Year 1 (Cohort 2)
Listening Comprehension
Oral Expression
Reading Comprehension
Correlations within Grade 5
in Year 3 (Cohort 2)
Listening Comprehension
Oral Expression
Reading Comprehension
Stability from Grades 1 to 3
(Cohort 1)
Stability from Grades 3 to 5
(Cohort 2)
Listening
Comprehension
Oral
Expression
Reading
Comprehension
Written
Expression
.26**
.52***
.42***
.25**
.31***
.53***
.26**
.58***
.39***
.45***
.47***
.56***
.13
.55***
.25**
.43***
.19*
.57***
.26**
.63***
.26**
.58***
.53***
.60***
.43***
.24**
.37***
.54***
.61***
.41***
.64***
.61***
Note. WIAT II = Wechsler Individual Achievement Test, 2nd edition.
*p ≤ .05. **p ≤ .01. ***p < .001.
Intraindividual differences and stability of handwriting modes. To evaluate individual differences across the handwriting modes and their stability across
development, children who had complete data for all relevant measures in both
Years 1 and 3 were identified; these results are reported in Table 4. In Grade 1,
manuscript writing and keyboarding were only correlated for speed of producing
the entire alphabet. By third grade (in both cohorts), both this correlation and the
correlation for automaticity of letter production were significant. When the outcome was letter accuracy, manuscript and keyboard letter writing were not correlated at any grade level (1, 3, or 5). This latter result shows that these writing modes
draw on unique processes in addition to the shared processes reflected in the significant correlations across modes.
TABLE 3
Descriptive and Inferential Comparison of Means and Standard Deviations
for Three Handwriting Modes at Two Stages of Development for Two
Cohorts Overlapping in Third Grade
Cohort 1
Grade 1a
Outcome
I. Descriptive statistics
Total correct first 15 sec
Manuscript
Keyboard
Cursive
Total accuracy
Manuscript
Keyboard
Cursive
Total time
Manuscript
Keyboard
Cursive
Cohort 2
Grade 3b
Grade 3c
Grade 5d
M
SD
M
SD
M
SD
M
SD
3.0
4.3
NA
2.3
3.6
NA
6.2
8.1
1.2
2.4
4.0
1.5
5.1
8.9
1.4
2.6
4.7
1.6
8.7
14.9
4.6
3.6
5.3
3.9
17.3
20.7
NA
6.1
5.9
NA
23.6
24.5
7.6
2.2
1.2
6.9
18.6
24.8
9.1
6.4
0.8
7.8
23.6
24.9
16.5
1.6
0.2
5.8
107.3
99.4
NA
25.6
27.4
NA
61.5
68.5
94.2
20.0
25.6
36.4
63.8
65.8
74.3
23.3
30.9
39.3
45.9
35.3
76.8
17.0
16.6
37.9
II. Inferential statisticse
Producing alphabet from memory correct in 15 sec
Grade
Task
Grade × Task
Producing alphabet from memory total correct
Grade
Task
Grade × Task
Producing alphabet from memory total time
Grade
Task
Grade × Task
Cohort 1:
Comparison of
Manuscript and
Keyboard
Cohort 2:
Comparison of
Manuscript,
Keyboard, and
Cursive
F
F
df
p
df
p
37.11 1, 118 .001 100.09 1, 76 .001
153.27 1, 118 .001 185.14 2, 152 .001
4.15 1, 118 .044 233.82 2, 152 .001
46.48 1, 117 .001 143.64 1, 77 .001
148.64 1, 117 .001 153.36 2, 154 .001
15.35 1, 117 .001 175.93 2, 154 .001
407.89 1, 115 .001 60.75 1, 78 .001
3.87 1, 115 .05
24.29 2, 156 .001
492.53 1, 115 .001 172.56 2, 156 .001
an = 128. bn = 122. cn = 113. dn = 106. eDegrees of freedom vary slightly because sometimes results
were not available for a specific measure because child did not complete the task following standardized procedures or administration errors were made.
76
77
.21*
Total time (N = 87)
.07
.46***
No. of total letters correct (N = 91)
Total time (N = 87)
.30**
.15
No. of total correct letters (N = 91)
II. Cohort 1, Grade 3
No. of correct letters in 15 sec (N = 92)
.11
rM&K
I. Cohort 1, Grade 1
No. of correct letters 15 sec (N = 92)
Outcome Measure
FING SUC
PHO ANAL
RAN
ORTH COD
PHO ANAL
RAN
RAN
ORTH COD
PHO ANAL
K
ORTH COD
RAN
ORTH COD
PHO ANAL
ORTH COD
PHO ANAL
RAN
FING SUC
M (by Pen)
ORTH COD
RAN
ORTH COD
PHO ANAL
ORTH COD
PHO ANAL
RAN
FING SUC
K
Significant Correlates
ORTH COD
PHO ANAL
RAN
ORTH COD
PHO ANAL
RAN
RAN
ORTH COD
M (by Pen)
Significant Correlates
TABLE 4
Correlations (r) Among Contrasting Language by Hand Modalities Within and Across Grades: Manuscript (M; by Pen)
and Keyboarding (K) in Cohort 1 and for Manuscript, Keyboarding, and Cursive (C) in Cohort 2
(continued)
78
.20
.31**
Total time (N = 76)
.36**
r1 M & K
No. of letter total correct letters (N = 75)
IV. Cohort 2, Grade 3
No. of correct letters in 15 sec (N = 73)
III. Stability Grades 1 to 3
No. of correct letters in 15 sec (N = 92)
No. of total correct letters (N = 91)
Total time (N = 87)
.30**
.36***
.27*
r2 M & C
r = .25*
r = .52***
r = .28**
M (by Pen)
.12
.10
.35**
r3 K & C
r = .23*
r = .17
r = .30**
K
Significant Correlates
TABLE 4 (Continued)
FING SUC
INHIB
INHIB/SW
RAN
ORTH COD
PHO ANAL
ORTH COD
RAN
INHIB
INHIB/SW
INHIB
M (by Pen)
FING SUC
PHO ANAL
ORTH COD
RAN
INHIB
INHIB/SW
FING SUC
RAN
FING SUC
INHIB
INHIB/SW
RAN
ORTH COD
K
Significant Correlates
FING SUC
ORTH COD
—
FING SUC
PHO ANAL
C
79
r = .33**
r = .41***
r = .42***
.41***
.24*
.48***
r = .39***
r = .00
r = .56***
.42***
–.09
.42***
FING SUC
INHIB
RAN
ORTH COD
INHIB/SW
ORTH COD
ORTH COD
FING SUC
INHIB
INHIB/SW
FING SUC
INHIB
RAN
ORTH COD
FING SUC
INHIB
INHIB/SW
ORTH COD
RAN
—
ORTH COD
RAN
INHIB/SW
INHIB
INHIB/SW
Note. N = the number of children with complete data for a particular outcome measure and set of common neuropsychological predictors; ORTH COD =
receptive orthographic coding; FING SUC = finger succession; PHO ANAL = phoneme analysis; RAN = rapid letter naming of letters; INHIB = inhibition;
INHIB/SW = inhibition/switching.
*p ≤ .05. **p ≤ .01. ***p ≤ .001.
r = .29**
r = .31**
r = .41***
.42***
Total time (N = 76)
VI. Stability Grades 3 to 5
No. of correct letters in 15 sec (N = 73)
No. of total correct letters (N = 75)
Total time (N = 76)
–.03
.30**
No. of total correct letters (N = 75)
V. Cohort 2, Grade 5
No. of correct letters in 15 sec (N = 73)
80
BERNINGER ET AL.
Cursive writing was analyzed and compared to the other modes in Cohort 2; see
Table 4. In Grade 3, manuscript and cursive writing were moderately correlated for
all outcomes (automaticity, accuracy, and total speed), but keyboarding and cursive were significantly correlated only for automaticity. In Grade 5, the results
were the same except that keyboarding and cursive were also correlated for total
speed.
The stability coefficients shown in Table 4 show that from Grades 1 to 3
automaticity and speed of letter writing are moderately stable for manuscript and
keyboard writing, and this stability increases in magnitude from Grade 3 to Grade
5. Cursive writing also is moderately stable from Grades 3 to 5, as shown in
Table 4.
Neuropsychological correlates of handwriting modes. Table 4 also reports the neuropsychological process measures that are significantly correlated
with each handwriting mode, organized by outcome measure, cohort, and grade
level. Each of the sets of correlated neuropsychological processes was entered into
multiple regressions for each outcome measure for a handwriting mode in each cohort at each grade level. Results, which are displayed in Table 5, are first considered by neuropsychological process and then by mode of writing at each grade
level.
In 19 of the 30 relevant analyses, RAN was significantly correlated with a writing outcome. Orthographic coding was more likely to be related to manuscript and
keyboarding (20 of the 30 relevant analyses) than to cursive writing (2 of the 6 of
the relevant analyses). Phoneme skills were very likely to be related to writing
skills in Grades 1 and 3 (12 of the 21 relevant analyses) but never in Grade 5. The
correlations between phonemes and writing probably reflect the role of letter
names in developing phonological awareness, a connection first observed by
Treiman (1993). Finger succession was more likely to be correlated in Grade 3 (8
of 15 relevant analyses) or 5 (4 of 9 relevant analyses) than in Grade 1 (1 of 5 relevant analyses). Inhibition or inhibition/switching (which can only be assessed in
Grade 3 and above; see the Methods section) correlated significantly with each of
the handwriting modes in Grades 3 and 5.
Manuscript writing in first grade. For Cohort 1 in Grade 1, only the RAN
task uniquely predicted manuscript letter retrieval and production in the first 15
sec (automaticity), only the phonemes task uniquely predicted the total number
of correctly written manuscript letters without time limits (accuracy) and only
the RAN and orthographic coding tasks (both of which require speeded processing of orthographic input) uniquely predicted the total time for writing the entire
alphabet in manuscript letters (writing speed). (See Table 5.) Thus, each of the
phenotypes found to be important in assessing dyslexia (Berninger et al., 2001)
was significantly correlated with and predicted uniquely at least some aspect of
TABLE 5
Unique Neuropsychological Predictors of Three Handwriting Modes
(Based on Significant Correlates in Table 4 Entered Into Multiple
Regressions for Specific Cohorts, Grades, and Modes)
Regression
Unique Predictors
Outcome
R2
F
df
Name
t
Cohort 1, Grade 1
Manuscript
No. correct in 1st 15 sec
Total correct
Total time
.38
.37
.45
6.08
5.99
13.76
3, 114***
3, 114***
2, 113***
RAN
PHO ANAL
RAN
ORTH COD
–2.87**
2.02*
2.63**
–2.44*
Keyboarding
No. correct in 1st 15 sec
Total correct
.37
.51
5.29
12.89
3, 103**
3, 113***
–2.23*
2.61**
.42
7.44
3, 109***
None
RAN
PHO ANAL
None
.39
.43
10.38
13.57
2, 119***
2, 120***
.48
8.14
4, 116***
.43
12.75
2, 117***
.31
.45
6.15
6.93
2, 117**
4, 113***
.44
4.52
5, 99***
.23
.55
5.78
8.89
.43
.36
No significant
correlates
.49
.36
.54
Total time
Cohort 1, Grade 3
Manuscript
No. correct in 1st 15 sec
Total correct
Total time
Keyboarding
No. correct in 1st 15 sec
Total correct
Total time
Cohort 2, Grade 3
Manuscript
No. correct in 1st 15 sec
Total correct
Total time
Cursive
No. correct in 1st 15 sec
Total correct
Total time
Keyboarding
No. correct in 1st 15 sec
Total correct
Total time
RAN
ORTH COD
PHO ANAL
RAN
FING SUC
2.91**
2.16*
4.10***
2.87**
1.97*
ORTH COD
RAN
None
None
3.32***
–2.25*
1, 108*
5, 106***
ORTH COD
INHIB/SW
INHIB
ORTH COD
INHIB/SW
FING SUC
3.01**
–2.10*
–2.40*
–2.17*
4.62***
2.10*
9.25
6.15
2, 83***
2, 83**
PHO ANAL
FING SUC
3.31***
–2.67**
5.28
7.93
8.05
6, 104***
2, 110***
5, 104***
RAN
RAN
ORTH COD
–2.34*
–2.81**
–3.34***
(continued)
81
82
BERNINGER ET AL.
TABLE 5 (Continued)
Regression
Outcome
Cohort 2, Grade 5
Manuscript
No. correct in 1st 15 sec
Total correct
Total time
Cursive
No. correct in 1st 15 sec
Total correct
Total time
Keyboarding
No. correct in 1st 15 sec
Total correct
Total time
R2
.33
.22
.32
F
Unique Predictors
df
4.19 3, 104**
ns
2.87*
Name
t
ORTH COD
2.61**
None unique
.40
.35
.23
5.98
13.92
5.75
3, 99***
1, 101***
1, 102*
INHIB/SW
INHIB/SW
ORTH COD
.47
No significant
correlates
.53
5.60
5, 102***
FING SUC
9.80
4, 102***
RAN
1.92 (p = .058)
3.73***
–2.40*
–2.38*
2.54*
Note. RAN = rapid letter naming of letters; PHO ANAL = phoneme analysis; ORTH COD = receptive orthographic coding; FING SUC = finger succession; INHIB/SW = inhibition/switching;
INHIB = inhibition.
*p ≤ .05. **p ≤ .01. ***p ≤ .001.
manuscript letter writing. These results support the hypothesis that handwriting
may play a role in typical reading development (e.g., Stempel-Mathey & Wolf,
1999) .
Keyboarding in first grade. For Cohort 1 in first grade, none of the correlated processes uniquely predicted automaticity or total speed in producing the alphabet letters by keyboard—all correlated skills as a set (or some other process underlying all four of them) appear to contribute. However, of the correlated
processes in Table 4 for this handwriting mode outcome, RAN and phoneme analysis uniquely predicted total accuracy in producing alphabet letters by keyboard
(see Table 5).
Manuscript writing in third grade. When Cohort 1 was in third grade, only
RAN uniquely predicted manuscript letter writing automaticity as it had when
these children were in first grade. Orthographic and phoneme analysis skills
uniquely predicted total accuracy for writing manuscript letters, but RAN and finger succession uniquely predicted total time for typing the alphabet. When Cohort
2 was in third grade, a different pattern of unique neuropsychological predictors
was observed for manuscript writing: orthographic coding and inhibition/switching for automaticity, inhibition for total correct letters, and inhibition/switching,
LANGUAGE BY HAND
83
orthographic coding, and finger succession for total writing speed (see Table 5).
Comparison of results across overlapping cohorts serves as a cautious reminder
that individual differences among students may outweigh age- or grade-related
processes at a given stage of writing development.
Keyboarding in third grade. When Cohort 1 was in third grade, orthographic coding and RAN uniquely predicted automaticity of keyboard letter writing, but no unique predictors were identified for the other outcomes for this handwriting mode. When Cohort 2 was in third grade, RAN uniquely predicted
automaticity and accuracy and orthographic coding uniquely predicted total time
for writing the entire alphabet. RAN replicated across the automaticity outcome
for keyboarding across the two cohorts. (See Table 5.)
Cursive writing in third grade. When Cohort 2 was in third grade, phoneme
analysis uniquely predicted automaticity of cursive letter writing. This observed
relationship between phonemes and cursive writing may explain why
multisensory/structured language teaching techniques that emphasize both phoneme awareness and cursive writing (Birsch, in press; Stempel-Mathey & Wolf,
1999) are effective for some children with learning disabilities. Finger succession
uniquely predicted total accuracy. Total time could not be assessed because no significant correlates with this outcome had been found. (See Table 5.)
Manuscript writing in fifth grade. Only orthographic coding uniquely predicted automaticity. (See Table 5.)
Keyboarding in fifth grade. When Cohort 2 was in fifth grade, RAN
uniquely predicted automaticity and accuracy. Orthographic coding uniquely predicted total time. (See Table 5).
Cursive writing in fifth grade. Inhibition/switching uniquely predicted automaticity (marginal) and accuracy. Orthographic coding uniquely predicted total
time. (See Table 5.)
Developmental changes in manuscript writing. Unique neuropsychological predictors varied with outcome measure for a particular handwriting mode in
Grades 1 and 3 and with cohort at Grade 3. Individual differences in orthographic
coding, phoneme skills, RAN, finger succession, inhibition, and inhibition/switching correlated and often contributed uniquely to various outcome measures for
manuscript writing during Grades 1 and 3, but by Grade 5 orthographic coding appeared to be the unique contributor, consistent with prior research (see Berninger
& Amtmann, 2003).
84
BERNINGER ET AL.
Developmental changes in keyboarding. Throughout the developmental
period studied (one cohort, Grade 1; two cohorts, Grade 3; one cohort, Grade 5),
both orthographic coding and RAN consistently contributed uniquely to various
keyboarding outcomes.
Developmental changes in cursive writing. Early in the process of learning cursive in Grade 3, both grapho-motor planning (finger succession) and oral
language skills (phoneme analysis) contributed uniquely. Possible reasons for
these findings include the following. The more complex sequential motor movements needed to form the loops and connecting strokes of cursive writing place
more demands on the grapho-motor planning system. Common oral names and
corresponding phoneme sounds may facilitate the transfer of abstract letter identity across letter formats—manuscript (first grapho-language) and cursive (second
grapho-language) that differ in component strokes but share a common link to the
aural/oral language system. Later in the process of learning cursive (fifth grade in
this study), inhibition and inhibition/switching and orthographic coding contribute
uniquely. Possible explanations for this developmental change toward greater reliance on executive functions and orthographic working memory include the following. Inhibition may help to overcome response competition to continue in forward
direction when reversal is needed in forming cursive letters (e.g., the return sweep
to left needed to close cursive s before a forward connecting stroke to the right). Inhibition/switching may regulate the process of switching from one letter to another
letter, which involves more complicated motor movements than is the case for
manuscript. As children gain experience with the cursive letter formats, they learn
to balance attention between (a) automatic access to long-term memory representations of cursive letter forms and motor routines for producing them, and (b) strategic coding into internal working memory of cursive letter forms from the external writing environment.
Other potential influences on handwriting modes. For Cohort 1 in Year
1, who had the least formal writing instruction, we examined the relationships between responses on a parent questionnaire relevant to preschool writing skills or
current home writing experience and student performance on test measures of
handwriting modes. None of the following was significantly correlated with any of
the handwriting mode outcomes: age at which child first used crayons or wrote alphabet letters, amount of time spent writing at home, or amount of assistance
needed with writing. For both cohorts at all grade levels, we examined parent responses about computers in the home and amount of time spent using computers at
home. None of these indicators of experience with computers was significantly
correlated with performance on keyboarding outcome measures.
LANGUAGE BY HAND
85
Fast Mapping in Learning to Spell Novel Spoken Words
An analysis of variance yielded a significant main effect for time, F(1, 235) = 8.47,
p < .004, and grade, F(1, 235) = 141.09, p < .001, but no significant Time × by
grade interaction. As a group, children improved from the first trial to the third
trial, and the third graders were more accurate than the first graders. Of the nine
pseudowords taught, the first graders increased in accuracy from the first to the last
trial: Trial 1 (M = 1.63, SD = 1.71), Trial 2 (M = 1.52, SD = 1.39), and Trial 3 (M =
1.77, SD = 1.59). The third graders increased in accuracy from the first to the last
trial: Trial 1 (M = 3.50, SD = 1.53), Trial 2 (M = 3.41, SD = 1.50), and Trial 3 (M =
3.94, SD = 1.33). Both groups showed a slight dip in the second teaching trial, consistent with growth trajectories that are discontinuous, showing tiny waves of progression and regression embedded in overall increasing growth. Overall, the range
of learning across the three trials (only two of which involved teaching) was zero to
five words correctly spelled.
The level of performance on the third trial (a testing trial) was used as evidence of
fast mapping in response to short-term instruction and entered as a predictor in multiple regression, along with other well-documented correlates of spelling achievement. As shown in Table 6, at both grade levels, the indicator of fast mapping in learning to spell words accounted for significant variance over and beyond the
orthographic and phoneme skills (both grade levels) and those skills plus RAN
(third-grade level). At both grade levels, these sets of unique predictors explained
about 50% of the individual differences in spelling skill (see Table 6). Moreover, this
indicator of fast mapping of word forms predicted spelling achievement not only in
TABLE 6
Fast Mapping in Learning to Spell Pseudowords During Year 1
(Grades 1 or 3), Orthographic Coding, Phoneme Awareness, and RAN as
Concurrent Predictors of Wechsler Individual Achievement Test, 2nd
Edition, Spelling Achievement in Multiple Regression
Overall Regression
F
df
R2
31.52
4,111***
.74
Unique Predictors
Cohort 1, Grade 1
Fast spelling mapping
Orthographic coding
Phoneme awareness
Cohort 2, Grade 3
Fast spelling mapping
Orthographic coding
Phoneme awareness
Rapid automatized naming
Unique Predictors
30.02
*p ≤ .05. **p ≤ .01. ***p ≤ .001.
4,110***
t
p
3.59
2.10
3.84
.001
.038
.001
4.58
2.66
3.69
–3.69
.001
.009
.000
.000
.74
86
BERNINGER ET AL.
the current year but also the subsequent year of the longitudinal study: For Cohort 1,
number of correctly spelled words on Trial 3 was significantly correlated with WIAT
II Spelling in first grade (r = .63, p < .001) and in second grade (r = .71, p < .001) and
with Woodcock–Johnson III (WJIII; Woodcock, McGrew, & Mather, 2001) Spell
Sounds in second grade (r = .48, p < .001). For Cohort 2, number of correctly spelled
words in Trial 3 was significantly correlated with WIAT II Spelling in third grade (r =
.54, p < .001) and in fourth grade (r = .55, p <.001) and with WJIII Spell Sounds in
fourth grade (r = .34, p < .001). At no grade level did concurrently administered RAN
letters, a measure of rapid naming of highly familiar orthographic symbols, explain
all the variance observed in performance on the indicator of fast mapping in spelling,
a measure of learning unfamiliar letter strings with phonological features: Cohort 1
(r = –.47, p < .001) and Cohort 2 (r = –.06, ns).
DISCUSSION
Interrelationships of Four Functional Language Systems
Theoretical significance. Functional language systems are not preformed
in the neurons any more than any functional brain system is (Piaget, 1970). Rather,
as Piaget envisioned, mental systems are constructed as the biological organism interacts with the environment. The kinds of environmental interactions encountered
by Language by Ear, Mouth, Eye, and Hand are qualitatively different, and hence
the developing functional systems for listening, speaking, reading, and writing are
somewhat the same because they draw on some common neural machinery for the
linguistic code and somewhat unique because they engage that neural machinery
variably to achieving a variety of different goals including (a) communicating and
interacting with others, (b) representing and operating on the external environment, (c) representing and operating on the internal mental environment during
thinking, and (d) regulating learning and behavior. Thus, language should not be
thought of as a system that can be described on the basis of the brain alone; it is best
understood in terms of functional systems that involve the brain as it interacts with
the external and internal environment (Berninger & Winn, in press). These systems, born of nature–nurture interactions, are somewhat stable over the course of
development but also flexible and dynamic.
Clinical and educational significance. Often educators and parents discuss language as if it is a separate, single mental function that can be contrasted
with other separate, single mental functions such as attention, memory, executive
functions, and so on. Neuropsychologists have an important contribution to make
in explaining that language is multifaceted, and children may differ in their relative
abilities in understanding and constructing language, both of which are influenced
LANGUAGE BY HAND
87
by the mode of input (ear or eye) and the mode of output (mouth or hand). Individual children and youth may have profiles of relative strengths and weaknesses in
specific functional language systems (e.g., for listening, speaking, reading, and
writing when the goal is to construct or express meaning), and these profiles are
somewhat flexible as well as somewhat stable across development. Instruction
throughout the elementary and even secondary grades should focus on developing
each of the language systems and not just reading because (a) each of the four language systems for achieving high-level meaning goals is necessary for overall language development and success in the adult world of work, and (b) some individuals may have unrecognized strengths in aural/oral language that should be
nurtured.
Interrelationships of Three Modes of Language by Hand
Theoretical significance. Letter production is a fundamentally important
process in written expression. Again, individual children may have profiles of relative strengths and weaknesses in specific grapho-motor systems for producing letters (manuscript writing, cursive writing, keyboarding). How well language is
communicated through the hand may depend, to a large extent, on which mode of
letter production is used. The finding that typically developing children show
intraindividual variation across three modes of letter production converges with a
prior finding based on large-scale cross-sectional studies that typically developing
elementary and middle school students varied considerably in whether they chose
to write in manuscript, cursive, or mixed modes; in fact, good writers often resorted to manuscript or mixed cursive and manuscript (Graham, Berninger, &
Weintraub, 1998). We did not focus on gender differences because that is a topic of
a larger investigation comparing the handwriting of typically developing readers
and writers and dyslexics and dysgraphics, which is in progress.
Clinical and educational significance. In assessing school-age children
referred for handwriting problems, whose cognitive and motor development are in
the normal range, measures of orthographic coding, phoneme awareness, RAN,
graphomotor planning (finger succession), and executive functions (inhibition, inhibition/switching) should be administered rather than measures of visual function
or motor function that do not assess planning for sequential finger maneuvers.
Children who have significant handwriting problems may respond to explicit
handwriting instruction (for review, see Berninger & Amtmann, 2003).
Keyboarding should not be used as alternative to poor handwriting, but rather
keyboarding should be taught because it is a culturally valid letter writing mode in
the 21st century. Children may also benefit from neuropsychological assessment
of the processes that underlie keyboarding, such as RAN (Bowers, 2001).
88
BERNINGER ET AL.
Role of Fast Mapping in Learning to Spell
Theoretical significance. Acquisition of phonological–orthographic mapping underlying spelling may rely both on mechanisms of aural/oral language
learning such as fast mapping as well as mechanisms that require conscious reflection and awareness and application of explicit strategies to achieve conscious
awareness. There is a myth that good spellers are born not taught. This myth may
survive because many individuals are able to learn to spell reasonably well by relying mainly on such fast-mapping mechanisms. The fast mapping mechanism may
be a dynamic mechanism of language learning for words that operates for both aural/oral and aural/written language. Fast mapping is not only automatic (quick and
effortless), it also involves mapping across different codes (e.g., visual objects that
are referents for language names; aural codes and oral codes of spoken language;
orthographic and phonological codes, see Table 6). Spelling, even when learned
through a fast-mapping mechanism, is not purely visual—both phonological and
orthographic information contribute and are probably mapped by the fast-mapping
mechanism (see Table 6).
Clinical and educational significance. Randomized, controlled study designs are needed to evaluate the potential advantage of combining learning activities that foster fast mapping with those that emphasize explicit, systematic instruction in awareness of the alphabetic principle and other strategies for making
connections between phonology and orthography. One hypothesis that should be
tested is whether explicit strategies are most effective for forging reflective
subword connections among phonological, orthographic, and morphological word
forms (e.g., Richards et al., 2005), and fast mapping is most effective for creating
automatic word connections between orthographic and phonological codes. Expert spelling probably requires both reflective and automatic processing
(Berninger & Richards, 2002).
CONCLUSIONS
Writing is not the inverse of reading (Read, 1981). It is not a purely motor or primarily visual activity. It is fundamentally Language by Hand, which shares some
common processes with other kinds of language (listening, speaking, and reading)
but also some distinct processes that are unique to writing. There are individual differences in how children learn the different modes of letter production for manuscript writing with a pen or a keyboard or cursive writing with a pen. The
fast-mapping mechanism that accounts for some aspects of aural/oral language
learning in the preschool years may account for some aspects of written spelling
LANGUAGE BY HAND
89
learning during the school-age years. Writing is a complex process that draws on
many neuropsychological processes and undergoes many neuropsychological
changes during the early school years. Research is beginning to shed light on those
processes and changes, but considerably more research is needed.
ACKNOWLEDGMENTS
Grant HD25858–15 from the National Institute of Child Health and Human Development supported this longitudinal research on typical writing development.
We thank Katherine Vaughan for her research coordination during Year 1, the
Year-1 testers (Jennifer Crane, Suzanna Eaton, Sandra Hiramatsu, Heather
Murphy, and Jennifer Norton), and the Year-3 testers (Leah Altemeier, Annie
Boyd, Noelia Garcia, Cindy Lin, Daniel Lovitt, and Pamala Trivedi). Laura Gould
and Marci Anderson-Youngstrom were also testers who assumed responsibility
for scoring and interrater reliabilities for handwriting measures.
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