J. Experimental Child Psychology 82 (2002) 185–199
www.academicpress.com

Orthographic learning during
reading: examining the role ofself-teaching q
Anne E. Cunningham, a,* Kathryn E. Perry, aKeith E. Stanovich, b and David L. Share ca

University of California, Berkeley, CA, USA
University of Toronto, Toronto, Ont., CanadacUniversity of Haifa, Mount Carmel, Haifa 31905, IsraelbReceived 10 August 2001; revised 18 February 2002

Abstract
Thirty-four second grade children read target homophonic pseudowords (e.g.,slurst/slirst) in the context of real stories in a test of the self-teaching theory of earlyreading acquisition. The degree of orthographic learning was assessed with three
converging tasks: homophonic choice, spelling, and target naming. Each of the tasks
indicated that orthographic learning had taken place because processing of target homophones (e.g., yait) was superior to that of their homophonic controls (e.g., yate).
Consistent with the self-teaching hypothesis, we obtained a substantial correlation(r ¼ :52) between orthographic learning and the number of target homophones correctly decoded during story reading. Hierarchical multiple regression analyses indicated that neither RAN tasks nor general cognitive ability predicted variance inorthographic learning once the number of target homophones correctly decoded during story reading had been partialed out. In contrast, a measure of orthographicqThis research was supported by a grant from the National Institute of Child Health andHuman Development to Anne E. Cunningham and a grant from the Natural Sciences andEngineering Research Council of Canada to Keith E. Stanovich.
Special thanks to Dr. Marcia Wilson and the teachers Nancy Palker, Leslie Mulligan, Pam
Rafanneli and the children at Beach Elementary School in Piedmont, California. Also to
Pamela Douglas and Jane Gould-Cauﬁeld for their assistance in data collection.
*
Corresponding author. Fax: +510-642-3555.
E-mail address: acunning@uclink.berkeley.edu (A.E. Cunningham).

0022-0965/02/$ - see front matter Ó 2002 Elsevier Science (USA). All rights reserved.
PII: S 0 0 2 2 - 0 9 6 5 ( 0 2 ) 0 0 0 0 8 - 5

186 A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199

knowledge predicted variance in orthographic learning once the number of targets
correctly decoded had been partialed. The development of orthographic knowledge
appears to be not entirely parasitic on decoding ability. Ó 2002 Elsevier Science(USA). All rights reserved.
Keywords: Reading acquisition; Self-teaching; Orthographic learning; Orthographic knowledge;
RAN

The self-teaching model of early reading acquisition (Jorm & Share, 1983;
Share, 1995) posits that the orthographic representations necessary for fast,eﬃcient, visual word recognition develop primarily as a function of phonological recoding (i.e., print-to-sound translation), and the opportunities toassociate print with sound that it provides. According to Share (1999):‘‘phonological recoding acts as a self-teaching device or built-in teacher enabling a child to independently develop the word-speciﬁc orthographic representations essential to skilled reading and spelling’’ (p. 96).
The self-teaching hypothesis is consistent with numerous studies thathave shown that eﬃcient phonological processing is a necessary, but not sufﬁcient, condition for orthographic learning (e.g., Juel, Griﬃth, & Gough,1986; Reitsma, 1989; Tunmer & Nesdale, 1985). That is, while virtually
no child with deﬁcient phonological processing skills develops reading ability with ease, some children with adequate phonological sensitivity lag behind in the development of word recognition eﬃciency. Thus, if
phonological processing is a necessary but not suﬃcient condition for thedevelopment of adequate word recognition skill, this implies that there
may be another cognitive ‘‘sticking point’’ for some children—that we
may ﬁnd a second critical locus of variance in word recognition skill. In fact,
empirical work and theoretical speculation has raised the possibility that theability to form, store, and access orthographic representations may be able
to account for some of the residual variance in word recognition skills not
explained by phonological factors (Barker, Torgesen, & Wagner, 1992;
Cunningham & Stanovich, 1990, 1993; Stanovich & West, 1989) and thatthese two sources of variance contribute diﬀerentially to reading diﬃculties(Castles, Datta, Gayan, & Olson, 1999; Manis, Custodio, & Szeszulski,
1993; Stanovich, Siegel, & Gottardo, 1997; Treiman, 1984).
The major aim of the present study was to provide a direct test of the selfteaching hypothesis via a replication and extension of Share’s (1999) recent
study. He asked second graders to read aloud short texts containing embedded pseudoword targets. Three days later, target spellings were correctly
identiﬁed more often, named more quickly, and spelled more accurately
than alternate homophonic spellings. Additional experiments ruled out alternative explanations of this ﬁnding.

A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199 187

The evidence for the self-teaching hypothesis has been either indirect oranecdotal with the exception of Share’s (1999) study. In his study, Share(1999) provided compelling evidence for the self-teaching hypothesis in ahighly regular script—Hebrew orthography. The generalizability and application of these ﬁndings to a less shallow and more prevalent orthography,
however, has yet to be examined. There is always the possibility that themechanisms of self-teaching might vary across orthographies as has beenfound for some other variables with important associations in early reading
acquisition (Wimmer, 1996; Wimmer, Mayringer, & Landerl, 2000).
Whether the self-teaching hypothesis holds up in a very diﬀerent and more
irregular orthography was thus tested in the present study. There is reason
to believe that orthographic learning is dependent upon phonological decoding even in a highly irregular but still fundamentally alphabetic script
such as English (e.g., Ehri, 1992; Ehri & Saltmarsh, 1995; Perfetti, 1992; Reitsma, 1983, 1989; Share, 1995), even though phonological decoding is amore complex process in learning to read English as compared to Hebrew(see Share & Levin, 1999).
With the exception of Share’s (1999) study in Hebrew and Reitsma’s
work in Dutch (Reitsma, 1983, 1989), previous examinations of the roleof decoding in English orthographic learning have used individual words
presented in isolation rather than connected text. Also, most investigations
have either directly taught word pronunciations or provided explicit feedback to children during reading as to the correct pronunciation of words.
If the self-teaching hypothesis applies to children’s independent everyday
reading of text, children should not be provided with outside assistance.
Rather, they should be left to their own devices, such as making incorrect
pronunciations of letter-strings, guessing or skipping words in their attempts
to read connected text. In order to assess children’s self-teaching abilities intheir proper context, examination of this hypothesis within a naturalistic
context (i.e., connected text and independent reading) is needed.
The present study also examined possible sources of variance in student’s
orthographic learning by including tasks tapping general cognitive ability,
rapid automatized naming (RAN) ability, and orthographic discrimination
ability. General cognitive ability is of course always a potential alternative
explanation of any individual diﬀerences obtained in any type of learning
task. Thus, its inclusion here will allow us to ascertain the speciﬁcity of
any individual diﬀerences in orthographic learning resulting from self-teaching that we may obtain. The RAN tasks included here relate to a more
speciﬁc hypothesis surrounding the issue of orthographic learning fromself-teaching. The RAN variable has been linked both theoretically andempirically with individual diﬀerences in orthographic processing skill(Bowers & Wolf, 1993; Manis, Seidenberg, & Doi, 1999; Manis, Doi, &Bhadha, 2000; however, see Torgesen, Wagner, Rashotte, Burgess, & Hecht,1997). Theoretically, Bowers and Wolf (1993) have argued that

188 A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199‘‘slow letter (or digit) naming speed may signal disruption of theautomatic processes which support induction of orthographic
patterns, which, in turn, result in quick word recognition’’ (p. 70).
We will examine this conjecture by analyzing whether RAN can predict
orthographic learning in the self-teaching paradigm after decoding accuracy
during story reading has been partialed out. Such an analysis relates to thespeciﬁcity of the RAN/orthographic processing relationship because, if
conﬁrmed, it would indicate that RAN is a predictor independent of decoding accuracy.

MethodDesignThe tasks administered to the participants were of two types: (1) connected text with post-test assessments of orthographic learning; and (2) measures designed to assess a variety of cognitive skills that have been linked
with early reading acquisition.
Participants
Thirty-four children (18 boys and 16 girls) from three second grade classrooms in a predominantly upper-middle class elementary school served as
participants. Testing took place at the end of the second grade school year,
during the months of May and June.
The self-teaching task
Ten short story texts (expository passages) were adapted from Share’s(1999) Hebrew stories and translated to a version more appropriate forNorth-American English speakers. Two versions of each story were createdand employed two alternate spellings of one of the following 10 pairs of homophonic pseudowords—yait/yate; slurst/slirst; stert/sturt; choom/chume;
stoon/stewn; woat/wote; rupe/roop; beel/beal; kear/keer; and troe/trow.
For each pair, half the sample saw one spelling, while the other half saw
the alternative spelling. These homophones pairs were selected for the experimental study from a pilot study employing a candidate pool of 59 homophonic pseudoword pairs. We chose pseudoword pairs that displayed
preference ratios as close as possible to 50:50 among a comparable groupof 48 second grade students. In the present study, each target homophone
appeared six times in the story. Apart from the diﬀerent target homophonespellings, the two versions of each story were identical. Our texts ranged inA.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199 189

length from 133 to 234 words (median length ¼ 186) and were designed topresent no diﬃculties for normal second grade readers. Each story was
printed on a separate page. The 10 stories were randomly assigned to two
sets: Stories 1–5 and 6–10.
The self-teaching task was based upon Share’s (1999) design and administered on an individual basis in four separate sessions. During the ﬁrst session children read ﬁve stories. Three days later, children participated in asecond session in which they were administered three orthographic learning
tasks (see descriptions below). After 4 days, children participated in a third
session during which they read the remaining 5 stories. Again, 3 days later,children participated in the fourth session during which they were administered the three orthographic learning tasks.
Story reading. Children were asked to read the stories aloud. The only assistance provided by the experimenter was in reading the title of the story.
The students did not receive any further help. If a student requested help
identifying a word, they were instructed to try and read it by themselves.
All sessions were audiotaped and the experimenter timed their story reading.
After each passage, the students were asked three comprehension questions
based upon the story (see Appendix A for example of stories and questions).
Target decoding accuracy score. The accuracy of the pronunciation of thetarget words was recorded online by the experimenter. An error was recorded if any change to the correct target pronunciation occurred. Independent analyses of the audiotapes was used to conﬁrm the online scoring. Of
the 60 possible correct pronunciations of the homophone targets (10 stories
times 6 target words), the mean number of correct pronunciations was 44.4
(SD ¼ 12:9; range ¼ 13–60); thus the overall decoding accuracy for the target homophones was roughly 74.0%—indicating that most of the targets
were successfully decoded when encountered in text. The raw number ofcorrect pronunciations was used in the analyses that follow.
Orthographic learning tasks
Three separate tasks were administered individually (in the following order) to determine the extent to which new orthographic information had
been assimilated three days later.
1. Homophonic pseudoword choice. Children were ﬁrst asked a question toprompt their recall of the target homophone (e.g., ‘‘do you remember thename of the coldest town in the world?’’). They were then shown four alternative spellings of the target homophone: (1) the original target spelling(e.g., yait); (2) the target homophone’s homophonic alternative (e.g., yate);(3) a letter substitution alternative, in which a letter of the target homophone was replaced by a visually similar letter (e.g., yoit); and (4) aletter transposition alternative, in which two adjacent letters of the target
homophone were transposed (e.g., yiat). The order of these alternatives

190 A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199

was counterbalanced. Children were asked to circle the word that was thesame as the name of the town/fruit/ﬂower they had read about in the story
3 days ago. The child’s score on this task was the total number of items correct (mean ¼ 7.5, SD ¼ 1:6, range ¼ 4–10).
2. Spelling task. Children were asked to spell the target homophone of thename of the town/fruit/ﬂower they had read about in the story 3 days previously. If the child could not recall the name of the target homophone,
the experimenter provided the ﬁrst sound (or blend) in the word. If the child
was then able to recall the target homophone, they were asked again to spellit. If the child still could not recall the correct target, the experimenter provided the homophone, and then asked the child to spell it. Only exact replications of the target homophones were coded as correct and the metric weanalyzed was the total number of items correct (mean ¼ 7.0, SD ¼ 2:1,
range ¼ 3–10). The number of homophonic spellings were also recorded.
3. Homophone naming task. The children were asked to name a series ofpseudohomophonic words presented on a Macintosh LC475 computer monitor one word at a time. The target spellings, both original (2 exposures) andhomophonic (2 exposures), were embedded in a list of 36 high frequency
words, some of which appeared more than once (as in natural text). These
stimuli were presented center screen, in a 24-point font size and remained visible until the child responded. There was a 2500-ms blank-screen interval between each stimulus presentation. The computer recorded latencies fromstimulus presentation to speech-onset for each stimulus and the entire session
was audiotaped so that reading accuracy could also be scored. In the analyses
that follow, the metric we analyzed was the mean naming time for the target
homophones. Metrics involving various diﬀerence and ratio scores (Allerup& Elbro, 1998) involving comparative target/homophone performance produced virtually identical results, but the diﬀerence scores were less reliable.
4. Orthographic learning composite score. An orthographic learning composite score was formed by taking the mean of the standard scores on thethree orthographic learning tasks: homophonic pseudoword choice, spelling, and homophone naming (the latter standard score was multiplied by)1 because it is a reaction-time rather than an accuracy score).
Cognitive measures
Prior to the experimental sessions, a number of background reading andcognitive measures were administered to the students during several individual and/or group sessions. The standardized measures we administered were:
the Peabody Picture Vocabulary Test (PPVT; Dunn & Dunn, 1981), Raven
Progressive Colored Matrices (RAVEN; Raven, 1962), Digit Span Subtest—WISC-III, and the Woodcock Reading Mastery Test (Word Attack
Subtest). Raw scores were employed in the analyses that follow. A composite cognitive ability score was formed by ﬁrst transforming the raw scores on

A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199 191

the Raven, the PPVT, and digit span measures to z-scores. These standard
scores were then averaged to obtain the cognitive ability composite score.
Additional cognitive measures that were administered were:
Rapid automatized naming tasks. The RAN tasks were individually administered tests of naming speed. Students were asked to read six 18  24 cards,
two of each type of stimuli: colors, letters, or numbers. The ﬁrst card contained 50 colored squares (ﬁve colors repeated ten times), the second 50 lower
case letters (ﬁve letters repeated ten times), and the third 50 numbers (ﬁvenumbers repeated ten times). Students were asked to name the colors/letters/numbers as quickly as they could. The participant’s total response timefor each card was recorded in seconds to the tenth of a second, and naming
errors were recorded by the experimenter on a separate score sheet. Two trials of each card were administered to each student, and scores were averaged
across the two trials. This task lasted approximately 5 min. The children’sscores on this task were the mean total time to read the 50 stimuli (mean colors ¼ 47.6, SD ¼ 9:8; mean letters ¼ 29.7, SD ¼ 5:0; mean numbers ¼ 31.4,
6.1). The mean number of errors was quite low (colors mean ¼ .19, lettersmean ¼ .13, numbers mean ¼ .07). In the analyses that follow, the metric
we analyzed was the mean naming time for colors, letters, and numbers.
A RAN composite score was formed by taking the mean of the standard
scores of the times on the three RAN tasks: colors, letters, and numbers.
Orthographic choice task. The orthographic choice task (Olson, Kliegl,
Davidson, & Foltz, 1985) is a group administered measure of orthographic
processing skill. Twenty-three pairs of phonologically similar letter-strings
were presented to the children on a sheet of paper. Each pair contained
one word and one pseudohomophone. The word pairs were: take–taik,
gote–goat, sleap–sleep, hole–hoal, rume–room, snoe–snow, face–fase,hert–hurt, sheep–sheap, smoak–smoke, bowl–boal, cloun–clown, word–
wurd, cote–coat, rain–rane, stoar–store, lurn–learn, nice–nise, scair–scare,skate–skait, true–trew, streem–stream, and wize–wise. The experimenter
told the children that each pair of letter strings contained one word thatwas spelled correctly and one that was spelled incorrectly. They were theninstructed to circle the correctly spelled word. This task lasted approximately 5 min. The child’s score on this task was the total number of items
correct (mean ¼ 21.9, SD ¼ 1:8, range ¼ 16–23). The split-half reliability(Spearman–Brown corrected) was .86.

Results
Orthographic learning
As indicated in Table 1, 74.7% of the choices made on the orthographic
learning task were choices of the target pseudohomophone. Only 12.9% of192 A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199
Table 1
Performance on the three orthographic learning tasks
Tasks

TargetHomophone

Substitution

Transposition

Homophonicpseudoword choice (%)

74.7

12.9

6.5

5.9

Target spellingcorrect
70.3

Homophonicspelling
13.5

Other

Target813

Homophone854

Spelling–production (%)
Homophone naming
RT (latency in ms)

16.2

the choices made were the homophonic alternative of the target pseudohomophone. The substitution and transposition alternatives were chosen only
6.5% and 5.9% of the time, respectively. That the target choice exceeded thatof all of the alternatives by a factor of almost three to one indicates that orthographic learning on the task was evident. A particularly important indication that orthographic learning had taken place was the fact that thetarget was chosen over the homophonic alternative by a factor of over ﬁve
to one.
On the spelling task, only exact reproductions of target homophones
were accepted as correct. As Table 1 indicates, 70.3% of the spellings wereidentical to those of the target pseudohomophone. In contrast, only
13.5% of the spellings mirrored that of the homophonic control (16.2% of
the spellings indicated some other spelling error). Thus, orthographic learning was indicated by the fact that the target was chosen over the homophonic alternative by a factor of over ﬁve to one.
On the post-test homophone naming task, each child read a total of 20
targets (10 targets each presented twice), and 20 homophone foils. Only fully
accurate pronunciations were accepted as correct. There was no signiﬁcantdiﬀerence in the pronunciation accuracy of either the original spelling or its
alternative spelling (targets ¼ 82%; homophones ¼ 80%). As Table 1 indicates, the mean pronunciation time for the target pseudohomophone was
813 ms and the mean pronunciation time for the homophonic control was
854 ms. An examination of naming latencies 1 revealed that pronunciation
times were signiﬁcantly faster (by 41 ms) for target homophones whose1
Latency scores (means and standard deviations) were based on the median of individual
children’s reaction times for correct pronunciations, calculated separately for targets andhomophones. Trials lost to equipment failure were 13% for targets and 11% for foils. The
average number of valid reaction times per child in the overall analysis was just over 17 (of 20)
for both types of items.

A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199 193

spellings had been encountered while reading text (the targets) than they
were for the alternative homophonic spellings, tð33Þ ¼ 2:64; p < :025.
Predictors of orthographic learning
Table 2 presents the relationships among the major variables in the study.
The critical variable is the ﬁrst one in the table—the orthographic learningcomposite score. According to the self-teaching hypothesis, the number oftargets in the passages correctly decoded should be correlated with the composite orthographic learning score. The substantial correlation obtained(r ¼ :52) conﬁrmed this key self-teaching prediction. The orthographic
learning composite also displayed a strong correlation (r ¼ :58) with a general measure of decoding ability (the Word Attack subtest of the Woodcock). The orthographic learning composite score displayed a correlationof ).35 ðp < :05Þ with the RAN composite score. Orthographic learning displayed no correlation at all with general cognitive ability (r ¼ :03).
In a series of regression analyses, we examined which individual diﬀerence variables predicted the degree of orthographic learning, building upon
the basic ﬁnding—one consistent with the self-teaching theory—that the
proportion of target homophones correctly decoded in the texts was a substantial predictor of orthographic learning. The hierarchical regression analyses reported in Table 3 build on this fundamental relationship. The
regression analyses reported there examined the critical question of whether
learning in this paradigm is due solely to target decoding accuracy, or
whether other variables such as general learning ability, rapid naming, orprior orthographic knowledge can account for signiﬁcant variance in orthographic learning once target decoding accuracy has been controlled.
Thus, we conducted three separate hierarchical regression analyses to examine the relative contribution of cognitive ability, rapid naming, and priororthographic knowledge to children’s orthographic learning due to selfteaching once target decoding accuracy had been partialed out. The ﬁrst
analysis (see Table 3), revealed that after controlling for target decoding accuracy, general cognitive ability did not predict orthographic learning. Cognitive ability is always a strong alternative hypothesis in a paradigm that
includes some type of learning. But the results of this analysis suggest thatorthographic learning has a level of speciﬁcity that goes beyond generallearning.
The RAN composite score did have a signiﬁcant zero-order correlation
with orthographic learning composite (the overall indicator of the amountof orthographic learning)—a ﬁnding that is consistent with the model put
forward by Bowers and Wolf (1993). However, the more critical issue iswhether rapid naming contributes to orthographic learning in this paradigm
after we have controlled for successful decoding. The second analysis
revealed that rapid naming did not independently predict orthographic

Variables

1

2

3

4

Orthographic learning measures
1. Orthographic learning composite
2. Homophonic choice
3. Spelling
4. Homophone naming target RT

.82
.89
).72

.70
).28

).47

Story reading performance
5. Target decoding accuracy

.52

.39

.48

).39

General ability measures
6. PPVT
7. Raven
8. Digit span composite
9. Cognitive ability composite

).09
).15
.28
.03

).25
).19
.24
).12

.03
).13
.04
).04

RAN measures
10. RAN colors
11. RAN numbers
12. RAN letters
13. RAN composite

).23
).37
).36
).35

).18
).30
).25
).27

).11
).23
).21
).20

Reading measures
14. WJ word attack
15. Orthographic choice

.58
.57

.32
.34

.53
.51

Mean
SD

0.0
0.0

7.5
1.6

7.0
2.1

Correlations greater than .33 are signiﬁcant at the .05 level.

5

6

7

).01 ).17.03 ).01
).39
.23
).22
.03

.11
).02
.62

).08
.59

.52

.26
.37
.42
.39

).40
).35
).26
).38

).27
).03
).08
).14

).11
.05
).03
).04

).45
).30
).45
).44

).47
).16
).33
).36

.68
.68
.88

.79
.91

.91

).56
).54

.52
.26

.11
.40

.01
.26

.19
.21

.18
.49

).27
).37

).37
).41

).29 ).34).30 ).40

44.4
12.9

34.4
3.6

18.4
3.6

13.1
2.6

0.0
0.0

47.6
9.8

31.4
6.1

29.7
5.0

813
187

8

9

10

11

12

13

0.0
0.0

14

15

.42
31.0
5.4

21.9
1.8

194 A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199

Table 2
Correlations between orthographic learning measures, target decoding accuracy, and other cognitive measures

A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199 195

Table 3
Hierarchical regression analyses predicting orthographic learning composite score
Step variableR

R2 change

F to enter

Final bFinal F

1. Target decoding accuracy
2. General ability comp

.520
.521

.271
.000

11.88
0.01

.520
.012

11.49
0.01

1. Target decoding accuracy
2. RAN composite

.520
.548

.271
.029

11.88
1.29

.451
).184

7.75
1.29

1. Target decoding accuracy
2. Orthographic choice

.520
.688

.271
.202

11.88
11.87

.401.465

8.84
11.87

**p < :01.

learning after target decoding accuracy was partialed. An identical analysis
was conducted using as the RAN composite score only letter and number
RAN (that is, eliminating color naming from the composite score). This alternative composite score also failed to reach signiﬁcance in the hierarchicalanalysis (variance explained ¼ .052, F ð1; 31Þ ¼ 2:39, p > 1), however it did
account for more unique variance. This potential relationship may deserve
further study.
The results of the ﬁrst two regression analyses suggest that orthographic
learning might occur solely as a function of decoding accuracy during story
reading. However, the third analysis displayed in Table 3 revealed thatorthographic knowledge predicted a signiﬁcant amount of variance inorthographic learning, over and above the contribution of target decoding
accuracy. This ﬁnding demonstrated that learning in this task was not justa function of successful decoding during the reading trials, but depended
on a pre-existing psychological or processing characteristic that the childbrought to the task, namely prior orthographic knowledge.

Discussion
In the present study, we found that second grade students demonstrated
robust evidence of orthographic learning three days after they were exposed
to novel English words in text, under conditions that simulated theself-teaching that is expected to occur in normal everyday reading contexts.
Children were able to more quickly and accurately identify, name, andreproduce these homophones, thus replicating Share’s (1999) study andextending his ﬁndings to a diﬀerent and less transparent orthography.
Not only did the present study demonstrate large diﬀerences between thetarget homophones and homophone controls, but we also obtained a substantial correlation (r ¼ :52) between orthographic learning and the numberof target homophones correctly decoded during story reading, just as would
be expected from the self-teaching hypothesis. Although the present study196 A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199

did not conduct controlled experiments for visual learning, Share’s (1999)
study (experiments two and three) demonstrated that word learning in thisparadigm cannot be attributed simply to visual exposure.
According to the self-teaching hypothesis, orthographic learning isachieved primarily by way of print-to-sound translation. Our ﬁndings addressed the question of whether the degree of success at ongoing print-tosound translation in a speciﬁc set of texts was the sole contributor to theamount of orthographic knowledge derived from those texts or whether
there were individual diﬀerences in reading-related processing characteristics
that mediated the amount of orthographic knowledge induced.
In any study of learning, general cognitive ability must always be considered to be an alternative hypothesis for any individual diﬀerences obtained.
We were surprised to ﬁnd that in fact this variable explained no unique variance in orthographic learning once target decoding accuracy had been controlled. Because Bowers and Wolf (1993) had posited a key independent link
between RAN and orthographic processing, we examined the ability of the
RAN composite variable to explain additional variance but found that itwas not a unique predictor. Somewhat more variance was explained usinga RAN composite score containing only letters and numbers but, again, thisdid not attain statistical signiﬁcance in a hierarchical analysis.
Whatever the eﬀect of RAN in producing individual diﬀerences in orthographic knowledge, at least in this study that eﬀect appeared to be masked
and/or overwhelmed by the potent eﬀects of decoding accuracy. Our results
are also consistent with the ﬁndings of Torgesen et al. (1997) who, in astudy of second-grade to fourth-grade growth in reading subskills, foundthat after word reading accuracy in second grade was partialed out, RAN
did not predict fourth-grade orthographic processing skill. It should be
noted, though, that the predictive potency of the RAN variable has tended
to be larger in studies where reading disabled children were disproportionately represented (Wolf, 1999; Wolf & Bowers, 1999) as opposed to studies
such as this one which utilize samples throughout the range of reading
ability.
Unlike general cognitive ability and the RAN composite, the orthographic choice task did explain unique variance in orthographic learning
as a result of self-teaching. Note that the orthographic choice task taps already existing orthographic knowledge whereas the orthographic learningcomposite score reﬂects knowledge gained from exposure to the particulartargets during these reading sessions. Thus, the ability of the orthographic
choice task to predict unique variance means that previous success at inducing orthographic knowledge predicts success at inducing such knowledge
from these exposures, once decoding accuracy has been controlled. In sum,the development of orthographic knowledge appears to be not entirely parasitic on decoding ability. Exactly what this orthographic skill is continues
to be a focus of much research and debate in the ﬁeld (e.g., Barker et al.,

A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199 197

1992; Berninger, 1994; Compton, 2000; Cunningham, Perry, & Stanovich,
2001; Cunningham & Stanovich, 1990, 1993; Ehri & Saltmarsh, 1995;
Manis, 1985; Manis et al., 2000; Reitsma, 1983, 1989; Venezky, 1999). Given
the present results, it does appear to be something more than just print exposure because that was controlled explicitly in the experiment, and target
decoding accuracy was controlled statistically.

Appendix A. Sample story text and comprehension questions
The coldest place in the world
North of Greenland is a place they say is the coldest place in the world.
The name of the city is Yait. In Yait there is snow and ice all year round.
The temperature in winter is always around 0° and it is dark most of theday. There is always a cold wind blowing from the north. But there are also
some good things about Yait.
You don’t have to hurry to put your food in the refrigerator to keep it
cold. And there’s always lots of ice for your drinks. In summer there is sunlight all day and all night, so you can go skiing and skating anytime youwant, even at night. In Yait you won’t have to put on sunscreen when
you go outside because the sun is never too hot. And there are no ﬂies in
Yait to worry about. There is also a lake that is always frozen, so you can’t
ever fall in. But most of all, the people are very nice and friendly.
Would you like to live in Yait?
Comprehension questions:
1. Name one activity you can do in this city?
2. Name one advantage of living in this cold city?
3. What are the people like in this city?

References
Allerup, P., & Elbro, C. (1998). Comparing diﬀerences in accuracy across conditions or
individuals: an argument for the use of log odds. Quarterly Journal of Experimental
Psychology, 51A, 409–424.
Barker, K., Torgesen, J. K., & Wagner, R. K. (1992). The role of orthographic processing skills
on ﬁve diﬀerent reading tasks. Reading Research Quarterly, 27, 334–345.
Berninger, V. (Ed.). (1994). Varieties of orthographic knowledge: Theoretical and developmental
issues (Vol. 1). Dordrecht, The Netherlands: Kluwer Academic.
Bowers, P. G., & Wolf, M. (1993). Theoretical links among naming speed, precise timing
mechanisms and orthographic skill in dyslexia. Reading and Writing: An Interdisciplinary
Journal, 5, 69–85.
Castles, A., Datta, H., Gayan, J., & Olson, R. K. (1999). Varieties of developmental reading
disorder: genetic and environmental inﬂuences. Journal of Experimental Child Psychology,
72, 73–94.

198 A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199
Compton, D. (2000). Modeling the growth of decoding skills in ﬁrst grade children. Scientiﬁc
Studies of Reading, 4, 219–259.
Cunningham, A. E., Perry, K. E., & Stanovich, K. E. (2001). Converging evidence for theconcept of orthographic processing. Reading and Writing: An Interdisciplinary Journal, 14,
549–568.
Cunningham, A. E., & Stanovich, K. E. (1990). Assessing print exposure and orthographic
processing skill in children: a quick measure of reading experience. Journal of Educational
Psychology, 82, 733–740.
Cunningham, A. E., & Stanovich, K. E. (1993). Children’s literacy environments and early word
recognition skills. Reading and Writing: An Interdisciplinary Journal, 5, 193–204.
Dunn, L. M., & Dunn, L. M. (1981). Peabody picture vocabulary test—revised. Circle Pines,
MN: American Guidance Service.
Ehri, L. C. (1992). Reconceptualizing the development of sight word reading and its
relationship to recoding. In P. Gough, L. C. Ehri, & R. Treiman (Eds.), Reading acquisition(pp. 107–144). Hillsdale, NJ: Erlbaum.
Ehri, L. C., & Saltmarsh, J. (1995). Beginning readers outperform older disabled readers inlearning to read words by sight. Reading and Writing: An Interdisciplinary Journal, 7, 295–
326.
Juel, C., Griﬃth, P. L., & Gough, P. B. (1986). Acquisition of literacy: a longitudinal study ofchildren in ﬁrst and second grade. Journal of Educational Psychology, 78, 243–255.
Jorm, A., & Share, D. (1983). Phonological recoding and reading acquisition. Applied
Psycholinguistics, 4, 103–147.
Manis, F. R. (1985). Acquisition of word identiﬁcation skills in normal and disabled readers.
Journal of Educational Psychology, 77, 78–90.
Manis, F. R., Custodio, R., & Szeszulski, P. A. (1993). Development of phonological andorthographic skill: a 2-year longitudinal study of dyslexic children. Journal of Experimental
Child Psychology, 56, 64–86.
Manis, F. R., Doi, L. M., & Bhadha, B. (2000). Naming speed, phonological awareness, andorthographic knowledge in second graders. Journal of Learning Disabilities, 33, 325–333.
Manis, F. R., Seidenberg, M. S., & Doi, L. M. (1999). See Dick RAN: rapid naming and thelongitudinal prediction of reading subskills in ﬁrst and second graders. Scientiﬁc Studies of
Reading, 3, 129–157.
Olson, R., Kliegl, R., Davidson, B., & Foltz, G. (1985). Individual and developmental
diﬀerences in reading disability. In T. Waller (Ed.), Reading research: Advances in theory
and practice (Vol. 4) (pp. 1–64). London: Academic Press.
Perfetti, C. A. (1992). The representation problem in reading acquisition. In P. B. Gough, L. C.
Ehri, & R. Treiman (Eds.), Reading acquisition (pp. 145–174). Hillsdale, NJ: Erlbaum.
Reitsma, P. (1983). Printed word learning in beginning readers. Journal of Experimental Child
Psychology, 36, 321–339.
Reitsma, P. (1989). Orthographic memory and learning to read. In P. G. Aaron, & R. M. Joshi(Eds.), Reading and writing disorders in diﬀerent orthographic systems (pp. 51–73).
Dordrecht/Norwell, MA: Kluwer Academic.
Raven, J. C. (1962). Standard colored progressive matrices (Set I). London: H.K. Lewis & Co.
Share, D. L. (1995). Phonological recoding and self-teaching: sine qua non of reading
acquisition. Cognition, 55, 151–218.
Share, D. L. (1999). Phonological recoding and orthographic learning: a direct test of the selfteaching hypothesis. Journal of Experimental Child Psychology, 72, 95–129.
Share, D. L., & Levin, I. (1999). Learning to read and write in Hebrew. In M. Harris, & G.
Hatano (Eds.), A cross-cultural perspective on learning to read. Cambridge: Cambridge
University Press.
Stanovich, K. E., & West, R. F. (1989). Exposure to print and orthographic processing. Reading
Research Quarterly, 24, 402–433.

A.E. Cunningham et al. / Journal of Experimental Child Psychology 82 (2002) 185–199 199

Stanovich, K. E., Siegel, L. S., & Gottardo, A. (1997). Converging evidence for phonologicaland surface subtypes of reading disability. Journal of Educational Psychology, 89, 114–127.
Torgesen, J. K., Wagner, R. K., Rashotte, C. A., Burgess, S., & Hecht, S. (1997). Contributionsof phonological awareness and rapid automatic naming ability to the growth of wordreading skills in second- to ﬁfth-grade children. Scientiﬁc Studies of Reading, 1, 161–185.
Treiman, R. (1984). Individual diﬀerences among children in reading and spelling styles. Journalof Experimental Child Psychology, 37, 463–477.
Tunmer, W. E., & Nesdale, A. R. (1985). Phonemic segmentation skill and beginning reading.
Journal of Educational Psychology, 77, 417–427.
Venezky, R. L. (1999). American English orthography. New York: Guilford Press.
Wimmer, H. (1996). The early manifestation of developmental dyslexia: evidence from German
children. Reading and Writing: An Interdisciplinary Journal, 8, 171–188.
Wimmer, H., Mayringer, H., & Landerl, K. (2000). The double-deﬁcit hypothesis anddiﬃculties in learning to read a regular orthography. Journal of Educational Psychology, 92,
668–680.
Wolf, M. (1999). What time may tell: towards a new conceptualization of developmental
dyslexia. Annals of Dyslexia, 49, 3–28.
Wolf, M., & Bowers, P. G. (1999). The double-deﬁcit hypothesis for the developmental
dsylexias. Journal of Educational Psychology, 91, 415–438.