Brain and Language 92 (2005) 185–203

www.elsevier.com/locate/b&l

Process dissociation of sight vocabulary and phonetic decodingin reading: A new perspective on surface and phonological dyslexias

Patricia McDougall,a,*,1 Ron Borowsky,b,*,1 G.E. MacKinnon,c and Shelley Hymeld

a St. Thomas More College, University of Saskatchewan, 1437 College Drive, Saskatoon, Saskatchewan, Canadab Department of Psychology, University of Saskatchewan, 9 Campus Drive, Saskatoon, SK, Canada S7N 5A5

c Department of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, Ont., Canada N2L 3GLd Department of Educational & Counselling Psychology and Special Education, Faculty of Education, University of British Columbia,

2125 Main Mall, Vancouver, British Columbia, Canada V6T 1Z4

Accepted 7 June 2004

Available online 30 July 2004

Abstract

Recent research on developmental dyslexia has suggested a phonological core deficit hypothesis (e.g., Manis, Seidenberg, Doi,

McBride-Chang, & Peterson, 1996; Stanovich, Siegel, & Gottardo, 1997) whereby pure cases of developmental phonological dyslexia

(dysfunctional phonetic decoding processing but normal sight vocabulary processing) can exist, but pure cases of developmental

surface dyslexia (dysfunctional sight vocabulary processing but normal phonetic decoding processing) should not. By applying Ja-

coby’s (1991) and Lindsay and Jacoby’s (1994) process dissociation procedure to the reading of regular and exception words, we

present a method that serves to estimate readers’ reliance on sight vocabulary and phonetic decoding during real word recognition.

These reliance estimates are then used in Castles and Coltheart’s (1993) regression-based approach to identify normal readers and

developmental dyslexics. This new method: (1) allows one to explore normal reading acquisition and both the delay and deviance

accounts of developmental dyslexia, (2) provides an alternative to matching dyslexics to both chronological-age and reading-age

control groups, and (3) uses only real words. We present evidence that pure cases of developmental surface dyslexia can be obtained

with both Castles and Coltheart’s measure as well as our own, and that developmental surface dyslexia is not simply a delayed reading

deficit. The theoretical importance and utility of estimates of reliance on sight vocabulary and phonetic decoding is discussed.

� 2004 Elsevier Inc. All rights reserved.

1. Introduction

We often take reading ability for granted. The cog-

nitive processes involved in reading have become so

well-learned that skilled word recognition has been de-

scribed by some as an automatic process (e.g., consider

the Stroop paradigm, where participants are to identifythe colour in which a word is presented, and perfor-

mance is decreased by the lack of congruency between

the word’s identity and its colour, such as the word

RED in the colour black, Stroop, 1935; see MacLeod,

1991, for a review; and Lindsay & Jacoby, 1994; as well

* Corresponding authors. Fax: +1-3069666630.

E-mail addresses: mcdougp@duke.usask.ca (P. McDougall),

ron.borowsky@usask.ca (R. Borowsky).1 Both authors contributed equally to this work.

0093-934X/$ - see front matter � 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.bandl.2004.06.003

as Spieler, Balota, & Faust, 1996, for a process disso-

ciation of the Stroop effect). The presence of reading

disabilities (or dyslexias) in the form of both develop-

mental and acquired cases serve to remind us that

reading can be a very effortful (or even impossible)

process for some.

The present research involves the development of ameasure for assessing reliance on Sight Vocabulary (SV;

i.e., naming a word using whole-word orthographic

representations to access a phonological lexical repre-

sentation) and Phonetic Decoding (PD; i.e., naming a

word using sub-lexical spelling-sound correspondences,

which will often involve phonological lexical access gi-

ven that a child’s spoken vocabulary is typically quite

large when learning to read). Fig. 1 illustrates a frame-work for examining SV and PD processing, which shows

how dual-route models and single-route models differ in

Fig. 1. A framework for sight vocabulary (SV) and phonetic decoding (PD) processing in reading (see Owen & Borowsky, 2004a, for a revised

version).

186 P. McDougall et al. / Brain and Language 92 (2005) 185–203

terms of subsystems and their grouping (see alsoBorowsky, Owen, & Fonos, 1999; and Owen &

Borowsky, 2004a, for experiments that examine the

nature and direction of connections between subsystems

in this framework). Specifically, dual-route models

specify more subsystems, whereby SV and PD process-

ing can function independently across two processes,

whereas single-route models specify more grouping

whereby SV and PD processing can only operate re-dundantly within one process.

Our measure of reliance on SV and PD processing

differs from current approaches (e.g., Castles & Colt-

heart, 1993; Manis, Seidenberg, Doi, McBride-Chang &

Peterson, 1996; Stanovich, Siegel, & Gottardo, 1997)

mainly in that it does not use nonwords (e.g., ‘‘gead’’)

for which we have no set criteria, and no means of

phonological lexical assistance, to assess reliance on PDprocessing (see also Borowsky & Besner, 1991, 1993,

2000, for discussion of an additive factors approach to

examining reliance on processing routes, and Borowsky,

Owen, & Masson, 2002, for discussion how list context

effects on pseudohomophone naming reflect the degree

to which PD processing involves phonological lexical

access). When engaged in PD processing during normal

reading acquisition, the greater the extent to which weexpect the words presented to us to be real words (a fair

expectation), the greater the likelihood of phonological

lexical assistance, and thus the more problematic it is to

use nonwords as diagnostic stimuli for PD processing.

This problem also demands that our theories be more

specific about what is meant by PD processing (or ‘‘as-

sembled phonology’’). The present paper provides a

solution for this problem by presenting a new, theoret-ically derived approach for estimating reliance on SV

and PD processing that uses only word stimuli. Beforedescribing this approach, we will briefly discuss the

traditional methods for identifying dyslexic subtypes,

and some of the problems associated with the traditional

methods that can also be dealt with using this new ap-

proach.

2. Current methods for identifying developmental dyslexias

Castles and Coltheart (1993) presented a very influ-

ential regression-based approach to identifying surface

versus phonological subtypes of developmental dyslexia

(e.g., Manis et al., 1996; Stanovich et al., 1997; and

several others since). This method involves plotting

‘‘normal’’ naming accuracy as a function of chrono-

logical age (where ‘‘normal’’ readers achieved a readingage score that was within 6 months of their chronolog-

ical age), calculating a 90% sample confidence interval

around this ‘‘normal’’ regression line, and then over-

laying the plot of ‘‘dyslexic’’ naming accuracy as a

function of chronological age (where ‘‘dyslexic’’ readers

achieved a reading age equivalency below 18 months of

their chronological age). A surface dyslexic pattern is

exemplified by lower-than-normal accuracy at namingexception words (e.g., ‘‘two’’), reflecting the commonly

held assumption that exception word naming selectively

involves SV processing. A phonological dyslexic pattern

is exemplified by lower-than-normal accuracy at naming

pronounceable nonwords (e.g., gead), reflecting the

commonly held assumption that nonword naming se-

lectively involves PD processing, an assumption that we

will return to later. Castles and Coltheart found 10 oftheir 53 participants to be relatively pure surface dys-

P. McDougall et al. / Brain and Language 92 (2005) 185–203 187

lexics, 8 to be relatively pure phonological dyslexics,and 27 to be both surface and phonologically dyslexic.

We note that this dissociation between pure surface

and pure phonological dyslexia, along with the ex-

istence of cases that illustrate both of these dyslexic

subtypes, presents a compelling illustration of the

(mathematically) independent relationship between SV

and PD processes (see also Hendricks & Kolk, 1997),

as do several cases of acquired dyslexia (e.g., Badde-ley, Ellis, Miles, & Lewis, 1982; Castles & Coltheart,

1993).

2 Stanovich et al. presented a reanalysis of Castles and Coltheart’s

data using a reading age control group, which resulted in about half

the number of phonological dyslexic cases (17) and surface dyslexic

cases (6). We note that the cases of surface dyslexia were not entirely

eliminated after controlling for reading age.

3. The control group problem

The most contentious issue regarding Castles and

Coltheart’s (1993) method has been the nature of theircontrol group. Castles and Coltheart chose to use a

chronological age control group (i.e., dyslexic readers

matched to the control group of good readers on the

basis of chronological age). The utility of chronological

age controls has been debated extensively in the litera-

ture, with some researchers arguing that ‘‘reading age’’

controls must be used instead (e.g., Bryant & Impey,

1986; Snowling, Bryant, & Hulme, 1996), and othersarguing that it is informative to make comparisons to

both types of control groups (e.g., Manis et al., 1996;

Stanovich et al., 1997). We agree with the latter re-

searchers that both types of control groups are infor-

mative, in that chronological age controls provide an

initial perspective on the dyslexic readers in terms of

how delayed they are compared to good readers of the

same age (a comparison that will continue to define whois and who is not dyslexic in our elementary schools,

given that children are always grouped according to

chronological age). In contrast, the reading age controls

allow one to examine whether any cases exemplify de-

viant processing. The logic here is that when comparing

dyslexics to chronological age controls, any differences

may reflect either delay in acquisition of the particular

reading process, or the operation of a nonnormal (or‘‘deviant’’) reading process, whereas the comparison of

dyslexics to reading age controls allows one to control

for delay (as the participants are matched on reading

age), such that any differences can only reflect the op-

eration of a deviant reading process.

When using a chronological age control group, the

examination of developmental surface dyslexia by

Manis et al. (1996) and Stanovich et al. (1997) revealedseveral cases of surface dyslexia (15 in each study;

N ¼ 51 and 68, respectively). In contrast, using a read-

ing age control group revealed only one case in each of

their studies that could be considered as illustrating

surface dyslexia. Based on the finding that surface dys-

lexia cases seem to disappear when controlling for dif-

ferences in reading age, these authors have argued that

developmental surface dyslexia most likely represents adelay deficit.2

With respect to developmental phonological dyslexia,

Manis et al. (1996) and Stanovich et al. (1997) found

evidence for several cases in their two studies (17 in each

study) when a chronological age control group was used

as an index of normal performance. Moreover, these

researchers found several cases of phonological dyslexia

in their studies when a reading age control group wasused (12 and 17, respectively). Based on the finding that

several cases of phonological dyslexia remain when

controlling for reading age, these authors have sug-

gested that developmental phonological dyslexia cannot

be considered a delay deficit, and thus it most likely

reflects some form of deviant processing.

4. The matching problem

The low numbers of surface dyslexia cases observed

when reading age is controlled (compared to when

chronological age controls are used) has led to the

suggestion that developmental surface dyslexia repre-

sents a developmental delay deficit (Manis et al., 1996;

Stanovich et al., 1997). An alternative interpretation ofwhy there are so few cases of developmental surface

dyslexia when participants are matched on reading age

has to do with how participants were matched. In both

the Manis et al. and Stanovich et al. studies, reading age

control participants were matched to the dyslexics on

the basis of their real word reading performance (i.e., the

Word Identification subtest from the Woodcock Read-

ing Mastery Test-Revised [WRMT-R; Woodcock,1987], and the Wide Range Achievement Test-Revised

[WRAT-R; Jastak & Wilkinson, 1984], respectively, in

these studies). Given that surface dyslexia is, by defini-

tion, an impairment of SV processing that specifically

serves to access lexical phonology for real words only,

we should not be surprised that cases of surface dyslexia

are reduced (relative to cases of phonological dyslexia)

when participants are matched on the basis of real wordreading performance. Clearly, it is important to not let

the participant matching procedure bias the identifica-

tion of dyslexic subtypes.

It is possible that this matching bias could have in-

fluenced Manis et al.’s (1996) and Stanovich et al.’s

(1997) findings regarding the nature of surface dyslexia.

We note that these authors have themselves acknowl-

edged this problem (e.g., Manis et al., 1996; Stanovich,Nathan, & Vala-Rossi, 1986), and that additional

188 P. McDougall et al. / Brain and Language 92 (2005) 185–203

problems with using reading age controls have beendebated in the literature (e.g., Coltheart, 1987; Jackson

& Butterfield, 1989; Stanovich & Siegel, 1994, but see

Bryant & Impey, 1986, and Snowling et al., 1996, for an

alternative view). Ideally, the types of stimuli that have

been used for the purpose of matching should not bias

one’s method for identifying cases of dyslexia. Given

that we are most interested in SV and PD processing in

the context of real word reading, it seems appropriate touse real words for the purposes of both matching and

identification. However, the use of nonwords as stimuli

for diagnosing PD processing and phonological dys-

lexia, along with using exception words as stimuli for

diagnosing SV processing and surface dyslexia, has be-

come somewhat of a convention in reading assessment.

5. The nonword problem

Accuracy in naming nonword stimuli is used as a

process-pure measure of PD processing ability in the

Castles and Coltheart (1993) regression-based method

described above. We argue that nonword stimuli can be

considered problematic for studying word reading pro-

cesses. For example, pronounce gead to yourself. Didyou pronounce it to rhyme with ‘‘bead’’ or ‘‘head,’’ and

was the ‘g’ hard (e.g., ‘‘gun’’) or soft (e.g., ‘‘gem’’)?

Which is the ‘‘correct’’ pronunciation, or are there sev-

eral ‘‘correct’’ pronunciations? Will another researcher

be as conservative or as liberal as you in selecting their

criteria for naming this nonword stimulus? Even if

consistent scoring criteria were adopted across labora-

tories, would we really be measuring PD ability as it isinvolved in real word reading? Recent research suggests

that both SV and PD processes are relied on to different

degrees when the reader is expecting all responses to

sound like real words that they know, than when all

responses are novel, or when some mixture of the two is

presented (e.g., Baluch & Besner, 1991; Borowsky et al.,

2002; Marmurek & Kwantes, 1996; Monsell, Patterson,

Graham, Hughes, & Milroy, 1992). Using an index oflexical access such as the magnitude of the word-fre-

quency effect during word identification, these studies

support the notion that PD is relied on more when the

stimulus set includes nonwords, and that SV is relied on

more when the stimulus set includes words (especially

exception or irregular words, which can only be named

correctly by relying on SV processing, e.g., yacht, pint).3

3 On this point, it is interesting to note that Castles and Coltheart

(1993) used a mixed presentation of their diagnostic exception word

and nonword stimuli, and found several more cases of phonological

developmental dyslexia than either Manis et al. (1996) or Stanovich

et al. (1997), who presented their diagnostic exception words and

nonwords in pure blocks. It would be interesting to examine whether

the discrepancy between the number of phonological developmental

dyslexics found in these studies could be partly attributable to whether

the diagnostic stimuli are presented in a mixed block or pure blocks.

However, and more importantly, the point here is thatnonwords may not make for a valid, process-pure

measure of PD in the context that we are interested in,

namely, a normal reading context in which real words

are expected. Although nonword naming performance

may reflect a reader’s ability to use PD when confronted

with letter strings that are both orthographically and

phonologically unfamiliar, it may not serve as a very

good measure of a reader’s reliance on PD duringphonological lexical access (arguably, a more common

occurrence during reading acquisition). Furthermore,

recent frameworks and models of visual word recogni-

tion have paid considerably more attention to the fact

that new readers typically have a large phonological

vocabulary consisting of familiar words prior to their

acquisition of orthographic representations (e.g.,

Borowsky et al., 1999; Harm & Seidenberg, 1999).

6. The process dissociation approach to estimating

reliance on SV and PD processes

Current frameworks and models of word recognition

have informed our development of this method. Briefly,

there are many dual-route models of word recognitionthat subscribe to the notion that there are two mathe-

matically independent routes involved in computing

pronunciations from print (see Fig. 2; e.g., Besner &

Smith, 1992; Borowsky et al., 1999; Coltheart, Curtis,

Atkins, & Haller, 1993; Coltheart, Rastle, Perry, Lang-

don, & Ziegler, 2001; Paap, McDonald, Schvaneveldt, &

Noel, 1987; Zorzi, Houghton, & Butterworth, 1998). We

are using the term ‘‘independent’’ in its strict mathe-matical sense, whereby the two processing routes can

contribute separately, or jointly, to the response. Gen-

erally speaking, reliance on PD processing can produce

correct pronunciations only for words with regular

spelling-sound correspondences, while reliance on SV

processing can produce correct pronunciations of words

with either regular or irregular spelling-sound corre-

spondences, as long as they are familiar to the readerand thus included in their SV. A recent method for de-

riving estimates of reliance on independent processes has

been provided by Jacoby (1991, process dissociation

procedure), and it has seen considerable development in

the literature (e.g., Jacoby, McElree, & Trainham, 1999;

Joordens & Merikle, 1993; Lindsay & Jacoby, 1994;

Fig. 2. Venn diagram of the relationship between sight vocabulary (SV)

and phonetic decoding (PD) reading processes.

P. McDougall et al. / Brain and Language 92 (2005) 185–203 189

Spieler et al., 1996; Trainham, Lindsay, & Jacoby,1997). This procedure can be applied to estimating re-

liance on PD and SV during real word recognition in a

manner similar to Lindsay and Jacoby’s (1994; see also

Hillstrom & Logan, 1997; Spieler et al., 1996; Trainham

et al., 1997) process dissociation of reliance on word

reading and colour naming in the Stroop task (i.e., name

the colour in which a word has been presented).

The procedure requires one experimental conditionthat allows either processing route to contribute to a

correct response, and another experimental condition in

which only one of the processing routes can contribute

to a correct response. Lindsay and Jacoby (1994) co-

gently argued that a congruent Stroop item (e.g., the

word BLUE presented in the colour blue) fulfils the

former experimental condition in that either word

reading or colour naming can contribute to a correctresponse (i.e., ‘‘blue’’). They further argued that an in-

congruent Stroop item (e.g., the word BLUE presented

in the colour red) fulfils the latter experimental condi-

tion in that only the colour naming process can con-

tribute to a correct response (i.e., ‘‘red’’). An analogous

situation exists with regular words and exception words

in models of basic reading processes that deal with the

distinction between SV and PD processing. Currentmodels have assumed that regular words (e.g., mint,

hint, flint, rave, save, and crave) are correctly pro-

nounced by either processing route (PD or SV), whereas

exception words (e.g., pint, have, yacht, and aisle) can

only be pronounced correctly by using one of the pro-

cessing routes (i.e., SV), but not the other. Thus, the

former experimental condition can be satisfied by having

children pronounce regular words, and the latter ex-perimental condition can be satisfied in a similar fashion

by having children pronounce exception words.

Given the assumption that these processing routes are

independent, two equations involving overall correct

response probability and two unknown reliance vari-

ables can be derived and solved. In the equations which

follow, PD represents reliance on PD, and SV represents

reliance on SV:

pðcorrectjregular wordsÞ ¼ PDþ SV� ðPD � SVÞ; ð1Þ

pðcorrectjexception wordsÞ ¼ SV� ðPD � SVÞ: ð2Þ

Eq. (1) states that the overall probability of correctpronunciation given regular words reflects the propor-

tion of trials that (the reader) relied on either processing

routine, minus their ‘‘intersection’’ (i.e., the proportion

of trials relying on both routes). The second equation

states that the overall probability of correct pronuncia-

tion given exception words reflects the proportion of

trials that relied on SV, minus the proportion of trials

involving both routes. The intersection of the two routes(i.e., PD*SV) is subtracted in the first equation because

the proportion of trials involving both routes is included

twice through the separate estimates of PD and SV re-liance, whereas the intersection is subtracted in the sec-

ond equation because any reliance on PD will yield an

incorrect pronunciation of an exception word.These

equations can be simplified to:

pðcorrectjregular wordsÞ ¼ PDþ SVð1� PDÞ; ð3Þ

pðcorrectjexception wordsÞ ¼ SVð1� PDÞ: ð4Þand rearranged for solving reliance on PD and SV:

PD ¼ pðcorrectjregular wordsÞ� pðcorrectjexception wordsÞ ð5Þ

SV ¼ pðcorrectjexception wordsÞ=ð1� PDÞ ð6Þ

Eq. (5) clearly illustrates how reliance on PD is esti-

mated by subtracting the proportion of correctly named

exception words from the proportion of correctly namedregular words. From what we know about regularity

effects during word naming for skilled readers of En-

glish, there is a small but reliable advantage for naming

regular words, and thus the estimate of reliance on PD

should usually be a small and positive value for skilled

readers (see also Manis, Szeszulski, Holt, & Graves,

1990; Olson, Kliegl, Davidson, & Foltz, 1985; Stanovich

& Siegel, 1994). These equations make intuitive senseonce one examines the possible extremes in perfor-

mance. For example, for the particular set of words

presented, if one was to never rely on PD but always rely

on SV, then you would not expect regular word naming

accuracy to exceed exception word naming accuracy

(assuming the use of stimuli that are well matched on

variables like word frequency, grade level, and length),

and the estimate of reliance on PD would equal zerowhile the estimate of reliance on SV would be equal to

the proportion of correctly named exception words. If

one was to never rely on SV, but always on PD, then one

would expect the proportion of correctly named excep-

tion words to equal zero, and thus the reliance on SV

will be zero, and reliance on PD would be estimated by

the proportion of correctly named regular words. In-

terestingly, if one correctly names fewer regular wordsthan exception words, then PD reliance must be inhib-

ited relative to SV reliance, and a negative value for PD

reliance would result. We note that negative values are

theoretically possible whenever this version of the pro-

cess dissociation procedure is used to estimate inde-

pendent processes (comparable to the situation where

accuracy in the incongruent condition of the Stroop task

exceeds accuracy in the congruent condition), and maygenerally represent the participant’s attempt to inhibit

the dominant process. For example, in the Stroop task,

if one was presented with a congruent item like the word

BLUE in the colour blue and is consciously trying to

inhibit the word, then the participant may experience

more difficulty resolving a congruent trial word code

190 P. McDougall et al. / Brain and Language 92 (2005) 185–203

(‘‘not BLUE’’) with the colour code (‘‘blue’’), than anincongruent trial word code (e.g., for the word RED in

the colour blue, the inhibited word code would be ‘‘not

RED’’) with the colour code (e.g., ‘‘blue’’; see Merikle &

Cheesman, 1987, for evidence of a reverse Stroop effect

when the proportion of incongruent trials is high).

These equations also assume that PD is the ‘‘domi-

nant’’ process (i.e., when both PD and SV processing are

completed at the same time, PD is relied on, and thuswould contribute to naming errors on exception words;

see also Lindsay & Jacoby’s (1994) assumption that

word naming dominates over colour naming in the

Stroop task). While we believe that this assumption is

appropriate for developing readers, one might prefer to

assume that SV is the dominant process for skilled

readers of English. Assuming that SV is the dominant

process would result in the following equations:

pðcorrectjregular wordsÞ ¼ PDþ SV� ðPD � SVÞ; ð7Þ

pðcorrectjexception wordsÞ ¼ SV ð8ÞEq. (7) can be simplified to:

pðcorrectjregular wordsÞ ¼ SVþ PDð1� SVÞ; ð9Þand both equations can be rearranged for solving reli-

ance on PD and SV:

SV ¼ pðcorrectjexception wordsÞ ð10Þ

PD ¼ ðpðcorrectjregular wordsÞ� pðcorrectjexception wordsÞÞ=ð1� pðcorrectjexception wordsÞÞ: ð11Þ

We will return to a discussion of these SV-dominantequations, and how they impact the identification of

dyslexics, in Section 9. To foreshadow, our identification

of dyslexics does not change dramatically as a function

of whether PD or SV is assumed to be the dominant

process.

7. The present study

The obtained estimates of reliance on PD and SV

processing can be used in the regression-based method

for identifying phonological and surface dyslexics in the

same way that previous researchers used nonword and

exception word naming accuracy (e.g., Castles & Colt-

heart, 1993; Manis et al., 1996; Stanovich et al., 1997).

In the current study, we present an extension of Castlesand Coltheart’s (1993) regression-based methodology

that: (1) allows us to explore normal reading develop-

ment and both the delay and deviance accounts of de-

velopmental dyslexia, (2) provides an alternative to

matching dyslexics to two separate control groups, and

(3) uses only real words. We present regression plots of

the dependent variables: (1) as a function of chrono-

logical age, and, (2) residualized on reading age, andthen plotted as a function of chronological age. This

type of ‘‘statistical matching’’ has been used by other

researchers (e.g., Stanovich & Siegel, 1994), and has

been argued to be advantageous in terms of maximizing

external validity over matching by sample selection (e.g.,

Jackson & Butterfield, 1989).

8. Method

8.1. Participants

In the first phase of data collection, 202 students in

grades 2 through 5 were recruited from 11 classrooms in

two elementary schools. In an effort to obtain a greater

number of challenged readers, we recruited an addi-tional 22 students in a second phase of data collection.

Remaining within the same school district, Phase 2 data

were collected from teacher-referred students in three

elementary schools. Specifically, teachers were asked to

nominate grade 2–5 students in their classroom who

were considered to be struggling readers and who were

viewed as doing more poorly than their classmates. The

majority of the students in Phase 2 (77%) were receivingremedial support beyond their regular classroom in-

struction in the areas of reading, spelling and language.

The total sample consisted of 224 students in grade 2

(n ¼ 59, 33 boys, mean age¼ 7 years 8 months), grade 3

(n ¼ 55, 21 boys, mean age¼ 8 years 8 months), grade 4

(n ¼ 63, 35 boys, mean age¼ 9 years 9 months), and

grade 5 (n ¼ 47, 24 boys, mean age¼ 10 years 8

months). All students received parental consent for theirparticipation.

8.2. Apparatus

Micro Experimental Laboratories (MEL) software

and a NEC Versa laptop computer controlled the

stimulus displays, the timing of events, and recorded

the data. The stimuli were presented in monochrome onthe laptop computer screen in a sans serif font measur-

ing 7mm in height and 5mm in width (with ascenders

10mm high). Participants controlled the rate of stimulus

presentation by pressing the space-bar on the computer

keyboard. Accuracy of participant’s naming response

was coded by the experimenter using a MEL button-

box.

8.3. Materials and procedure

Phase 1 data were collected in March, April, and May

of the school year. The second phase of testing was

conducted one year later during the month of May. All

data were collected during 35–45min individual sessions

conducted in a quiet room within the school setting.

4 A goal for future research would be to treat the reliance estimates

as inferential (i.e., generalizable to the reading of an entire population

of words) instead of just descriptive. To do so would require one to use

a sample of words that would be representative of the population of

words (as ever-changing as that population must be, but it certainly

must include more low frequency, and ‘‘unknown’’ words than what

we used). Following this logic, and assuming that no single person

knows all of the words of the language, such a sample of words must

include some ‘‘unknown’’ words in order for it to be convincing as an

inferential test. Clearly, we are not there yet, but it is possible to create

larger sets of graded (and matched) regular and exception words in

order to work towards an inferential test. In its current form, the

reliance estimates speak to the issue of correct responding given the set

of words used, and cannot be taken strictly as estimating all-or-none use

of SV and PD processes. Consider an error in naming a regular word as

an example. Our assumption of mathematical independence between

SV and PD processes clearly allows either, or both, processes, to

contribute to correct responding. With respect to misreading a regular

word, it would be misread if the child is unfamiliar with a particular

spelling-sound mapping. Such mappings may be regular in the

language, but one still needs to be exposed to them over time in order

to learn them. The child made an error because the relevant

information was not available to them through either the PD or SV

process, but that does not imply that neither process was engaged, just

that neither process could provide a correct response.

P. McDougall et al. / Brain and Language 92 (2005) 185–203 191

Each participant was seated in front of a laptop com-puter with the experimenter sitting directly beside. At

the outset of the session participants were instructed

that they were going to see some things to read on the

computer screen, some of which they might find easy

and some they might find hard, that they were not ex-

pected to know all of them, and that we just wanted

them to try as hard as they could to read them.

Each of the specific tasks are described below in theorder in which they were administered. Unless otherwise

specified, each task involved isolated word naming using

the laptop computer to present the stimuli. Each trial

involved the following sequence of events: (1) a fixation

cross appeared, (2) the participant initiated the trial by

pressing the space bar, (3) a stimulus appeared after

275ms, (4) the stimulus remained on the screen for a set

length of time (see each task below), and (5) the par-ticipant’s naming response was coded by the experi-

menter as correct or incorrect. Following each trial,

participants were not given any feedback regarding the

accuracy of their response.

8.4. Word identification subtest

The Word Identification subtest from the WoodcockReading Mastery Tests-Revised, Form H (Woodcock,

1987) is a widely used standardized measure of sight

vocabulary (SV) and phonetic decoding (PD) ability

together. The Word Identification subtest contains a

total of 106 items (no practice trials) including both

regular and exception words. In the present study, we

followed the standardized protocol provided for this

subtest and gave the prescribed instructions. However,instead of entering the stimulus list at varying starting

points (based on the experimenter’s estimate of the

child’s reading ability) and establishing a basal level, we

had all participants begin with the first item. We utilized

the exit or ceiling criteria prescribed for the subtest

whereby testing is discontinued when the participant

makes six errors in a row that end with the last item in a

subset (see WRMT-R manual). Consistent with stan-dardized administration, each stimulus was presented on

the screen for 5000ms. If a participant provided no re-

sponse during this five second period, the item was co-

ded as an error. Previous research from our lab has

shown that the traditional (i.e., easel) and computerized

methods of administering this subtest are comparable

(Borowsky, McDougall, Hymel, & MacKinnon, 1997).

8.5. Process dissociation task

The process dissociation task was designed to mea-

sure reliance on SV versus PD without using nonword

stimuli. The task involved a set of 55 pairs of regular

and exception words (see Appendix A) plus four prac-

tice words, presented in a fixed, pre-randomized order

(i.e., block 1 contained a randomized order of the first66 items, block 2 contained a randomized order of items

67–76, and block 3 was a randomized set of items 77–

110). In order to ensure that the PD and SV processes

involved in exception word reading are the same as for

regular word reading, each pair of regular and exception

words was matched on word frequency, initial phoneme,

word length, and grade level. Graded word lists from

seven commercially available Informal Reading Inven-tories (IRIs) were used to estimate word frequency

ranges that are typical of words at different grade levels.

Words were compiled without replacement across IRIs

to yield samples of words from Primer to Grade 6 (n’s

varied from 82 to 121 words per level). While there was

some overlap across samples from adjacent graded lists

in the distributions of word frequency [using the The

Word Frequency Guide (Zeno, Ivens, Millard, & Duvv-uri, 1995)], a nonoverlapping word frequency range for

each grade was defined using the range from the median

frequency and above at a particular level to just below

the frequency of the median at an adjacent lower level.

This procedure yielded the following ranges: Pre-Primer

Words, >2133; Primer, 1431–2133; Grade 1, 1430–679;

Grade 2, 678–194; Grade 3, 193–59; Grade 4, 58–13;

Grade 5, 12–8; and Grade 6, 7–5. These ranges werethen used to estimate the grade levels of the words used

in this task. Words in block 1 ranged from pre-primer to

grade 3.5 whereas block 2 contained words at the 3.5

grade level and block 3 words ranged from grades 4 to

7.4 Each item remained on the screen for a 5000ms in-

terval and participants were instructed to respond as

quickly and accurately as possible. When the participant

did not respond within the 5000ms interval, the item

5 Due to equipment problems, one participant was unable to

participate in the Castles and Coltheart (1993) diagnostic naming task,

and two participants were unable to participate in the orthographic

choice task.6 We did not encounter any division by zero problems because they

are restricted to one scenario. In order to obtain a zero in the

denominator of Eq. (6), it is necessary for PD reliance to equal 1,

which would imply that the participant named all regular words

correctly, and named none of the exception words correctly. If no

exception words have been named correctly, then the numerator of Eq.

(6) also becomes zero. In this case, the division by zero problem could

be handled by substituting a value slightly less than 1 for PD reliance

(e.g., .999), resulting in an estimate of SV reliance equal to zero. It

makes sense that SV reliance should equal zero in this case, because

any degree of reliance on SV should have yielded an accuracy value for

naming exception words that is greater than zero.

192 P. McDougall et al. / Brain and Language 92 (2005) 185–203

was coded as an error. Testing was discontinued if aparticipant made errors on all of the items in block 2

(only one participant stopped at this point).

8.6. Castles and Coltheart (1993) exception and

nonword stimuli

Castles and Coltheart’s (1993) set of 30 inconsistent/

exception words and 30 pronounceable nonwords wereincluded in the present study. This set of stimuli was

included to measure what we refer to as SV and PD

‘‘ability’’ (i.e., nonword naming performance may reflect

a participant’s ability to use PD when confronted with

unfamiliar letter strings), and to allow for comparison

with our process dissociation task (which measures a

participant’s reliance on PD and SV when confronted

with real words). In contrast to Castles and Coltheart,who presented each item for unlimited time, we re-

stricted the stimulus presentation duration to 10,000ms.

Items were presented in a random order and partici-

pants were encouraged to respond as accurately as

possible. If a participant did not respond within 10 s, an

error was recorded for that item.

8.7. Orthographic choice task

The orthographic choice task consisted of 50 experi-

mental trials plus two practice trials and was designed to

measure orthographic lexical knowledge. The task was

adopted from Manis et al. (1996), although we dropped

two of their original 52 experimental items because of

overlap with the Word Identification subtest of the

WRMT-R (described above). Items were presented inrandom order. The experimenter initiated each trial by

pressing the space bar, following which participants saw

two horizontally presented stimuli; one was a correctly

spelled word (e.g., train) and the other was a pseud-

ohomophone (e.g., trane). Participants were instructed

to indicate which was the correctly spelled word by

pressing the left or right key on a button-box which

corresponded with the position of the items on thescreen. Although participants were encouraged to re-

spond as quickly and accurately as possible, each pair of

items remained on the screen until the participant re-

sponded.

8.8. Position analysis task

The position analysis task adopted from Manis et al.(1996) is an orally administered phonemic awareness

task and did not involve the use of a computer. The task

was designed to measure the ability to sequence, seg-

ment and produce a variety of phonemes and consisted

of 24 nonwords plus four practice trials. In an effort to

ensure that participants had in fact correctly heard an

item, they were first asked to listen to and repeat a

nonword (e.g., say ‘‘grive’’). After correctly repeatingthe item, participants were asked to identify a phoneme

that came either before or after a designated sound (e.g.,

‘‘What sound comes before the sound ‘‘r’’ in ‘‘grive’’).

The stimulus list was presented in a fixed order and

organized such that the first 12 experimental items in-

volved naming a sound after the target phoneme and the

second 12 involved naming sounds before the target

phoneme. Target sounds varied across items to includeeight items with a target sound in the initial consonant

cluster, eight items with a target sound in the final

consonant cluster and eight items which involved vowel

sounds. Consistent with the instructions utilized by

Manis et al. participants were encouraged to respond

with phonemes, but were not penalized for using letter

names. Feedback regarding the correctness of responses

was provided on the practice trials only. Verbatim re-sponses were recorded by the experimenter. If a partic-

ipant did not respond within 10 s, the item was coded as

incorrect.

9. Results

Sample size, minimum, maximum, mean, upper, andlower 95th percentile confidence interval around the

mean, and standard deviation for each measure, as a

function of reading ability, are given in Table 1.5 Castles

and Coltheart’s exception word and nonword naming

accuracy represent the number of correct responses out

of 30. The estimates of reliance on PD were computed

by use of Eq. (5) as described in the introduction, and

can range between 1 and )1 (although the typical rangeis between 1 and close to 0, as a negative value will only

occur when a participant names exception words with

greater accuracy than regular words, which occurred in

only five cases in our sample). As mentioned earlier, a

negative value in this context may represent inhibition

of reliance on the process (i.e., PD). The estimates of

reliance on SV were computed by Eq. (6) as described in

the introduction, and can range from 1 to 0.6 Age and

Table 1

Sample size, minimum, maximum, mean, upper, and lower 95% confidence limits around the mean, and standard deviations of measures for good

and poor (i.e., Dyslexic) readers

EX NW PD SV AGE R.AGE ORTH POSANA

Good readers

N 197 197 198 198 198 198 196 198

Min 2.000 3.000 )0.055 0.164 85.000 81.000 20.000 2.000

Max 23.000 30.000 0.436 1.000 137.000 222.000 48.000 24.000

Mean 14.645 20.503 0.130 0.887 111.152 121.722 35.643 19.056

95% Upper 15.237 21.404 0.140 0.907 113.035 125.066 36.497 19.599

95% Lower 14.053 19.602 0.119 0.868 109.268 118.379 34.789 18.512

SD 4.213 6.412 0.076 0.140 13.442 23.857 6.063 3.875

Poor readers

N 26 26 26 26 26 26 26 26

Min 2.000 0.0 )0.109 0.180 88.000 80.000 20.000 5.000

Max 14.000 20.000 0.273 0.857 141.000 102.000 40.000 24.000

Mean 8.231 10.269 0.125 0.585 112.115 92.462 29.654 15.385

95% Upper 9.827 12.640 0.158 0.674 117.672 95.424 31.745 17.558

95% Lower 6.634 7.899 0.092 0.497 106.559 89.500 27.563 13.211

SD 3.953 5.869 0.082 0.220 13.756 7.333 5.176 5.382

EX, exception word naming accuracy; NW, nonword naming accuracy; PD, reliance on phonetic decoding; SV, reliance on sight vocabulary;

AGE, chronological age; R.AGE, reading age; ORTH, orthographic choice task accuracy; and POSANA, position analysis task accuracy.

P. McDougall et al. / Brain and Language 92 (2005) 185–203 193

reading age are expressed in number of months. Or-

thographic choice task accuracy represents the number

of correct choices out of 50. Position analysis task

accuracy represents the number of correct responses outof 24.

To be considered as a ‘‘good’’ reader, a participant

was required to score higher than the 15th percentile on

the Word Identification subtest of the WRMT-R (i.e.,

no less than 1 SD below the mean of the normative

distribution). Participants who scored equal to or less

than the 15th percentile on this subtest were considered

to be dyslexic, or ‘‘poor’’ readers (i.e., 16 of the 202students from the first phase of data collection, and 10

of the 22 students from the second phase).

Independent t tests revealed significant, systematic

differences between the good and poor readers (in fa-

vour of good readers) on almost all measures, support-

ing the validity of the groups (i.e., Castles & Coltheart’s

exception words (EX), tð221Þ ¼ 7:346, p < :001; Castles& Coltheart’s nonwords (NW), tð221Þ ¼ 7:719,p < :001; reliance on SV (SV), tð222Þ ¼ 9:576, p < :001;

Table 2

Matrix of correlations between measures

EX NW PD

NW .740�

PD ).219� .018

SV .859� .816� .046

AGE .552� .360� ).265�

R.AGE .787� .740� ).275�

ORTH .790� .627� ).302�

POSANA .454� .612� .030

EX, exception word naming accuracy; NW, nonword naming accuracy;

AGE, chronological age; R.AGE, reading age; ORTH, orthographic choice* p < :01.

reading age from the Word Identification subtest of the

WRMT-R (R. AGE), tð222Þ ¼ 6:205, p < :001; ortho-graphic choice task (ORTH), tð220Þ ¼ 4:807, p < :001;and the position analysis task (POSANA),tð222Þ ¼ 4:321, p < :001), except for reliance on PD

(PD), tð222Þ ¼ :276, p ¼ :783, and chronological age

(AGE), tð222Þ ¼ :343, p ¼ :732. The apparent equiva-

lency between good and poor readers on PD is com-

promised by an interaction with AGE, which will be

discussed later.

Overall correlations among these measures (including

all participants) are presented in Table 2. Most pairwisecorrelations are significant and positive, and reflect the

typical relationships between measures related to read-

ing skill (as indexed by R.AGE), and thus to each other.

All significant correlations involving PD reliance were

negative, suggesting that reliance on PD decreases with

age, reading age, and orthographic knowledge. Given

that this is an index of reliance on PD, and not an index

of PD ability, these negative correlations appeal to ourintuitions that reliance on PD would decrease as a

SV AGE R.AGE ORTH

.431�

.677� .522�

.674� .505� .764�

.493� .184� .471� .352�

PD, reliance on phonetic decoding; SV, reliance on sight vocabulary;

task accuracy; and POSANA, position analysis task accuracy.

7 Our method for statistical matching differs somewhat from

Stanovich and Siegel’s (1994) method, who fully partial the influence of

the to-be-controlled variable from the dependent variable (DV) and

other independent variables (IVs) by way of a standard, simultaneous

multiple regression. We remove the influence of the to-be-controlled

variable (i.e., reading age) from the DV only by first residualizing the

DV on the to-be-controlled variable, and then regressing the residu-

alized DV on the IVs. We prefer our approach in that it removes only

the variability that is shared between the DV and reading age from the

DV only, and not from the other IVs (e.g., chronological age), and so

avoids removing everything that is related to reading age from the

other IVs. Our approach also allows us to plot performance as a

function of an intact IV (i.e., chronological age), and thus apply the

regression-based approach for identifying dyslexic cases. However, we

have performed all of the relevant multiple regressions that represent

Stanovich and Siegal’s full partialing approach, only to find the same

pattern of results, with the same effects holding at a ¼ :05.

194 P. McDougall et al. / Brain and Language 92 (2005) 185–203

function of increasing age and reading experience (asindexed by AGE and R.AGE), and as a function of an

increasing number (and quality) of representations in

the orthographic lexicon (as indexed by EXC and

ORTH). This distinction between reliance and ability is

also illustrated by the lack of correlations between PD

reliance and a phonemic awareness task (POSANA),

and a traditional measure of PD ability (NW). The lack

of a significant correlation between PD and SV relianceis also of no surprise given the independent nature of the

equations that produced them.

9.1. Identification of dyslexic subgroups using Castles

& Coltheart’s task

Our analysis of Castles and Coltheart’s (1993) task

reflects some changes in method that we felt were im-portant to implement in our study. First, instead of

trying to capture the good readers’ scatterplot perfor-

mance within a parallelogram-like figure, which Castles

and Coltheart (and others, e.g., Manis et al., 1996;

Stanovich et al., 1997) have done by drawing confidence

limits around the regression line, we computed 95%

confidence sample ellipses in order to capture the good

readers’ performance (see Fig. 3). These ellipses areGaussian bivariate confidence intervals centered on the

sample means of the x and y variables. The unbiased

sample SDs of x and y determine major axes, whereas

the sample covariance between x and y determines the

orientation. We argue that Gaussian bivariate ellipses

are much better at capturing the continuous and distri-

butional nature of the data, and more closely reflect the

underlying distribution of the data than do confidencelimits around a regression line. Using confidence limits

around a regression line to capture data involves an

assumption that the distribution of the variable along

the x axis is rectangular, whereas using the Gaussian

bivariate ellipse assumes that both the x and y variables

are better approximated by a normal distribution. Vi-

sual inspection of our data supported the assumption

that our variables are best approximated by a normaldistribution. Second, instead of attempting to match

dyslexic participants to (multiple) control group partic-

ipants, we adopted a reasonable criterion for discrimi-

nating between good and poor readers, and plotted

exception word and nonword accuracy: (1) as a function

of chronological age (see Figs. 3A and C), and (2) re-

sidualized on reading age, and then plotted as a function

of chronological age (see Figs. 3B and D).The regression of exception word naming accuracy

on chronological age yielded significant relationships for

both good readers (slope¼ .189, intercept¼)6.352,tð195Þ ¼ 10:587, p < :001) and poor readers (slope¼.225, intercept¼)17.040, tð24Þ ¼ 6:196, p < :001; see

Fig. 3A). A multiple regression of exception word ac-

curacy on chronological age and group (i.e., good vs.

poor readers) did not yield a significant interaction be-tween age and group (slope¼ .037, intercept¼ 4.337,

p ¼ :473). Ten of the poor readers demonstrated lower

exception word accuracy than would be expected given

their age, as compared to the good readers. These in-

dividuals can be considered as illustrating a surface

dyslexic profile when compared to good readers of

similar age.

The regression of residual exception word namingaccuracy (i.e., the residuals from regressing exception

word accuracy on reading age) on chronological age

resulted in significant relationships for both good read-

ers (slope¼ .035, intercept¼)3.634, tð195Þ ¼ 2:397,p < :001) and poor readers (slope¼ .164, intercept¼)20.143, tð24Þ ¼ 5:370, p < :001; see Fig. 3B). A multi-

ple regression of residual exception word accuracy on

chronological age and group did produce a significantinteraction between age and group (slope¼ .129, inter-

cept¼ 12.875, tð219Þ ¼ 3:110, p < :01), indicating that

dyslexic participants tend to start lower on exception

word accuracy and elicit a steeper developmental tra-

jectory than good readers when differences due to

reading age are removed. Four of the poor readers ex-

hibited lower residual exception word accuracy than

would be expected given their age, as compared to thegood readers. These four individuals are a subset of the

10 individuals identified above by regressing exception

word accuracy on chronological age. These individuals

illustrate a surface dyslexic profile when compared to

good readers of similar age, after removing the effects of

reading age from exception word naming accuracy.

Thus, when one statistically controls for reading age,

only four of the 10 surface dyslexic participants fallbelow the good readers in terms of their exception word

naming accuracy.7

The regression of nonword naming accuracy on

chronological age yielded significant relationships for

both good readers (slope¼ .183, intercept¼ .179,

tð195Þ ¼ 5:811, p < :001) and poor readers (slope¼ .246,

intercept¼)17.040, tð24Þ ¼ 6:459, p < :01; see Fig. 3C).

Fig. 3. (A) Exception word naming accuracy, (B) residual exception word naming accuracy, (C) nonword naming accuracy, (D) residual nonword

naming accuracy, (E) sight vocabulary (SV) reliance, (F) residual SV reliance, (G) phonetic decoding (PD) reliance, and (H) residual PD reliance,

plotted as a function of age and reading ability.

P. McDougall et al. / Brain and Language 92 (2005) 185–203 195

196 P. McDougall et al. / Brain and Language 92 (2005) 185–203

A multiple regression of nonword accuracy on chrono-logical age and group did not yield a significant inter-

action between age and group (slope¼ .063,

intercept¼ 17.680, p ¼ :484). Eleven of the poor readers

elicited lower nonword accuracy than would be expected

given their age, as compared to the good readers. These

individuals illustrate a phonological dyslexic profile

when compared to good readers of similar age. Eight of

these individuals overlapped with the 10 participantswho showed the surface dyslexic profile when exception

word naming accuracy was plotted as a function of

chronological age and group. Thus, when reading age is

not statistically controlled, there is evidence for three

pure phonological dyslexics and two pure surface dys-

lexics using Castles and Coltheart’s (1993) diagnostic

task.

The regression of residual nonword naming accuracy(i.e., the residuals from regressing nonword naming ac-

curacy on reading age) on chronological age resulted in

a nonsignificant negative trend for good readers

(slope¼).039, intercept¼ 4.759, tð195Þ ¼ �1:592,p ¼ :113) and a significant positive relationship for poor

readers (slope¼ .157, intercept¼)21.116, tð24Þ ¼ 2:364,p < :05; see Fig. 3D). A multiple regression of residual

nonword accuracy on chronological age and group didproduce a significant interaction between age and group

(slope¼ .196, intercept¼ 30.634, tð219Þ ¼ 2:759,p < :01), indicating that the developmental trajectory in

terms of nonword naming accuracy starts lower and is

significantly steeper for poor readers than for good

readers when differences due to reading age are re-

moved. Five of the poor readers elicited lower residual

nonword accuracy than would be expected given theirage, as compared to the good readers. These five indi-

viduals are a subset of the 11 individuals identified

above by regressing nonword accuracy on chronological

age. These individuals illustrate a phonological dyslexic

profile when compared to good readers of similar age,

after removing the effects of reading age from nonword

naming accuracy. Thus, when one statistically controls

for reading age, only 5 of the 11 phonological dyslexicparticipants fall below the good readers in terms of their

nonword naming accuracy. Two of these five phono-

logical dyslexic individuals overlapped with the four

participants who showed the surface dyslexic profile

when residual exception word naming accuracy was

plotted as a function of chronological age and group.

Thus, when reading age is statistically controlled, there

is evidence for three pure phonological dyslexics andtwo pure surface dyslexics using Castles and Coltheart’s

(1993, task). It is worth noting that these individuals are

not exactly the same as the pure dyslexics identified

above when reading age was not controlled. Specifically,

only two of the three pure phonological dyslexics iden-

tified when reading age was not controlled overlap with

the three pure phonological dyslexics identified when

reading age was controlled, whereas only one of the twopure surface dyslexics identified when reading age was

not controlled overlap with the two pure surface dys-

lexics identified when reading age was controlled.

9.2. Identification of dyslexic subgroups using the

process dissociation estimates of reliance on SV and PD

Following the same method that we used for theanalysis of Castles and Coltheart’s (1993) diagnostic

task above, we plotted SV and PD reliance estimates: (1)

as a function of chronological age (see Figs. 3E and G),

and (2) residualized on reading age, and then plotted as

a function of chronological age (see Figs. 3F and H).

The regression of SV reliance estimates on chrono-

logical age yielded significant relationships for both

good readers (slope¼ .005, intercept¼ .340, tð196Þ ¼7:489, p < :001) and poor readers (slope¼ .012, inter-

cept¼).807, tð24Þ ¼ 6:062, p < :001; see Fig. 3E). A

multiple regression of SV reliance estimates on chrono-

logical age and group did yield a significant interaction

between age and group (slope¼ .007, intercept¼ 1.488,

tð220Þ ¼ 3:848, p < :001), indicating that the develop-

mental trajectory of SV reliance starts lower and is

significantly steeper for poor readers than for goodreaders. Thirteen of the poor readers elicited lower SV

reliance than would be expected given their age, as

compared to the good readers. Thus, these individuals

illustrate a surface dyslexic profile when compared to SV

reliance estimates of good readers of similar age. Of the

10 surface dyslexics identified by Castles and Coltheart’s

(1993) method, eight are also identified as surface

dyslexic by this method.The regression of residual SV reliance estimates on

chronological age resulted in a nonsignificant relation-

ship for good readers (slope¼).000, intercept¼ .040,

p ¼ :743) and a significant relationship for poor readers

(slope¼ .010, intercept¼)1.301, tð24Þ ¼ 5:664, p <:001;see Fig. 3F). A multiple regression of residual SV reli-

ance on chronological age and group produced a sig-

nificant interaction between age and group (slope¼ .011,intercept¼ 1.381, tð220Þ ¼ 6:034, p < :001), indicating

that the developmental trajectory of SV reliance starts

lower and is significantly steeper for poor readers than

for good readers when reading age is statistically con-

trolled. Nine of the poor readers elicited lower residual

SV reliance than would be expected given their age, as

compared to the good readers. These nine individuals

are a subset of the 13 individuals identified above byregressing SV reliance on chronological age. These

individuals illustrate a surface dyslexic profile when

compared to good readers of similar age, after removing

the effects of reading age from SV reliance. Thus, when

one statistically controls for reading age, nine of the 13

surface dyslexic participants fall below the good readers

in terms of their SV reliance. Of the four surface

8 Although we have argued in favour of assuming PD as the

dominant process for developing readers, it is informative to compare

our results to what is obtained when SV is assumed to be the dominant

process. The main difference when SV dominant equations are used is

that there are now significant positive correlations between PD reliance

and all other variables in Table 2. Specifically, the correlations

involving PD reliance are (with): EXC .669, NW .764, SV .644, AGE

.291, R.AGE .640, ORTH .526, and POSANA .474. Note that the

presence of a significant positive correlation between PD and SV

reliance estimates does not compromise the assumption of mathemat-

ical independence between PD and SV processes, as this assumption

involves both unique and shared contributions of SV and PD reliance

(see Fig. 2; i.e., the presence and sign of a correlation between PD and

SV reliance does not support nor refute the assumption of mathemat-

ical independence). The other correlations involving SV reliance are

(with): EXC .898, NW .766, AGE .510, R.AGE .745, ORTH .754, and

POSANA .455. In summary, when one assumes SV dominance, SV

and PD reliance estimates become correlated, but the correlations

involving the SV reliance estimates and all other variables remain

similar in comparison to when PD dominance is assumed. However,

the correlations involving the PD reliance estimate and all other

variables change dramatically when one assumes SV dominance (i.e.,

they all become positive and significant). Most importantly, however,

the identification of dyslexic subtype cases did not change dramatically

when SV was assumed to be the dominant process. After removing the

influence of reading age, the following number of dyslexic cases were

identified (original cases identified assuming PD dominance are in

square brackets): 0 [0] pure phonological dyslexics, 5 pure surface

dyslexics [all overlapping with the original 7], and 1 mixed cases

[overlapping with the original 2].

P. McDougall et al. / Brain and Language 92 (2005) 185–203 197

dyslexics identified by Castles and Coltheart’s (1993)method (when reading age is statistically controlled), all

four are also identified as surface dyslexic by this

method.

The regression of PD reliance estimates on chrono-

logical age yielded a significant negative relationship for

good readers (slope¼).002, intercept¼ .344, tð196Þ ¼�5:100, p < :001) and no significant relationship for

poor readers (slope¼ .002, intercept¼).049, p ¼ :197;see Fig. 3G). A multiple regression of PD reliance on

chronological age and group did produce a significant

interaction between age and group (slope¼ .003, inter-

cept¼ .736, tð220Þ ¼ 3:106, p < :01), which indicates

that good readers decrease their reliance on PD as a

function of age at a rate that is statistically different

from that of poor readers. Only one of the poor readers

elicited lower PD reliance than would be expected giventheir age, as compared to the good readers. This indi-

vidual illustrates a phonological dyslexic profile when

compared to good readers of similar age. However, this

individual overlaps with the 13 participants who showed

the surface dyslexic profile when SV reliance was plotted

as a function of chronological age and group (this in-

dividual is also diagnosed as showing both surface and

phonological dyslexia using Castles & Coltheart’s, 1993,task). Thus, when reading age is not statistically con-

trolled, the process dissociation method involving esti-

mates of reliance on SV and PD yields no evidence for

any pure phonological dyslexics in our study, and evi-

dence for 12 pure surface dyslexics.

The regression of residual PD reliance estimates on

chronological age resulted in a significant negative re-

lationship for good readers (slope¼).001, intercept¼.119, tð196Þ ¼ �2:800, p < :01) and a nonsignificant

positive trend for poor readers (slope¼ .002, inter-

cept¼).240, tð24Þ ¼ 1:620, p ¼ :118; see Fig. 3H). A

multiple regression of residual PD reliance on chro-

nological age and group did produce a significant

interaction between age and group (slope¼ .003, inter-

cept¼ .478, tð220Þ ¼ 2:665, p < :01), which indicates

that good readers decrease their reliance on PD as afunction of age at a rate that is statistically different

from that of poor readers when differences due to

reading age are controlled. Two of the poor readers

elicited lower residual PD reliance than would be ex-

pected given their age, as compared to the good readers.

These two individuals illustrate a phonological dyslexic

profile when compared to good readers of similar age,

after removing the effects of reading age from the esti-mates of PD reliance. Thus, when one statistically con-

trols for reading age, only two phonological dyslexic

participants fall below the good readers in terms of their

PD reliance. These two phonological dyslexic individu-

als overlap with the nine participants who showed the

surface dyslexic profile when residual SV reliance was

plotted as a function of chronological age and group.

Thus, when reading age is statistically controlled, theprocess dissociation method involving estimates of reli-

ance on SV and PD yields no evidence for any pure

phonological dyslexics in our study, and evidence for

seven pure surface dyslexics. These seven pure cases of

surface dyslexia overlap with the 12 pure dyslexics

identified above when reading age was not controlled.

Of the two pure surface dyslexics identified by Castles

and Coltheart’s (1993) method (when reading age isstatistically controlled), both are also identified as sur-

face dyslexic by this method.8

10. Discussion

The process dissociation method of estimating reli-

ance on SV and PD processing presents a somewhatdifferent picture of developmental dyslexia than current

regression-based approaches for identifying dyslexic

participants (e.g., Castles & Coltheart, 1993; Manis et

al., 1996; Stanovich et al., 1997). In general, the process

dissociation method appears to yield a slightly larger

number of surface dyslexic cases, but a dramatically

lower number of phonological dyslexic cases than Cas-

tles and Coltheart’s regression-based approach. Whenplotting performance as a function of chronological age

(and ignoring differences in reading age), our application

of Castles and Coltheart’s method yielded 10 cases of

surface dyslexia and 11 cases of phonological dyslexia,

9 Given that reliance on PD is estimated by subtracting exception

word accuracy from regular word accuracy, and that the grade level

difficulty of these words did not exceed grade 7, one potential criticism

is that a floor effect may be biasing our method from being sensitive

enough to detect more cases of lower than normal reliance on PD (i.e.,

cases of phonological dyslexia). One would expect PD reliance (as

measured by the size of the regular word naming advantage) to

decrease as a function of increasing word frequency. Indeed, the

negative correlations between PD reliance and age (or reading age)

support this notion. Thus, if the words are highly familiar, then

estimates of reliance on PD will be lower than if the words are less

familiar. We note, however, if such a floor effect was present, it should

be restricted to the older or more experienced readers. If one was

interested in applying this process dissociation method specifically to

older readers (e.g., grades 4–5), then additional lower frequency (or

higher grade level) items should be included.

198 P. McDougall et al. / Brain and Language 92 (2005) 185–203

eight of which were overlapping cases. To contrast, ourprocess dissociation method yielded 13 cases of surface

dyslexia, and only one case of phonological dyslexia,

which was an overlapping case (i.e., both surface and

phonologically dyslexic). Removing the influence of

reading age did not change the number of pure cases of

surface (n ¼ 2) and phonological (n ¼ 3) dyslexia ob-

tained through Castles and Coltheart’s method (al-

though the identity of some of these cases did change asa function of removing the influence of reading age). In

contrast, our process dissociation method yielded seven

cases of pure surface dyslexia (constituting a subset of

the 13 cases obtained when reading age was not statis-

tically controlled), two overlapping cases, and no cases

of pure phonological dyslexia when the effects of reading

age were removed.

One reason for the discrepancy in the frequency ofphonological dyslexia diagnoses between these two

methods has to do with the presence of nonword

stimuli in the traditional diagnostic tests (e.g., Castles

& Coltheart, 1993; Manis et al., 1996; Stanovich et al.,

1997). Given that, by definition, there is no established

criterion for the pronunciation of any nonword, it

seems quite plausible that any criteria for scoring

nonword pronunciations simply serve to increase thevariability in ‘‘accuracy’’ rate, and would likely de-

crease the observed ‘‘accuracy’’ rate for some readers.

Thus, it seems that nonword naming accuracy may

estimate an unstable (and possibly inflated) frequency

of phonological dyslexia cases because of the necessary

invention of pronunciation criteria for each stimulus.

As discussed in the introduction, we know that par-

ticipants will perform differently during the namingtask when nonwords are included (either in mixed or

pure blocks), compared to when only words are pre-

sented. We all have the expectation that, when reading

text, the orthography should correspond to a familiar

phonology (aside from the occasional novel term in

technical texts), and this may be particularly salient

when we are learning to read. Just as the influence of

semantic context has been argued to be particularlyrelevant for poorer readers (e.g., Stanovich, 1980; West

& Stanovich, 1988), so may the influence of this ex-

pectancy for familiar sounding words, accounting for

why poorer readers could be more susceptible to

making nonword naming errors.

More importantly, it is not clear how nonword

naming accuracy can be taken as a measure of PD

ability during real word recognition (i.e., when thephonological lexical system can provide assistance to

the PD process). Indeed, some children were surprised

(and a bit offput) by the presentation of the nonwords

used in this study. Given all of the time spent in school

learning real words, and expecting tests of their reading

ability to involve real words, perhaps we should not be

surprised that some children do not enjoy trying to

read such orthographically and phonologically strangestimuli. Clearly, if the participant expects (through in-

structions or experience with the task) the pronuncia-

tions of the stimuli to correspond to real words that

they may have at least heard before, then we have an

opportunity to study processes like SV and PD as they

pertain to natural word recognition and reading.

Nonword naming accuracy may very likely be reflect-

ing something different from PD ability during realword recognition.

A second reason for this discrepancy in frequency of

phonological dyslexic cases between our study and

previous research concerns the matching problem de-

scribed in the introduction. Our process dissociation

method allows us to use real words both for (statisti-

cal) matching and for dyslexic subtype identification.

Traditional approaches that use nonwords for dyslexicsubtype identification do not use the same type of

stimulus for (individual) matching, and thus the

matching method itself can bias the identification of

phonological dyslexia (e.g., Manis et al., 1996; Stano-

vich et al., 1997). Specifically, if one matches poor

readers to good readers on the basis of real word

naming accuracy (as is the case when one matches on

reading age from a test like the Word Identificationsubtest of the WRMT-R), then one should expect more

differences between poor readers and good readers on

an unmatched variable like nonword naming accuracy

as opposed to a matched variable like word naming

accuracy. Matching participants and identifying dys-

lexic cases on the basis of different stimuli will serve to

bias the dyslexia diagnosis in favour of the subtype

that is measured by the unmatched variable (i.e.,phonological dyslexia as measured by nonword naming

accuracy).

Taken together, these reasons for the discrepancy in

frequency of phonological dyslexia between our process

dissociation method and traditional methods that em-

ploy nonwords as diagnostic stimuli suggest that the

traditional methods may result in inflated frequencies of

phonological dyslexia.9 Despite our criticisms about

P. McDougall et al. / Brain and Language 92 (2005) 185–203 199

using nonwords to assess PD processing, the prevalenceof this method in the literature requires an interpretation

of the results from our application of Castles and

Coltheart’s (1993) diagnostic task. Indeed, given what is

known about dyslexia from the traditional methods, it

would be premature to suggest that they be abandoned

in favour of this new method that we are only beginning

to explore.

10.1. Castles and Coltheart’s method

If we focus our interpretation of the results to those

that do control for the effects of reading age, using

Castles and Coltheart’s (1993) method we see an al-

most equal frequency of pure surface dyslexia and pure

phonological dyslexia. This finding challenges previous

results in that Manis et al. (1996) and Stanovich et al.(1997) found that, compared to when chronological age

controls are used, using reading age controls eliminated

almost all cases of surface dyslexia, whereas relatively

more cases of phonological dyslexia tend to remain.

Given that both Manis et al. and Stanovich et al. used

different stimuli than Castles and Coltheart (and each

other), it is possible that the influence of reading age

could be affected by the choice of stimuli. Using Cas-tles and Coltheart’s stimuli, our results would suggest

that both surface and phonological dyslexia are simi-

larly influenced by reading age, and that just over half

of the cases of each subtype could be considered as

‘‘delay’’ cases in that they no longer appear dyslexic

when reading age is statistically controlled (see also

Footnote 2). The remaining half demonstrate ‘‘devi-

ant’’ processing in that these individuals do appeardyslexic when reading age is statistically controlled.

These observations can be contrasted with Manis et al.

and Stanovich et al.’s conclusion that surface dyslexia

is most likely a delay deficit, whereas phonological

dyslexia can be characterized as being mainly a devi-

ance deficit.

However, we have argued that using different types of

stimuli for matching and identification, and/or usingnonwords for the purpose of assessing PD processing,

may be problematic (e.g., Castles & Coltheart, 1993;

Manis et al., 1996; Stanovich et al., 1997). Thus, we turn

to a discussion of the process dissociation method for

identifying dyslexic cases.

10.2. The process dissociation method

Although we are measuring something new and

different in ‘‘reliance’’ on PD and SV processes (or

actual usage of PD and SV during real word reading)

by applying the process dissociation procedure, instead

of measuring a specific ‘‘ability’’ to use these processes

(i.e., particularly in the case of using nonword naming

performance as a measure of PD processing that is

strictly sublexical), the estimates do make intuitivesense with respect to their correlations (or lack thereof)

with other indices of linguistic proficiency. Reliance on

SV was significantly correlated with both exception

word and nonword naming accuracy, chronological

and reading age, orthographic lexical knowledge and

phonemic awareness. Reliance on PD was negatively

related with exception word naming accuracy, chro-

nological and reading age, and orthographic lexicalknowledge, and unrelated to nonword naming accu-

racy and phonemic awareness when PD dominance

was assumed, whereas these correlations were all po-

sitive when SV dominance was assumed. In general

then, reliance on SV is positively related to other in-

dices of linguistic proficiency, whereas reliance on PD

tends to be either negatively related or unrelated to

these indices if it is assumed to be the dominant pro-cess, and positively related to these indices if SV is

assumed to be the dominant process. If PD dominance

is to be assumed for the developing reader, then as a

function of development (i.e., both reading age and

chronological age), it appears that reliance on SV in-

creases, whereas reliance on PD decreases (see also

Doctor & Coltheart, 1980).

Treating good and poor readers as separate groups,the process dissociation method yielded significant in-

teractions between group and age on both SV and PD

reliance estimates, regardless of whether reading age was

controlled. When reading age was controlled, good

readers no longer showed a positive relationship be-

tween reliance on SV and age, whereas poor readers did.

Thus, reliance on SV increased as a function of chro-

nological age, and the effect can be attributed to readingage level for good readers, but not so for poor readers.

This finding suggests that lower than normal reliance on

SV (i.e., surface dyslexia) is not simply a developmental

delay deficit (in contrast to Manis et al., 1996 and

Stanovich et al., 1997; see also Harm & Seidenberg,

1999, for a model that was developed on the assumption

that surface dyslexia is mainly a developmental delay

deficit).Reliance on PD, on the other hand, decreased as a

function of age for good readers regardless of whether

reading age is controlled, and remained unrelated to age

for poor readers. So, while good readers decreased their

reliance on PD as a function of age, poor readers did

not, and there appeared to be a trend for poor readers to

even increase their reliance on PD as a function of age

when reading age was controlled.When reading age was controlled, the process disso-

ciation method revealed seven cases of pure surface

dyslexia, two overlapping cases that showed both sur-

face and phonological dyslexia, and no pure cases of

phonological dyslexia. Thus, these findings also suggest

that developmental surface dyslexia cannot be simply a

delay deficit [as has been suggested by Manis et al.

200 P. McDougall et al. / Brain and Language 92 (2005) 185–203

(1996), Stanovich et al. (1997), and Harm and Seiden-berg (1999)], as participants were statistically matched

on reading age. Furthermore, we obtained no evidence

for any pure cases of phonological dyslexia, which

stands in contrast to the rather high frequency of pho-

nological dyslexic cases reported by the methods which

used nonword stimuli to identify phonological dyslexia

(i.e., Castles & Coltheart, 1993), and some which also

matched dyslexic readers to reading age control partic-ipants based on word reading performance, leaving only

nonword naming performance free to vary (i.e., Manis

et al., 1996; Stanovich et al., 1997). Given that our

method used the same type of stimuli for dyslexic sub-

type identification as it does for matching (i.e., real

words serve both purposes), and did not use nonword

stimuli to assess PD processing during word recognition,

we feel that the process dissociation method has somevery desirable characteristics for assessing PD process-

ing when reading real words, and as a potential tool for

identifying cases of dyslexia.

Although we have framed our discussion on devel-

opmental dyslexia in terms of SV and PD processing

reliance deficits, we are not wedded to a particular

processing model of word recognition, nor are we ad-

vocating any particular type of representation (e.g.,localist versus distributed). We simply note that the

estimates of reliance could be interpreted in terms of

any processing architecture that involves (or can be

reduced to) two mathematically independent processes

(i.e., the two processing routes can effectively function

separately prior to the response, and a response can be

made, albeit not always correctly, on the basis of ei-

ther, or both, processing routes). The characteristic ofhaving two independent processing routes would seem

to capture many models of visual word recognition

[e.g., the lexical and nonlexical routes in Coltheart et

al.’s, 1993, 2001 dual route cascade model, and inter-

active-activation models such as Jacobs, Rey, Ziegler,

and Grainger (1998), MROM-P model; the semantic

and nonsemantic routes in Plaut, McClelland, Seiden-

berg, and Patterson (1996), Borowsky and Masson’s(1996), and Masson and Borowsky’s (1998), connec-

tionist models; see also Borowsky et al. (1999) and

Owen and Borowsky (2004a), for experiments that

evaluate the nature and direction of sublexical and

lexical influences between orthographic and phonolog-

ical processing, and a framework that captures many

models of visual and spoken word recognition]. Indeed,

a recent hybrid connectionist dual-process model de-veloped by Zorzi et al. (1998) illustrates the advantages

of maintaining dual nonsemantic processing routes

within a connectionist architecture. In this model, an

addressed phonology route corresponds to SV pro-

cessing, while an assembled phonology route corre-

sponds to PD processing. It is our hope that SV and

PD reliance estimates can be utilized in the develop-

ment of such simulation models (e.g., a model’s reli-ance on each route could be weighted by empirically

derived reliance estimates for the population being

studied), and result in more ‘‘ecologically valid’’ im-

plementations of models of basic reading processes.

10.3. Do SV and PD cognitive processes correspond to

independent neurophysiological processes?

Owen, Borowsky, and Sarty (2002) have recently

shown functional magnetic resonance imaging evidence

in support of independent neurophysiological pathways

that correspond to SV and PD processing. Specifically,

familiar exception words (i.e., SV-reliant stimuli) were

processed in both the occipito–temporal regions (i.e., a

ventral pathway) and insular cortices, whereas less fa-

miliar exception words and pseudohomophones (i.e.,PD-reliant stimuli) were processed more so in the su-

perior occipital and inferior parietal lobule, as well as

Broca’s area (i.e., a dorsal pathway). Other researchers

have also noted this type of ventral–dorsal correspon-

dence to SV and PD processes. Pugh et al. (2000) have

argued that the dorsal (temporo–parietal) pathway is

associated with the rule-based analyses engaged during

PD processing, and that the ventral (occipito–tempo-ral) pathway is associated with memory-based word

identification engaged during SV processing. Similar

distinctions between a ventral SV-type system and a

more dorsal PD-type system are discussed by Simos et

al. (2002), and Posner and Raichle (1994), who also

discuss the role of the insular cortex in processing fa-

miliar stimuli, as well as an automatic/controlled pro-

cessing distinction between the ventral and dorsalpathways. Some of our current neuroimaging research

explores the relationship between SV and PD reliance

estimates and activation along the ventral and dorsal

pathways. A central hypothesis in this research is that

the SV and PD reliance estimates will be related to

functional reliance on ventral and dorsal pathways as

measured by blood oxygenation level dependent

(BOLD) volume of activation, or degree of activation.Post-remediation imaging will also be used in this

study to evaluate the efficacy of training that targets

either SV or PD processing.

We realize that the process dissociation method of

estimating reliance on SV and PD requires further de-

velopment before it can contend with all that is known

about word identification and dyslexia based on tradi-

tional methods. The process dissociation procedurelooks promising in this regard in that it has recently seen

considerable development not only in terms of ac-

counting for naming accuracy performance, but also

with respect to accounting for reaction time data (Ja-

coby et al., 1999), through a type of counter model (e.g.,

Ratcliff & McKoon, 1997). We believe that this proce-

dure offers a novel perspective on dual-process models,

P. McDougall et al. / Brain and Language 92 (2005) 185–203 201

and that it is worthwhile to continue to explore amethod that promises to alleviate the need for using

artificial nonword stimuli, and thus the practice of using

different (or nonword) stimuli for matching participants

and identifying dyslexic cases, in the assessment of word

reading performance. The use of real words instead of

nonwords for examining PD processing in models of

visual word recognition could lead to a converging ap-

proach for studying the basic operation of this process(e.g., consider the multitude of experiments that have

used nonword stimuli to examine PD processing, many

of which have had considerable influence on the devel-

opment of models of visual word recognition and other

basic reading processes). Moreover, this approach can

be argued as more ecologically valid given that models

of visual word recognition are concerned with describing

reading processes as they pertain to the reading ofreal-world stimuli (i.e., real words). We have recently

reported evidence in support of the assumption of

independence in our application of the process dissoci-

ation procedure (Owen & Borowsky, 2004b), and we are

also currently examining the estimates of reliance on SV

and PD obtained when pseudohomophones are used.

Like our word stimuli, pseudohomophones can be

considered to have a single ‘‘correct’’ response and thusmaintain a similar expectation for ‘‘sounding familiar’’

as words do (see also Laxon, Smith, & Masterson, 1995,

and Goswami, Ziegler, Dalton, & Schneider, 2001; for

discussion of children’s pseudohomophone reading

effects, and Borowsky & Masson, 1999, and Borowsky

et al., 2002, for discussion on interpreting such effects).

We hope that other researchers will join us in further

evaluating this process dissociation method for esti-mating reliance on SV and PD processing.

Acknowledgments

This research was supported by the Social Sciences

and Humanities Research Council of Canada in the

form of a grant to the authors, and the Natural Sciencesand Engineering Research Council of Canada in the

form of a grant to R.B. We thank Larry Jacoby, Steve

Lindsay, and especially Max Coltheart, for their in-

sightful reviews.

Appendix A. Exception and regular word pairs used in theprocess dissociation task

have had, one with, says saw, two too, door dark,

move must, most much, own off, once well, head hand,does days, learn leave, give girl, world which, front

food, heard hear, where while, both board, grow grew,

love land, gone goes, full feel, four free, won win,

wood wore, heart heat, broad brown, prove proud,

spread speech, touch twice, none nine, climb cliff, footfool, whom home, month mouth, breath bridge, bread

brain, bought bound, bush bunch, thread thrust, flood

flame, tour torn, sweat sweep, earn ease, swear swell,

comb coil, dread ditch, steak stack, dough dodge,

hearth hoarse, sieve snatch, sew sag, mow mug, caste

carve, tread truce.

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