KATHOLIEKE UNIVERSITEIT LEUVEN

Faculteit Psychologie en Pedagogische Wetenschappen

Centrum voor Orthopedagogiek

& Laboratorium voor Experimentele ORL

EARLY LITERACY DEVELOPMENT IN CHILDREN

AT RISK FOR DYSLEXIA

A longitudinal study of the general magnocellular theory

Proefschrift aangeboden tot het

verkrijgen van de graad van

Doctor in de Psychologie

door Bart Boets

o.l.v. Prof. Dr. Pol Ghesquière

Prof. Dr. Jan Wouters

2006

Bart Boets, Early literacy development in children at risk for dyslexia: A longitudinal study of the general magnocellular theory. Dissertation submitted to obtain the degree of Doctor in the Psychological Sciences, March 2006. Supervision: Prof. Dr. Pol Ghesquière and Prof. Dr. Jan Wouters

Developmental dyslexia is a hereditary neurological disorder that affects about 5 to 10% of children and

adults. It is characterized by severe reading and spelling difficulties that are persistent and resistant to usual teaching methods and remedial efforts. Although the predominant aetiological view postulates that dyslexia results from a phonological deficit, recent research demonstrated an additional deficit in low-level auditory and visual temporal processing, which might be causal to the observed phonological and literacy problems. In the auditory modality, it has been hypothesised that the temporal processing deficit interferes with the accurate detection of rapid acoustical changes in speech, and subsequently disrupts the development of adequate phonological representations and later reading and spelling skills. Alternatively, it has been suggested that an analogous problem in the visual magnocellular subsystem interferes with the development of orthographic skills and thus also complicates literacy development. Although the auditory and the visual research line were originally developed independently, they both converge in postulating that dyslexics suffer from a deficiency in temporal information processing. As a consequence, the hypothesis of a general temporal processing deficit was formulated and an attempt was undertaken to relate the hypothesised cross-modal deficits to one underlying biological cause. Accordingly, the ‘general magnocellular theory’ postulates that the neural magnocellular deficit is not restricted to the visual pathway but can be generalized to all modalities. Furthermore, the theory assumes an explicit causal relation between the auditory and visual temporal processing skills on the one hand and the development of phonological, orthographic and subsequent reading and spelling skills on the other.

The aim of the present dissertation was to evaluate the validity of the general magnocellular theory by means of a longitudinal prospective study. We studied a group of preschool children at high family risk for dyslexia and compared them with a group of well-matched low-risk control children. Both groups were followed up through early literacy development. In a series of studies we evaluated different parts of the hypotheses that constitute the general magnocellular theory. In the first study (manuscript 1) we tested whether preschool children at family risk of dyslexia presented a deficit in auditory temporal processing and we examined the relation between auditory and phonological processing. The results indicated that preschool children at family risk for dyslexia presented a significant deficit in letter knowledge and phonological awareness, but not in any of the administered auditory measures. However, tone-in-noise detection and in particular frequency modulation detection were significantly related to phonological awareness. In the second study (manuscript 2), we further explored the assumptions of the auditory temporal processing deficit hypothesis by examining speech perception in the same groups of preschoolers, and relating these measures to the auditory and phonological data. For categorical perception, both groups performed identically on the identification task, but the risk group presented a marginally significant deficit on the discrimination task. On the speech-in-noise task, the children at family risk for dyslexia presented a slight but significant deficit, particularly in the most difficult listening conditions. Furthermore, speech parameters were significantly related to phonological awareness and low-level auditory processing. In a next study (manuscript 3), we focused upon sensory processing in the visual modality. To assess visual magnocellular processing, we tested sensitivity to coherent motion in random dot kinematograms. Although no significant differences were observed between the high versus low risk group, sensitivity to coherent motion was significantly related to emerging orthographic skills. Integration of the visual data with the results obtained in manuscript 1 indicated an exclusive relation between dynamic visual processing and orthographic skills on the one hand, and dynamic auditory processing and phonological skills on the other.

While in the first three manuscripts preschool data were analysed comparing the high risk versus low risk group, in study 1 of manuscript 4 we described the crucial retrospective analysis comparing children developing literacy problems versus children developing normal literacy skills. Therefore, children were reclassified based upon family risk status and first grade literacy achievement. A reanalysis of the data demonstrated that children showing both the increased family risk and literacy problems at the end of first grade presented a significant preschool deficit in letter knowledge, phonological awareness, rapid automatic naming, frequency modulation detection, coherent motion detection, categorical perception and speech-in-noise perception. This indicated that the sensory deficit preceded the literacy problem, leaving open the possibility of a causal influence of sensory processing upon literacy development. In a final study (manuscript 4, study 2), the plausibility of this causal relation was evaluated using path analysis. In particular, we demonstrated that dynamic auditory processing was related to speech perception which on its turn was related to phonological awareness. Similarly, dynamic visual processing was related to orthographic ability. Eventually, phonological awareness and orthographic ability – together with verbal short-term memory - were unique predictors of reading and writing development.

In the last chapter we concluded that our study provided substantial evidence for the validity of the general magnocellular theory, although a more fine-grained inspection of the data (particularly at the level of the individual subjects) also revealed some conflicting findings.

Bart Boets, De ontwikkeling van lees- en spellingsvaardigheden bij jonge kinderen met risico op dyslexie: Een longitudinale studie van de algemene magnocellulaire theorie. Proefschrift aangeboden tot het verkrijgen van de graad van doctor in de Psychologie, maart 2006. Promotoren: Prof. Dr. Pol Ghesquière en Prof. Dr. Jan Wouters

Dyslexie is een erfelijk bepaalde neurologische aandoening waarvan de prevalentie wordt geraamd op 5

à 10% van de bevolking. De leerstoornis wordt gekenmerkt door ernstige lees- en spellingsproblemen die moeilijk te verhelpen zijn door middel van de gebruikelijke didactische maatregelen en remediëringsinspanningen. Hoewel algemeen wordt aangenomen dat dyslexie veroorzaakt wordt door een tekort in de fonologische vaardigheid, wijst recent onderzoek ook op basale tekorten in auditieve en visuele temporele informatieverwerking die mogelijkerwijs aan de basis liggen van de fonologische en literaire problemen. Binnen de auditieve modaliteit wordt verondersteld dat het temporele informatieverwerkingstekort interfereert met de accurate detectie van snelle akoestische wisselingen in het spraaksignaal. Door dit subtiele spraakperceptieprobleem wordt de ontwikkeling van het fonologische systeem en de verdere lees- en spellingsvaardigheid bemoeilijkt. Daarnaast wordt verondersteld dat een analoog probleem in de visuele modaliteit, meer bepaald in het magnocellulaire subsysteem, interfereert met de ontwikkeling van orthografische vaardigheden, waardoor eveneens de ontwikkeling van de literaire vaardigheid wordt bedreigd. Hoewel de auditieve en de visuele onderzoekslijn aanvankelijk onafhankelijk ontwikkeld werden, komen ze beiden tot de conclusie dat personen met dyslexie moeilijkheden hebben met temporele informatieverwerking. Als een gevolg werd dan ook de hypothese van een algemeen informatieverwerkingstekort geformuleerd en werd een poging ondernomen om deze veronderstelde cross-modale tekorten te herleiden tot één onderliggende biologische oorzaak. Dienovereenkomstig postuleert de general magnocellular theory dat dyslexie het gevolg is van een magnocellulaire disfunctie die zich niet enkel manifesteert binnen het visuele systeem, maar alle sensorische modaliteiten betreft. Voorts veronderstelt de theorie een expliciet causaal verband tussen enerzijds de basale temporele informatieverwerkingsvaardigheden en anderzijds de ontwikkeling van fonologische, orthografische en lees- en spellingsvaardigheden.

Voorliggend doctoraatsonderzoek beoogt een evaluatie van de algemene magnocellulaire theorie aan de hand van een prospectieve longitudinale studie. Hiertoe werd een groep kleuters met familiaal risico op dyslexie gedurende twee jaar opgevolgd (vanaf begin derde kleuterklas tot eind eerste leerjaar) en vergeleken met een nauwkeurig gematchte controlegroep. Systematisch werden de verschillende deelhypothesen van de theorie onderzocht. In de eerste studie (manuscript 1) onderzoeken we of kleuters met verhoogd familiaal risico een tekort in auditieve temporele informatieverwerkingsvaardigheden vertonen en exploreren we de relatie tussen deze vaardigheden en fonologische bedrevenheid. De resultaten geven aan dat de risicokleuters tekorten vertonen in letterkennis en fonologisch bewustzijn, maar niet in auditieve vaardigheden. Nochtans wordt er wel een significant verband geobserveerd tussen fonologisch bewustzijn enerzijds en toon-in-ruis detectie en frequentiemodulatie detectie anderzijds. In de volgende studie (manuscript 2) onderwerpen we de kleuters aan enkele spraakperceptieproeven en worden deze resultaten gerelateerd aan de auditieve en fonologische data. Voor categorale perceptie presteert de risicogroep (rand-)significant zwakker op de discriminatietaak, hoewel we geen groepsverschillen observeren op de identificatietaak. Ook voor spraakverstaan in ruis presteert de risicogroep significant zwakker dan de controlegroep, en dit vooral in de moeilijkste luistercondities. Verder zijn de spraakparameters significant gerelateerd aan fonologisch bewustzijn en aan de basale auditieve vaardigheden. In een volgende studie (manuscript 3) richten we ons op informatieverwerking in de visuele modaliteit. Om de kwaliteit van visueel magnocellulair functioneren te bepalen, wordt de gevoeligheid voor coherente bewegingsdetectie gemeten. Opnieuw worden er geen significante groepsverschillen geobserveerd, maar de gevoeligheid voor coherente bewegingsdetectie houdt wel verband met ontluikende orthografische vaardigheden. Integratie van de visuele data met de resultaten verkregen in manuscript 1, suggereert een exclusief verband tussen enerzijds de bedrevenheid in dynamische visuele informatieverwerking en orthografische vaardigheden en anderzijds dynamische auditieve informatieverwerking en fonologische bekwaamheid.

In de eerste drie manuscripten worden de voorschoolse resultaten van de risicogroep telkens vergeleken met deze van de controlegroep. In manuscript 4 (studie 1) worden de kinderen opnieuw in groepen ingedeeld op basis van het behaalde lees- en spellingsniveau aan het einde van het eerste leerjaar in combinatie met het familiale risico. Vervolgens worden de kleuterdata retrospectief hergeanalyseerd. Deze heranalyse toont aan dat kinderen met een uitermate verhoogd risico op dyslexie (o.b.v. familiaal risico én lees- en spellingsachterstand aan het einde van het eerste leerjaar) op voorschoolse leeftijd al ernstige tekorten vertonen in letterkennis, fonologisch bewustzijn, snel serieel benoemen, frequentiemodulatie detectie, coherente bewegingsdetectie, categorale perceptie en spraakverstaan in ruis. Bovendien tonen deze resultaten duidelijk aan dat het perceptuele probleem voorafgaat aan het lees- en spellingsprobleem, waardoor het dus inderdaad een potentiële causale invloed zou kunnen uitoefenen. In een laatste studie (manuscript 4, studie 2) wordt de plausibiliteit van dergelijke causale relatie geëxploreerd aan de hand van causale padanalyse. Hierbij wordt aangetoond dat dynamische auditieve informatieverwerking verband houdt met spraakperceptie, wat op zijn beurt gerelateerd is

aan fonologisch bewustzijn. Op gelijkaardige wijze blijkt dynamische visuele informatieverwerking in verband te staan met orthografische vaardigheden. Ten slotte blijken fonologisch bewustzijn en orthografische bekwaamheid, samen met verbaal korte-termijn geheugen, unieke predictoren te zijn voor de ontwikkeling van lees- en spellingsvaardigheden.

In het laatste hoofdstuk concluderen we dat ons onderzoek in het algemeen substantiële evidentie biedt voor de validiteit van de algemene magnocellulaire theorie. Nochtans levert een meer diepgaande analyse van de data (in het bijzonder op individueel subjectniveau) toch ook heel wat bevindingen op die tegenstrijdig zijn met de theorie.

Dankwoord

Hier ligt het dan, mijn proefschrift – het resultaat van ruim vier jaar gedreven

wetenschappelijk werk. Het was een hele klus waarbij ik gelukkig de hulp kreeg van talrijke

mensen. Ik wil hen dan ook van harte danken.

In de eerste plaats dank ik mijn promotoren Pol Ghesquière en Jan Wouters voor hun

deskundige begeleiding. Pol, ik besef dat een belangrijk deel van je opdracht bestond uit het

intomen van mijn steeds verder uitdijende onderzoeksdrang. Ik dank je dan ook voor het

vertrouwen en de vrijheid die je me bood om dit onderzoeksproject op persoonlijke wijze uit

te werken zonder de haalbaarheid ervan uit het oog te verliezen. Jan, het was een plezier om

met een gepassioneerde wetenschapper als jij samen te werken. Het aanstekelijke

enthousiasme waarmee je mijn onderzoeksresultaten telkens onthaalde, werkte bijzonder

stimulerend en inspirerend. Hartelijk dank!

Daarnaast wil ik nog een bijzonder woord van dank uitspreken voor Erik Vandenbussche die

ook aan de basis van dit onderzoek stond, maar onverwachts en veel te vroeg overleed. Ik

hoop dat hij trots zou zijn op dit proefschrift dat mede gedragen werd door zijn fascinatie voor

de neurowetenschappen.

Astrid van Wieringen, Karl Verfaillie, Patrick Onghena en Peter de Jong wil ik danken voor

hun deelname aan mijn projectcommissie. Hun kritische opmerkingen en suggesties waren

erg waardevol. I am also very grateful and honoured that Prof. Stuart Rosen, Prof. Wied

Ruijssenaars and Prof. Karl Verfaillie accepted to be members of the jury.

Verder dank ik Astrid van Wieringen voor haar gewaardeerde hulp bij de uitbouw van de

auditieve onderzoekslijn; Johan Laneau en Tom Francart voor hun hulp bij de constructie van

de auditieve stimuli en het auditieve testplatform; Alexander Nowinski voor het

programmeren van de visuele stimuli; Willy Leung voor de creatie van de tekenfilmpjes die

het mogelijk maakten om zelfs kleuters te enthousiasmeren voor psychofysica; Mieke van

Ingelghem voor de talloze overlegmomenten en discussies; Peter Stiers voor zijn advies

omtrent de uitbouw van de visuele onderzoekslijn; Hugo Boets en Wivine Decoster voor het

lenen van hun mooie stem om de filmfragmentjes en fonologische taakjes in te spreken; Geert

Verbeke en Patrick Onghena voor hun methodologisch en statistisch advies; Els Gadeyne

voor haar tips omtrent Structural Equation Modeling; Peter de Jong en Femke Scheltinga voor

het beschikbaar stellen van fonologisch testmateriaal; het Dutch Dyslexia Research team voor

het beschikbaar stellen van hun spraakstimuli; Caroline Van Eccelpoel, Kathelijne Jordens,

Ellen Boon, Sara Vanderhallen, Sandra Schoonbrood, Leen Van Rie en Dorien Schrouwen

voor hun assistentie bij de dataverzameling, het segmenteren en chronometreren van

eindeloze spraakfragmenten en de uitwerking van de pseudohomofoontaak.

Ik wil ook uitdrukkelijk mijn dank betuigen aan de vele kinderen die deelnamen aan het

onderzoek. Ik ben nog steeds verwonderd dat ze bereid waren om dagenlang (!) naar vreemde

piepjes te luisteren of naar hypnotiserende stipjes op een scherm te staren. Ik dank ook hun

ouders en de betrokken scholen en directies.

Verder wil ik mijn collega’s van het ‘vijfde verdiep’ danken voor de hartelijke werksfeer. In

het bijzonder dank ik Bert De Smedt voor de talloze kleine en grote adviezen en informele

overlegmomenten, Leen Cleuren voor het tekstueel nalezen van mijn proefschrift en Joris Van

Puyenbroeck voor de reanimatie van mijn gecrashte en doodverklaarde hard-disk met alle

onderzoeksgegevens.

Ook nog een heel bijzondere dankjewel voor Lieve, niet enkel omdat ze me daadwerkelijk

heeft geholpen bij het ontwerp van de filmscenario’s en het programmeren van de

spraakexperimenten, maar vooral voor haar onmisbare steun als lieve levensgezel.

Tot slot dank ik mijn dochtertje Kato voor haar stralende ochtendlach waarmee ze me telkens

de energie gaf om de dag met hernieuwde moed en inspiratie te starten (na alweer een veel te

korte nacht).

Bart Boets, maart 2006

The greater becomes the volume of our sphere of knowledge,

the greater also becomes its surface of contact with the unknown.

J. Sageret

Dit proefschrift werd mede mogelijk gemaakt dankzij een beurs bij het Fonds voor

Wetenschappelijk Onderzoek – Vlaanderen. Een deel van de apparatuur werd bovendien

gesponsord door het Koningin Fabiola Fonds.

This doctoral dissertation was funded by a grant of the Fund for Scientific Research –

Flanders. Part of the technical equipment was funded by the Queen Fabiola Fund.

Table of contents

General introduction

1

Manuscript 1 Auditory temporal information processing in preschool children at

family risk for dyslexia: Relations with phonological abilities and

developing literacy skills

13

Manuscript 2 Speech perception in preschoolers at family risk for dyslexia:

Relations with low-level auditory processing and phonological

ability

51

Manuscript 3 Coherent motion detection in preschool children at family risk for

dyslexia

87

Manuscript 4 The development of early literacy skills among children at high

risk for dyslexia: An empirical evaluation of the general

magnocellular theory

107

Study 1 Auditory, visual, speech perception and phonological deficits in

preschool children at high risk for dyslexia

112

Study 2 Modeling relations between sensory processing, speech

perception, orthographic and phonological ability and literacy

achievement

130

General conclusions and future perspectives 161

1

General introduction ___________________________________________________________________________

At the end of kindergarten, every preschooler looks forward to learning to unravel the

mystery of reading and writing. Whereas the majority of children fairly easily assimilates

these skills in primary school, a small group of about 5 to 10 % of children is confronted with

severe reading and spelling difficulties that are persistent and resistant to the usual teaching

methods and remedial efforts. These reading and spelling impairments, characteristic for

developmental dyslexia, might have far-reaching academic, occupational and psycho-social

consequences. Historically, there has been a longstanding discussion about the aetiology of

these ‘unexpected’ literacy problems. The origin has been sought in the visual, the auditory as

well as in the cognitive-linguistic domain. Recently, a new comprehensive theory has been

put forward, integrating several aspects of the visual, the auditory and the cognitive-linguistic

research tradition. The present doctoral research project aims at empirically evaluating this

general magnocellular theory of dyslexia.

Theoretical framework

At present, the predominant aetiological view postulates that dyslexia results from a

cognitive deficit that is specific to the representation and processing of speech sounds: the

phonological deficit theory (Rack, 1994; Snowling, 2000). However, during the last decades

there has been a growing number of studies demonstrating a deficit in low-level auditory and

visual processing in subjects with dyslexia and it has been suggested that these sensory

deficits might be causal to both the observed phonological and literacy problems (Farmer &

Klein, 1995).

In the auditory modality, most emphasis has been given to temporal auditory

processing. It has been demonstrated that dyslexics have problems processing short, rapidly

presented and dynamic changing acoustic stimuli (e.g. Talcott & Witton, 2002; Tallal, 1980;

Van Ingelghem et al., 2005). Besides, dyslexics also tend to present subtle speech perception

2

problems (McBride-Chang, 1995). Consequently, it has been hypothesised that the basic

deficit in perceiving auditory temporal cues causes a problem for the accurate detection of the

acoustical changes in speech. Accordingly, the speech perception problem causes a cascade of

effects, starting with the disruption of normal development of the phonological system and

resulting in problems learning to read and spell (Talcott & Witton, 2002; Tallal, 1980; Wright

et al., 1997).

Alternatively, it has been suggested that the literacy problems of some dyslexics can

be traced back to a specific visual problem, in particular a problem in magnocellular visual

processing (Stein & Walsh, 1997). Anatomically as well as functionally, the visual system can

be divided in two independent but linked subsystems: the magnocellular (or transient)

pathway and the parvocellular (or sustained) pathway. The magnocellular system is highly

sensitive to low spatial and high temporal frequency stimulation and it responds preferentially

to the onset and offset of the stimulus. Consequently, it is predominantly a flicker or motion

detecting system. In psychophysical as well as neurophysiologic studies, it has been

demonstrated that individuals with dyslexia show a decreased sensitivity to stimuli within the

magnocellular range (e.g. Cornelissen et al., 1995; Eden et al., 1996; Demb, Boynton &

Heeger, 1997; Lovegrove, 1996). This evidence for a magnocellular dysfunction has further

been confirmed by anatomical studies showing abnormalities of the magnocellular layers of

the lateral geniculate nucleus (LGN) in the dyslexic brain (Livingstone, Rosen, Drislane &

Galaburda, 1991). Regarding the specific mechanism by which magnocellular visual

dysfunction may limit normal literacy development, there is still much speculation. The

answer probably lies in the anatomical connections from the magnocellular pathway to the

posterior parietal cortex (PPC). The PPC is known to be involved in normal eye movement

control, visuospatial attention, visual search and peripheral vision – all factors that are

obviously involved in the development of orthographic skills and subsequent reading and

spelling skills (Stein & Walsh, 1997; Stein & Talcott, 1999; Stein, 2001). Since the PPC is

dominated by magnocellular input, it is very well possible that slight impairments in

magnocellular functioning might multiply up to greater deficits in PPC functioning.

Although historically the auditory and the visual research lines have been developed

independently, they both converge in postulating that dyslexics suffer from a deficiency in

temporal information processing. As a consequence, the hypothesis of a general temporal

processing deficit or pan-modal deficit is formulated (Eden, Stein, & Wood, 1995; Farmer &

Klein, 1995; Frith & Frith, 1996; Stein & Talcott, 1999). In this hypothesis the reading and

spelling problems of dyslexic people are attributed to a deficiency in the temporal processing

3

capacity of both the auditory and the visual system. In this way, the theory suggests an

explicit causal relation between the auditory and visual temporal processing skills on the one

hand, and the development of phonological, orthographic and subsequent reading and spelling

skills on the other. Recently, an attempt has also been undertaken to relate the hypothesised

cross-modal deficiencies to one underlying biological cause. The general magnocellular

theory (Stein & Walsh, 1997; Stein, 2001) postulates that the magnocellular deficit is not

restricted to the visual pathway but could be generalized to all modalities (visual and auditory

as well as tactile). Although a similar magnocellular/parvocellular distinction has not been

described for the auditory brain areas, preliminary anatomical evidence for the auditory

variant of a magnocellular deficit has been found by Galaburda and colleagues (Galaburda,

Menard & Rosen, 1994), reporting an increased proportion of smaller neurons in the medial

geniculate nucleus of the dyslexic brain.

The general magnocellular theory, unique in its ability to account for most of the

manifestations of dyslexia, is undoubtedly attractive. Nevertheless, it also has its problems,

with a major one being its lack of empirical validation. Indeed, many studies that have been

reported as corroborating evidence for the theory, actually present such methodological

shortcomings that they are not able to offer substantial empirical validity.

For instance, in the most rigorous version, the general magnocellular theory postulates

that subjects with dyslexia show deficits across all the aforementioned skills. However,

probably as a consequence of the separate and different research traditions, most studies only

investigated a subgroup of skills (e.g. only auditory processing, only visual processing, only

speech perception or only phonological ability), and almost none studied the whole spectrum

of deficits in the same group of subjects. As a consequence, these studies are simply not able

to demonstrate the postulated cross-modal deficit. Noteworthy exceptions are studies by

Cestnick (2001), Van Ingelghem et al. (2001) and Witton et al. (1998), reporting the co-

occurrence of auditory and visual temporal problems in certain individuals with dyslexia (but

see Ramus et al., 2003, and White et al., in press, for some strong counterevidence).

Further, most of these studies only reported group means, hence giving the impression

that deficits were more or less uniform in the dyslexic group (i.e. that all subjects were

affected). However, a more detailed inspection of individual results demonstrated that in fact

only a rather small proportion of dyslexics presented these sensory deficits, even in the

presence of an overall group difference (Ramus et al., 2003; Rosen, 2003). Of course, this

4

observation makes it questionable whether the sensory problem would be at the basis of the

literacy problem.

Finally, almost all studies have been investigating adult or school-aged subjects; only

very few studied preschoolers. As a consequence, despite its inherent developmental aspect,

the general magnocellular theory has almost exclusively been built upon cross-sectional data

collected in adult and school-aged subjects. Unfortunately however, these data do not provide

any indication regarding the cause of the observed deficits or regarding the nature of the

studied interrelations. Indeed, based on adult and school-aged studies, the possibility cannot

be ruled out that the observed sensory deficits are the result rather than the cause of

differences in literacy ability and reading experience.

Aim of the doctoral research project

This doctoral research project aims to evaluate the empirical validity of the general

magnocellular theory. As aforementioned, the general magnocellular theory comprises an

integration of the auditory temporal processing deficit hypothesis and the visual

magnocellular theory. Besides, it also incorporates the phonological deficit theory, since it

does not deny the presence of phonological problems, but rather considers them as secondary

to a more basic auditory deficit.

In order to investigate the whole spectrum of postulated deficits, we administered a

broad battery of tests assessing low-level auditory and visual processing, speech perception,

phonological ability, orthographic skills, and reading and spelling skills1. Table 1 presents an

overview of all tests that have been applied. By measuring all these abilities in the same

subjects and by analysing the data at an individual level, we are able to verify whether

subjects with dyslexia present a consistent cross-modal pattern of deficiencies.

Moreover, in order to evaluate the hypothesised developmental and causal aspect of

the theory, we studied preschool children who not yet received any formal reading instruction,

and followed them up through early literacy development. In particular, we studied a group of

preschool children at high family risk for dyslexia and compared them with a group of well-

matched low-risk control children. The use of this longitudinal prospective design allows us

to verify whether the postulated sensory deficits precede the literacy problem, and whether

they demonstrate a predictive relation with later reading and writing skills.

5

Table 1

Overview of the tests administered in the present study.

Preschool measures First grade measures

Non-verbal intelligence

Phonological processing

Phonological Awareness

First sound identity

End sound identity

Rhyme identity

Rhyme fluency

Rapid Automatic Naming

Colour naming

Picture naming

Verbal short-term memory

Digit span forward

Nonword repetition test

Letter knowledge

Productive letter knowledge

Receptive letter knowledge

Auditory processing

Gap-in-noise detection

Frequency modulation detection

Tone-in-noise detection

Coherent motion detection

Speech perception

Speech-in-noise perception

Categorical perception (identification)

Categorical perception (discrimination)

Reading

One-minute real-word reading test

Pseudoword reading test

Real-word reading accuracy

Real-word reading speed

Pseudoword reading accuracy

Pseudoword reading speed

Spelling

Pseudohomophone task

ADHD Questionnaire

Outline of the doctoral thesis

The doctoral thesis consists of four manuscripts – two of them have been published,

one is currently under revision, and the last one will soon be submitted as two separate papers.

Throughout a series of psychophysical studies we evaluate different aspects of the general

magnocellular theory. Since all studies investigate the same group of preschoolers using

6

partly the same test instruments, this necessarily implies some overlap between the

manuscripts, especially with respect to the method sections.

In the first manuscript (Boets, Wouters, van Wieringen, & Ghesquière, 2006a) we

describe the procedure that we applied to select a group of preschool children at high family

risk for dyslexia, and the way this group was matched to a control group. Next, we tested

whether preschool children at family risk for dyslexia present a deficit in auditory temporal

processing and we examined the relation between auditory and phonological processing.

Auditory processing was assessed by means of three psychophysical threshold tests: gap-in-

noise detection, 2 Hz FM-detection and tone-in-noise detection. Phonological processing was

assessed by a broad test battery comprising tasks for rapid automatic naming, verbal short-

term memory and phonological awareness. The results of this study indicated that preschool

children at family risk for dyslexia present a significant deficit in letter knowledge and

phonological awareness, but not in any of the administered auditory measures. However,

tone-in-noise detection and in particular frequency modulation detection were significantly

correlated with phonological awareness, suggesting a relation between low-level auditory

processing and phonological processing.

In the second manuscript (Boets, Ghesquière, van Wieringen, & Wouters, under

revision), we further explore the assumptions of the auditory temporal processing deficit

hypothesis by examining speech perception in the same groups of preschoolers, and relating

these measures to the auditory and phonological data. For categorical perception of a place-

of-articulation speech continuum, both groups performed identically on the identification task,

but the risk group presented a marginally significant deficit on the discrimination task. On the

speech-in-noise task, the children at family risk for dyslexia presented a slight but significant

deficit, particularly in the most difficult listening conditions. Furthermore, speech parameters

were significantly related to phonological awareness and low-level auditory processing.

In a next study (manuscript 3; Boets, Wouters, van Wieringen, & Ghesquière, 2006b),

we focus upon sensory processing in the visual modality. To assess visual magnocellular

processing, we tested sensitivity to coherent motion in random dot kinematograms. Although

no significant differences were observed between the high versus low risk group, sensitivity

to coherent motion was significantly related to emerging orthographic skills. Hence,

integration of the visual data with the auditory results obtained in manuscript 1, indicated an

exclusive relation between coherent motion sensitivity and orthographic skills on the one

hand, and FM sensitivity and phonological skills on the other. These results suggest that basic

visual and auditory sensitivity is likely to play an important role in the development of fine-

7

grained orthographic and phonological representations necessary for successful reading

development.

While in the first three manuscripts preschool data were analysed comparing the high

risk versus low risk group, in study 1 of manuscript 4 we describe the crucial retrospective

analysis comparing children developing literacy problems versus children developing normal

literacy skills. Therefore, children were reclassified based upon family risk status and first

grade literacy achievement. A reanalysis of the data demonstrated that children showing both

the increased family risk and literacy problems at the end of first grade, presented a

significant preschool deficit in letter knowledge, phonological awareness, rapid automatic

naming, frequency modulation detection, coherent motion detection, categorical perception

and speech-in-noise perception. These results confirm that the sensory deficit precedes the

literacy problem, leaving open the possibility of a causal influence of sensory processing upon

literacy development. In a final study (manuscript 4, study 2), the plausibility of this causal

relation is evaluated using path analysis. In particular, we demonstrated that dynamic auditory

processing is related to speech perception which on its turn is related to phonological

awareness. Similarly, dynamic visual processing is related to orthographic ability. Eventually,

phonological awareness and orthographic ability – together with verbal short-term memory -

are unique predictors of reading and writing development.

The thesis ends with a critical discussion of the theoretical, methodological and

practical relevance of this doctoral research project. Finally, some suggestions for future

research are outlined.

8

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11

Notes

1 The most elaborated version of the general magnocellular theory also considers the

accompanying motor problems of dyslexics as a consequence of the single underlying

neurological deficit (Stein, 2001). In the present study however, no motor skills were

assessed.

13

Manuscript 1

Auditory temporal information processing in preschool children at family

risk for dyslexia: Relations with phonological abilities and developing

literacy skills1

Abstract

In this project the hypothesis of an auditory temporal processing deficit in dyslexia was tested by examining

auditory processing in relation to phonological skills in two contrasting groups of five-year-old preschool

children, a familial high risk and a familial low risk group. Participants were individually matched for gender,

age, nonverbal IQ, school environment and parental educational level. Psychophysical thresholds were estimated

for gap detection, frequency modulation detection and tone-in-noise detection using a three-interval forced

choice adaptive staircase paradigm embedded within a computer game. Phonological skills were measured by

tasks assessing phonological awareness, rapid serial naming and verbal short-term memory. Significant group

differences were found for phonological awareness and letter knowledge. In contrast, none of the auditory tasks

differentiated significantly between both groups. However, both frequency modulation and tone-in-noise

detection were significantly related to phonological awareness. This relation with phonological skills was not

present for gap detection.

Introduction

Developmental dyslexia is characterised by serious reading and spelling difficulties

that are persistent and resistant to the usual didactic measures and remedial efforts. At present

it is well established that a major cause of these problems lies in the phonological domain (see

Snowling, 2000 for a review). One hypothesis maintains that this phonological deficit results

from a more fundamental deficit in the basic perceptual mechanisms that are responsible for

auditory temporal information processing.

The auditory temporal hypothesis originated from studies on children with specific

language impairments (SLI) and was later extended to dyslexia. The empirical evidence

started with Tallal’s repetition task (Tallal, 1980). In this temporal order judgement (TOJ)

14

task two complex tones with different fundamentals were presented in pairs at various inter-

stimulus intervals (ISI) and the listener responded with two button presses to identify the

order of the stimuli presented. Tallal found that children with dyslexia, in comparison to

normal readers, were impaired in discriminating and sequencing pairs of short-lived stimuli

with short ISI, and concluded that the dyslexic deficit was specific to processing stimuli that

are brief and occur in rapid succession. Moreover, she found a high correlation between this

basic perceptual processing of non-speech signals and phonological skills (r = .81). Following

further evidence that dyslexic and SLI children had great difficulty discriminating syllables

containing stop consonants (such as /ba/ and /da/), the claim of a temporal deficit was

extended to apply to both non-linguistic and linguistic auditory stimuli (Tallal & Piercy, 1973;

Tallal, Miller, & Fitch, 1993). Since discrimination of such syllables critically depends on

accurate detection of the rapid frequency changes in the first milliseconds of voicing,

inaccurate detection of these formant transitions would inevitably interfere with the

identification of the phonological cues that are typical for spoken language. This hypothesis

of a direct association between basic auditory processing and speech or language processing

was strengthened by demonstrating that speech stimuli with lengthened transitions were much

better discriminated (Tallal & Piercy, 1975). From this association sprang the claim that the

temporal auditory problem caused the language problem, and subsequently the deficient

phonological and reading development. During decades this supposed causal mechanism has

been put forward as a plausible explanation of dyslexia.

Since the formulation of this theory there have been multiple studies exploring the

auditory temporal abilities of individuals with dyslexia. While the bulk of studies has been

done on adults, a minority of recent studies focused on school aged children and some very

few on preschoolers. In line with the scope of our study, we will mainly restrict our report to

psychophysical studies using specific non-speech stimuli to examine younger subjects.

Probably the most straightforward way to measure temporal processing is a gap-

detection task; this task estimates the smallest detectable interruption in an auditory stimulus.

Van Ingelghem and colleagues (Van Ingelghem et al., 2001; 2005) found a significant gap-

detection deficit in 11-year-old dyslexic children compared to normal reading children.

Moreover, the results on the task were significantly related to both real word reading and

nonword reading (r = -0.57 and r = -0.60 respectively). These results were replicated in a

broader study in dyslexic and normal reading children matched for sex, age and intellectual

ability (Van Ingelghem, Boets, van Wieringen, Ghesquière, & Wouters, 2004). The observed

15

results are in line with McCroskey and Kidder (1980), but are not consistent with

observations reported by McAnally and Stein (1996), Schulte-Körne, Deimel, Bartling, and

Remschmidt (1998) and Adlard and Hazan (1998). Hautus, Setchell, Waldie and Kirk (2003)

also observed higher gap detection thresholds in dyslexic subjects, but these thresholds were

only significantly higher for the young reading-impaired subjects (aged 6-9 years) and not for

the older ones (aged 10 years up to adulthood). The authors interpreted these results as

suggestive for a passing maturational lag in temporal acuity in children with dyslexia. In an

interesting study of Fischer and Hartnegg (2004), investigating a large group of subjects

covering an age-range of 7 to 22 years, a higher proportion of subjects with dyslexia were

unable to perform a gap-detection task even at its easiest level. However, within the group of

participants for whom a threshold value could be assigned, there was no significant difference

between dyslexics and normal readers.

Studdert-Kennedy and Mody (1995) challenged Tallal’s auditory theory and argued

that the observed phonological impairments in dyslexics are in origin speech-specific and

cannot be attributed to a more general lower-level auditory deficit. Besides this fundamental

criticism they also postulated that stimulus processing should only be regarded as temporal

when the defining features of the stimuli are changing in time and not merely because of their

rapid and brief presentation. This new temporal concept resulted in a new series of studies

that investigated auditory temporal processing in dyslexia using “dynamic stimuli” (see

Talcott et al., 2000). Most of these studies were carried out on adult samples and

demonstrated a relative impairment in sensitivity to amplitude modulation (AM) (McAnally

& Stein, 1997; Menell, McAnally, & Stein, 1999; Rocheron, Lorenzi, Fullgrabe, & Dumont,

2002) and frequency modulation (FM) (e.g. Stein & McAnally, 1995). In addition, Witton et

al., (1998) found that sensitivity to 2 and 40 Hz FM, for both dyslexics and controls,

significantly correlated with phonological decoding skills. This relationship between FM

sensitivity and phonological ability has also been demonstrated by Talcott et al. (1999) in a

random group of children. More recently, Van Ingelghem et al. (2005) demonstrated a

significant difference in FM sensitivity in a group of 11-year-old dyslexic children compared

to normal reading children. However, in a similar but broader well-controlled study with IQ-

matched control subjects, this difference could not be replicated (Van Ingelghem et al., 2004).

These studies with ‘dynamic’ stimuli again point to an auditory temporal processing

deficit as a possible cause of dyslexics’ phonological problems. Accurate tracking of

amplitude and frequency changes is exactly what is needed for the perception of speech,

16

which is characterised by temporal and spectral variations. Since speech perception is the

basis for developing phonological skills, it is likely that impairments in AM and FM detection

affect phonological skill development via speech perception (McBride-Chang, 1996).

With respect to preschool subjects, as far as we know, there have only been a few

longitudinal studies applying psychophysical measures. Heath and Hogben (2004) and Share

and colleagues (2002) administered Tallal’s repetition test to a large unselected group of

kindergarten children and followed them up until respectively second and third grade.

However, neither of both research groups was able to predict grade two or three literacy

scores based on the auditory data collected in preschool. Conversely, Benasich and Tallal

(2002) administered an operantly conditioned head-turn version of the repetition test to

infants 7.5 months of age born into families who were either positive or negative for family

history of language impairment (SLI). Not only did these authors observe significantly poorer

thresholds for children born into risk families, but they also demonstrated that rapid auditory

processing thresholds at 7.5 months of age were the single best predictor of language

development at two years of age and together with gender predicted up to 40 % of variance in

language outcome at three years of age. Unfortunately, information about literacy

development and its relation with rapid processing thresholds is currently not yet available for

these children. In contrast with the sparse psychophysical studies, there is a growing number

of neurophysiologic studies focusing on the temporal characteristics of speech processing in

very young subjects that already demonstrated promising results comparing genetically high

risk versus low risk children (e.g. Jyväskylä Longitudinal Study of Dyslexia, see Lyytinen et

al., 2001; Dutch Dyslexia Research Programme; Molfese, 2000).

Notwithstanding the large number of studies demonstrating an auditory deficit in

dyslexics, the explicit causality of the auditory hypothesis has never been established directly

by means of a longitudinal study. Here we report data from a longitudinal study that explores

(i) the development of basic auditory skills, speech perception, phonological abilities and

reading skills over a two-year time period from the beginning of the last year of kindergarten2

up to the end of the first year of primary school; (ii) the mutual relations between these

abilities and the way they influence each other over time. In this paper we will discuss the

first results about the relation between auditory temporal processing skills and phonological

skills in two contrasting groups of preschool children, i.e. a genetically high risk and a

genetically low risk group.

17

Auditory processing was assessed by means of three psychophysical threshold tests:

one for gap-detection in noise (GAP), one for 2 Hz FM-detection (FM) and one for tone-in-

noise detection (TN). With the GAP-detection task we tested the hypothesis of a deficit in

‘rapid and brief’ temporal processing. With the FM-detection task we verified the hypothesis

of a deficit in the processing of ‘dynamic stimuli’. The TN-task was included as a non-

temporal control task to verify the specificity of any observed temporal deficit, i.e. we wanted

to examine whether a deficit might be the result of failing performance on auditory

psychophysical tasks in general. Phonological processing was assessed by administering a

broad test battery comprising tasks for rapid serial naming, verbal short-term memory and

phonological awareness. Developing literacy skills were measured using a letter knowledge

task.

In this study we aimed to answer the following questions. First, is it possible to obtain

reliable results while administering such complex psychophysical tasks to very young

subjects? Second, do genetically high risk children, in comparison to low risk children,

perform significantly worse on phonological tasks? Third, do genetically high risk children, in

comparison to low risk children, perform significantly worse on psychophysical tasks for

auditory temporal processing? Fourth, are these auditory processing abilities related to

phonological and developing literacy skills?

Method

Participants

Sixty-two five-year-old children were included in the study. Half of the participants

were children of ‘dyslexic families’, the so-called high-risk group (HR); the other half were

control children of ‘normal reading families’, the so-called low-risk group (LR). Since

dyslexia tends to run strongly in families, preschoolers with dyslexic relatives are more likely

than other children to develop reading problems. Gilger, Pennington, and DeFries (1991)

estimate that roughly between 30 % and 50 % of such children will become reading disabled.

The HR children were recruited by means of referrals and public announcements to

encourage families with a child entering the last year of nursery school and having at least one

member with a formal diagnosis of dyslexia to engage in the study. Following this

recruitment, we received over 300 registrations. Based on a short semi-structured telephone

interview we selected 162 potential candidate families who were sent three questionnaires.

18

One questionnaire investigated in detail the reading and spelling (dis)abilities of all family

members up to third degree and investigated the general development of the preschooler. The

two other questionnaires were translations and adaptations of the Adult Reading History

Questionnaire (Lefly & Pennington, 2000) and investigated the reading experiences and

educational level of each parent. We assessed educational level using the ISCED-scale

(International Standard Classification of Education by UNESCO, 1997), by converting

classifications on the original seven-point scale to a three-point scale. Out of these 162

potential candidates we selected 31 preschoolers based on the following criteria: having at

least one first-degree relative being diagnosed as reading disabled by an authorized

educational psychology service; being native Dutch speaker; born in 1998 and entering last

year of nursery school at the beginning of the study; no history of brain damage, long term

hearing loss or visual problems. Of the 31 selected HR children 3 reported with reading

problems in only one (first-degree) relative, 5 reported with reading problems in two (first-

degree) relatives, 12 with reading problems in three (first-degree or extended) relatives and 11

with reading problems in four or more (first-degree or extended) relatives.

To further increase the number of future dyslexic children in our test population we

selected relatively more male than female preschoolers (ratio: 18M/13F). Finally, to exclude

the “garden variety” poor readers whose literacy is poor due to a low IQ, we selected

proportionally more children out of gifted and higher educated families (see Snowling, 2000).

Children of the LR group met the same selection criteria, with the restriction that they

were not allowed to show any history of speech or language problems and that none of their

family members might have suffered any learning or language deficiencies. For every

individual HR child we searched for the best matching LR control child based on five criteria:

(1) educational environment, i.e. same nursery school, (2) gender, (3) age, (4) non-verbal

intelligence, and (5) parental educational level. In effect we selected the particular control

child out of the group of same-sex classmates of the HR child. All these children –including

the HR child- were administered an adapted version of the Raven Coloured Progressive

Matrices (RCPM) (Raven, Court, & Raven, 1984), a collective non-verbal intelligence test

measuring spatial reasoning. To assure the child’s motivation and attention during testing, we

integrated the test procedure within a game-like fairy tale.

From this group we selected the best overall matching LR control child. For the first

two criteria (educational environment and gender) matching was perfect; for the remaining

three criteria matching was as good as possible within the restrictions of having to choose

19

within a concrete class group. For the age criterion we were able to select all control children

within an age-difference range of maximal five months. For the IQ criterion only four

children differed more than one standard deviation with their matched counterpart. Since the

RCPM is very age-sensitive in young children, we used age corrected norms to calculate this

non-verbal IQ score. Concerning parental educational level we gave relatively more

importance on getting a good fit for maternal than for paternal educational level. In this way

we were able to match 21 children in a perfect way for maternal educational level and 15 for

paternal educational level.

Table 1 gives descriptive characteristics of both groups and test statistics. The mean

age for both the HR and LR group was 5 years and 4 months, not being statistically different

(p = .83). The nonverbal IQ scores were slightly above population average (107 for HR group

and 111 for LR group) and neither differed significantly (p = 0.07). Fisher’s Exact Test also

confirms that both groups did not differ in frequency distribution of the different educational

categories (p = .71 for maternal and p = .43 for paternal educational level).

Table 1

Characteristics of participants : descriptive statistics, paired t-tests and Fisher’s Exact Test.

HR

(n = 31)

LR

(n = 31)

p

Nonverbal IQ (M / SD)a

107 (14)

111 (13)

.07

Age in months (M / SD) 64 (3) 64 (3) .83

Frequency distribution of Low Middle High Low Middle High

Maternal Educational Level 3 8 20 1 9 21 .71

Paternal Educational Level 6 14 11 2 16 13 .43

Note. a Transformed RCPM scores with M = 100, SD = 15.

Apparatus

Phonological Tests

Tests were selected to reflect the three traditional domains of phonological skills

(Wagner & Torgesen, 1987). Phonological awareness was measured by three sound identity

tasks and a rhyme fluency task. Verbal short-term memory was measured by a digit span test

20

and a non-word repetition test. Rapid automatic naming was assessed by administering a

colour and an object rapid naming task.

Sound identity tasks. The child was required to choose from four alternatives the word

that had the same (a) first sound, (b) end sound or (c) end rhyme as a given word (de Jong,

Seveke, & van Veen, 2000, adapted by van Otterloo & Regtvoort). The distracter alternatives

were systematically constructed to prevent guessing. All words were high frequent one-

syllabic Dutch words. Each item consisted of a row of five pictures. The first picture

represented the given word and was separated from the other pictures by a vertical line. All

items were named for the child. The first-sound and end-sound identity tasks both consisted

of ten items, preceded by two practice items, and had a maximum score of ten. The rhyme

identity task consisted of twelve items, preceded by two practice items, and had a maximum

score of twelve.

Rhyme fluency test. Participants were presented a one-syllable word and were required

to produce as many as possible rhyming (non-)words within a twenty second time period. To

familiarize them with the task, the experimenter already offered an example rhyme word for

every target item. Since we were interested in measuring rhyming abilities irrespective of

vocabulary knowledge, the test score was the total number of phonologically correct

responses, regardless of whether it was a real Dutch word or not. The test consisted of eight

items, gradually getting more difficult, and was preceded by two practice items.

Based on the factor analysis of the phonological data (see Results section), we

recalculated the results of the rhyme fluency test to create a purer measure of rhyming ability,

uncontaminated by fluency. For this Simple rhyme test each item was scored in binary

fashion, and treated as correct if at least one rhyming response (word or nonword) was

produced. The maximum score on the test was eight.

Nonword repetition test. A nonword repetition test (NRT) is frequently used as a pure

measure of verbal short-term memory. Since neither the nonwords nor the constituent

syllables of the nonwords used in the NRT correspond to existing words, the use of long-term

memory representations to support recall of the nonwords is prevented. The test was

developed after a Dutch adaptation (Scheltinga, 2003) of the nonword repetition test reported

by Gathercole, Willis, Baddeley, and Emslie (1994). This Dutch version of the NRT was

again adapted for the use with Flemish children. The Flemish NRT consisted of four

21

categories of nonwords, varying in word length from two to five syllables. Each category

contained 12 nonwords. All 48 nonwords and two test items were recorded on a CD and were

presented once. In contrast to Gathercole et al. (1994), the presentation order of the words was

determined by word length; starting with all the two-syllabic words and climbing gradually up

to the five-syllabic words. The test consisted of 48 nonwords, preceded by two practice items,

and had a maximum score of 48.

Digit span forward. The test assessed the immediate serial recall of spoken lists of

digits between 1 and 9. Prior to testing, children were asked to count from 1 to 10 to

familiarize them with the counting string and to reduce the influence of possible differences in

digit knowledge. After a practice session, three trials of each list length were presented,

starting at a sequence of two digits. Testing continued with increasing list length until the

child failed on two of three trials of the same list length. The test score was the total number

of correctly recalled lists. In order to standardize assessment, all lists were recorded and

presented on CD at a rate of one digit per second. For each list length, the stimuli of the first

two trials were taken from the WISC-III (Wechsler, 1992). The third trial was selected from

the Working Memory Battery for Children (see Gathercole & Pickering, 2000).

Rapid automatic naming. The test assessed the rapid serial naming for two types of

familiar symbols: colours and objects (van den Bos, Zijlstra, & Spelberg, 2002). The objects

represented five high-frequent, one-syllabic words: boom (‘tree’), eend (‘duck’), stoel

(‘chair’), schaar (‘scissors’) and fiets (‘bicycle’). The colours were represented by small

rectangles in black, blue, red, yellow and green. For each type of symbol one test card was

given, consisting of 50 symbols in a random order (5 columns of 10 symbols). The child was

instructed to name the symbols on a card as fast and accurately as possible. Prior to testing,

the child was required to name the symbols in the last column of a card to determine whether

he/she was familiar with all the presented symbols. For each card, the number of errors and

the time to completion were recorded. Subsequently, the time to completion was transformed

to the number of symbols named per second. As such, a higher score on the test corresponded

to a higher naming speed.

22

Letter knowledge

To get a preliminary idea about the stage of reading development we administered a

letter knowledge task, since many studies have consistently proven this task to be the best

predictor of the later development of literacy skills (see e.g. Elbro & Scarborough, 2003, for a

recent overview). To test for the receptive and productive letter knowledge, the 16 most

frequently used letters in Dutch books for children were selected (Rolf & Van Rijnsoever,

1984).

Productive letter knowledge. Sixteen printed letters were presented on a card. The

child had to name each of these letters. Both the sound and the name of a letter were

considered correct. The maximum score was 16.

Receptive letter knowledge. Sixteen printed letters were presented on a card. The

experimenter named all letter sounds in random order. After each sound, the child had to

indicate the printed letter that matched the sound. The maximum score was 16.

Auditory tests

Audiometric pure-tone detection. Prior to administering any auditory psychophysical

test we assessed all children on an audiometric pulsed pure-tone detection task to check for

any hearing loss. All but one child obtained a PTA-score below the 25 dB HL criterion. For

this child showing mild hearing loss, all further auditory testing was administered with

increased stimulus amplitude proportionally to the hearing loss. Since detailed inspection of

all her test results didn’t show any anomalies, her data were not excluded from further

analyses.

GAP-detection test. In the GAP-detection test, white noise stimuli were used. The

target stimulus was a white noise stimulus containing a silent gap. The reference stimulus was

an uninterrupted white noise. Stimuli were cosine gated on and off with 50 ms rise and fall

times. Gap rise and fall times were 0.5 ms. 64 target stimuli were constructed, comprising 32

gap sizes, varying between 100 ms and 0.1 ms. Gap length decreased with a factor 1.2 from

100 ms towards 6.5 ms. From here on gap length decreased with a fixed step size of 0.4 ms.

In order to prevent participants from using overall duration as a cue for detection, the length

23

of both the target and the reference stimulus was varied randomly from presentation to

presentation (van Wieringen & Wouters, 1999). In the target stimulus, the length of the

markers (i.e. noise components surrounding the gap) varied between 250 and 650 ms

including on and off set (i.e. 250, 400, 500 and 650 ms). The length of the reference stimulus

was 750, 900 or 1050 ms including on and off set. Stimuli were presented monaurally at

70 dB SPL with an inter stimulus interval (ISI) of 400 ms.

FM-detection test. In the FM-detection test, stimuli can be defined as x(t) = Asin

[2πfct + β sin (2πfmt)] in which β is the modulation index (β=∆f/fm), fc the carrier frequency,

fm the modulation frequency and ∆f the frequency deviation. The target stimulus was a 2 Hz

frequency modulation (fm) of a 1 kHz carrier tone (fc) with varying modulation depth ∆f.

Modulation depth decreased with a factor 1.2 from 100 Hz towards 11 Hz. From a ∆f of

11 Hz, a step size of 1 Hz was used. Frequency modulation in the target stimulus was

sinusoidal and the modulation envelope was always in sine phase. The reference stimulus was

a pure tone of 1 kHz (β=0). The length of both the reference and the target stimulus was

1000 ms including 50 ms cosine-gated onset and offset. Stimuli were presented monaurally at

70 dB SPL with an ISI of 350 ms.

Tone-in-noise detection test. In the tone-in-noise (TN) detection task participants had

to detect pure tone pulses within a one-octave noise signal, centered around 1 kHz (from 707

to 1414 Hz). Noise was presented monaurally at 55 dB SPL with an ISI of 300 ms. The length

of both the target and the reference stimulus was 1620 ms, including 20 ms linearly gated rise

and fall times. For the target stimulus noise contained two pure 1 kHz pulses of 440 ms,

including 20 ms linearly gated on and offset. The first pulse started 320 ms after stimulus

onset, the second one 960 ms after stimulus onset. The amplitude of the pulses varied with a

signal-to-noise ratio (SNR) between +25 dB and –20 dB. From +25 to -3 dB SNR amplitude

decreased with a 4 dB step size. From here on amplitude decreased with a 1 dB step size.

All stimuli for GAP, FM and TN were generated in MATLAB 5.1 and saved as 16-bit

wav-files (sample frequency 44100 Hz) on the hard disc of a Dell Latitude C800 and Toshiba

Satellite 1400-103 portable computer. They were presented using an integrated audio PC-card

and routed to an audiometer (Madsen OB622) in order to control the level of presentation.

The stimuli were presented monaurally over calibrated TDH-39 headphones.

24

Psychophysical procedure

For all GAP, FM and TN tests a similar psychophysical procedure was used.

Thresholds were estimated using a three-interval forced-choice oddity paradigm. The

subject’s task was to identify the ‘odd’ stimulus, the one that sounded different from the other

two. The length of the gap, the depth of modulation and the amplitude of the sinusoidal pulses

were adjusted adaptively using a two-down, one-up rule, which targeted the threshold

corresponding to 70.7 % correct responses (Levitt, 1971). In all tests a threshold run was

terminated after eight reversals. Thresholds for an individual run were calculated by the

geometric mean of the values of the last four reversals. For each participant three threshold

estimates were determined for every experiment. Prior to auditory data collection, participants

were given a short period of practice, comprising supra-threshold trials, to familiarise them

with the stimuli and the task.

The forced-choice oddity paradigm was controlled by APEX, a software module

developed for psycho-acoustical and psycho-electrical auditory testing (Laneau, Boets,

Moonen, van Wieringen, & Wouters, 2005). In order to make the rather boring

psychophysical tests more interesting and child friendly we integrated them in an interactive

video game with intro and outro animation movies, aimed to transform the abstract

meaningless acoustical signal into a concrete and well known ‘daily life signal’. This concept

of testing was based upon earlier work of Soderquist and Shilling (1992) and Wightman and

co-workers (Allen, Wightman, Kistler & Dolan, 1989; Wightman, Allen, Dolan, Kistler &

Jamieson, 1989). During testing, the three intervals of every trial were visually represented on

the screen by an identical character. The characters were animated synchronously with the

presentation of the corresponding sound interval in order to create a psychological link

between the moving object on the screen and the presented sound signal. The child’s task was

to choose, by pointing to the touch screen, which of the three characters corresponded to the

target sound (i.e. sounded different from the other two). Immediately after the child touching

the screen, visual feedback was given in the form of a moving cartoon. After correct selection,

feedback was much more spectacular – and as such much more reinforcing- than after

incorrect selection. During the sequence of trials constituting a single run, APEX also

provided a more global reinforcement by adding little smiley faces to a rising ladder structure

for every correct response and removing them again after every incorrect response. At the end

25

of the run the number of smiley faces was evaluated and the child was rewarded

proportionally.

For the GAP-detection experiment the video game displayed a teasing mouse who is

trying to wake up three sleeping snakes because she wants to play with them. The child had to

predict which one is going to wake up by listening to the specific interrupted noise sound

‘ssss-ssss’. For the FM-detection experiment the introductory movie showed a mother dragon

sitting in front of three eggs. The child’s task was to predict which egg is ready to hatch, by

listening to the baby dragon crying inside (‘wouwouwouw’). For the TN-detection task we

presented an introductory animation movie about a little monkey waiting desperately at the

school gate for his father to pick him up by car. Three cars arrive at the school (making noise

sound) and the child had to identify the father’s car by listening to him honking his horn (=

the pure tone pulses).

Data collection

All auditory and phonological data were collected within a 70-day period between the

second and the fourth month of the last year in kindergarten. Intelligence testing (RCPM)

took place one month earlier. Data collection was carried out by qualified psychologists and

audiologists. Testing took place in a quiet room at the children’s school. Since the LR child

was selected out of the HR child’s classmates, we could always test both children in exactly

the same circumstances.

All phonological tests and the letter knowledge task were administered individually in

one day during three sessions. After every subtest children were rewarded by receiving little

stickers or stamps.

Auditory data were collected during two consecutive days. Testing always started with

the pure-tone audiogram. Then we administered one run of the TN test since this is

conceptually the easiest psychophysical task. Consequently we administered one run of the

GAP and FM test in a contra balanced way. This sequence was continued until we had three

threshold estimates for every experiment.

26

Statistical Analysis

Prior to analysis all data were individually checked for unexpected outliers. This

resulted in the removal of only two unreliable phonological test scores for one subject of the

dyslexic group.

All results were analysed in a paired wise manner, comparing HR versus LR group at

the level of the matched individuals. Although both groups did not show a significant

difference on any of the matching criteria, we decided to rule out any possible influence of

age, nonverbal intelligence or parental educational level by controlling for these variables in

our analyses. As such, we analysed the data using Mixed Model Analysis (MMA) with school

as a random variable (1 to 31) and participant group (HR versus LR) as the fixed between-

subject variable (Littell, Milliken, Stroup & Wolfinger, 1996). Age, nonverbal IQ and

educational level of both mother and father were added as fixed (co)variables. Additionally,

for the auditory data we also computed a series of Repeated Measures MMA with threshold

run (1 to 3) as the within-subject variable, participant group as the between-subject variable

and with the same covariates as mentioned above. MMA was chosen not merely to allow a

paired wise comparison, but also because of its robustness in analysing semi-normally

distributed data (Verbeke & Lesaffre, 1997). In order to approach a normal distribution for

more variables, the GAP and FM thresholds and the results on the letter knowledge task were

log-transformed prior to MMA. To explore the internal consistency of the different

phonological tasks, Cronbach’s alpha-coefficients were calculated. To explore the structure

and the mutual relationships of the phonological tests a principal component factor analysis

was done with varimax rotation. Relationships between variables were analysed using

Spearman correlation coefficients.

Results

Phonological skills and Letter Knowledge

Descriptive statistics, MMA results and reliabilities (Cronbach’s α) for all measures

are displayed in Table 2. The internal consistency of the simple rhyme task, the nonword

repetition test and the letter knowledge task was good. The reliability of the rhyme, first

phoneme and end phoneme identity tasks was somewhat lower, probably because these tasks

appeared to be rather difficult3.

27

For the colour and picture rapid naming tasks we examined whether there might have

been a speed-accuracy trade-off. Since there was no significant correlation between naming

speed and error number and since the quantity of errors did not differ between both groups,

we did not correct the speed scores for error rate.

Table 2

Phonological abilities and letter knowledge: descriptive statistics and p-values for paired wise MMA, controlling

for nonverbal IQ, age and parental educational level.

HR LR

Measures Cronbach α Maximum M SD M SD p

Phonological Awareness

Rhyme fluency - - 14.3 9.6 18.9 8.2 .02

Simple rhyme .88 8 6.2 2.4 7.3 1.8 .05

Rhyme identity .69 12 8.7 2.7 9.8 1.9 .23

First-sound identity .59 10 4.6 2.1 5.6 2.4 .20

End-sound identity .63 10 4.5 2.2 5.9 2.4 .02

Rapid Automatic Naming

Colour naming - - 0.6 0.1 0.7 0.2 .12

Picture naming - - 0.6 0.1 0.7 0.2 .11

Verbal Short Term Memory

Digit span - 21 7.0 1.6 6.9 1.5 .66

Nonword repetition test .84 48 16.8 5.3 20.3 7.1 .08

Letter Knowledge .90 32 5.6 6.6 8.2 6.9 .03

Factor Phonological Awareness - - -0.73 1.2 0.00 1.0 .04

Factor Rapid Automatic Naming - - -0.19 0.9 0.00 1.0 .30

Factor Verbal STM - - -0.14 0.9 0.00 1.0 .72

Although the results on almost any phonological task were in the expected direction

with the HR-group scoring less well than the LR-group, not all these group differences turned

out to be statistically significant. Considering the tasks meant to measure phonological

awareness, the scores of the HR-group were significantly lower for the rhyme fluency test and

for the end phoneme identity test, with the results on the simple rhyme task being marginally

significantly lower. For the rapid serial naming tests neither the picture naming nor the colour

naming task differed significantly between both groups. On the tests of verbal short-term

memory only the nonword repetition test showed a slight tendency towards differing between

both groups.

28

It is noteworthy that some of the group differences have been tempered by applying

the strict controlling MMA design. For example, by comparing both groups without

controlling for nonverbal IQ, age and parental educational level, we also found significant

group differences for the nonword repetition test and simple rhyme test, and marginally

significant differences on the first phoneme identity task4.

With respect to developing literacy skills, we found a significant group difference on

the log-transformed letter knowledge scores.

Because the number of participants was not big enough to perform a reliable

confirmatory factor analysis, an exploratory principal component factor analysis with varimax

rotation was carried out to examine the data structure. Since we wanted to explore the unique

relation between the individual phonological sub skills and the auditory processing skills,

unrelated (i.c. orthogonal) phonological factors were calculated. This analysis revealed that

the rhyme fluency task disturbed the assumed threefold phonological structure. A post hoc

explanation for this phenomenon might be that this fluency task depended only marginally on

rhyming skills, while depending mostly on skills as flexible and creative thinking.

Replacement of this test by the described simple rhyme test, which was a pure rhyme

measure, resulted in an excellent three-factor structure (based on the eigenvalue criterion).

The first factor had heavy loadings of the three sound identity tasks and the simple rhyme

task, and a more modest loading of the nonword repetition test (see Table 3). As a

consequence, this factor could be reliably labelled as the Phonological Awareness Factor

(PhAw). The second factor was completely determined by heavy loadings of both the colour

and the picture naming task, and as such could be regarded as the Rapid Automatic Naming

Factor (RAN). Finally, the nonword repetition test and the digit span loaded heavily on the

last factor, namely the Verbal Short Term Memory Factor (VSTM).

Table 2 displays descriptive statistics and MMA results for these three latent

phonological factors. To assist in the interpretation of these results, factor values were

transformed to effect sizes relatively to the mean and standard deviation of the LR-group. As

can be seen, both groups did not differ on Verbal Short Term Memory and Rapid Automatic

Naming, but they differed significantly on Phonological Awareness (p = .04).

29

Table 3

Principal component factor analysis with varimax rotation: factor loadings of the phonological measures.

Factor 1

Phonological

Awareness

Factor 2

Rapid Automatic

Naming

Factor 3

Verbal STM

Simple rhyme .68

Rhyme identity .83

First-sound identity .82

End-sound identity .80

Colour naming .91

Picture naming .92

Digit span .83

Nonword repetition test .36 .74

Note. Only factor loadings above .35 have been depicted.

Auditory measures

For every auditory experiment a paired wise Repeated Measures MMA was computed

with group as between-subject variable (HR versus LR) and threshold run as within-subject

variable (run 1 to 3). Results can be summarised as follows: (a) neither for GAP, nor for FM,

nor for TN-detection there was a significant group effect (p = .08, p = .33 and p = .66,

respectively); (b) the three auditory tests showed a significant effect of threshold run (p <

.001, p = .01 and p < .001, respectively) and (c) for none of the tests the group by run

interaction was significant (p = .67, p = .21 and p = .47, respectively). For every auditory

experiment, post-hoc analysis revealed that none of the three threshold measures

differentiated significantly between HR and LR group. Furthermore, for every experiment

there was only a significant learning effect from the first to the second run; the second and

third run did not differ significantly from each other.

Importantly, in contrast to the phonological data, the results were not influenced by

applying the conservative MMA design. Even while analysing the auditory data without any

covariates added, the null-results were virtually identical.

Although the Repeated Measures MMA revealed a general learning effect from the

first to the second threshold run, this tendency did certainly not apply to all subjects. For

many of them, the first threshold was better than the second or third, or the second threshold

was better than the third. Moreover, since we are interested in threshold estimations as an

indicator of a subject’s sensory capability, average threshold (or the average of the last two

30

threshold runs) might not be the most appropriate measure; especially not in this age group

that traditionally shows a high intrasubject variability (Wightman & Allen, 1992). Because

our interest is in the best level of performance a subject is able to reach, a more reasonable

estimator of threshold is each subject’s “best” performance, or the lowest threshold of the

three estimates for every experiment (see Wightman et al., 1989). While using a three-interval

oddity paradigm, the probability of progressing to the next more difficult level just by chance

is only 11.1 % (1 to 9). This means that the probability of progressing two consecutive levels

by guessing is very small (only 1.23 %). Hence, on an average of 35 trials for every run and

every experiment, it turns out to be very implausible that this “best threshold” would just be

the result of lucky guessing instead of reflecting the real sensory capability limit. Threshold

estimates and test statistics for the best and second best threshold and for the mean of the two

best thresholds are given in Table 4. For every auditory experiment, Mixed Model Analysis

showed there was no significant group effect.

Table 4

Auditory Measures: Descriptive statistics and p-values for paired wise MMA, controlling for nonverbal IQ, age

and parental educational level

HR LR

Measures M SD M SD p

PTA (dB HL)

Best GAP1 (ms)

Best GAP2 (ms)

AV GAP ½ (ms)

Best FM1 (Hz)

Best FM2 (Hz)

AV FM ½ (Hz)

Best TN1 (dB SNR)

Best TN2 (dB SNR)

AV TN ½ (dB SNR)

13.3

4.2

7.4

5.8

6.0

10.4

8.2

-8.0

-6.9

-7.5

5.1

2.4

6.7

4.3

3.8

6.2

4.6

2.3

2.4

2.3

13.4

4.2

5.8

5.0

5.4

9.0

7.2

-9.0

-7.5

-8.2

6.0

3.1

4.3

3.6

2.2

6.3

4.0

1.8

1.4

1.5

.94

.88

.30

.40

.91

.28

.41

.71

.94

.81

Note. PTA: Pure Tone Average, BestGAP1-2: best and second best GAP threshold, AV GAP ½: average of the

two best GAP thresholds, BestFM1-2: best and second best FM threshold, AV FM ½: average of the two best

FM thresholds, BestTN1-2: best and second best TN threshold, AV TN ½: average of the two best TN

thresholds

Spearman rank correlations between the best and the second best threshold estimate

for every experiment appeared to be satisfactory and were rs = .79 for GAP, rs = .75 for FM

and rs = .83 for TN, all being significant at the p < .0001 level.

31

Table 5

Spearman correlation coefficients: age, nonverbal intelligence and auditory measures.

AV GAP ½ AV FM ½ AV TN ½

Age

Nonverbal IQ

AV GAP ½

AV FM ½

0.10

-0.32*

0.15

-0.16

0.27*

-0.13

-0.18

0.36**

0.43***

Note. AV GAP ½: average of the two best GAP thresholds in ms, AV FM ½: average of the two best FM

thresholds in Hz, AV TN ½: average of the two best TN thresholds in SNR.

Note. * p < .05, ** p < .01, *** p < .001

Table 5 shows the Spearman rank interrelations between the different auditory

psychophysical measures, and their relation to age and nonverbal IQ. None of the auditory

tasks seemed to be related to age, and only GAP-detection showed a significant relation to

nonverbal intelligence. All auditory tasks appeared to be significantly correlated with each

other, with the relation between FM and TN being the most substantial one.

Relations between phonological and auditory skills

To analyse the relationship between participants’ auditory processing skills and their

phonological abilities and developing literacy skills, Spearman correlation coefficients were

calculated between the participants’ GAP, FM and TN thresholds on the one hand, and the

raw and combined phonological and letter knowledge scores on the other hand. Table 6 offers

an overview of these correlations for total group (TG), HR-group and LR-group. Correlations

have been partialed out for the possible influence of age and nonverbal IQ.

For the total group both FM and TN-detection were significantly related to all

variables measuring phonological awareness skills, and consequently they were also

significantly related to the composite Phonological Awareness factor (rs = -.48 and -.35

respectively). In contrast, GAP-detection was not related to any of the phonological variables.

FM-detection was the only auditory variable being significantly related to Letter Knowledge.

For both groups separately, slightly different relationships could be observed. In the

HR-group only TN-detection was significantly related to Phonological Awareness and its

constituent subtasks, whereas in the LR-group FM-detection was exclusively related to this

32

Phonological Awareness factor. Remarkably, in the HR-group both FM and TN-detection

appeared to be significantly related to Picture Naming (rs = -.41). The GAP-detection task

again appeared to be unrelated to any of the phonological variables. In the subgroups the

power of the Spearman test was too weak to reveal a relation between Letter Knowledge and

any of the auditory variables.

Individual deviance analysis

Since one of the goals of this study was to explore early indicators of dyslexia and in

view of the fact that group comparisons might mask significant individual differences, we

also carried out analyses on the subject level. To decide which individual did and did not

show abnormal performance, we adopted the two-step criterion as suggested by Ramus et al.

(2003). Applying this procedure, the criterion for deviance has been placed on 1.65 standard

deviations of the mean of the LR-group. In a normal distribution this corresponds to the fifth

percentile and as such it is a fairly strict criterion. However, if a LR subject may occasionally

show abnormal performance, this would make the criterion much more stringent by

excessively influencing the LR mean and standard deviations. Moreover, the occurrence of

these low scoring LR subjects might be especially probable in this preschool population since

there is still a chance that even children of the LR group would become dyslexic. For this

reason, the criterion has been applied in two steps: (1) compute the control mean and standard

deviation and identify LR subjects who qualify for abnormal performance according to the

1.65 SD criterion (typically, this applied to 1 or 2 LR subjects for each measure);

(2) recompute the LR mean and standard deviation excluding these deviant LR subjects, and

identify HR subjects who are outside +/- 1.65 SD.

Table 6

Spearman partial correlation coefficients, partialed out for age and nonverbal IQ.

Simple

rhyme

Rhyme

identity

First-sound

identity

End-sound

identity

Colour

naming

Picture

naming

Digit

span

Nonword

repetition

Factor

PhAW

Factor

RAN

Factor

VSTM

Letter

knowledge

Total group (N = 61)

AV GAP ½

AV FM ½ -0.33* -0.34** -0.43*** -0.45*** -0.24 -0.48**** -0.29*

AV TN ½ -0.29* -0.36** -0.28* -0.24 -0.35**

HR group (N = 30)

AV GAP ½ -0.36

AV FM ½ -0.43* -0.34 -0.33 -0.32 -0.41* -0.34 -0.31

AV TN ½ -0.39* -0.55** -0.50** -0.41* -0.52** 0.36

LR group (N = 31)

AV GAP ½

AV FM ½ -0.42* -0.50** -0.59*** -0.60***

AV TN ½

Note. AV GAP ½: average of the two best GAP thresholds in ms, AV FM ½: average of the two best FM thresholds in Hz, AV TN ½: average of the two best TN thresholds

in SNR.

Note. Only correlations with a p-value above .10 have been depicted.

Note. * p < .05, ** p < .01, *** p < .001 and **** p < .0001

34

Individual scores for the psychophysical measures (average of best and second best

threshold) and for the phonological factors are plotted in figure 1. A distribution analysis on

the data of the ‘restricted’ LR-group confirmed the normality of these variables. As such, the

1.65 SD deviance criterion corresponded indeed to the postulated fifth percentile. A deviance

analysis on Phonological Awareness revealed that 12 HR subjects out of 30 showed abnormal

performance, this corresponds to 40 % of the HR group. In the LR group 5 subjects out of 31

(= 16 %) had abnormal performance. For Rapid Automatic Naming 3 subjects out of each

group had abnormal performance (corresponding to 10 %) and for Verbal STM 3 HR versus 2

LR subjects showed deviant scores (10 % versus 6 %). Considering the auditory measures, for

both FM and GAP detection there were 8 HR subjects versus 4 LR subjects who showed

abnormal performance (26 % versus 13 %). For TN there were 3 HR subjects (10 %) versus 1

LR subject (3 %) having deviant results.

To summarize, again Phonological Awareness turned out to discriminate best between

both groups by having a significant higher proportion of deviant subjects in the HR group

(Fisher Exact Test, p = .04). For both GAP detection and FM detection the proportion of

subjects showing abnormal performance was twice as high in the HR group compared to the

LR group. However, this tendency was not significant (Fisher Exact Test, p = .17 for both

tests).

By looking at the individual scores for all subjects showing abnormal performance in

at least one measure, it becomes clear that there is no straightforward regularity or tendency

between these measures. Auditory deficits appeared to be largely mutually unrelated with

some subjects differing only on one task, others on two or three tasks, but without any

consistency. The same applied for deficits on the phonological factors. Moreover, the relation

between auditory deficiencies on the one hand and phonological deficiencies (more

specifically in phonological awareness) on the other hand was even more ambiguous. Some

subjects suffered really serious deficits in auditory processing without showing any

phonological problems. Conversely, other subjects obtained deviant phonological results

while demonstrating perfectly intact auditory processing. Finally, in some subjects

phonological and auditory processing deficits appeared to be partially related.

Fig 1: Individual Z-scores on phonological and auditory measures. The solid line indicates the mean for all LR subjects above

Pc 5; the dashed line indicates the chosen deviance criterion (1.65 SD deviating of the LR mean after excluding deviant LR

subjects). Deviant individuals are identified by their pair number.

36

Discussion

Feasibility of psychophysical testing in preschoolers

One of the main objectives of this study was to explore the feasibility of administering

complex psychophysical tests to very young subjects. Based on our results, this research

question can be answered entirely confirmative. Not only did the children perform

surprisingly accurately, but they also really enjoyed the auditory tasks.

While comparing the auditory thresholds of our LR-subjects with results on identical

tasks administered to 11-year old normal reading children (Van Ingelghem et al., 2004), the

preschoolers showed an overall weaker discrimination capacity (GAP: 4.2 versus 2.1 ms, FM:

5.4 versus 3.3 Hz, TN: -9.0 versus -12.7 dB SNR). The higher thresholds of preschoolers

compared to older children and adults is a general observation that has been demonstrated in

numerous auditory studies across all kind of discrimination tasks (e.g. Allen, et al., 1989;

Irwin, Ball, Kay, Stillman, & Rosser, 1985; Jensen & Neff, 1993; Morrongiello, Kulig, &

Clifton, 1984; Schneider & Trehub, 1992; Trehub, Schneider, & Henderson, 1995). However,

it remains unclear whether this weaker discrimination reflects an underlying sensory

immaturity or has a cognitive origin (e.g. non-optimal listening strategies, fluctuations in

attention, etc.). Another general observation in psychophysical testing with preschoolers

concerns the higher inter- and intrasubject variability. As Wightman and colleagues

demonstrated, the intrasubject variability might mainly result from central non-auditory

attentional factors (e.g. Oh, Wightman & Lutfi, 2001; Wightman, et al., 1989; Wightman,

Callahan, Lutfi, Kistler & Oh, 2003). For this reason, the way we reduced this excessive

variability by opting for a ‘best threshold analysis’, can be justified. In contrast, the

intersubject variability is not a result of error variance, but reflects reliable differences in the

speed of neural development (e.g. Allen & Wightman., 1994). Because of this substantial

intersubject variability, caution is required while interpreting averages and group

performances (Wightman & Allen, 1992).

Phonological abilities and letter knowledge

With respect to the phonological data, the exploratory factor analysis convincingly

revealed the three-dimensional phonological structure as postulated by Wagner and Torgesen

(1987). Moreover, both the group analysis and the individual deviance analysis clearly

37

demonstrated the robustness of the phonological deficit hypothesis in dyslexia-prone children.

Even at a preschool age, the HR-children already showed a significant deficit in phonological

awareness, not only at the rhyme-level but also at the level of the phonemes. These results are

consistent with other longitudinal prospective studies that revealed similar deficits in

genetically at risk children (e.g. Elbro, Borstrom, & Petersen, 1998; Gallagher, Frith, &

Snowling, 2000; Pennington & Lefly, 2001; Scarborough, 1989; 1990; 1998). The group

differences on the factors rapid automatic naming and verbal short-term memory were

insignificant but in the expected direction with the HR-group scoring less well than the LR-

group. These results are in line with findings by Elbro et al. (1998). However, some

researchers did find significant differences on verbal short-term memory (Pennington &

Lefly, 2001 – significant group difference in first grade, but not in kindergarten) and rapid

automatic naming (de Jong & van der Leij, 2003; Pennington & Lefly, 2001), but only while

comparing HR dyslexic subjects versus LR normal readers. This means they retrospectively

reanalysed the kindergarten data after having diagnosed their subjects in second or third

grade. Since currently the children in our study are still attending kindergarten, we do not yet

know who will finally become dyslexic, and consequently we obviously cannot carry out this

analysis yet.

Although most children hardly knew any letters at the beginning of the last year in

kindergarten (on average about three or four letters), the group difference was already

significant. Again this result is in line with any of the previously mentioned longitudinal

studies. Since both letter knowledge and phonological awareness have consistently been

proven to be among the best single preschool predictors of literacy development (see e.g. the

impressive meta-analysis of Scarborough, 1998, based on 61 studies), and since the HR

versus LR group differed especially on these measures, it is likely that the familial high risk

group will contain a disproportionally high number of future cases of dyslexia.

Auditory processing skills

We studied GAP and FM-detection in order to investigate auditory temporal (‘rapid

and brief’ versus ‘dynamic’) processing. In line with the temporal hypothesis, we expected

these tasks to differentiate between both risk groups and to be related to specific pre-reading

skills like phonological processing and letter knowledge. However, we did not find any

significant differences in auditory processing between the HR and the LR group, neither at a

group level nor in individual deviance analyses. Although there were twice as many subjects

38

showing abnormal performance for GAP and FM-detection in the HR-group, this tendency

did not reach significance. Assuming the correctness of the causal auditory hypothesis, this

lack of significance might be attributed either to the typically greater interindividual

variability in children (cfr. supra) or to the fact that we did not study a well-defined clinical

group but only a risk group that still might show substantial overlap with the non-affected

control group. Moreover, Bishop et al. (1999) demonstrated in a twin study on SLI children

that in contrast to the highly heritable phonological skills, auditory skills depend less on

genetic and more on environmental influences. As such, our finding of a phonological deficit

in combination with relatively intact auditory skills in this genetic high risk group

corresponds well with the results of Bishop and colleagues.

Recently, an alternative explanation has been put forward to explain the variably

observed auditory deficits in subjects with specific language impairment and dyslexia. Bishop

and colleagues (Bishop & McArthur, 2004; Bishop & McArthur, 2005; McArthur & Bishop,

2004) and Wright and Zecker (2004) suggested that these subjects might not suffer from a

specific chronic auditory deficit, but rather from a more general and passing auditory

maturational delay (estimated to encompass about three or four years). In that perspective the

differential sensitivity of a task will be at best when administered in the age period that the

measured skill is sharply improving in typically developing subjects. However, in spite of

having administered our auditory tasks during a sensitive developmental period - it has been

demonstrated that normally developing five-year-old subjects undergo a rapid maturation of

both spectral and temporal auditory abilities5 (e.g. Irwin et al., 1985; Jensen & Neff, 1993;

Thompson, Cranford & Hoyer, 1999; Wightman et al., 1989) - we did not observe the

hypothesized developmental delay in the HR group. This is markedly in contrast with results

obtained by Hautus et al. (2003), who demonstrated that younger reading-impaired children

(aged 6-9 years) had significantly higher gap-detection thresholds than age-matched controls

while older dyslexic subjects (aged 10-13 and adults) did no longer differ from controls.

Relations between auditory and phonological abilities

With regard to relations between auditory processing and phonological processing,

both FM and TN-detection thresholds were significantly related to phonological awareness

and -to some extent- to rapid automatic naming and letter knowledge. In contrast, the GAP-

detection task was completely unrelated to any phonological measure. These results suggest

that it is not the specific temporal aspect of auditory processing that is related to developing

39

phonological abilities, since the temporal GAP-detection task was not related whereas the

non-temporal TN-detection task turned out to be significantly related to phonological

processing. Instead it appears as if the common spectral or frequency sensitive characteristic

of TN and FM-detection causes the relation with phonological ability. At a neurophysiologic

level this might imply that a more accurate phase-locking system or smaller and more sharply

tuned auditory filters are somehow related to better phonological processing (e.g. Carney,

Heinz, Evilsizer, Gilkey & Colburn, 2002; Moore, 1997). This is in line with many studies

demonstrating an impairment in frequency discrimination in dyslexic subjects (e.g. Ahissar,

Protopas, Reid & Merzenich, 2000; Amitay, Ben-Yehudah, Banai & Ahissar, 2002; Cacace,

McFarland, Ouimet, Schrieber, & Marro, 2000; Fischer & Hartnegg, 2004). Talcott et al.

(2002) reported similar observations from a large-scale primary school study in which

auditory frequency resolution differed between groups of children with different literacy

skills. In the same way, Hulslander et al. (2004) observed a significant correlation between

FM-thresholds and scores on a phoneme awareness composite, even while controlling for

individual differences for full-scale IQ. Evidence supporting the neurophysiologic explanation

for a deficit in frequency sensitivity has been put forward by McAnally and Stein (1996) who

demonstrated that dyslexics were less able to generate neural discharges phase-locked to the

temporal fine structure of the acoustic stimuli. Data consistent with these findings were also

reported by Baldeweg, Richardson, Watkins, Foale, and Gurzelier (1999), Dougherty,

Cynader, Bjornson, Edgell, and Giaschi (1998) and Schulte-Körne et al. (1998). Recently,

Amitay, Ahissar and Nelken (2002) also found evidence for a deficit in tone-in-noise

detection in adult dyslexic subjects. However, their results were conflicting with McAnally’s

findings of a phase-locking deficit in dyslexics (for a similar conclusion see also Hill, Bailey,

Griffiths & Snowling, 1999).

The significant relation we observed between the auditory measures and phonological

awareness is in line with the results of Share et al. (2002) who also found a reliable

concurrent correlation between a non-linguistic TOJ task and phoneme segmentation at

school entry. Unfortunately, this relation could not be interpreted in a causal way since the

auditory measures were not able to predict any later phonological or reading skills. Instead,

Share and colleagues speculated that the association between early temporal processing and

phonological awareness might be the result of a higher-order common cause of an unspecified

metalinguistic nature. Similarly, the substantial concurrent correlation we found between

phonological and spectral auditory measures should not be interpreted in a directional or

causal way. After all, while inspecting individual data in the deviance analysis, it is clear that

40

there is no obvious straightforward relation between deficits in auditory measures and deficits

in phonological skills. Although deviant auditory processing tends to be a risk indicator for a

deficit in phonological development, intact auditory processing is certainly not a sufficient

prerequisite for developing normal phonological skills. More generally, we have to conclude

that we are not able to demonstrate a consistent and convincing pattern in individual

deficiencies, neither within the auditory skills, nor within the phonological sub-skills, nor

within the relation between auditory and phonological skills.

Conclusion

To conclude, phonological awareness and letter knowledge turn out to be the best

indicators to differentiate between preschool children with low versus high familial risk of

developing dyslexia. In contrast, none of the auditory processing tasks is able to differentiate

significantly between both groups. However, auditory spectral tasks (FM and TN-detection

thresholds) are highly significantly related to phonological awareness. This relation is not

present for a specific temporal GAP-detection task. Nevertheless, identifying deviant subjects

in auditory spectral processing in order to predict deficiencies in phonological skills and

subsequent reading development does not yet seem to be a viable option, since at the level of

individual subjects the relation between auditory and phonological skills seems to be much

less straightforward.

41

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Notes

1 Published as Boets, B. Wouters, J., van Wieringen, A., & Ghesquière, P. (2006). Auditory

temporal information processing in preschool children at family risk for dyslexia: Relations

with phonological abilities and developing literacy skills. Brain and Language, 97, 64-79. 2 In the Belgian school system formal instruction starts in Grade 1 at six years. This means in

kindergarten no reading instruction is offered. This is in contrast to the kindergarten group

studied by Share et al. (2002), who already received formal reading instruction. 3 It is worth mentioning that for both HR and LR-group, the mean scores on the sound identity

tasks are well above chance level. This is in contrast to a Dutch study where an odd-one-out

categorization version of these tests was administered and where children of the same age

were not able to exceed chance level on the last-sound and first-sound categorization test (de

Jong & van der Leij, 1999). 4 Interestingly, this change in significance was caused almost completely by the influence of

maternal educational level. 5 For instance, regarding frequency discrimination, an almost linear improvement has been

observed from 4 to 9 years, after which adult levels of performance are obtained (Jensen &

Neff, 1993; Thompson et al., 1999). Similarly, with respect to GAP detection thresholds,

Wightman et al. (1989) and Irwin and colleagues (1985) observed a sharp maturational

improvement from 3 years on up to 7 years and extending slightly up to 11 years before

reaching adult levels.

51

Manuscript 2

Speech perception in preschoolers at family risk for dyslexia:

Relations with low-level auditory processing and phonological ability1

Abstract

We tested categorical perception and speech-in-noise perception in a group of 5-year-old preschool children

genetically at risk for dyslexia, compared to a group of well-matched control children and a group of adults.

Both groups of children differed significantly from the adults on all speech measures. Comparing both child

groups, the risk group presented a slight but significant deficit in speech-in-noise perception, particularly in the

most difficult listening condition. For categorical perception a marginally significant deficit was observed on the

discrimination task but not on the identification task. Speech parameters were significantly related to

phonological awareness and low-level auditory measures. Results are discussed within the framework of a causal

model where low-level auditory problems are hypothesized to result in subtle speech perception problems that

might interfere with the development of phonology and reading and spelling ability.

Introduction

Every beginning reader of an alphabetic writing system is confronted with the

challenge to learn to segment the quasi-continuous speech signal in distinct and specified

phoneme units in order to map them upon their corresponding graphemes. Whereas the

majority of readers fairly easily establishes stable grapheme to phoneme relations, there is a

small group of about 5 to 10 percent of children and adults that is not able to master fluent

reading and spelling skills despite conventional instruction, adequate intelligence and

sufficient socio-cultural opportunities. In recent years, there has been a growing consensus

that these reading and spelling impairments, characteristic of dyslexia, are related to specific

deficits in the representation and use of phonological information (for a review see Rack,

1994; Snowling, 2000). Phonological deficits have been demonstrated in three broad areas

52

(Wagner & Torgesen, 1987): phonological awareness (e.g. Manis, Custodio & Szeszulski,

1993), retrieval of phonological codes from long-term memory (rapid automatic naming) (e.g.

Bowers & Swanson, 1991; Wolf 1986), and verbal short-term memory (e.g. Catts, 1989).

Moreover, several prospective longitudinal studies have suggested a causal link between

sensitivity to the phonological structure of words and later progress in reading acquisition

(e.g. Wagner, Torgesen & Rashotte, 1994), although the relationship between reading and

phonological awareness is probably reciprocal (Morais, Bertelson, Cary & Alegria, 1979).

Research in the underlying neurological dysfunction of dyslexia suggests that the

phonological processing deficits as such may result from a more fundamental deficit in the

basic perceptual mechanisms that are responsible for auditory information processing.

Dyslexics tend to have difficulties processing linguistic and non-linguistic stimuli that are

short and enter the nervous system in rapid succession (for reviews see e.g. Farmer & Klein,

1995; Habib, 2000; McArthur & Bishop, 2001). Recent studies in this context focus more

specifically on an impaired perception of dynamic aspects in the auditory signal itself, like

amplitude and frequency modulations (Menell, McAnally & Stein, 1999; Talcott et al., 1999;

Talcott et al., 2000; Talcott et al., 2002; Van Ingelghem et al., 2005; Witton et al., 1998). In

these studies, persons with dyslexia show difficulties tracking acoustic changes over time.

Besides, subjects with reading impairments also tend to have difficulties with speech

perception (for a review see McBride-Chang, 1995). In particular, it has been suggested that

they have difficulty extracting discrete phonological representations from phonetic features

embedded within the speech signal (Manis et al., 1997; Serniclaes, Sprenger-Charolles, Carré

& Demonet, 2001). The resulting deficient phonological representations can be expected to

interfere with or delay the process of becoming phonemically aware, which in turn can lead to

problems in learning to read or write (Joanisse, Manis, Keating, & Seidenberg, 2000;

McBride-Chang, 1996; Watson & Miller, 1993). In this way, even subtle speech perception

problems might have considerable consequences for learning to read (Werker & Tees, 1987).

Since speech perception also heavily depends upon adequate tracking of fast changing

transitions in frequency and amplitude, there could very well be a causal path relating the

observed deficits in auditory processing, speech perception and phonological ability. More

concretely, it has been hypothesised that dyslexic’s low-level auditory processing problems

might result in a subtle speech perception problem, which might interfere with the

development of fine-grained phonological representations, which in turn impacts upon

reading and spelling ability (Talcot & Witton, 2002; Tallal, 1980; 1984; Wright et al., 1997).

53

Even though this causal hypothesis is widely recognized (and hotly debated, see e.g.

Blomert & Mitterer, 2004; Denenberg, 1999; Mody, Studdert-Kennedy & Brady, 1997;

Nittrouer, 1999; Rosen & Manganari, 2001), only few studies have examined the

developmental pathway exploring the relations between these different skills in detail. Some

major prospective studies are coming about (e.g. Jyväskylä Longitudinal study of Dyslexia,

see Lyytinen et al., 2001; Dutch Dyslexia Research Program, NWO, 1996), but until now

only limited reports have been published. Here, we report data from a longitudinal study that

explores the development of basic auditory skills, speech perception, phonological abilities

and reading skills in a group of 5-year-old preschool children genetically at risk for dyslexia,

compared to a group of well-matched control children. In a previous paper (Boets, Wouters,

van Wieringen & Ghesquière, 2006), we already reported the absence of a significant group

difference for any of three administered auditory measures (i.e. gap-in-noise detection,

frequency modulation detection and tone-in-noise detection), in the presence of a significant

difference for phonological awareness and letter knowledge. Moreover, spectral auditory

tasks (particularly 2 Hz frequency modulation detection) turned out to be highly significantly

related to phonological awareness. In this paper, we will focus upon speech perception

abilities, assessed in the same group of preschool children. In particular, we consider the

question whether a deficit in speech perception may already be observable in preschool

children at risk for dyslexia and we investigate the relationship between speech perception

and auditory and phonological processing.

Dyslexia research has mainly used two major experimental paradigms to study speech

perception: (i) the perception of speech presented in background noise and (ii) categorical

perception of stop consonants.

Although it is widely acknowledged (and there is much supporting anecdotal

evidence) that subjects with reading problems have particular difficulty with the perception of

speech under noisy listening conditions, so far only a limited number of empirical studies

have addressed this issue in well-defined clinical groups of subjects with dyslexia. Brady,

Shankweiler and Mann (1983) showed that nine-year-old poor readers had more difficulty in

repeating words presented in noise than age-matched controls, though both groups did not

differ in the perception of speech without noise. Similarly, Chermak, Vonhof and Bendel

(1989) found that adults with broadly defined learning disabilities had poorer word

identification in noise than a control group of adults without learning disabilities. The same

results were obtained by Watson and Miller (1993), presenting CV syllables in a background

54

of cafeteria noise. Two studies presenting meaningful sentences in noise found that children

with hearing or language impairments (Stollman, Kapteyn & Sleeswijk, 1994) or with

language-based learning disabilities (Bradlow, Kraus & Hayes, 2003) showed poorer overall

sentence-in-noise perception than a non-affected control group. Moreover, both studies

demonstrated that children with language problems were more affected by more adverse

listening conditions than the control group.

Recently, some neurophysiologic studies also provided evidence for anomalies in

dyslexics’ neural encoding of speech stimuli embedded in background noise. Wible, Nicol

and Kraus (2002) demonstrated a deficiency in the neural representation of repeated speech

stimuli in noise but not in quiet in 11-year old children diagnosed with reading problems.

Moreover, the robustness of the physiologic response in noise was significantly correlated

with behavioral measures of speech discrimination, with a phonological composite measure

and with spelling ability. In the same way, Cunningham, Nicol, Zecker, Bradlow and Kraus

(2001) reported that a group of reading disabled children (aged 10 to 13 years) showed poorer

speech discrimination coupled with diminished neurophysiologic cortical and subcortical

responses in background noise compared to normal children. There were no differences

between the groups in quiet listening conditions, neither in behavioral perception, nor in

electrophysiology. Similar results, indicative of abnormal timing of cortical responses to

speech stimuli in background noise in learning-impaired children, were reported by Warrier

and colleagues (Warrier, Johnson, Hayes, Nicol, & Kraus, 2004). Moreover, they

demonstrated that a commercial phonological/language training program not only enhanced

their phonetic decoding skills, but also improved the timing of the cortical responses in these

children.

Taken together, even though these studies are diffuse in terms of methodology and

participant inclusion criteria, the findings indicate that subjects with dyslexia or more broadly

defined learning or language impairments have problems perceiving speech in the presence of

background noise. However, evidence of a speech-in-noise perception deficit is not yet

unequivocal since some studies failed to find a discrepancy (Pennington, Van Orden, Smith,

Green & Haith, 1990; Snowling, Goulandris, Bowlby & Howell, 1986) or could only

demonstrate it under very specific noisy listening conditions (viz. intermittent noise, see

Bailey, Griffiths, Hill, Brent, & Snowling, 2002).

The categorical perception experiments are based on the general principle that

perceptual discrimination between speech sounds belonging to different phoneme categories

55

is better than that between sounds falling within the same category, irrespective of the

magnitude of the acoustic (physical) difference between the sounds (Liberman, Harris,

Hoffman & Griffith, 1957). In identification experiments this is expressed in the

discontinuous perception of a series of speech stimuli that vary continuously along a single

acoustic dimension. Normal listeners exhibit a strong tendency toward categorical perception

of speech along certain phonetic dimensions, such as place of articulation (e.g. /ba/ vs. /da/) or

voicing status (e.g. /ba/ vs. /pa/). Several studies suggest that children and adults with dyslexia

are less categorical than average readers in the way they perceive phonetic contrasts (Breier,

Fletcher, Denton & Gray, 2004; Godfrey, Syrdal-Lasky, Millay & Knox, 1981; Lieberman,

Meskill, Chatillon & Schupack, 1985; Maassen et al., 2001; Manis et al., 1997; Reed, 1989;

Steffens, Eilers, Gross-Glenn & Jallad, 1992; Serniclaes et al., 2001; van Beinum et al., 2005;

Werker & Tees, 1987). Some studies could even demonstrate a weaker categorical perception

in toddlers (Gerrits, 2003) and six-month-old infants (Richardson, Leppänen, Leiwo &

Lyytinen, 2003) at family risk for dyslexia (see also Benasich & Tallal, 2002, and Molfese,

2000, for similar speech perception studies in infants). However, a minority of studies

obtained non-significant results (Blomert & Mitterer, 2004; Brandt & Rosen, 1980; Nittrouer,

1999) or only observed the categorical perception deficit in the most severely phonologically

or linguistically impaired dyslexics (Joanisse et al., 2000; Manis et al., 1997). Interestingly,

Serniclaes and colleagues (Serniclaes et al., 2001; Serniclaes, Van Heghe, Mousty, Carré, &

Sprenger-Charolles, 2004) demonstrated that in addition to a weaker discrimination between

categories, dyslexics presented a higher sensitivity for acoustic differences within the same

phoneme category. This higher sensitivity for ‘allophones’ (i.e. variants of the same phoneme

category) indicates that the internal structure of the categories is less coherent. Whereas the

perception of speech with allophonic rather than phonemic categories probably would not

raise major problems, it might pose a problem for the acquisition of written language (at least

in alphabetic systems). Indeed, if the phonological representations are not well specified, the

one-to-one relation between graphemes and phonemes will be difficult to establish.

To understand the speech perception problem in dyslexia, Serniclaes et al. (2004) and

Maassen et al. (2001) suggested looking at it from a developmental perspective. It has been

demonstrated that newborns possess predispositions for discriminating all potential phoneme

categories in the world’s languages; they can even discriminate among a range of phonetic

categories that are not phonemic in their own mother language. However, after exposure to

the sounds of their native language, they gradually obtain higher sensitivity for language-

specific phonetic cues (by assigning different weights to different acoustic parameters) and

56

simultaneously they lose their sensitivity to the boundaries that are not linguistically relevant

(MacKain, Best & Strange, 1981; Nittrouer, 1996; Werker & Tees, 1984). Whereas in

normally developing children these irrelevant predispositions are usually deactivated by about

the age of one year, it has been suggested that in dyslexics this ‘weighting shift’ has not been

completed adequately, resulting in the persistence of more or differently divided categories

than are necessary for perceiving phonemes (Serniclaes et al., 2004).

Notwithstanding the empirical evidence that speech perception measures are able to

differentiate relatively reliably between adult and school-aged dyslexic and normal reading

subjects, the differentiating and predictive power of these tasks has been studied only in a

limited way in preschoolers. In this study we want to deal with this issue by examining

categorical perception and speech-in-noise perception in two contrasting groups of preschool

children. Moreover, by correlating the speech data to previously collected auditory and

phonological data, we aim to shed light on the relation between low-level auditory processing,

speech perception, phonological skills and literacy development. Although theoretically it has

been argued that there might be a causal relation between these abilities, it is important to

note that with this study we do not aim to establish whether the relation is causal in any way.

Nevertheless, by assessing all these abilities in the same children, this study is one of the first

to empirically investigate the hypothesized interrelations. Moreover, by studying preschool

children who never received any formal reading instruction, we can rule out the possibility

that observed group differences or interrelations are merely a consequence of differences in

literacy skills or reading experience.

Method

Participants

Sixty-two five-year-old children attending the last year of kindergarten were included

in the study (36 boys / 26 girls). Half of the participants were children of ‘dyslexic families’,

the so-called high-risk group (HR); the other half were control children of ‘normal reading

families’, the so-called low-risk group (LR). Since dyslexia tends to run strongly in families,

preschoolers with dyslexic relatives are more likely to develop reading problems. Gilger,

Pennington and DeFries (1991) estimate that roughly between 30 and 50 % of such children

will become reading disabled. Moreover, in view of the fact that the HR group already

57

demonstrated a significant deficit in phonological awareness and letter knowledge, and since

these measures have consistently been proven to be the best single preschool predictors of

literacy development (Scarborough, 1998), it is very likely that this group will contain a

disproportional high number of future cases of dyslexia.

All children were native Dutch speakers without any history of brain damage, long

term hearing loss or visual problems. Additionally, at the moment of data collection they did

not present any gross hearing deficiencies (audiometric pure-tone average < 25 dB HL). The

HR children were selected on a basis of having at least one first-degree relative with a

diagnosis of dyslexia. The LR children showed no history of speech or language problems

and none of their family members suffered any learning or language problem. For every

individual HR child the best matching LR control child was selected based on five criteria:

(1) educational environment, i.e. same nursery school, (2) gender, (3) age, (4) nonverbal

intelligence, and (5) parental educational level. Nonverbal intelligence was assessed by an

adapted version of the Raven Coloured Progressive Matrices (RCPM) (Raven, Court, &

Raven, 1984), a collective non-verbal intelligence test measuring spatial reasoning. Parental

educational level was assessed using the ISCED-scale (International Standard Classification

of Education by UNESCO, 1997), by converting classifications on the original seven-point

scale to a three-point scale. At the time of collecting the speech data, the mean age for both

the HR and LR group was 5 years and 8 months, not being statistically different [paired t(30)

= 0.22, p = .83]. The nonverbal IQ scores were slightly above population average (107 for HR

group and 111 for LR group) and did not differ significantly [paired t(30) = 1.88, p = .07].

Fisher’s Exact Test also confirms that both groups did not differ in frequency distribution of

the different educational categories (p = .71 for maternal and p = .43 for paternal educational

level). Further details about the participants and the selection procedure are described in Boets

et al., (2006).

For reasons of comparison, the speech tests were also administered to a group of

eleven normal hearing and reading adults, aged 24-43 years (5 males / 6 females).

58

Tasks and materials

Tests for speech perception

In order to assess the speech processing capacities, two speech perception tasks were

administered: one measuring speech-in-noise perception and one measuring categorical

speech perception.

Speech-in-noise perception task. In the speech-in-noise perception task, seven lists of

ten high-frequency monosyllabic words (taken from the Göttingerlist II, recorded by Wouters,

Damman & Bosman, 1994; see also van Wieringen & Wouters, 2005) were presented

monaurally with an inter-stimulus interval of 7 seconds. Simultaneously, a continuous

stationary speech noise with an identical spectrum as the average spectrum of the word lists

was presented to the same ear, at a fixed level of 70 dB SPL. Words were presented at -1, -4

and –7 dB signal-to-noise ratio (SNR). Before administration of the six test lists (3 x 2), one

list was presented at an SNR of +4 dB as a practice list. The child’s task was to repeat the

words as accurately as possible, resulting in a percentage correct word score for every test list.

Categorical perception. A ten-point speech continuum ranging from /bAk/ to /dAk/

(i.e. the Dutch word for ‘box / basket’ and ‘roof’) was presented by means of a categorical

perception task. For every child, a 2AFC forced-choice identification task was administered,

followed by a same-different (AX) discrimination task. Stimuli were identical to the ones

developed and described by van Beinum et al. (2005). Stimuli were based on natural speech

and were constructed by linearly interpolating the transition of the second formant (F2) from

/b/ to /d/. The manipulated part of the signal was a 100 ms interval at the beginning of the

vowel. There were ten interpolation steps and the F2 onset ranged from 1100 to 1800 Hz. The

/bAk/-starting point was characterized by a constant F2 value of 1100 Hz. For each

intermediate signal, the F2 onset was gradually situated at higher frequencies, making the fall

of the transition steeper in every step. At the/d/-endpoint of the continuum the transition was

falling from 1800 Hz to 1100 Hz. The interpolation resulted in a 10-point continuum, the total

length of each item being 600 ms, consisting of (a) a vocal murmur 170 ms; (b) burst 10 ms;

(c) vowel /A/ 250 ms, divided into 100 ms transition duration and 150 ms steady state; (d)

occlusion period [k] (silence) 55 ms; (e) release [k] 115 ms (van Beinum et al., 2005). Stimuli

were presented monaurally at a comfortable listening level of 70 dB SPL.

59

In the identification task, subjects were sitting in front of a computer screen, depicting

a picture of a basket and a roof, representing the possible response options (/bAk/ vs. /dAk/).

The ten stimuli of the continuum were presented twelve times in a random order, the same

stimulus not being presented more than two consecutive times. The child was instructed to

repeat the perceived word and point to the corresponding picture. Participants were given an

unlimited time to respond. No direct feedback was given. The task was preceded by a pre-test,

presenting each of the two endpoint stimuli five times.

In the discrimination task, subjects were instructed to listen to two stimuli presented

with an ISI of 600 ms and to determine whether they sounded ‘the same’ or ‘different’.

Children had to respond by pointing to one of two pictures on the screen representing “the

same” or “different”. No direct feedback was given. In order to obtain a bias-free measure of

discriminability, the task contained physically different as well as identical pairs. The

different pairs, comprising each of the seven 3-step comparisons (1-4, 2-5, 3-6, etc), were

presented 6 times; the identical pairs (1-1, 2-2, etc) were presented twice. The pairs of stimuli

were presented in two blocks of 31 stimuli (3x7 + 1x10). Presentation order of the stimuli was

randomized and the internal order of a different stimulus pair was balanced over both series.

The task was preceded by 10 practice items, comprising pairs that were clearly different or

identical.

Stimulus presentation and response registration was controlled by E-prime, a software

module for psychological experiments (Schneider, Eschman & Zuccolotto, 2002). In order to

make the rather boring psychophysical tests more interesting and child friendly, they were

integrated in an interactive videogame with an introductory and concluding movie and

animated buttons. Prior to testing, the child was presented an animation movie about a cat

waking up in a basket (= /bAk/ in Dutch) and climbing on the roof (= /dAk/ in Dutch). After

the cat disappearing out of view, the movie explained that the child could know where the cat

actually is – in the /bAk/ or on the /dAk/ -by listening very carefully.

For both the categorical perception task and the speech-in-noise perception task, as

well as for the other auditory psychophysical tasks, stimuli were presented monaurally over

calibrated TDH-39 headphones using the integrated audio PC-card from a portable computer

routed to an audiometer (Madsen OB622).

60

Phonological tests

Phonological skills were assessed by a test battery comprising eight tests (see Boets et

al., 2006). A principal component factor analysis with varimax rotation confirmed that the

battery reflected the three traditional phonological domains: (a) phonological awareness: high

loadings from first-sound and end-sound identity task, rhyme identity task and simple rhyme

task, (b) rapid automatic naming: high loadings from both the colors and objects rapid

automatic naming tasks and (c) verbal short-term memory: high loadings from the non-word

repetition task and the digit span forward task. Details about the composition of the battery

can be found in Boets et al. (2006); in this paper we will only refer to the factor scores.

Tests for low-level auditory processing

Auditory processing was assessed by means of three psychophysical threshold tests:

gap-detection in noise (GAP), 2 Hz frequency modulation detection (FM) and tone-in-noise

detection (TN) (see Boets et al., 2006). In the GAP-detection test, subjects had to detect a

silent interval (gap) in a white noise stimulus. Threshold was defined as the minimum gap

length required for detecting the silent interval. In the FM-detection test participants had to

detect a 2 Hz sinusoidal frequency modulation of a 1 kHz carrier tone with varying

modulation depth. Threshold was defined as the minimum depth of frequency deviation

required to detect the modulation. In the TN-detection task participants had to detect two pure

tone pulses (1 kHz, length = 440 ms) within a one-octave noise signal, centered around 1 kHz

(from 707 to 1414 Hz, length = 1620 ms). Threshold was defined as the lowest signal-to-noise

ratio (SNR) required for detecting the tone pulses. For all three psychophysical tests,

thresholds were estimated using a forced-choice adaptive staircase paradigm embedded within

an interactive computer game (Laneau, Boets, Moonen, van Wieringen & Wouters, 2005).

For each participant three threshold estimates were determined for every experiment. For the

correlation data we present here, the average of the best and second best threshold was used as

an indicator of auditory sensitivity. A detailed description of the stimuli, procedure and results

can be found in Boets et al. (2006).

Data collection was carried out by qualified psychologists and audiologists. Testing

took place in a quiet room at the children’s school. Since the LR child was selected from the

HR child’s classmates, both children could always be tested in exactly the same

circumstances.

61

Statistical analysis

First, both groups of children were compared to each other. To account for the

clustered nature of the data (i.e. matched pairs), data were analyzed in a pair wise manner

using Mixed Model Analysis (MMA) (Littell, Stroup & Freund, 2002)2. Although both

groups did not show a significant difference on any of the matching criteria, any possible

influence of age, nonverbal intelligence or parental educational level was ruled out by

additionally controlling for these variables. As such, a series of (repeated) MMA’s was

calculated with pair number as a random variable (1 to 31) and participant group (HR versus

LR) as the fixed between-subject variable3. Age, nonverbal IQ and educational level of both

mother and father were added as fixed (co)variables. Second, child and adult data were

compared to each other using MMA as well. For this comparison no random factor or

covariables were specified. Post-hoc analyses were corrected for multiple comparisons using

Tukey procedure. Third, to extract the essential parameters, the speech-in-noise data and the

identification data were submitted to a logistic fitting and groups were compared on the

resulting parameters. Prior to analysis, the slope-parameters of these curves were log10-

transformed to obtain normally distributed data. In order to take into account the variable

quality of the fits, the inverse standard error of the estimated parameters was added to the

model as a weight variable. Finally, in order to determine the relations between speech

perception on the one hand and auditory and phonological processing on the other, Pearson

correlations were calculated, partialed out for individual differences in nonverbal intelligence

and with the inverse standard error of the estimated parameters added as a weight variable.

Results

Speech-in-noise perception

Prior to analysis, data from one subject were removed because of irregularities during

testing. Average results of the speech-in-noise perception test are depicted in Fig. 1. A paired

wise Repeated Measures MMA with proportion correctly perceived words as dependent

variable, group as between-subject variable (HR versus LR), SNR as within-subject variable

(-1, -4 and -7 dB SNR) and with the same covariates as mentioned above, revealed a

significant effect for group (p = .05), a significant effect for SNR (p < .0001) and no

significant group x SNR interaction effect (p = .22). Post-hoc analysis revealed that both

62

groups only differed at the -7 dB SNR-level (p = .01). A Repeated Measures MMA

comparing the child data with an adult group (n = 8), indicated that both the HR and the LR

group differed significantly from the adult group (p < .0001) with the group x SNR interaction

being significant as well (p = .03).

0

0.2

0.4

0.6

0.8

1

-7 dB -4 dB -1 dB

Signal to noise ratio (SNR)

Pro

porti

on c

orre

ct re

spon

ses

HR group

LR group

Adults

Fig. 1: Mean scores relating the proportion correctly perceived words to the relative level of the presented words

(SNR) [HR group: n = 30, LR group: n = 31; adult group: n = 8].

The result of the perception process can be represented by a psychometric function

relating the percentage correctly perceived words to the relative intensity (SNR) of the

presented words. For every subject, a logistic function relating percent correct responses to

SNR level was fitted to the data using the Nonlinear function of SAS-9.1 and a maximum

likelihood criterion. The fitted function had the following form:

PC = 100 / [1 + exp ( 4 * slope * ( SRT - SNR)/ 100)]

where SNR is the signal-to-noise ratio of the presented stimulus, SRT (or Speech Reception

Threshold) is the estimated signal level required for 50 % correct responses and slope is the

estimated slope parameter of the logistic function. Both the SRT and the slope of the fitted

function are informative: SRT indicates the signal-to-noise ratio required for a fixed level of

performance and the slope reflects the rate at which performance changes with changes in the

level of the signal (Allen & Wightman, 1994).

63

Table 1 shows weighted group means for HR, LR and adult group. In order to take

into account the variable quality of the fits, the inverse standard error of the estimated

parameters was added to the model as a weight variable. A paired wise weighted MMA with

the same covariates as mentioned above demonstrated that both child groups differed

significantly for SRT (p = .04) and (log-transformed) slope (p = .02). This means that the HR

group required a significantly easier signal-to-noise ratio than the LR group in order to

perceive 50 % of the presented words correctly. Moreover, the steeper slope in the HR group

also indicates a faster deterioration of performance while progressing from easy towards

difficult listening conditions. A weighted MMA showed that the adult group differed

significantly from both child groups regarding SRT (p < .0001), but for slope only the adult-

LR difference was significant (adults vs. LR: p = .04; adults vs. HR: p = .77).

Table 1

Descriptive statistics for speech perception tests. Standard deviations are given in parentheses after the mean.

HR group LR group Adult group

Speech-in-noise

N

Weighted SRT (dB)

Weighted slope (%/dB)

Identification

N

Weighted PSE

Weighted slope

Discrimination

N

Mean discriminability

30

-3.3 (1.8)

8.9 (2.5)

25

4.2 (4.0)

16.9 (5.7)

25

0.34 (0.54)

31

-3.8 (2.2)

6.5 (1.9)

23

4.4 (4.1)

21.3 (5.3)

23

0.71 (0.63)

8

-6.4 (1.0)

9.0 (1.1)

11

5.9 (5.4)

75.5 (8.0)

11

1.41 (0.51)

Categorical perception – Identification

Only subjects obtaining 70 % correct responses or more on the pre-test were included

in the analysis, excluding 4 subjects (1HR/3LR). This liberal criterion was chosen since our

data show that on average toddlers misperceive the original /bAk/ sound as /dAk/ in more

than 20% of the presentations (see Fig. 2)4. For the remaining 58 subjects, identification

64

curves were visually inspected to evaluate whether they showed a gradual increase in /dAk/-

responses while progressing from stimulus 1 to stimulus 10. As a result, two additional

subjects (1HR/1LR) were excluded, since they consequently perceived all test stimuli as

/dAk/. (For these subjects, the proportion of /dAk/ answers varied between 84 and 100%,

regardless of the presented stimulus.)

For the remaining 56 subjects, a paired wise Repeated Measures MMA with

‘proportion /dAk/ responses’ as dependent variable, group as between-subject variable (HR

versus LR), stimulus as within-subject variable (1 to 10) and with the same covariates as

mentioned above, revealed no significant effect for group (p = .52), a significant effect for

stimulus (p < .0001) and no significant group x stimulus interaction effect (p = .59). Post-hoc

analysis revealed that for every single stimulus the group difference was insignificant. In

contrast, while comparing the child data with an adult group (n = 11), a Repeated Measures

MMA indicated that both the HR and the LR group differed significantly from the adult group

(p < .01) with the group x stimulus interaction being significant as well (p < .01).

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9 10

Stimulus number

Pro

porti

on /d

Ak/

resp

onse

s

HR group

LR group

Adults

Fig 2: Mean identification functions relating the proportion /dAk/ responses to the stimulus number of the place-

of-articulation continuum [HR group: n = 29, LR group: n = 27; adult group: n = 11].

Furthermore, each individual identification curve was submitted to a logistic

transformation in order to estimate the phoneme boundary (i.e. the interpolated 50 % cross-

over point or Point of Subjective Equivalence) and the slope in this point. A logistic function

relating percentage /dAk/-responses to stimulus level (1 to 10) was fitted to the data using the

Nonlinear function of SAS-9.1 and a maximum likelihood criterion. The fitted function had

the following form:

65

PC = 100 / [1 + exp ( 4 * slope * ( PSE - stim)/ 100)]

where stim is the presented stimulus (1 to 10), PSE is the Point of Subjective Equivalence (50

% /bAk/-responses, 50 % /dAk/-responses) and slope is the slope parameter of the logistic

function. Both the PSE and the slope of the fitted function are informative. The PSE indicates

the phoneme boundary and the slope indicates the range of uncertainty in distinguishing one

phoneme category from another. A high slope value indicates a small uncertainty range and

suggests a highly consistent ability to categorise a speech contrast; whereas, a low slope value

indicates a large range of uncertainty and suggests difficulties in identifying the speech

stimuli (Maassen et al., 2001).

Psychometric functions could be fitted for all 56 participants. However, for 8 subjects

(4HR/4LR) the phoneme boundary was estimated to be below stimulus 1, since they even

perceived the /bAk/ endpoint as /dAk/ in more than 50 % of the presentations. These subjects

were excluded from further analysis. For the remaining 48 subjects a paired wise MMA with

the same covariates as mentioned above and with the inversed standard error of the estimated

parameters as a weight variable showed no significant differences between both groups for

PSE (p = .89) and (log-transformed) slope (p = .21). Without adding any covariates the slope

difference resulted in a p-value of .08. In contrast, in a weighted MMA the adult group

differed significantly from both child groups regarding PSE (p < .001), and (log-transformed)

slope (p < .0001), indicating that the adult phoneme boundary had moved up towards /dAk/

and showed a more abrupt perceptual transition (see Table 1 and Fig. 2).

Since many toddlers failed to show perfect performance at the end points of the

continuum, a second and more accurate model was also fitted to the data, taking into account

the variability in identifying the original /bAk/ and /dAk/ stimuli:

PC = min + (max - min) / [1 + exp ( 4 * slope * ( IP - stim)/ (max - min))]

where stim is the presented stimulus (1 to 10), min is the estimated minimum asymptote

(= estimated performance for the original /bAk/ stimulus), max is the estimated maximum

asymptote (= estimated performance for the original /dAk/ stimulus), IP is the estimated

inflection point between min and max and slope is the slope parameter of the logistic

function. Using the parameters estimated by this function, the results comparing children and

66

adult groups were virtually identical to the ones obtained with the simpler function described

above.

Categorical perception – Discrimination

Again, only subjects achieving 70 % correct responses or more on the pre-test were

included in the analysis. This resulted in the removal of data of 14 subjects (6HR/8LR). Most

of these subjects were also excluded from the identification analysis because they revealed too

many errors in that pre-test too or because they did not pass the PSE>1 criterion.

Basically, there are two commonly used methods to score results when assessing

categorical perception with a discrimination task (see Serniclaes et al., 2004). One possibility

is to take into account only the responses to pairs of different stimuli and to calculate the

proportion of (correctly) assigned different responses for every pair. The other possibility is to

take the mean of the correct responses to both the different and the identical pairs

(= proportion ‘hits’ and ‘correct rejections’). Actually, this second option is the most

interesting, since it provides a genuine measure of discriminability irrespective of response

bias (the d’ coefficient). Hence, in the current study this second type of scores was calculated.

In Fig. 3 the mean “discriminability’ functions for the HR, LR and adult group are

displayed. For every stimulus pair a distribution free and response-bias free index of

discriminability (–ln eta) was calculated according to signal detection theory (Wood, 1976):

-ln eta = ½ ln [P(Hit) * P(Correct Rejection) / P (Miss) * P (False Alarm)]

Discriminability equals zero when performance is at chance. It increases with increasing

discrimination accuracy without influences of bias to respond ‘same’ or ‘different’.

Discriminability is maximal at the -ln eta value of 4.6; this value is obtained when the

probabilities for the correct ‘difference’ (= Hit) and correct ‘same’ responses (= Correct

Rejection) are both 0.99, the value assigned (for computational purposes) when the actual

probabilities were 1.00 (Maassen et al., 2001).

A paired wise Repeated Measures MMA with ‘discriminability’ as dependent variable,

group as between-subject variable (HR versus LR), stimulus pair as within-subject variable

(1-4, 2-5, etc.) and with the same covariates as mentioned above, showed a marginally

significant effect for group (p = .056), a significant effect for stimulus pair (p = .0004) and no

significant group x stimulus pair interaction effect (p = .48). Post-hoc analysis revealed that

67

the group difference was significant for pairs 4 -7, 5-8 and 6-9. Without adding any

covariates, the global repeated group difference also turned out to be significant (p = .03).

Further, for every individual a mean discriminability score over all pairs was calculated (see

Table 1). MMA with covariates added showed no significant group difference (p = .07), but

without any covariates this difference was significant in favour of the LR group (p = .04).

Comparison of the child data with the adult group (n = 11) showed that the HR and LR group

differed significantly from the adult group (p < .001 and p < .01 respectively) with the

interaction group x stimulus pair being significant as well (p < .0001). Regarding the mean

discriminability scores, the difference between the HR and LR group on the one hand and the

adult group on the other was also significant (p < .001 and p < .01 respectively).

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

1-4 2-5 3-6 4-7 5-8 6-9 7-10Stimulus pair

Dis

crim

inab

ility

HR group

LR group

Adults

Fig. 3: Mean discrimination scores (- ln eta) as a function of stimulus pair [HR group: n = 25, LR group: n = 23;

adult group: n = 11].

Relations between speech perception and phonological and auditory processing

In table 2 correlations are presented between speech perception ability on the one hand

and auditory and phonological processing on the other. Since SRT and the slope of the

phoneme boundary are the most representative parameters for our speech perception tasks,

only correlations with these parameters are presented. Correlations were partialed out for

individual differences in nonverbal intelligence and the inversed standard error of the

estimated speech parameter was added as a weight variable.

68

The results of the speech-in-noise test (SRT) were significantly related to phonological

awareness, implying that a better speech-in-noise perception corresponded with better

phonological processing. Similarly, for categorical perception the abruptness of the phoneme

boundary (expressed by the slope parameter) was significantly related to phonological

awareness. This implies that a higher slope value, indicative of a small uncertainty range in

distinguishing one phoneme category from the other, was related to better phonological

awareness5. Considering the relations between low-level auditory processing and speech

perception, the positive relation between tone-in-noise detection and speech-in-noise

perception (SRT) was the most straightforward. Further, sensitivity to FM was also related to

speech-in-noise perception, and tone-in-noise detection was related to the abruptness of the

perceptual shifting from one phoneme category to the other.

Table 2

Relations between speech perception and phonological and auditory processing: Weighted Pearson correlation

coefficients partialed out for individual differences in nonverbal IQ.

Speech Reception Threshold

(n = 60)

Slope Phoneme Boundarya

(n = 47)

GAP detection a 0.07 -0.03

FM detection a 0.27* -0.22

TN detection 0.35** -0.30*

Phonological awareness -0.44*** 0.53***

Rapid automatic naming -0.08 -0.05

Verbal short-term memory -0.06 0.16

Note. a Scores were log10-transformed prior to analysis.

* p < .05, ** p < .01, *** p < .001

Individual deviance analysis

As mentioned by Manis and colleagues (1997), there are two possible explanations for

the mixed pattern of speech-perception results observed in literature. First, the group

differences between normal reading and reading-impaired subjects are real, but small and

therefore difficult to detect. Second, group comparisons might mask larger speech-perception

deficits that are found only in specific subgroups of dyslexics. To verify this second option,

we carried out analyses at the subject level. To decide which individual did and did not show

abnormal performance, we adopted the two-step criterion as suggested by Ramus et al.

69

(2003). Applying this procedure, the criterion for deviance was placed at 1.65 standard

deviations of the ‘purified’ mean of the LR-group, after first having excluded all deviant LR-

subjects (by applying a similar 1.65 SD criterion, usually resulting in the removal of one or

two deviant LR subjects). A distribution analysis on the speech results of the ‘restricted’ LR-

group confirmed the normality of the data. Hence, the 1.65 SD deviance criterion corresponds

to the fifth percentile. For the various speech measures, Table 3 presents the number of

deviant subjects in each risk group. Although the HR group generally presented a higher

proportion of deviant subjects, the difference was not significant.

Table 3

Individual deviance analysis for speech perception tests.

HR

# of deviants

LR

# of deviants

Fisher Exact

(2-sided p-value)

Speech-in-noise

SRT

Identification

Slope of phoneme boundarya

Discrimination

Mean discriminability

4 of 30 (13%)

11 of 25 (44%)

4 of 25 (16%)

1 of 31 (3%)

6 of 23 (26%)

1 of 23 (4%)

.20

.23

.35

Note. a Scores were log10-transformed prior to analysis.

Individual Z-scores for speech perception parameters (i.e. SRT and the Slope of

phoneme boundary) and for phonological and auditory measures, relative to the mean of the

‘restricted’ LR group are depicted in Appendix. Scores deviating more than 1.65 SD (in the

deficient direction) are printed in bold. Inspection of the individual scores reveals that there is

no straightforward regularity or tendency between these measures. For the speech perception

data for instance, deficits in categorical perception and speech-in-noise perception appear to

be largely mutually unrelated. The same applies for deficits in phonology and low-level

auditory processing: some subjects differ only on one of the tasks, others on two or three, but

without consistency (see also Boets et al., 2006). To investigate the relation between speech

perception deficiencies on the one hand and phonological and auditory deficiencies on the

other, we constructed some additional variables indicating whether a specific skill is impaired

or not. Because of the high intrasubject variability (even for scores measuring the same

overall skill), averaging across these measures might underestimate the presence of a deficit

70

in this skill. Instead, a skill was labeled as deficient if at least one of its measures scored

beyond the 1.65 SD cutoff criterion. As such, PHONOLOGY is a binary variable indicating

whether a subject demonstrates a deficit in phonological awareness, rapid automatic naming

or verbal short-term memory. Similarly, AUDITORY is a binary variable indicating whether

a subject demonstrates a deficit in at least one of the auditory measures (viz. gap-in-noise

detection, tone-in-noise detection and FM detection) and SPEECH is a binary variable

indicating whether a subject shows deviant scores for SRT or for the slope parameter of the

identification function. As mentioned above, some subjects were not able to pass the 70%

pretest or PSE>1 criterion of the identification task. Since we felt this failure might also offer

important information concerning speech perception abilities, these subjects were also labeled

as deviant on the SPEECH variable.

To explore the interrelation between deficiencies in these different skills, we

calculated the number of subjects showing isolated versus (partially) overlapping deficits. Fig.

4 depicts a schematic overview of this analysis. Results indicate that there indeed exists a

small subgroup of subjects showing consistent deficits across auditory, speech and

phonological processing. However, and more importantly, the overview convincingly

demonstrates that the relation between these deficits is far from clear. Contrary to the causal

hypothesis, there are also many subjects with severe deficits in one of the skills while being

completely unimpaired in the other ones, or subjects showing only partially overlapping

deficits.

Fig. 4: Distribution of auditory, speech perception and phonological deficits in the total sample of 62 preschool

children. The number of impaired subjects is indicated both in absolute numbers and in percentages.

SPEECH PHONOLOGY

AUDITORY

11 (18%)

3 (5%)

5 (8%)

4 (6%)

9 (15%)

7 (11%)

6 (10%)

No deficits = 17 (27 %)

71

Discussion

In this study we tested speech perception and phonological and auditory processing in

two groups of five-year-old preschool subjects, i.e. a genetically high risk and a genetically

low risk group. Data were compared between both risk groups, and in addition speech

perception abilities were contrasted with those of adult subjects.

Comparing child and adult speech perception data, we observed a significant and

consistent difference to the advantage of the adults. Adults demonstrated better speech-in-

noise perception thresholds, a better categorical perception with a shifted and more abrupt

phoneme boundary, and better general discriminability. It is well recognized that preschoolers

perform more poorly than older children and adults across all kinds of psychoacoustic tasks

(Wightman & Allen, 1992). This weaker performance has also been demonstrated for the

specific speech perception tasks at hand (e.g. Allen & Wightman, 1994; Elliot, 1979; Hazan

& Barrett, 2000; Johnson, 2000; Mills, 1975; Serniclaes et al., 2004). However, it is still an

ongoing discussion whether this weaker performance reflects an underlying sensorial

immaturity or rather has a cognitive origin (e.g. non-optimal listening strategies, fluctuations

in attention, etc.).

Regarding the child data, comparing both risk groups, results are less straightforward.

With respect to the speech-in-noise experiment, the repeated measures analysis revealed that

the HR group perceived significantly less words correctly than the LR group, particularly in

the most difficult listening condition. This observation was also confirmed by significant

group differences for the estimated SRT and slope parameters. Moreover, the steeper slope in

the HR group indicated a faster deterioration of performance while progressing from easy

towards difficult listening conditions. These results are in line with earlier data reporting a

speech-in-noise perception deficit in subjects with dyslexia and/or language impairments

(Brady et al., 1983; Bradlow et al., 2003; Chermak et al., 1989; Stollman et al., 1994). In

particular, the observation that the HR children were more adversely affected by a decreasing

signal-to-noise ratio is consistent with findings by Bradlow et al. (2003) and Stollman et al.

(1994). Interestingly, a similar noise exclusion deficit in dyslexic subjects has recently also

been described in the visual modality (Sperling, Lu, Manis & Seidenberg, 2005). Moreover,

these authors demonstrated that visual contrast thresholds in high-noise conditions were

significantly related to several language measures, suggesting that the dyslexic deficit may

not be specific for auditory speech-in-noise perception, but might be a cross-modal deficit in

noise exclusion.

72

With respect to the categorical perception task, both child groups performed similarly

on the identification task, eventuating into analogous results for the repeated measures

analysis and for the estimated phoneme boundary and slope. However, we observed a

(marginally) significant difference to the advantage of the LR group on the more sensitive

discrimination task (although the differing stimulus pairs did not particularly include the ones

crossing the phoneme boundary). Consequently, these results corroborate only partly with the

mass of evidence provided by most other studies (see introduction section). A straightforward

explanation for this less pronounced group difference is obviously the fact that we did not

study a well-defined clinical group but only an at-risk group that still might show substantial

overlap with the control group. Alternatively, it is plausible that differences in categorical

perception will become more evident as development continues (possibly as a consequence of

differently developing phonological and reading abilities, as suggested by Serniclaes et al.,

2001). This would imply that the identification and discrimination curves of normal reading

children will evolve towards those of the adult group, whereas those of the dyslexic children

will continue lagging behind. Indeed, Hazan and Barrett (2000) demonstrated that the

development of phonemic categorization extends well beyond the age of 12 years before

reaching adult levels. Moreover, Maassen and colleagues (2001) revealed that dyslexic

children perform worse than age-matched control children but similar to reading-age younger

controls on a voicing identification task, suggesting a developmental delay associated with

reading-level instead of a qualitative difference. In a similar way, Bishop and McArthur

(2004; McArthur & Bishop, 2004) and Wright and Zecker (2004) have hypothesized that

dyslexic subjects might not suffer from a specific chronic perceptual deficit, but rather from a

more general and passing maturational delay. Accordingly, a task would only have enough

differential sensitivity when administered in the age period the measured skill is sharply

improving in typically developing subjects, and maybe this might not yet have been the case

with categorical perception.

Considering the relations between these variables, it should be reiterated that in a

previous paper describing the same sample of preschoolers (Boets et al., 2006), we already

reported a considerable correlation between auditory spectral processing and phonological

awareness [r(FM, Phonological Awareness) = -0.48, p < .0001; r(TN, Phonological

Awareness) = -0.35, p < .01; Spearman correlations partialed out for individual differences in

age and non-verbal IQ]. Likewise, in the actual study we also observe a significant relation

between speech perception on the one hand, and auditory processing and phonological

awareness on the other. Moreover, since these relations are already present in preschool

73

children that have not yet learned to read, our data indicate that the observed interrelations

precede literacy development and are not merely the result of differences in reading

experience or reading skills.

Although our correlation analysis indicates that there are some general relations

between aspects of auditory processing, speech perception and phonological ability, the

results of the individual deviance analysis demonstrate that the theoretical implications of

these correlations should not be overestimated. Actually there was only a very small subgroup

showing consistent deficiencies across all processing skills, while the majority of subjects

showed isolated or only partially overlapping deficits. This means for instance that contrary to

the hypothesized ontogenetic pathway, there was a substantial group of subjects showing

considerable auditory processing problems without presenting any problems in speech

perception or phonology. In the same way, there were many subjects presenting speech

perception problems despite intact auditory processing or without interference with the

phonological development. Further on, there was also a group of subjects showing

phonological problems without evidence of auditory or speech perception problems. In sum,

the overall picture indicated that about 30 % of the subjects were unimpaired, 30 % presented

an isolated deficit, 30 % presented a deficit in two skills and a small subgroup of about 10 %

presented consistent deficiencies across the three processing skills. Considering the

proportion of HR subjects in these differently affected groups, an interesting pattern was

revealed: whereas the HR subjects only represented 32 % of the unaffected group, their

proportion grew to 56 and 61 % of the groups showing respectively one and two deficits, and

they ultimately represented up to 100 % of the group showing deficits across all the

processing skills.

Of course, one has to be cautious while interpreting these data. For instance, we have

to acknowledge that it is not realistic that about 70 percent of the children in our combined

HR and LR sample would present one or more deficits in the assessed skills. This is certainly

an overestimation due to the liberal criterion applied. Moreover, our data only represent a

momentary measurement of skills that are still highly developing at this age. Therefore,

interindividual differences in speed of maturation might also have influenced the observed

number of aberrantly scoring subjects.

Several explanations for the relatively small group of subjects showing overlapping

deficits can be proposed. First, it might be argued that the administered tasks were not

sensitive enough to detect very subtle deficits. However, it is important to stress that the tasks

at hand have not been chosen accidentally, but because literature has proven them to

74

differentiate reliably between reading-impaired and normal reading subjects (regarding the

auditory tasks, see e.g. Talcott et al., 2003; Van Ingelghem et al., 2005; regarding the

discriminative power of the phonological tasks, see e.g. Scarborough, 1998). Second, since

the assessed skills are still maturing at this preschool age, it is very well possible that they will

influence each other during further literacy development, resulting in an increased

interrelation in future. Third, it might be necessary to revise or refine the theoretically

suggested developmental pathway in some way. Indeed, parallel to the growing evidence that

only a small subgroup of dyslexics presents low-level auditory problems (Ramus et al., 2003;

Rosen, 2003), it becomes increasingly clear that also only a minority of dyslexics shows

speech perception problems (Manis et al., 1997). Joanisse and colleagues (2000) for example

studied three groups of dyslexics (a language impaired, a phonologically impaired and a

globally reading delayed group) and demonstrated that speech perception problems only

occur in dyslexic subjects showing additional expressive and/or receptive language problems.

They also observed several dyslexics with phonological impairments whose speech

perception was normal, confirming that phonological impairments are not necessarily

secondary to a speech perception deficit. Accordingly, these authors argue that although the

language impaired and phonologically impaired dyslexic groups exhibit similar deficits in

phonology and reading, their causes may differ, with one involving a speech perception

deficit, and the other involving higher-order differences in phonological representation or

processing. The third globally reading delayed group of their sample did not show any

deficits in speech perception or phonology at all, suggesting still another cause for their

reading deficit. Consequently, together with these authors we would contend that in order to

fully understand the deviant developmental pathway of dyslexia, it will be necessary to take

into account the considerable heterogeneity of this group and to recognize that subjects

presenting similar patterns of reading problems must not necessarily have the same

underlying deficits. In this way it is plausible that the hypothesized causal path (from auditory

processing to speech perception to phonology to reading) might be true for a specific

subgroup of dyslexics, but it probably is not representative for the whole population of

reading impaired subjects.

75

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84

Notes

1 Manuscript submitted for publication in Brain and Language. Co-authors: P. Ghesquière, A.

van Wieringen & J. Wouters. 2 Traditional statistics (e.g. ANOVA and regression analysis) require independence of errors,

but this assumption is not met in a dependent sample design. Mixed models allow modelling

this dependent nature of the data. By entering pair number as a random factor in all analyses,

these models are an extension of the paired t-test, while offering the possibility to include

more sophisticated options such as the incorporation of covariates and repeated measurement

analyses. 3 All MMA’s used the Kenward-Roger degrees of freedom estimation method that is more

robust against violations to assumptions of normality (Kowalchuk, Keselman, Angina &

Wolfinger, 2004). 4 This finding should not be too surprising: in the study of van Beinum et al. (2005) (using

identical stimuli and a similar paradigm) normal reading adults also misperceived the /bAk/

stimulus as ‘dak’ in 10% of the presentations. 5 Noteworthy, another indicator for a relation between categorical perception and

phonological awareness is the observation that almost all excluded subjects (based on the

>70% on the pretest or PSE>1 criterion) score bad on the phonological awareness factor (the

average score of all excluded subjects was .89 SD’s below the mean of the LR group).

Appendix: Individual Z-scores relative to the mean of the ‘restricted’ LR group for speech perception, phonological ability and auditory sensitivity. Scores deviating more than 1.65 SD’s (in the direction that indicates deficiency) are depicted in bold.

Nr Group SRT Slope Phoneme

Boundarya TN GAPa FMaPhon

Awareness RANVerbal

STM AUDITORY PHONOLOGY SPEECH

1 HR -0.41 -1.15 -0.11 -0.16 1.07 0.46 0.27 0.56 3 HR 1.31 0.68 -0.90 -1.46 -1.64 0.23 0.32 DEVIANT 5 HR 0.73 -2.53 0.42 -0.92 2.08 -0.83 0.85 -0.36 DEVIANT DEVIANT 7 HR -0.47 -2.26 0.69 -0.68 0.19 -1.61 -0.63 1.15 DEVIANT 9 HR 0.00 0.41 -1.87 -1.16 -1.34 0.89 -0.56 -1.03 11 HR 1.99 0.85 1.33 0.73 -0.23 -0.51 -0.02 DEVIANT 13 HR 1.55 -0.40 0.34 -0.33 -0.32 0.20 0.40 -0.10 15 HR 0.17 -2.19 -2.94 -0.70 0.58 -0.13 0.45 0.68 DEVIANT 17 HR 2.59 -0.11 3.12 1.59 -2.29 0.07 -0.17 DEVIANT DEVIANT DEVIANT 19 HR 0.48 -7.48 0.51 -0.07 -0.57 -2.68 0.74 -0.95 DEVIANT DEVIANT 21 HR 0.01 2.65 -0.91 -0.69 0.26 -0.25 -0.25 -0.32 23 HR 0.27 0.51 0.14 -0.48 1.05 1.36 -1.24 DEVIANT 25 HR 1.49 1.39 -1.15 -2.40 0.70 0.08 DEVIANT DEVIANT 27 HR -0.89 2.76 0.95 -0.50 4.43 -0.91 -1.14 0.22 DEVIANT 29 HR 0.66 4.58 -0.01 4.09 1.64 -2.02 -0.64 -0.03 DEVIANT DEVIANT 31 HR 1.14 -8.89 0.52 -0.28 0.90 -3.78 0.44 -0.06 DEVIANT DEVIANT 33 HR 1.59 0.78 -2.40 2.29 -1.17 -1.86 0.63 0.72 DEVIANT DEVIANT 35 HR 1.31 -0.54 4.12 4.22 6.75 -2.71 -0.80 -0.91 DEVIANT DEVIANT 37 HR -2.19 4.42 -2.49 0.17 -1.59 1.28 -0.02 -2.55 DEVIANT 39 HR 0.26 -0.26 -2.23 -1.08 -1.98 -0.02 -2.56 -2.14 DEVIANT 41 HR 1.04 1.29 1.58 1.15 0.17 -0.70 -0.95 0.30 43 HR 2.21 -0.52 -1.51 -0.68 0.10 1.32 -1.34 -0.96 DEVIANT 45 HR -1.25 -3.17 0.96 11.36 2.95 -0.31 -0.61 1.03 DEVIANT DEVIANT 47 HR 0.47 -3.46 2.19 -0.89 0.54 -2.43 0.54 2.24 DEVIANT DEVIANT DEVIANT 49 HR -0.24 -4.49 0.87 7.44 5.34 -4.39 0.01 -1.64 DEVIANT DEVIANT DEVIANT 51 HR 0.51 2.89 2.07 3.29 -1.07 -3.03 -0.88 DEVIANT DEVIANT DEVIANT 53 HR 0.91 -2.17 1.05 -0.16 0.51 -4.67 -1.13 -0.51 DEVIANT DEVIANT 55 HR 0.39 -3.12 0.87 0.01 5.49 -0.92 -0.77 -0.78 DEVIANT DEVIANT 57 HR 2.46 -7.88 1.31 -0.72 3.15 -2.22 -1.70 -2.00 DEVIANT DEVIANT DEVIANT 59 HR -0.43 0.59 -0.72 0.08 0.82 -1.65 -0.13 -0.74 DEVIANT 61b HR -0.93 -0.64 -1.46 2.52 -0.87 DEVIANT DEVIANT

Appendix, continuation

Nr Group SRT Slope Phoneme

Boundarya TN GAPa FMaPhon

Awareness RANVerbal

STM AUDITORY PHONOLOGY SPEECH

2 LR 0.61 0.69 -0.72 -0.32 1.15 -0.57 0.36 DEVIANT 4 LR 0.99 -1.65 0.78 0.13 1.39 -0.25 -1.00 0.59 DEVIANT 6 LR -0.64 -0.53 -0.74 -0.68 -0.40 -1.14 0.87 1.12 8 LR 0.46 2.88 2.01 0.27 1.38 -2.57 -1.97 -0.39 DEVIANT DEVIANT

10 LR 0.91 -2.60 1.57 0.75 -0.73 1.00 -1.68 1.19 DEVIANT DEVIANT 12 LR 0.57 -0.20 0.27 0.56 -3.01 0.40 -2.83 DEVIANT DEVIANT 14 LR 0.15 -2.86 -0.38 -0.57 0.47 0.67 -0.87 0.80 DEVIANT 16 LR -0.47 7.59 -2.14 0.07 -0.10 0.87 0.22 -0.88 18 LR 0.67 -0.27 -0.49 -0.17 0.99 0.15 -0.20 -0.17 20 LR 2.11 -1.08 0.45 0.68 0.46 1.13 -2.53 DEVIANT DEVIANT 22 LR 0.06 0.47 -1.19 0.37 0.00 -0.12 1.09 -0.06 24 LR 0.48 -2.59 0.87 0.12 -0.41 -0.71 -0.40 -1.43 DEVIANT 26 LR -0.61 0.51 1.05 6.15 5.39 -1.30 1.88 -0.52 DEVIANT 28 LR -1.92 -0.04 -1.63 -0.61 0.07 -0.22 0.00 1.39 30 LR 0.38 0.43 -0.59 1.31 -3.14 0.05 -0.41 DEVIANT DEVIANT 32 LR -1.93 2.97 -0.83 -1.42 -1.71 1.38 -1.17 -1.34 34 LR 0.60 1.05 2.31 0.42 0.39 0.37 -0.55 DEVIANT DEVIANT 36 LR -0.98 3.03 -0.64 -0.07 -1.30 0.55 -0.91 1.66 38 LR -0.44 0.56 0.61 -0.49 -0.57 0.61 1.34 -0.68 40 LR -0.90 3.58 -1.63 -1.08 -2.05 1.99 -0.88 0.14 42 LR 1.41 0.87 0.14 4.72 -1.76 -3.44 -0.35 DEVIANT DEVIANT DEVIANT 44 LR 1.01 2.94 0.34 0.78 6.63 -1.34 0.14 1.46 DEVIANT 46 LR 0.26 0.96 -0.65 1.93 -0.20 -0.13 -1.62 DEVIANT DEVIANT 48 LR 0.69 0.09 0.61 0.89 -0.72 -0.09 1.11 0.62 50 LR -2.22 -3.02 1.31 3.46 -0.83 0.38 0.48 -0.68 DEVIANT 52 LR 1.17 5.58 -0.64 -0.07 -1.02 0.62 -0.86 0.00 54 LR 1.53 -1.63 0.42 -0.41 1.35 -1.18 0.66 -0.31 56 LR -0.71 -3.49 0.87 9.92 -0.27 1.18 -0.15 -1.49 DEVIANT DEVIANT 58 LR -0.04 0.12 0.60 -1.03 0.89 -0.27 2.03 -0.83 60 LR 1.34 -4.75 -1.34 -0.75 -0.48 -2.00 0.16 0.17 DEVIANT DEVIANT 62 LR -0.61 -0.10 -0.71 -0.49 -0.78 -1.15 2.19 DEVIANT Note. a Scores were log10-transformed prior to analysis. b Because of missing scores on two phonological tests, no phonological factors could be calculated for subject 61. Notwithstanding, based on the scores of the administered tests, it was determined that this subject showed a deficit in phonological awareness. Note. For the Phoneme Boundary Slope, Phonological Awareness, RAN and Verbal STM a higher Z-score indicates better processing; in contrast, for all other measures a higher Z-score indicates reduced sensory sensitivity.

87

Manuscript 3

Coherent motion detection in preschool children at family risk for dyslexia1

Abstract

We tested sensitivity to coherent motion (CM) in random dot kinematograms in a group of five-year-old

preschool children genetically at risk for dyslexia, compared to a group of well-matched control children. No

significant differences were observed, either in a group analysis or in an individual deviance analysis.

Nonetheless, CM-thresholds were significantly related to emerging orthographic skills. In a previous study on

the same subjects (Boets, van Wieringen, Wouters, Ghesquière, 2006), we demonstrated that both risk groups

already differed on measures of phonological awareness and letter knowledge. Moreover, auditory spectral

processing (especially 2 Hz FM detection) was significantly related to phonological ability. In sum, the actual

visual and previous auditory data combined, seem to suggest an exclusive relation between CM sensitivity and

orthographic skills on the one hand, and FM sensitivity and phonological skills on the other.

Introduction

Developmental dyslexia is a specific failure to acquire reading and spelling skills

despite adequate intelligence and education, affecting around 5-10 % of children and adults.

The predominant etiological view postulates that dyslexia results from a phonological deficit

(Snowling, 2000). However, extensive research during the last decade also demonstrated a

specific sensory processing deficit in individuals with dyslexia and it has been suggested that

this deficit might be causal to both the observed phonological and literacy problems (Farmer

& Klein, 1995; Stein, 2001). To investigate the assumed causality of this sensorial deficit

hypothesis we assessed auditory and visual processing in two contrasting groups of five-year-

old preschool children, a genetically high risk and a genetically low risk group. In a previous

paper (Boets et al., 2006) we reported the absence of a significant group difference for any of

three administered auditory measures, in the presence of a significant difference for

phonological awareness and letter knowledge. However, spectral auditory tasks (particularly 2

Hz frequency modulation detection) turned out to be highly significantly related to

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phonological awareness. In this paper, we will focus upon sensory processing in the visual

modality, assessed in the same group of preschool children. In particular, we consider the

question whether a deficit in coherent motion processing may already be observable in

preschool children at risk of dyslexia and we investigate the relationship between motion

processing and developing literacy skills.

Within the visual modality, dyslexia research has mainly focused upon sensory

processing in the magnocellular visual pathway. Early studies using stimuli that assess the

peripheral visual system (e.g., contrast sensitivity and flicker sensitivity paradigms)

demonstrated that dyslexics tend to show a deficit in processing stimuli with low spatial and

high temporal resolution (for a review, see Lovegrove, 1996, but see Skottun, 2000 for a

critical revision). More recently, interesting results have also been obtained with stimuli that

imply higher level magnocellular functioning such as coherent motion detection tasks (CM).

These tasks, relying predominantly upon processing in area V5/MT of the cortex, have proven

to differentiate relatively reliable between groups of dyslexic and normal reading subjects

(Witton et al., 1998; Raymond & Sorensen, 1998; Talcott, Hansen, Assoku, & Stein, 2000;

Hansen, Stein, Orde, Winter, & Talcott, 2001; Ridder, Borsting, & Banton, 2001; Talcott et

al., 2003; Cornelissen, Richardson, Mason, Fowler, & Stein, 1995; Everatt, Bradshaw, &

Hibbard, 1999; Van Ingelghem, Boets, van Wieringen, Ghesquière, & Wouters, 2004;

Wilmer, Richardson, Chen, & Stein, 2004). Moreover, functional imaging studies have

confirmed that activation of area V5/MT in response to coherent motion stimuli was not as

robust in dyslexics compared to controls (Eden et al., 1996). Demb, Boyner and Heeger

(1997) have even demonstrated a reliable relation between the magnitude of the

hemodynamic BOLD-response in extrastriate area MT and overall reading skills in dyslexic

subjects. In psychophysical studies too, sensitivity to motion stimuli has been related to

(nonword) reading ability (Van Ingelghem et al., 2004; Witton et al., 1998, Talcott et al.,

1998), orthographic ability (Talcott et al., 2000; Talcott et al., 2002; Van Ingelghem et al.,

2004) and letter position encoding (Cornelissen et al., 1998). However, evidence of a motion

coherence deficit in dyslexia is not yet unequivocal since some studies failed to find

differentiating thresholds (Kronbichler, Hutzler, & Wimmer, 2002; Ramus et al., 2003;

Amitay, Ben-Yehudah, Banai, & Ahissar, 2002; Hulslander et al., 2004). Moreover, a deficit

in motion processing might not be an exclusive characteristic of dyslexia, since it has also

been demonstrated in other developmental disorders like for example autism (Milne et al.,

2002) and Williams-syndrome (see e.g., Atkinson et al., 1997).

89

Regarding the specific mechanism by which coherent motion sensitivity may limit

normal literacy development, there is still much speculation. Since CM-thresholds are a

robust measure of magnocellular processing and since this visual subsystem is mostly

involved in encoding spatial information, it is probable that poor magnocellular functioning

might result in uncertainty about letter position while reading and writing (Cornelissen et al.,

1998). Furthermore, a magnocellular deficit has also been related to binocular instability and

poor eye movement control, visual attention and visual search – all factors that might interfere

with the development of orthographic skills and subsequent reading and spelling skills (Stein

& Talcott, 1999; Stein, 2001; Talcott et al., 2000).

Notwithstanding the considerable empirical evidence that CM-thresholds are able to

differentiate reliably between adult and school-aged dyslexic and normal reading subjects, the

differentiating and predictive power of this task has never been investigated in preschool

children. In this study we want to address this issue. Furthermore, to investigate the specific

relation between sensory processing and different aspects of literacy development, we will

also integrate the previously administered phonological measures and the 2 Hz FM detection

thresholds in our correlation analyses. This has been done since in a series of former studies

CM detection has gradually been linked to FM detection (see e.g. Witton et al., 1998; Talcott

et al., 2000; Talcott et al., 2002; Talcott et al., 2003). According to Talcott, Witton and

colleagues both psychophysical tasks could be regarded as ‘dynamic’ stimuli tasks by relying

upon long-duration stimuli that require the perception of a dimension changing in time

(‘perception of rate’). While CM detection depends on the successful detection and

integration of local motion signals over both time and space, FM detection depends on

tracking the dynamic changes in the frequency of a tone over time. Interestingly, in some

recent studies where FM and CM-detection tasks have been administered to the same subjects

(school-aged children), it has been demonstrated that orthographic skills co-vary most

strongly with CM sensitivity, whereas phonological skills co-vary most strongly with FM

sensitivity (Talcott et al., 2000; Talcott & Witton, 2002). In this study we will explore

whether these specific relations might already be present in preschool subjects.

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Method

Participants

Sixty-two five-year-old children attending the last year of kindergarten were included

in the study (36 boys / 26 girls). Half of the participants were children of ‘dyslexic families’,

the so-called high-risk group (HR); the other half were control children of ‘normal reading

families’, the so-called low-risk group (LR). All children were native Dutch speakers without

any history of brain damage, long term hearing loss or visual problems. Additionally, at the

moment of data collection they did not present any gross deficiencies in visual acuity

(Landolt-C single optotypes Snellen acuity > 0.85) and/or audiology (audiometric pure-tone

average < 25 dB HL). The HR children were selected on a basis of having at least one first-

degree relative with a diagnosis of dyslexia. The LR children showed no history of speech or

language problems and none of their family members suffered any learning or language

problems. For every individual HR child we selected the best matching LR control child

based on five criteria: (1) educational environment, i.e. same nursery school, (2) gender, (3)

age, (4) nonverbal intelligence, and (5) parental educational level. Nonverbal intelligence was

assessed by an adapted version of the Raven Coloured Progressive Matrices (RCPM) (Raven,

Court, & Raven, 1984), a collective non-verbal intelligence test measuring spatial reasoning.

Parental educational level was assessed using the ISCED-scale (International Standard

Classification of Education by UNESCO, 1997), by converting classifications on the original

seven-point scale to a three-point scale. Further details about the participants and the selection

procedure are described in Boets et al., (2006).

Table 1 gives descriptive characteristics of both groups. At the time of collecting the

visual data the mean age for both the HR and LR group was 5 years and 8 months, not being

statistically different (p = .83). The nonverbal IQ scores were slightly above population

average (107 for HR group and 111 for LR group) and did not differ significantly (p = 0.07).

Fisher’s Exact Test also confirms that both groups did not differ in frequency distribution of

the different educational categories (p = .71 for maternal and p = .43 for paternal educational

level).

91

Table 1

Characteristics of participants and coherent motion detection thresholds.

HR LR

Measures M SD M SD p

Nonverbal IQ

Age in months

Best CM1 (% coherence)

Best CM2 (% coherence)

AV CM ½ (% coherence)

107

68

0.19

0.22

0.20

14

3

0.11

0.13

0.12

111

68

0.17

0.19

0.18

13

3

0.08

0.09

0.09

.07 a

.83 a

.28 b

.14 b

.19 b

Note: BestCM1-2: best and second best CM threshold, AVCM ½: average of the two best CM thresholds; a paired t-test; b paired wise MMA controlled for nonverbal IQ, age and parental educational level.

Apparatus

Phonological tests. Phonological skills were assessed by a broad test battery

comprising eight tests. A principal component factor analysis with varimax rotation

confirmed that the battery reflected the three traditional phonological domains: (a)

phonological awareness (AWARENESS): high loadings from first-sound and end-sound

identity task, rhyme identity task and simple rhyme task, (b) rapid automatic naming (RAN):

high loadings from both the colours and objects rapid automatic naming tasks and (c) verbal

short-term memory (VSTM): high loadings from the nonword repetition task and the digit

span forward task. Details about the composition of the battery can be found in Boets et al.,

(2006); in this paper we will only refer to the factor scores.

Productive letter knowledge. This task was intended as a preliminary measure of

literacy development. The sixteen most frequently used letters in Dutch books were presented

on a card and the child had to name each of these letters. Both the sound and the name of a

letter were considered correct.

CM-detection test. For the CM-detection test, children were sitting in a low-luminance

(mesopic) environment at 40 cm distance from an Elo Intuitive 1725L 17’’ touch screen (75

Hz refresh rate) on which the random dot kinematograms (RDK) were displayed. The display

resolution was set to 640 x 480 pixels. The stimuli were generated online by a portable

computer (Dell Latitude C800 and Toshiba Satellite 1400-103) and comprised of two

rectangular patches, each containing 1103 randomly moving high luminance white dots on a

black background (dot size = 1 pixel or 0.07° diameter, dot density = 2.5 dots/deg2,

92

velocity = 7.3 deg/sec, life time = 5 video frames or 200 msec, maximal duration of stimulus

presentation = 6 sec, luminance of dots = 125 cd/m2, luminance of background = 0.39 cd/m2,

Michelson contrast = 99.4 %). At a viewing distance of 40 cm each patch of dots subtended

16 x 27.2° visual angle, separated horizontally by 3.8°. The target patch was segregated into

three horizontal strips (see Gunn et al., 2002); in the middle strip a variable proportion of dots

were moving coherently in horizontal direction, reversing direction every 330 msec – creating

as such the impression of ‘an emerging road in the snow’. All other dots were moving

randomly in a Brownian manner. The two patches were presented simultaneously and the

subject had to identify the patch containing the strip with coherently moving dots. Threshold

was defined as the smallest proportion of coherently moving dots required for detection of the

middle strip with reversing dot motion. Thresholds were estimated using a two-down, one-up

adaptive staircase paradigm, which targeted the threshold corresponding to 70.7 % correct

responses (Levitt, 1971). Percentage coherence in the middle strip of the target patch started

at 100 % and decreased with a factor of 1.16. After four reversals factor 1.14 was used. A

threshold run was terminated after eight reversals and thresholds for an individual run were

calculated by the geometric mean of the values of the last four reversals. For every subject

four thresholds were determined. Prior to data collection, participants were given a short

period of practice, comprising supra-threshold trials, to familiarise them with the stimuli and

the task.

To ensure the child’s attention and motivation we integrated the psychophysical test in

a computer game with animation movies, aimed to transform the abstract meaningless stimuli

into a concrete and well-known ‘daily life signal’. Before administering the CM-detection

experiment children watched an introductory animation movie about a little dog and a little

bear getting lost in the snow (see Fig. 1A). The children were asked to help them find their

way home again by visually inspecting each stimulus patch and reporting which patch

contains the road to get home (inspired by work of Atkinson et al., 2003). Immediately after

the child’s response, corresponding auditory feedback was presented.

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A B

Fig. 1. Screenshots of the animation movies. (A) A frame of the introductory and concluding movie used to

animate the CM detection experiment, (B) a frame of the introductory and concluding movie used for the FM

detection experiment.

FM-detection test. In this test participants had to detect a 2 Hz sinusoidal frequency

modulation of a 1 kHz carrier tone with varying modulation depth. Threshold was defined as

the minimum depth of frequency deviation required to detect the modulation. Modulation

depth decreased with a factor 1.2 from 100 Hz towards 11 Hz, from where a fixed step size of

1 Hz was used. The length of both the reference and the target stimulus was 1000 ms

including 50 ms cosine-gated onset and offset. Stimuli were generated in MATLAB 5.1 and

saved as 16-bit wav-files (sample frequency 44100 Hz) on the hard disc of the same portable

computers as used for the CM-experiment. They were presented using an integrated audio

PC-card and routed to an audiometer (Madsen OB622) in order to control the level of

presentation. The stimuli were presented monaurally over a calibrated TDH-39 headphone at

70 dB SPL with an ISI of 350 ms. FM-thresholds were estimated using a three-interval

forced-choice oddity paradigm embedded within an interactive computer game with

animation movies (see Fig. 1B) (Laneau, Boets, Moonen, van Wieringen & Wouters, 2005).

Similarly to the CM-experiment a two-down, one-up adaptive staircase procedure was used

94

and threshold was calculated as the geometric mean of the values of the last four of eight

reversals. After a short period of practice, three thresholds were determined for every subject.

For the correlation data we present here, the average of the best and second best threshold was

used as an indicator of FM sensitivity. A more detailed description of the stimuli, procedure

and results can be found in Boets et al., (2006).

Data collection was carried out by qualified psychologists and audiologists. Testing

took place in a quiet room at the children’s school. Since the LR child was selected from the

HR child’s classmates, both children could always be tested in exactly the same

circumstances.

Statistical Analysis

Prior to analysis, psychophysical thresholds were log10-transformed in order to obtain

normally distributed data. All results were analysed in a paired wise manner, comparing HR

versus LR group at the level of the matched individuals. Although the groups did not show a

significant difference on any of the matching criteria, we decided to rule out any possible

influence of age, nonverbal intelligence or parental educational level by controlling for these

variables in our analysis. As such, we analysed the data using Mixed Model Analysis (MMA)

with pair number (school) as a random variable (1 to 31) and participant group (HR versus

LR) as the fixed between-subject variable (Littell, Milliken, Stroup, & Wolfinger, 1996). Age,

nonverbal IQ and educational level of both mother and father were added as fixed

(co)variables. Relationships between variables were analysed using Spearman correlation

coefficients, partialed out for the influence of nonverbal intelligence.

Results

A paired wise Repeated Measures MMA with group as between-subject variable (HR

versus LR), threshold run as within-subject variable (run 1 to 4) and with the same covariates

as mentioned above, revealed no significant effect for group (p = .20), a significant effect for

threshold run (p < .0001) and no significant group x run interaction effect (p = .25). Post-hoc

analysis revealed that none of the four CM threshold measures differentiated significantly

between HR and LR group. Furthermore, there was only a significant learning effect from the

first to the second run; the second, third and fourth run did not differ significantly from each

other.

95

Although the Repeated Measures MMA revealed a general learning effect from the

first to the second run, this tendency did not apply to all subjects. For many of them, the first

threshold was better than the second, third or fourth. Moreover, since we are interested in

threshold estimation as an indicator of a subject’s true sensory capability – i.e. the best level

of performance a subject is able to reach, regardless of interfering factors like for example

fluctuations in concentration and motivation – average threshold (or the average of the last

two or three threshold runs) might not be the most appropriate measure. To cope with the high

intrasubject variability which is typical for younger children (see e.g. Wightman & Allen,

1992), a more reasonable estimator is each subject’s ‘best’ performance, or the lowest

threshold of the different runs. For this reason we used the average of the best and second best

threshold as a true indicator of a child’s CM sensitivity. The Spearman rank correlation

between this best and second best threshold estimate appeared to be very satisfactory (rs = .91,

p < .0001), indicating a reliable threshold estimation.

Threshold estimates and test statistics for the best and second best threshold and for

the average of the two best thresholds are given in Table 1. Although the observed difference

was in the expected direction with the HR-group scoring less well than the LR-group, the

coherent motion detection task did not differentiate significantly between the groups. It is

worth mentioning that these null-results were not merely the consequence of applying such a

strict controlling MMA design, as the results were virtually identical when the analysis was

repeated without any covariates added.

Since group comparisons might mask major individual differences, we also carried out

an analysis at the subject level. To decide which individual did and did not show abnormal

performance, we adopted the two-step criterion as suggested by Ramus et al., (2003).

Applying this procedure, the criterion for deviance was placed at 1.65 standard deviations of

the ‘purified’ mean of the LR-group, after first having excluded all deviant LR-subjects (by

applying a similar 1.65 SD criterion, resulting in the removal of two deviant LR subjects). A

distribution analysis on the coherent motion thresholds of the ‘restricted’ LR-group confirmed

the normality of the data. Hence, the 1.65 SD deviance criterion corresponds to the fifth

percentile and is thus a fairly strict criterion. The individual deviance analysis for the

averaged best and second best coherent motion threshold revealed that the proportion of

subjects showing abnormal performance was equal in both groups: four subjects in the HR

group and four subjects in the LR group scored below the fifth percentile. This corresponded

to 13 % of each group.

96

Table 2

Spearman (partial) correlations for total group of subjects (n = 61/62)

Age RCPM

Letter

Knowledge AWARENESS RAN VSTM AV FM ½ AV CM ½

Letter Knowledge

AWARENESS

RAN

VSTM

AV FM ½

AV CM ½

-0.08

-0.08

0.25*

0.07

0.15

0.18

0.26*

0.26*

-0.21 0.19

-0.16

-0.30*

-

0.46***

0.03

-0.02

-0.40**

-0.33**

0.42***

-

-0.08

-0.04

-0.49****

-0.18

0.09

-0.02

-

-0.02

-0.08

0.01

-0.07

-0.10

0.02

-

-0.08

-0.10

-0.36**

-0.48****

-0.12

-0.05

-

0.29*

-0.29*

-0.11

-0.06

-0.04

0.26*

-

Note. Coefficients above the diagonal are partial correlations after removing variance attributable to individual

differences in nonverbal intelligence (RCPM).

* p < .05, ** p < .01, *** p < .001, **** p < .0001

Table 2 shows Spearman rank interrelations between psychophysical thresholds,

phonological ability and letter knowledge, and their relation to age and nonverbal IQ. Neither

CM, nor FM were related to age, and only CM-detection showed a significant relation to

nonverbal intelligence. To exclude the variance attributable to individual differences in

intelligence, all further correlations have been partialed out for the influence of nonverbal IQ.

Table 3 offers similar Spearman rank correlations for both risk groups separately.

Table 3

Spearman partial correlations for LR and HR group separately (after removing variance attributable to individual

differences in nonverbal intelligence)

Letter

Knowledge AWARENESS RAN VSTM AV FM ½ AV CM ½

Letter Knowledge

AWARENESS

RAN

Verbal STM

AV FM 1/2

AV CM 1/2

-

0.46**

-0.14

-0.18

-0.38**

-0.19

0.17

-

-0.04

-0.14

-0.35*

0.09

0.17

-0.11

-

0.30

-0.33*

-0.25

0.01

-0.08

-0.29

-

0.07

-0.16

-0.31*

-0.61****

0.13

-0.16

-

0.08

-0.41**

-0.29

0.11

0.06

0.44**

-

Note. Coefficients above the diagonal concern the LR group (n = 31), coefficients under the diagonal concern the

HR group (n=30).

* p < .10, ** p < .05, *** p < .01, **** p < .001

FM-detection was highly significantly related to Phonological Awareness and – to a

lesser extent - to Productive Letter Knowledge. There was no correlation with Verbal STM

and only in the HR group the correlation with Rapid Automatic Naming was significant. The

relation with Phonological Awareness seemed to be the most robust in the LR-group where 37

% of the variance in phonological awareness could be predicted from sensitivity to FM. CM-

97

detection on the other hand, turned out to be completely unrelated to any phonological

measure, but was significantly related to Letter Knowledge. However, this relation only

seemed to hold for the LR-group.

In previous research CM-detection has been found to be specifically related to

orthographic skills. Since it is impossible to administer a pure orthographic test at this

preschool age, we considered Letter Knowledge as the best approximate measure to obtain an

indication about orthographic ability. After all, resolving a letter knowledge task relies on

recognizing the visual features of the written symbol on the one hand, and retrieving the

corresponding linguistic information on the other. Therefore, Letter Knowledge might be

regarded as a measure that reflects both orthographic and phonological skills. To create a

more ‘pure’ orthographic measure we extracted all the phonological aspects out of the letter

knowledge task by statistically removing all the variance due to differences in phonological

awareness, rapid automatic naming and verbal short-term memory. Concretely, we calculated

the Spearman correlation between CM and Letter Knowledge and added nonverbal IQ,

AWARENESS, RAN and VSTM as partial variables. In doing so, we still observed a

significant relation between CM and the orthographic aspects of Letter Knowledge, both in

the total group (rs = -.27, p = .04) and in the LR-group (rs = -.41, p = .03). For the HR-group

this relation was considerable but not significant (rs = -.31, p = .11). This means, that again

the relation turned out to be the most substantial in the LR-group where 17 % of the variance

in orthographic skills could be predicted from sensitivity to CM-detection. Interestingly, in

contrast, the correlation between FM and the orthographic aspect of Letter Knowledge was

not significant (in neither of the two groups). Considering the whole of these observations,

this seems to imply an exclusive relation between FM and phonological awareness on the one

hand, and CM and orthographic skills on the other (see Figs. 2A and 2B).

Although FM and CM appeared to be related to different and dissociated aspects of

literacy development, they also shared some common variance. Indeed, both in the total group

and in the LR-group FM and CM were significantly related to each other. Again, this relation

was not present in the HR-group. This might imply that both psychophysical tasks (at least in

the LR-group) rely upon some common neurological mechanism involved in ‘dynamic

processing’. Importantly, this hypothesised common mechanism is not involved in

psychophysical processing in general, since CM proved to be completely unrelated to two

other auditory measures that were also administered to the same subjects (i.e. gap detection in

broadband noise and tone-in-noise detection; for details see Boets, et al., 2006).

98

Fig. 2. (A) Thresholds for detecting coherent motion plotted against a Productive Letter Knowledge measure for

31 HR and 31 LR subjects. (B) Thresholds for detecting 2 Hz FM of a 1000 Hz tone plotted against a combined

measure of Phonological Awareness for 30 HR and 31 LR subjects. HR subjects: filled diamonds; LR subjects:

empty squares.

Discussion

In this study we tested sensory processing and phonological and orthographic abilities

in five-year-old preschool subjects who never received any formal reading instruction. As

reported previously, a significant deficit in phonological awareness and letter knowledge

could be demonstrated in the dyslexia-prone HR group. Consequently, since both letter

knowledge and phonological awareness have consistently been proven to be the best

preschool predictors of literacy development (see e.g. Scarborough, 1998), it is likely that the

genetically high risk group will contain a disproportionally high number of future cases of

dyslexia.

For CM detection we did not observe any significant differences between the high and

low risk groups, either at a group level, or in the individual deviance analysis. Although these

results are consistent with a minority of studies that also failed to find any group differences

(Kronbichler et al., 2002; Amitay et al., 2002; Ramus et al., 2003; Hulslander et al., 2004),

they clearly conflict with the mass of evidence provided by most other studies. The same

applied to the FM detection data that did not reveal a group difference either. A

straightforward explanation for this lack of a group difference might be the fact that we did

not study a well-defined clinical group but only an at risk group that still might show

substantial overlap with the control group. Moreover, Bishop et al., (1999) demonstrated in a

0,8

1

1,2

1,4

1,6

1,8

2

0 5 10 15 20

Productive Letter Knowledge

log

CM

(%)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

-3 -2 -1 0 1 2 3

Phonological Awareness

log

FM (H

z)

99

twin study on SLI children that in contrast to the highly heritable phonological skills, auditory

skills depend less on genetic and more on environmental influences. As such, our finding of a

phonological deficit in combination with relatively intact sensorial skills in this genetic high

risk group corresponds well with the results of Bishop and colleagues.

Considering the relations between sensory processing and the orthographic and

phonological sub skills of literacy development, our preschool data convincingly confirm

previous results from adults and school-aged children (Talcott et al., 2000; Talcott & Witton,

2002). Even while taking into account the influence of general cognitive ability, sensitivity to

CM seems to be uniquely related to orthographic skills, whereas sensitivity to FM seems to be

specifically related to phonological skills. This relation appears to be the most robust in the

LR group where 17% of the variance in orthographic skills could be predicted from

differences in sensitivity to CM and 37% of the variance in phonological awareness could be

predicted from sensitivity to FM.

The finding of these more substantial correlations in the LR/control group compared to

the HR/dyslexic group is in line with most other studies (Rosen, 2003). Ahissar, Protopapas,

Reid, and Merzenich (2000) have hypothesized that the suppression of these correlations in

adult dyslexics might be due to variably compensated literacy skills, in contrast to the lagging

sensory skills. However, our data do not provide much support for this interpretation, since

we observed a similar pattern in preschool children who have not even been diagnosed or

detected as being dyslexic. Actually, the children studied had not yet received any formal

reading instruction; indeed, they have had no opportunity to compensate or be treated for their

undetected impairments.

With respect to the causality of the observed relations between literacy skills and basic

sensory measures, it has been suggested that better sensorial sensitivity might be a

consequence and not a cause of better literacy skills (Talcott & Witton, 2002). Indeed, based

on adult and school-aged data, the possibility cannot be ruled out that reading experience (or

print exposure) improves CM and FM detection performance rather than vice versa. In fact, it

would not be too far-fetched to expect visual and auditory skills of good readers to be more

finely tuned than those of dyslexics by virtue of their more highly trained orthographic and

phonological systems. However, this study on preschool subjects demonstrated that there

already exists a reliable preceding relation between sensory and preliminary literacy skills,

even before having received any instruction or before having been exposed extensively to a

lot of print. Therefore, it seems unlikely to consider the sensorial deficits in dyslexics as a

consequence of lack of reading experience. Instead, the results of our study seem to be

100

consistent with the general hypothesis that basic sensory processing skills do influence (albeit

in a facilitating or in an inhibiting way) the development of phonological, orthographic and

reading abilities.

To conclude, phonological awareness and letter knowledge turn out to be the best

indicators to differentiate between preschool children with low versus high genetic risk of

developing dyslexia. In contrast, neither visual coherent motion detection nor auditory 2 Hz

frequency modulation detection is able to differentiate significantly between both groups.

Nevertheless, there is a significant relation between these dynamic sensory measures and

developing literacy skills, even while taking into account the influence of differences in

intelligence: sensitivity to CM is uniquely related to orthographic skills and not to

phonological ability, whereas sensitivity to FM is specifically predictive for emerging

phonological skills and not for orthographic skills. In sum, these results suggest that basic

visual and auditory sensitivity is likely to play an important role in the development of fine-

grained orthographic and phonological representations necessary for successful reading.

101

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Notes

1 Published as Boets, B., Wouters, J., van Wieringen, A., & Ghesquière, P. (2006). Coherent

motion detection in preschool children at family risk for dyslexia. Vision Research, 46 (4),

527-535.

107

Manuscript 4

The development of early literacy skills among children at high risk for

dyslexia: an empirical evaluation of the general magnocellular theory

Abstract

We tested low-level auditory and visual processing, speech perception, phonological ability and letter knowledge

in a group of 5-year-old preschool children at high family risk for dyslexia (HR, n = 31), compared to a group of

well-matched low risk control children (LR, n = 31). Based on family risk status and first grade literacy

achievement (literacy delay: LD versus normal literacy: LN) children were categorized in three groups (HR-LD,

HR-LN and LR-LN) and preschool data were retrospectively reanalyzed. Compared to the LR-LN group, the

HR-LD group presented a significant deficit in letter knowledge, phonological ability, 2 Hz frequency

modulation detection, coherent motion detection, categorical perception and speech-in-noise perception.

Whereas for phonological ability the HR-LN group scored in between both other groups or at the level of the

HR-LD group, for sensory processing they performed as well as the LR-LN subjects. This longitudinal study is

among the first to demonstrate that the sensory deficit typically observed in adult and school-aged dyslexic

subjects, does precede the literacy problem and is not merely the result of lacking reading experience. Hence,

this leaves open the possibility of a potential causal influence of sensory processing upon literacy development

in a way as postulated by the general magnocellular theory.

In a subsequent study, the plausibility of this causal relation was demonstrated using structural equation

modeling. In particular, we demonstrated that dynamic auditory processing was related to speech perception,

which on its turn was related to phonological awareness. In the same way, dynamic visual processing was related

to orthographic ability. Subsequently, phonological awareness and orthographic ability – together with verbal

short-term memory - were unique predictors of reading and writing development.

Introduction

Developmental dyslexia is a specific learning disability that affects around 5 to 10 %

of children and adults. It is characterised by severe reading and spelling difficulties that are

persistent and resistant to usual teaching methods and remedial efforts. Historically, there has

108

been a longstanding discussion about the aetiology of these specific literacy problems. The

origin has been sought in the visual, the auditory as well as in the cognitive-linguistic domain.

At present, the predominant aetiological view postulates that dyslexia results from a

cognitive deficit that is specific to the representation and processing of speech sounds: this is

the phonological deficit theory (Snowling, 2000). However, during the last decades there has

been a growing number of studies demonstrating a deficit in low-level auditory and visual

processing in subjects with dyslexia and it has been suggested that this sensory deficit might

be causal to both the observed phonological and literacy problems (Farmer & Klein, 1995). In

the auditory modality, most emphasis has been given to temporal auditory processing. It has

been demonstrated that dyslexics have problems processing short, rapidly presented and

dynamic changing acoustic stimuli (e.g. Talcott & Witton, 2002; Tallal, 1980; Van Ingelghem

et al., 2005). Besides, dyslexics also tend to present subtle speech perception problems

(McBride-Chang, 1995). It has been hypothesised that the basic deficit in perceiving auditory

temporal cues causes a problem for the accurate detection of the rapid acoustical changes in

speech. Consequently, the speech perception problem causes a cascade of effects, starting

with the disruption of normal development of the phonological system and resulting in

problems learning to read and spell (Talcott & Witton, 2002; Tallal, 1980; Wright et al.,

1997). In this way, the supporters of the auditory temporal processing deficit theory do not

deny the existence of the phonological deficit, but rather see it as secondary to a more basic

auditory impairment (Ramus, 2003).

Alternatively, it has been suggested that the literacy problems of some dyslexics can

be traced back to a specific visual problem, in particular a problem in magnocellular visual

processing (Stein & Walsh, 1997). Anatomically as well as functionally, the visual system can

be divided in two independent but linked subsystems: the magnocellular (or transient)

pathway and the parvocellular (or sustained) pathway. The magnocellular pathway originates

from the ventral layers 1 and 2 of the lateral geniculate nucleus (LGN) and projects mainly to

the dorsal stream of visual processing which terminates at the posterior parietal cortex

(Livingstone & Hubel, 1987). It contains cells with large somas (magnocells) and highly

myelinated axons, hence promoting fast conduction velocities. It is highly sensitive to low

spatial and high temporal frequency stimulation and it responds preferentially to the onset and

offset of the stimulus. Consequently, the magnocellular system is predominantly a flicker or

motion detecting system. In psychophysical studies, it has been demonstrated that individuals

with dyslexia show a decreased sensitivity to stimuli within the magnocellular range (e.g.

Cornelissen et al., 1995; Lovegrove, 1996). This evidence for a magnocellular dysfunction

109

has further been confirmed by visual evoked potential and fMRI studies (Eden et al., 1996;

Demb, Boynton & Heeger, 1997) and by anatomical studies showing abnormalities of the

magnocellular layers of the LGN in the dyslexic brain (Livingstone, Rosen, Drislane &

Galaburda, 1991). Regarding the specific mechanism by which magnocellular visual

dysfunction may limit normal literacy development, there is still much speculation. The

answer probably lies in the anatomical connections from the magnocellular pathway to the

posterior parietal cortex (PPC). The PPC is known to be involved in normal eye movement

control, visuospatial attention, visual search and peripheral vision – all factors that are

obviously involved in the development of orthographic skills and subsequent reading and

spelling skills (Stein & Walsh, 1997; Stein & Talcott, 1999; Stein, 2001). Since the PPC is

dominated by magnocellular input, it is very well possible that slight impairments in

magnocellular functioning might multiply up to greater deficits in PPC functioning.

Although historically the auditory and the visual research lines have been developed

independently, they both converge in postulating that dyslexics suffer from a deficiency in

temporal information processing. As a consequence the hypothesis of a general temporal

processing deficit or pan-modal deficit is formulated (Eden, Stein, & Wood, 1995; Farmer &

Klein, 1995; Frith & Frith, 1996; Stein & Talcott, 1999). In this hypothesis the reading and

spelling problems of dyslexic people are attributed to a deficiency in the temporal processing

capacity of both the auditory and the visual system. Support for this pan-modal deficit

hypothesis was provided by studies reporting the co-occurrence of auditory and visual

temporal problems in certain individuals with dyslexia (Witton et al., 1998; Cestnick, 2001;

Van Ingelghem et al., 2001). Recently, an attempt has also been undertaken to relate the

hypothesised cross-modal deficiencies to one underlying biological cause. The general

magnocellular theory (Stein & Walsh, 1997; Stein, 2001) postulates that the magnocellular

deficit is not restricted to the visual pathway but could be generalized to all modalities (visual

and auditory as well as tactile). Although a similar magnocellular/parvocellular distinction has

not been described for the auditory brain areas, anatomical evidence for the auditory variant

of a magnocellular deficit has been found by Galaburda and colleagues (Galaburda, Menard

& Rosen, 1994), reporting an increased proportion of smaller neurons in the medial geniculate

nucleus of the dyslexic brain.

Notwithstanding the obvious attractiveness of the general magnocellular theory in

providing a unifying framework for all manifestations of dyslexia, it also has important

shortcomings and has been facing growing criticism in recent years (Ramus, 2001; Ramus,

2003; Ramus et al., 2003; White et al., in press). Besides failures to replicate an auditory or

110

visual deficit in dyslexia, an important line of criticism concerns the finding that the deficit –

when observed- does not always seem to imply ‘rapid/temporal/magnocellular’ processing.

Indeed, in some studies ‘rapid/magnocellular’ processing is found to be intact, whereas

‘slow/parvocellular’ processing is found to be impaired (see e.g. Skottun, 2000 for an

extensive critical review of the visual evidence; Rosen, 2003 for an evaluation of the auditory

evidence). Another line of criticism emphasizes that the sensory problems are usually only

observed in a relatively small proportion of dyslexics. Of course this makes it questionable

whether the sensory problem could be regarded as the principal cause of dyslexia (Bishop,

Carlyon, Deeks & Bishop, 1999; Ramus, 2003; Rosen, 2003).

A more general critique regarding the (extended) magnocellular theory concerns the

lack of empirical evidence, since the postulated causality of the theory has not yet been

confirmed by longitudinal data. Actually, only very few studies have been undertaken to

assess preschool sensory processing difficulties in children that will ultimately become

dyslexic. Some major prospective studies are coming about (e.g. Jyväskylä Longitudinal

Study of Dyslexia, see Lyytinen et al., 2001; Dutch Dyslexia Research Program, NWO, 1996;

Benasich & Tallal, 2002), but until now only limited reports have been published. Moreover,

three exceptional studies assessing auditory (Heath & Hogben, 2004; Share, Jorm, MacLean

& Matthews, 2002) and auditory and visual (Hood & Conlon, 2004) temporal order

judgement tasks in an unselected population of preschoolers, yielded mixed results relating

preschool sensory thresholds to later literacy development. This means that, although the

magnocellular theory considers dyslexics’ reading and spelling problems as the result of a

deviant developmental pathway in early childhood, the theory has almost exclusively been

based upon data collected in adult and school-aged subjects. Unfortunately however, these

data should not be interpreted in a causal way! It is evident that the mere finding of sensory

deficits in dyslexic subjects or the observation of significant relations between sensory

thresholds and aspects of literacy ability does not tell us anything about the direction of this

relation. Indeed, based on adult and school-aged studies the possibility cannot be ruled out

that the observed sensory deficits are rather the result than the cause of differences in literacy

ability and reading experience. In fact, some researchers did suggest that it would not be too

far-fetched to expect auditory and visual skills of good readers to be more finely tuned than

those of dyslexics by virtue of their more highly trained phonological and orthographic

systems (Talcott & Witton, 2002).

With this longitudinal study we aim to fill in this empirical gap and take a first step

towards exploring the hypothesised causality issue. Concretely, we assessed basic auditory

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and visual skills, speech perception, phonological ability and letter knowledge in a group of 5-

year-old preschool children at family risk for dyslexia, compared to a group of well-matched

control children1. Auditory processing was assessed by means of three psychophysical tests:

gap-detection in noise (GAP), 2 Hz FM-detection (FM) and tone-in-noise detection (TN).

With the GAP-detection task, we tested the hypothesis of a deficit in ‘rapid and brief’

temporal processing. With the FM-detection task, we verified the hypothesis of a deficit in the

processing of ‘dynamic stimuli’. The TN task was included as a non-temporal control task to

verify the specificity of any observed auditory deficit, i.e. we wanted to examine whether a

deficit might be the result of failing performance on psychophysical tasks in general. To

assess visual magnocellular processing, we measured sensitivity to coherent motion (CM) in

random dot kinematograms. This task was chosen since it has been shown by single cell

recordings of activity in cortical area MT/V5 (strongly supplied by cells from the

magnocellular pathway) in response to coherent motion stimuli, that random dot

kinematograms provide a sensitive measure of magnocellular processing (Britten, Shalden,

Newsome & Movshon, 1992). To evaluate speech perception, we measured categorical

perception of a place-of-articulation speech continuum and we administered a speech-in-noise

perception task. Phonological processing was assessed by a broad test battery comprising

tasks for rapid automatic naming, verbal short-term memory and phonological awareness.

Finally, developing literacy skills were measured using a letter knowledge task.

In the first place, all preschool data were analysed comparing the familial high risk

(HR) versus low risk (LR) group. The results of this group comparison have been described in

three previous papers (Boets, Wouters, van Wieringen & Ghesquière, 2006a; Boets, Wouters,

van Wieringen, Ghesquière, 2006b; Boets, Ghesquière, van Wieringen & Wouters, under

revision) and can be summarised as follows. On every task the HR group demonstrated lower

performance than the LR group, with the group difference being statistically significant for

phonological awareness, letter knowledge and speech-in-noise perception. For the other tasks

the difference was in the expected direction but did not reach statistical significance. Further

on, significant correlations were observed between spectral auditory processing (FM- and TN-

detection), speech perception and phonological awareness on the one hand, and between

visual magnocellular processing (CM) and the specific orthographic aspect of letter

knowledge on the other.

Of course, the lack of a significant group difference for the sensory measures might be

attributed to the fact that we did not study a well-defined clinical group but only a risk group

that might still show substantial overlap with the non-affected control group. To clarify this

112

aspect, we followed up these children through first grade of primary school and assessed their

reading and spelling skills to determine whether or not they present literacy difficulties.

Accordingly, in study 1 of this paper we will describe the crucial retrospective analysis

comparing preschool (future) dyslexic children versus preschool (future) normal readers. We

postulate that if the sensory problems observed in adult and school-aged dyslexics are indeed

at the basis of the literacy problem, they should also be observable in preschool subjects who

will be diagnosed as dyslexic some time later.

Subsequently, in study 2 of the current paper we will explicitly evaluate the

hypothesised causal path by modeling the relations between sensory processing, speech

perception, orthographic and phonological ability and literacy development using structural

equation modeling procedures. According to the general magnocellular theory, we expect

visual magnocellular processing to influence literacy achievement by means of orthographic

ability. Likewise, we postulate that low-level auditory processing will influence phonological

processing by means of speech perception. Further on, we expect phonological ability (i.e.

phonological awareness, rapid automatic naming and verbal short-term memory) to determine

literacy development.

STUDY 1: Auditory, visual, speech perception and phonological deficits in

preschool children at high risk for dyslexia

Method

Participants

Sixty-two children were included in the study (36 boys / 26 girls) and followed from

one year before the onset of formal reading instruction till one year into reading instruction.

Half of the participants were children of ‘dyslexic families’, the so-called high-risk group

(HR); the other half were control children of ‘normal reading families’, the so-called low-risk

group (LR). All children were native Dutch speakers without any history of brain damage,

long-term hearing loss or visual problems. Additionally, at the moment of data collection they

did not present any gross deficiencies in visual acuity (Landolt-C single optotypes Snellen

acuity > 0.85) and/or audiology (audiometric pure-tone average < 25 dB HL). The HR

113

children were selected on a basis of having at least one first-degree relative with a diagnosis

of dyslexia. The LR children showed no history of speech or language problems and none of

their family members suffered from any learning or language problem. For every individual

HR child we selected the best matching LR control child based on five criteria: (1)

educational environment, i.e. same school, (2) gender, (3) age, (4) nonverbal intelligence, and

(5) parental educational level. Nonverbal intelligence was assessed by an adapted version of

the Raven Coloured Progressive Matrices (RCPM) (Raven, Court, & Raven, 1984), a

collective non-verbal intelligence test measuring spatial reasoning. Parental educational level

was assessed using the ISCED-scale (International Standard Classification of Education by

UNESCO, 1997), by converting classifications on the original seven-point scale to a three-

point scale. At the time of collecting the first kindergarten data, the mean age for both the HR

and LR group was 5 years and 4 months, not being statistically different. The nonverbal IQ

scores were slightly above population average (107 for HR group and 111 for LR group) and

did not differ significantly. Both risk groups represented children from relatively higher

educated parents, with Fisher’s Exact Test confirming that both groups did not differ in

frequency distribution of the different parental educational categories. Further details about

the participants and the selection procedure are described in Boets et al. (2006a).

Tasks and materials

Measures at preschool age

Phonological tests

Tests were selected to reflect the three traditional domains of phonological skills

(Wagner & Torgesen, 1987). Phonological awareness was measured by three sound identity

tasks and a rhyme task. Verbal short-term memory was measured by a digit span test and a

nonword repetition test. Rapid automatic naming was assessed by administering a colour and

an object rapid naming task. Here we will only present a brief description of the tests; further

details can be found in Boets et al. (2006a).

Sound identity tasks. The child was required to choose from four alternatives the word

that had the same (a) first sound, (b) end sound or (c) end rhyme as a given word (de Jong,

Seveke, & van Veen, 2000, adapted by van Otterloo & Regtvoort). The distracter alternatives

were systematically constructed to prevent guessing. Each item consisted of a row of five

114

pictures. The first picture represented the given word and was separated from the other

pictures by a vertical line. All items were named for the child. The first-sound and end-sound

identity tasks both consisted of 10 items, preceded by two practice items, and had a maximum

score of 10. The rhyme identity task consisted of 12 items, preceded by two practice items,

and had a maximum score of 12.

Rhyme test. Participants were presented a one-syllable word and were required to

produce a rhyming (non-)word. The test consisted of 8 items, gradually becoming more

difficult, and was preceded by two practice items.

Non-word repetition test. Upon hearing a non-word, presented from a CD, the child

had to repeat it as accurately as possible. The test consisted of 48 non-words, varying in word

length from two to five syllables, was preceded by two practice items and had a maximum

score of 48 (Scheltinga, 2003).

Digit span forward. The test assessed the immediate serial recall of spoken lists of

digits presented from a CD. Testing started with a sequence of two digits and continued with

increasing list length until the child failed on two of three trials of the same length. The test

score was the total number of correctly recalled lists (De Smedt, Verschaffel & Ghesquière,

2004).

Rapid automatic naming: pictures. The child was presented a card depicting 50

pictures of five familiar objects in random order and was instructed to name them as fast and

accurately as possible (van den Bos, Zijlstra, & Spelberg, 2002). Total naming time was

recorded irrespective of accuracy. Subsequently, the time to completion was transformed to

the number of symbols named per second. As such, a higher score on the test corresponded to

a higher naming speed.

Rapid automatic naming: colours. This test was identical to the automatic picture-

naming test, but now a card depicting 50 small rectangles in five familiar colours was

presented to the child.

Productive letter knowledge

This task was intended as a preliminary measure of literacy development. The sixteen

most frequently used letters in Dutch books were presented on a card and the child had to

name each of these letters. Both the sound and the name of a letter were considered correct.

115

Tests for low-level auditory processing

Auditory processing was assessed by means of three psychophysical threshold tests

(see Boets et al., 2006a). In the GAP-detection test, subjects had to detect a silent interval

(gap) in a white noise stimulus. Threshold was defined as the minimum gap length required

for detecting the silent interval. In the FM-detection test participants had to detect a 2 Hz

sinusoidal frequency modulation of a 1 kHz carrier tone with varying modulation depth.

Threshold was defined as the minimum depth of frequency deviation required to detect the

modulation. In the TN-detection task participants had to detect two pure tone pulses (1 kHz,

length = 440 ms) within a one-octave noise signal, centered around 1 kHz (from 707 to 1414

Hz, length = 1620 ms). Threshold was defined as the lowest signal-to-noise ratio (SNR)

required for detecting the tone pulses. For all three auditory tests, thresholds were estimated

using a three-interval forced-choice oddity paradigm embedded within an interactive

computer game with animation movies (Laneau, Boets, Moonen, van Wieringen & Wouters,

2005). The length of the gap, the depth of modulation and the amplitude of the sinusoidal

pulses were adjusted adaptively using a two-down, one-up rule, which targeted the threshold

corresponding to 70.7 % correct responses (Levitt, 1971). A threshold run was terminated

after eight reversals and the threshold for an individual run was calculated by the geometric

mean of the values of the last four reversals. After a short period of practice, comprising

supra-threshold trials to familiarize the participants with the stimuli and the task, three

threshold estimates were determined for every experiment. For the data we present here, the

average of the best and second best threshold was used as an indicator of auditory sensitivity.

For all auditory testing – also for the speech perception tests – the same calibrated

equipment was used. Stimuli were saved as 16-bit wav-files on the hard disc of a Dell

Latitude C800 and Toshiba Satellite 1400-103 portable computer. They were presented using

an integrated audio PC-card and routed to an audiometer (Madsen OB622) in order to control

the level of presentation. Stimuli were presented monaurally over calibrated TDH-39

headphones. A more detailed description of the stimuli, procedure and equipment can be

found in Boets et al. (2006a).

Visual magnocellular test

For the CM-detection test, children were sitting in a low-luminance (mesopic)

environment at 40 cm distance from an Elo Intuitive 1725L 17’’ touch screen (75 Hz refresh

116

rate) on which the random dot kinematograms (RDK) were displayed. The display resolution

was set to 640 x 480 pixels. The stimuli were generated online by the same portable

computers as used for the auditory experiments and comprised of two rectangular patches,

each containing 1103 randomly moving high luminance white dots on a black background

(dot size = 1 pixel or 0.07° diameter, dot density = 2.5 dots/deg2, velocity = 7.3 deg/sec, life

time = 5 video frames or 200 msec, maximal duration of stimulus presentation = 6 sec,

luminance of dots = 125 cd/m2, luminance of background = 0.39 cd/m2, Michelson

contrast = 99.4 %). At a viewing distance of 40 cm each patch of dots subtended 16 x 27.2°

visual angle, separated horizontally by 3.8°. The target patch was segregated into three

horizontal strips; in the middle strip a variable proportion of dots were moving coherently in

horizontal direction, reversing direction every 330 msec. All other dots were moving

randomly in a Brownian manner. The two patches were presented simultaneously and the

subject had to identify the patch containing the strip with coherently moving dots. Threshold

was defined as the smallest proportion of coherently moving dots required for detection of the

middle strip with reversing dot motion. Similarly to the auditory experiments a two-down,

one-up adaptive staircase procedure was used and threshold was calculated as the geometric

mean of the values of the last four of eight reversals. After a short period of practice, four

thresholds were determined for every subject. For the data we present here, the average of the

best and second best threshold was used as an indicator of visual magnocellular sensitivity.

As was the case with the auditory experiments, the CM-detection experiment was integrated

within a computer game with animation movies and an extensive reinforcement system to

make it attractive and applicable for very young children (see Boets et al., 2006b).

Tests for speech perception

Speech-in-noise perception task. In the speech-in-noise perception task seven lists of

ten high frequent monosyllables (taken from the Göttingerlist II, see van Wieringen &

Wouters, 2005) were presented monaurally with an inter-stimulus interval of 7 seconds.

Simultaneously a continuous stationary speech noise, with an identical spectrum as the

average spectrum of the word lists, was presented to the same ear, at a fixed level of 70 dB

SPL. Words were presented at -1, -4 and -7 dB signal-to-noise ratio (SNR). Before

administration of the six test lists (3 x 2), one list was presented at an SNR of +4 dB as a

practice list. The child’s task was to repeat the words as accurately as possible, resulting in a

percentage correct word score for every test list.

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Categorical perception. A ten-point speech continuum ranging from /bAk/ to /dAk/

(i.e. the Dutch word for ‘box / basket’ and ‘roof’) was presented by means of a categorical

perception task. For every child, a 2AFC forced-choice identification task was administered

followed by a same-different (AX) discrimination task. Stimuli were identical to the ones

developed and described by van Beinum et al. (2005) and were constructed by linearly

interpolating the transition of the second formant (F2) from /b/ to /d/. Stimuli were presented

monaurally at a comfortable listening level of 70 dB SPL. Similarly as in the other sensory

experiments, the task was again integrated within a videogame.

In the identification task, the ten stimuli of the continuum were presented twelve times

in a random order. The child was instructed to repeat the perceived word and point to one of

two pictures on a screen, depicting the possible response options. The task was preceded by a

pre-test, presenting each of the two endpoint stimuli five times.

In the discrimination task, subjects were instructed to listen to two stimuli presented

with an ISI of 600 ms and to determine whether they sounded ‘the same’ or ‘different’.

Children had to respond by pointing to one of two pictures on the screen representing ‘the

same’ or ‘different’. In order to obtain a bias-free measure of discriminability, the task

contained physically different as well as identical pairs. The different pairs, comprising each

of the seven 3-step comparisons (1-4, 2-5, 3-6, etc.), were presented 6 times (with balanced

internal order); the identical pairs (1-1, 2-2, etc.) were presented twice. The task was preceded

by 10 practice items, comprising pairs that were clearly different or identical. Further details

regarding the speech tests can be found in Boets et al. (under revision).

Literacy measures at the end of first grade

To assess literacy skills, a standardised spelling test (Dudal, 1997) and six

standardised reading tests were administered at the end of Grade 1 (after receiving one year of

formal reading instruction). The One-Minute Real-Word Reading test (Brus & Voeten, 1973)

and the Pseudo-Word Reading test (van den Bos, Spelberg, Scheepstra, de Vries, 1994)

measure respectively real-word reading and non-word reading, but they do not differentiate

between reading accuracy and reading speed. To discern between these aspects of reading, we

constructed and administered four additional reading tests based on the description provided

by de Jong and Wolters (2002): the Real-Word Reading Accuracy test, the Real-Word

Reading Speed test, the Non-Word Reading Accuracy test and the Non-Word Reading Speed

118

test2. To make these tests applicable for the diagnostic process, age norms were collected in a

large-scale pilot study.

Data collection

Data collection was carried out by qualified psychologists and audiologists. All tests

were administered individually during several sessions adding up to approximately 11 hours

of testing for every subject. After every subtest children were rewarded with little gadgets.

Testing took place in a quiet room at the children’s school. Since the LR child was selected

from the HR child’s classmates, both children could always be tested in exactly the same

circumstances. Phonological and auditory data, and speech perception and CM data were

collected during respectively the first and second trimester of kindergarten. Literacy measures

were collected in the last month of grade 1.

Statistical analysis

Prior to analysis, all data were individually checked for unexpected outliers. As a

consequence, for one subject two unreliable phonological test scores were removed and for

another subject the speech-in-noise perception data were discarded. Further, the speech-in-

noise data and the identification data were submitted to a logistic fitting to estimate the

essential parameters. In order to take into account the variable quality of these fits, the inverse

standard error of the estimated parameter was added to the model as a weight variable. To

obtain a normal distribution, the thresholds for GAP, FM and CM, the results on the letter

knowledge task and the fitted slope parameter of the speech identification data were log10-

transformed. Generally, all data were analyzed using Mixed Model Analysis (MMA) (Littell,

Stroup & Freund, 2002), taking into account the clustered nature of the data (i.e. matched

pairs attending the same school). Although the original HR and LR group as well as the

reading defined groups (see supra) did not differ for age, nonverbal intelligence or parental

educational level, these variables were additionally controlled for in our analyses. Concretely,

a series of (repeated) MMA’s was calculated with school (= pair number) as a random

variable and participant group as the fixed between-subject variable3. Age, nonverbal IQ and

educational level of both mother and father were added as fixed (co)variables. All post-hoc

analyses were corrected for multiple comparisons using the Tukey procedure (α = .05).

119

Results

Defining literacy groups at the end of Grade 1

Descriptive statistics summarising the performance of the HR and LR group on the

reading and spelling measures are shown in Table 1. A paired wise MMA incorporating the

same covariates as mentioned above revealed a significant group difference for every single

literacy measure (p < .01).

Table 1

Mean performance (and standard deviations) on literacy achievement tests at the end of Grade 1 for the High-

Risk and Low-Risk group.

HR

(n = 31)

LR

(n = 31)

M SD M SD

Spelling

One-Minute Real-Word Reading

Pseudo-Word Reading

Real-Word Reading Accuracy

Real-Word Reading Speed

Non-Word Reading Accuracy

Non-Word Reading Speed

46

16

17

22

60

14

40

13

8

9

13

33

11

23

53

22

22

28

78

21

54

5

9

9

10

32

11

26

The contemporary definition of dyslexia for the Dutch language area (Committee on

Dyslexia of the Health Council of the Netherlands, see Gersons-Wolfensberger &

Ruijssenaars, 1997) emphasises that the diagnosis does not only depend upon the observation

of severe reading and spelling problems (< Pc 10), but also requires these problems to be

persistent and resistant to the usual teaching methods and remedial efforts. However, after one

year of formal reading instruction, this additional criterion of persistence cannot yet be

verified. As a consequence, we will refer to the children currently demonstrating impaired

reading and spelling ability as ‘literacy delayed’ instead of ‘dyslexic’. To determine the

reading groups, a LITERACY composite score was calculated averaging the standardised

results (relative to population data) of all reading and spelling tests. In line with the <Pc10

criterion postulated by the Health Council of the Netherlands, a cut-off point of –1.3 SD ’s on

the LITERACY composite was taken as a criterion to delineate the literacy-delayed group.

Applying this criterion resulted in 3 literacy-delayed subjects in the LR group (3/31 = 9 %),

120

and 9 in the HR group (9/31 = 29 %), indicating a relatively increased risk of 3.0 for the

genetically at risk subjects [χ2(1) = 3.7, p < .05]. Hence, on the basis of familial risk status and

current literacy achievement, we defined four groups: a HR normal literacy (HR-LN) and a

HR literacy-delayed (HR-LD) group, and a LR normal literacy (LR-LN) and a LR literacy-

delayed (LR-LD) group. In the further analyses we will compare the results of the HR-LD,

HR-LN and LR-LN groups. Data of the LR-LD participants were excluded because the size of

the group (n = 3) was too small to provide meaningful comparisons and because we wanted to

restrict our clinical group to children showing both (1) the familial risk for dyslexia and (2)

already presenting a significant delay in reading and spelling development.

Table 2 provides descriptive statistics for the three groups, indicating that they did not

differ significantly regarding age, nonverbal intelligence and parental educational level4.

Since the groups were defined based on literacy achievement, it is evident that for all reading

and spelling measures the HR-LD group differed significantly (p < .0001) from the two other

groups (HR-LN and LR-LN), who themselves did not differ from each other.

Table 2

Characteristics of the participants.

HR-LD

(n = 9)

HR-LN

(n = 22)

LR-LN

(n = 28)

M SD M SD M SD

Age in months

Non-verbal IQ

Maternal educational level

Paternal educational level

63

108

2.4

2.4

3

7

0.7

0.5

64

106

2.6

2.1

3

16

0.7

0.8

64

111

2.6

2.4

3

14

0.6

0.6

Note. ANOVA revealed that there were no significant group differences for any of the subject characteristics

(Tukey contrasts p < .05). Parental educational level was calculated from ordinal data.

Group comparisons for preschool measures

Table 3 shows the performance of the three groups of children on the phonological

tests and the letter knowledge task. For every test, except the first-sound identity task and the

digit span task, the HR-LD group scored significantly below the LR-LN group. Furthermore,

it is interesting to note that the HR-LN group, in spite of a hitherto normal reading and

spelling development, scored in between both other groups. For the end-sound identity task

121

and the non-word repetition test, the difference between the HR-LN and the LR-LN group

was even significant, suggesting that familial risk is continuous rather than discrete.

Table 3

Mean performance (and standard deviations) on preschool letter knowledge, phonological and sensory tests for

the High Risk Literacy-Delayed, High Risk Literacy-Normal and Low-Risk Literacy-Normal groups.

HR-LD HR-LN LR-LN

M SD M SD M SD

Letter Knowledge

Phonological Awareness

Simple rhyme

Rhyme identity

First-sound identity

End-sound identity

Rapid Automatic Naming

Colour naming

Picture naming

Verbal Short-Term Memory

Digit span

Nonword repetition test

Composite AWARENESS

Composite RAN

Composite VSTM

Composite PHONOLOGY

AV GAP (ms)

AV FM (Hz)

AV TN (dB SNR)

AV CM (% coherence)

SRT (dB)

Phoneme Boundary

Slope of Phoneme Boundary

Mean discriminability (-ln eta)

0.3a

5.1a

8.0a

3.9

4.0a

0.58 a

0.58 a

6.4

16.1a

-1.49a

-0.87a

-0.62

-1.65a

7.4

11.1a

-6.2

0.28a

-2.8a

5.0

17.5

0.15

0.5

2.8

3.2

1.8

1.4

0.06

0.09

1.5

3.9

1.5

0.4

0.6

1.3

4.3

5.8

2.1

0.18

1.7

4.7

7.2

0.27

2.6ab

6.6ab

9.0ab

4.9

4.7a

0.65ab

0.65ab

7.3

17.1a

-0.75ab

-0.49ab

-0.31

-0.85a

5.1

7.0b

-8.0

0.17b

-3.6b

3.8

16.7

0.44

3.8

2.2

2.4

2.2

2.4

0.13

0.13

1.6

5.8

1.3

0.9

0.9

1.2

4.3

3.4

2.2

0.06

1.6

3.5

5.2

0.61

3.3b

7.3b

10.1b

5.9

6.3b

0.71b

0.71b

6.9

21.3b

0.00b

0.00b

0.00

0.00b

5.1

6.9b

-7.8

0.18b

-3.9b

4.3

21.7

0.74

3.5

1.8

1.5

2.3

2.3

0.16

0.16

1.6

6.5

1.0

1.0

1.0

1.0

3.8

3.8

1.5

0.09

1.2

4.1

5.4

0.63

Note. Pairs with different subscript letters differ significantly (MMA controlled for non-verbal IQ, age, parental

educational level and school environment; Tukey contrasts p < .05). For SRT, Phoneme Boundary and Slope of

Phoneme Boundary weighted group means and SD ’s are presented.

122

A principal component factor analysis with varimax rotation confirmed that the test

battery excellently reflected the traditional three-fold phonological structure: (a) the

phonological awareness factor had high loadings of the three sound identity tasks and the

simple rhyme task, (b) the rapid automatic naming factor had high loadings of both the colour

and object rapid naming tasks and (c) the verbal short-term memory factor was completely

determined by high loadings of the non-word repetition and the digit span task (for details, see

Boets et al, 2006a). Consequently, for every phonological factor summarizing composite

scores were calculated averaging the standardised scores of their constituent tests

(AWARENESS, RAN, VSTM). In addition, a general phonological composite averaging the

standardised scores of all phonological tests was also calculated (PHONOLOGY). Statistics

for these four phonological composites are displayed in table 3. To assist in the interpretation

of the results, composite values were transformed to effect sizes relatively to the mean and

standard deviation of the LR-LN group. As can be seen, the HR-LD group scored

significantly lower than the LR-LN group on AWARENESS, RAN and PHONOLOGY, with

the HR-LN group again scoring in between. For VSTM group differences did not reach

significance.

Table 3 shows the results on the auditory measures and the visual CM-detection task.

For FM and CM detection the HR-LD group scored significantly worse than the two other

groups, who themselves did not differ from each other. Also for GAP and TN detection the

thresholds of the HR-LD group were increased, but this difference did not reach significance.

Average results of the speech-in-noise perception test are depicted in Fig. 1. A

Repeated Measures MMA with proportion correctly perceived words as dependent variable,

group as between-subject variable, SNR as within-subject variable and with the same

covariates as mentioned above, revealed a significant main effect for group (p = .004) and

SNR (p < .0001) with the group x SNR interaction being insignificant (p = .50). Post-hoc

analysis revealed that the HR-LD group differed significantly from both other groups, who

themselves did not differ from each other. Additionally, to estimate the Speech Reception

Threshold (SRT: the signal level required for 50 % correct responses), for every subject a

logistic function was fitted to the data (for details, see Boets et al., under revision). In order to

take into account the variable quality of the fits, the inverse standard error of the estimated

parameters was added to the model as a weight variable. Table 3 shows weighted group

means for SRT. As was the case with the Repeated Measures analysis, MMA with the weight

123

variable and the same covariates as mentioned above demonstrated that the HR-LD group

required a significantly easier signal-to-noise ratio than the two other groups to perceive 50 %

of the presented words correctly.

0

0.2

0.4

0.6

0.8

1

-1 dB -4 dB -7 dB

Signal-to-noise ratio (SNR)

Pro

porti

on c

orre

ct re

spon

ses HR-LD

HR-LN

LR-LN

Fig. 1: Mean scores relating the proportion correctly perceived words to the relative level of the presented words

(SNR).

For the categorical perception data, only subjects obtaining 70 % correct responses or

more on the pre-test were included in the analyses, excluding 4 subjects for the identification

task and 14 subjects for the discrimination task. A justification of this relatively liberal

criterion can be found in Boets et al. (under revision). For the identification data (see Fig. 2), a

Repeated Measures MMA with ‘proportion /dAk/ responses’ as dependent variable, group as

between-subject variable, stimulus (1 to 10) as within-subject variable and with the same

covariates as mentioned above, revealed no significant effect for group (p = .68), a significant

effect for stimulus (p < .0001) and no significant group x stimulus interaction effect (p = .71).

Again, data were submitted to a logistic fitting to estimate the phoneme boundary (i.e. the

interpolated 50 % cross-over point) and the slope in this point. The slope of the fitted function

is the most relevant parameter: it represents the range of uncertainty in distinguishing one

phoneme category from another and is thus indicative of the ability to categorise a speech

continuum in a consistent way. Table 3 shows weighted group means for the estimated

phoneme boundary and slope5. MMA with a similar weight variable and the same covariates

as mentioned above demonstrated no significant differences between groups.

124

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9 10

Stimulus number

Pro

porti

on /d

Ak/

resp

onse

s

HR-LD

HR-LN

LR-LN

Fig. 2: Mean identification functions relating the proportion /dAk/ responses to the stimulus number of the place-

of-articulation continuum.

-0.8-0.6-0.4-0.2

00.20.40.60.8

11.21.41.6

1-4 2-5 3-6 4-7 5-8 6-9 7-10

Stimulus pair

Dis

crim

inab

ility

HR-LD

HR-LN

LR-LN

Fig. 3: Mean discrimination scores (- ln eta) as a function of stimulus pair.

For the discrimination data (see Fig. 3), a distribution and response-bias free index of

dicriminability (- ln eta)6 was calculated according to signal detection theory (Wood, 1976).

Although Fig. 3 clearly demonstrates that the HR-LD group presented a reduced

discriminability (in particular for stimulus pair 3-6), this effect was not significant in a

Repeated Measures MMA with covariates incorporated [group effect: p = .09; stimulus effect:

p = .01; group x stimulus interaction: p = .17]. However, repeating the analysis without any

fixed covariates added, did result in a significant group effect (p = .02), with the HR-LD

group differing significantly from the LR-LN group. The same pattern of results was obtained

125

for the mean discriminability score calculated over all pairs (see Table 3). With the covariates

added the group difference was not significant, but without these covariates the HR-LD group

differed significantly from LR-LN.

Individual deviance analysis

Since one of the goals of this study was to explore early indicators of dyslexia and in

view of the fact that group comparisons might mask significant individual differences, we

also carried out analyses at the subject level. To decide which individual did and did not show

abnormal performance, we adopted the two-step criterion as suggested by Ramus et al.

(2003). Applying this procedure, the criterion for deviance was placed at 1.65 standard

deviations of the ‘purified’ mean of the LR-LN group, after first having excluded all deviant

LR-LN subjects (by applying a similar 1.65 SD criterion, typically resulting in the removal of

one or two deviant LR-LN subjects for each measure). A distribution analysis on the data of

the ‘restricted’ LR-LN group confirmed their normality, indicating that the 1.65 SD criterion

corresponds to the fifth percentile. Individual scores for the sensory measures and for the

PHONOLOGY and LITERACY composite are plotted in Fig. 4, with Table 4 presenting the

number of deviant subjects in each group (see Appendix for an overview of all individual

deviancy data). Inspection of the individual data reveals three main findings. First, for the

sensory measures and in particular for PHONOLOGY, it is evident that the HR-LD group

showed an increased proportion of deviant subjects. Second, it is clear that not all HR-LD

subjects scored in the deviant range. Whereas for PHONOLOGY this applied for about ¾ of

the group, for the sensory measures this typically applied for about one third or the half of the

group. Third, inspection of the scores of the children of the HR-LD group reveals no

straightforward pattern relating deficiencies across different processing skills. Although there

might be a tendency for children with auditory deficiencies to present also more severe

phonological deficits, this pattern does not extent to speech perception. Further, it is striking

that exactly the two HR-LD subjects without phonological deficiencies were among the ones

presenting the most severe visual problems. However, contrary to the expectation, they were

also among the ones showing the most severe speech perception problems. Thus, in sum, we

have to conclude that we are not able to demonstrate a consistent pattern of deficits across

auditory, visual, speech perception and phonological processing abilities for the subjects of

the HR-LD group.

126

Fig. 4: Individual Z-scores for the LITERACY and PHONOLOGY composite and for all sensory measures.

Except for LITERACY, the solid line indicates the mean for all LR-LN subjects above Pc 5; the dashed line

indicates the chosen deviance criterion (1.65 SD deviating of the ‘restricted’ LR-LN mean). Deviant individuals

are identified. For LITERACY, individuals have been depicted relatively against population average with the

dashed line indicating the -1.3 SD criterion.

Note. For LITERACY, PHONOLOGY and the Phoneme Boundary Slope a higher Z-score indicates better

processing; in contrast for all other measures a higher Z-score indicates reduced sensory sensitivity.

127

Table 4

Proportion of deviant subjects for each reading group.

HR-LD

(n = 9)

HR-LN

(n = 22)

LR-LN

(n = 28)

PHONOLOGY

GAP-detection

FM-detection

TN-detection

CM-detection

SRT

Slope of Phoneme Boundarya

78 %

33 %

44 %

11 %

33 %

33 %

55 %

45 %

23 %

14 %

9 %

5 %

5 %

36 %

14 %

14 %

7 %

4 %

11 %

4 %

28 %

Note. aFor the Slope of Phoneme Boundary both the subjects scoring below 1.65 SD of the restricted LR-LN

mean, as well as the excluded subjects for whom no reliable identification data could be obtained were

considered as being deviant.

Discussion

In this study we investigated low-level auditory and visual processing, speech

perception and phonological ability in 5-year-old preschool subjects who did not yet receive

any formal reading instruction. Data were retrospectively analysed, comparing three groups of

subjects defined by first grade literacy achievement and family risk for dyslexia. Since it is

questionable to firmly assess dyslexia in subjects that have received only one year of reading

instruction, we preferred to refer to the impaired group as literacy-delayed instead of dyslexic.

However, evidence for the validity of defining reading groups at the end of first grade has

been provided by research demonstrating that differences among children in reading and

spelling (dis)abilities are quite stable over time, and that the majority of those identified as

having reading difficulties in first grade continue to read poorly throughout their school years

and beyond (McCardle, Scarborough & Catts, 2001). Moreover, our clinical group was

defined by showing both the actual literacy delay and the increased family risk.

Comparing the HR-LD group with the LR-LN group, we observed a significant

difference to the advantage of the LR-LN group for letter knowledge, phonological ability

(i.e. phonological awareness and rapid automatic naming; for verbal short-term memory the

difference was only significant on the non-word repetition test), FM- and CM-detection, and

128

speech-in-noise perception. Without taking into account any covariates, the results on the

categorical perception task also differentiated between both groups. With respect to the

phonological data, these results are consistent with the phonological deficit hypothesis and are

in line with other prospective longitudinal studies revealing similar deficits in genetically at

risk children (for a review see the impressive meta-analysis of Scarborough, 1998, based on

61 studies, indicating that letter knowledge and phonological awareness are the best single

preschool predictors of literacy development). Regarding the sensory data, the significant

group difference for FM- and CM-detection is consistent with a whole series of

psychophysical studies typically demonstrating that these measures differentiate reliably

between adult and school-aged dyslexic and normal reading subjects (for FM see e.g. Stein &

McAnally, 1995; Van Ingelghem et al., 2005; Witton et al., 1998; for CM see e.g.

Cornelissen, Richardson, Mason, Fowler & Stein, 1995; Everatt, Bradshaw & Hibbard, 1999;

Hansen, Stein, Orde, Winter & Talcott, 2001; Raymond & Sorensen, 1998; Ridder, Borsting

& Banton, 2001; Talcott, Hanssen, Assoku & Stein, 2000; Talcott et al., 2003; Van

Ingelghem, Boets, van Wieringen, Ghesquière & Wouters, 2004; Wilmer, Richardson, Chen

& Stein, 2004; Witton et al., 1998). Interestingly, some authors have postulated that both

tasks depend upon ‘dynamic’ stimulus processing (i.e. requiring the perception of a dimension

changing in time), suggesting that the sensory deficit might be specific for dynamic

processing (Talcott et al., 2000; Talcott & Witton, 2002). With respect to speech perception,

again our results are in line with a whole range of studies demonstrating a deficit in speech-in-

noise perception (e.g. Bradlow, Kraus & Hayes, 2003; Brady, Shankweiler & Mann, 1983)

and categorical perception (e.g. Breier, Fletcher, Denton & Gray, 2004; Godfrey, Syrdal-

Lasky, Millay & Knox, 1981; Lieberman, Meskill, Chatillon & Schupack, 1985; Maassen et

al., 2001; Manis et al., 1997; Reed, 1989; Steffens, Eilers, Gross-Glenn & Jallad, 1992;

Serniclaes et al., 2001; Werker & Tees, 1987) in adult and school-aged dyslexic subjects. In

particular, our data also corroborate two studies providing rare evidence of speech perception

problems in toddlers (Gerrits, 2003) and six-month-old infants (Richardson, Leppänen, Leiwo

& Lyytinen, 2003) at family risk for dyslexia. Besides the reduced speech discriminability,

particularly for speech contrasts near the phoneme boundary, we would like to emphasize the

remarkable speech-in-noise perception deficit in the HR-LD group. Regardless of the actual

signal-to-noise ratio, children of this group perceived on average about 10 % fewer words

than their peers. Applied to a regular classroom situation, typically characterized by a lot of

background noise, this implicates that a considerable amount of communication and

instruction is missed by these children. Undoubtedly, this might have far-reaching

129

implications for scholar development in general and language and phonological development

in particular.

For the two other auditory tasks, GAP- and TN-detection, the thresholds of the HR-LD

subjects were increased but did not differ significantly from the HR-LN group. For TN-

detection this lack of a difference was expected according to the auditory temporal deficit

hypothesis since TN-detection was merely taken up as a non-temporal control task. For GAP-

detection in contrast, both the theory and previous empirical evidence (e.g. Hautus, Setchell,

Waldie & Kirk, 2003; McCroskey & Kidder, 1980; Van Ingelghem et al., 2001, 2004, 2005)

predicted that the dyslexia-prone HR-LD group should show a significant deficit.

Nevertheless, our failure replicating a significant deficit in GAP-detection does not stand

alone: several other studies also failed to demonstrate a GAP-detection deficit in dyslexia

suggesting the task might not be robust enough to differentiate reliably between dyslexic and

normal reading subjects (Adlard & Hazan, 1998; McAnally & Stein, 1996; Schulte-Körne,

Deimel, Bartling & Remschmidt, 1998). In line with these findings, Phillips and colleagues

proposed that a between-channel variant of the GAP-detection task (where the pre and post

gap markers occupy different critical bands) might be a more appropriate measure to probe

the perceptual mechanisms involved in stop consonant speech discrimination (Phillips,

Taylor, Hall, Carr & Mossop, 1997).

Focussing upon the results of the HR-LN group reveals an interesting pattern. For

phonological ability their scores were situated either in between both other groups or even at

the level of the HR-LD group. This means that in spite of a hitherto normal literacy

development, they presented a mild phonological deficit, suggesting that familial risk is

continuous rather than discrete (for a similar conclusion see Pennington & Lefly, 2001;

Snowling, Gallagher & Frith, 2003). In contrast, for the sensory measures, the scores of the

HR-LN group were almost identical to those obtained by children of the LR-LN group. This

suggests that whereas the occurrence of a phonological problem seems to be largely

genetically determined (or at least by familial risk status) and partly irrespective of actual

literacy achievement, the presence of a sensory problem rather seems to depend on the co-

occurrence of a real literacy deficit. This conclusion is well in line with data of Bishop and

colleagues (Bishop et al., 1999) and Olson and Datta (2002) who demonstrated in twin studies

that in contrast to the highly heritable phonological skills, sensory skills depend less on

genetic and more on environmental influences. Within the framework of an integrative

neurobiological theory of dyslexia, Ramus (2004) recently suggested that these co-occurring

sensori(motor) problems might be the consequence of elevated levels of foetal testosterone

130

(that are mostly influenced by non-genetic factors). As a consequence, his model predicts an

increased prevalence of the sensorimotor syndrome in male dyslexics. Although our final

sample of literacy-delayed subjects is too small (6 boys / 3 girls) to make any firm

conclusions, our data are at least consistent with this hypothesis: 6 of the 6 male subjects (100

%) present at least one deviant score for auditory, visual or speech perception processing

versus 2 out of 3 female subjects (67 %).

The observation of reduced sensory processing by subjects of the HR-LD group

(evidenced both by an increased proportion of deviant subjects and a significant group

difference), makes it plausible that this sensory deficit is somehow related to their problems in

literacy achievement. However, the concurrent findings that (1) not all literacy-delayed

subjects are affected by a sensory problem, whereas (2) some normal reading subjects do also

present sensory problems, suggest that sensory skills might only play a limited role in literacy

development. Noteworthy however, the same pattern of results is also observed for

phonological ability, indicating that even phonology is not able to explain the whole story of

dyslexia.

It should be clear that the observation of a sensory deficit in preschool children, who

ultimately will develop dyslexia, does not imply that this deficit is causally related to the

reading problem. One should be cautious interpreting these data in a causal way. But on the

other hand, this study convincingly demonstrates that the sensory problems typically observed

in dyslexia are not merely the result of lacking reading experience. On the contrary, the

sensory deficit evidently does precede the literacy problem. Hence, this leaves open the

possibility of a potential causal influence of sensory processing upon literacy development in

a way as postulated by the general magnocellular theory. In the next study we will further

explore the plausibility of these interrelations using structural equation modeling.

STUDY 2: Modeling relations between sensory processing, speech

perception, orthographic and phonological ability and literacy achievement

The general magnocellular theory does not only assume the presence of an underlying

sensory deficit in subjects with dyslexia, but also proposes a specific (causal) relation between

sensory sensitivity, speech perception, orthographic and phonological ability and literacy

131

achievement. Although many studies have mentioned correlations between (a) auditory

processing and phonological or reading abilities (e.g. Amitay, Ahissar & Nelken, 2002;

Ramus et al., 2003; Richardson, Thomson, Scott & Goswami, 2004; Talcott et al., 1999; Van

Ingelghem et al., 2005; Witton et al., 1998), (b) between speech perception and phonological

or reading abilities (e.g. Bradlow et al., 2003; Breier et al., 2004; De Weirdt, 1988; Godfrey et

al., 1981; Maassen et al., 2001; Mayo, Scobbie, Hewlett & Waters, 2003; Nittrouer, 1996;

Ramus et al., 2003) and (c) between visual magnocellular processing and orthographic or

reading skills (e.g. Cornelissen et al., 1998; Demb, Boynton & Heeger, 1997; Van Ingelghem

et al., 2004), only very few have systematically investigated the relations between these

variables. A noteworthy exception is a series of studies by Talcott, Witton and colleagues

demonstrating an exclusive relation between visual magnocellular processing and

orthographic skills on the one hand and auditory processing and phonological ability on the

other (Talcott, Hansen, et al., 2000; Talcott, Witton, et al., 2000; Talcott & Witton, 2002).

Further exceptions are three studies illuminating interrelations using structural equation

modeling. McBride-Chang (1996) investigated the relations of speech perception and

phonological processing to reading, and concluded that the best fitting model was one in

which the effect of speech perception on reading was mediated by its relation with

phonological awareness and rapid automatic naming. Two other studies (Schulte-Körne,

Deimel, Bartling & Remschmidt, 1999; Watson & Miller, 1993) additionally included non-

linguistic measures for auditory temporal processing into the model. Although both confirmed

that the influence of speech perception on reading and spelling was mediated by phonological

processing, neither of them were able to obtain a good fit with the low-level auditory

variables integrated into the model.

In this study we will evaluate the postulated relations between sensory processing,

speech perception, orthographic and phonological ability and literacy development using

structural equation modeling (path analysis). Structural equation modeling is a statistical

method to model the (causal) interrelations between a group of variables, and subsequently

test the plausibility of the theoretical model compared to the observed data (Kline, 1998). In

line with the assumptions of the general magnocellular theory and the traditional phonological

theory we constructed and tested the structural model depicted in Fig. 5. In this model

dynamic auditory processing is postulated to determine speech perception, which on its turn

determines phonological ability. Although we mainly expect an influence of speech

perception upon phonological awareness, in line with the theoretical arguments and empirical

data provided by McBride-Chang (1996) we will also allow rapid automatic naming and

132

verbal short-term memory to be determined by speech perception. Further on, all three

phonological sub skills are hypothesised to act upon literacy development. In the same way,

dynamic visual (magnocellular) processing is postulated to determine orthographic ability,

which on its turn also determines reading and writing development. In accordance with the

established phonological theory (Wagner & Torgesen, 1987), we allow the phonological sub

skills to be mutually correlated. Likewise, the measures for auditory and visual sensitivity are

also allowed to correlate since they both depend upon dynamic sensory processing (Talcott &

Witton, 2002).

Fig. 5: Figure showing the basic structural model tested in the path analysis.

Method

Participants

The participants were the same as those involved in study 1.

133

Tasks and materials

Preschool measures. Largely the same data were used as in study 1. However, in order

to manage the complexity of the estimated models, we only included preschool sensory

measures that demonstrated significant differentiating power. This means that only the data

for FM detection, CM detection and speech-in-noise perception were retained in the model,

together with letter knowledge and data representing the three phonological sub skills. The

restriction of the low-level sensory tests to FM and CM detection also offered the additional

advantage of being in line with the current research tradition regarding ‘dynamic’ sensory

processing (e.g. Witton et al., 1998). As such, it offered the possibility to replicate some

recent findings in adults and school-aged children demonstrating that orthographic skills co-

vary most strongly with CM sensitivity, whereas phonological skills co-vary most strongly

with FM sensitivity (Talcott, Hansen, et al., 2000; Talcott, Witton, et al., 2000; Talcott &

Witton, 2002). Finally, the exclusion of the categorical perception data was also partially

motivated by the large number of missing or unreliable test scores.

First grade literacy measures. To assess reading skills, data of the same six

standardised reading tests described in study 1 were used. To assess writing skills, we

administered a pseudohomophone task in addition to the standardised spelling test described

in study 1. In this pseudohomophone task 40 familiar words (known by > 90 % of Belgian

six-year-olds; see Schaerlaekens, Kohnstamm & Lejaeghere, 1999) were presented on a

screen together with a non-word with the same pronunciation (e.g. ‘blauw’ vs ‘blouw’).

Simultaneous with the visual presentation the word was also auditory presented and used in

an example sentence. The child’s task was to point to the correctly spelled word. The test was

integrated in a computer game in a way that upon correct responding a jigsaw piece popped

up revealing a funny image throughout the task. Stimulus presentation and response

registration was controlled by E-prime, a software module for psychological experiments

(Schneider, Eschman & Zuccolotto, 2002).

Statistical analysis

For two subjects showing a missing test score, data were estimated using the LISREL

multiple imputation option. To obtain a normal distribution, results on the letter knowledge

task and thresholds for FM and CM were log10-transformed. Further on, CM and FM

134

thresholds were multiplied by -1 to obtain a positive definite covariance matrix; hence a

higher score on a measure indicated a better capability.

Structural equation modeling (SEM) was used to examine the hypotheses. Analysis of

the covariance matrices was conducted using LISREL 8.71 (Jöreskog & Sorbom, 2004) and

solutions were generated on the basis of maximum-likelihood estimation (Boomsma &

Hoogland, 2001). Given that we had multiple indicators of the theoretical constructs, we

wished to combine measures to simplify the models and increase reliability. However,

because of the low ratio of participants to free parameters, it was not appropriate to estimate

latent variables for the path models (Tanaka, 1987). Instead, we used observed composite

variables, using aggregated scores to define the constructs for which we had multiple

measures. To further control the number of free parameters to be estimated, models were

estimated separately for the prediction of reading and writing development.

Data screening of the (composite) variables indicated partial data non-normality, both

at the univariate and the multivariate level7. Therefore, in all models the asymptotic

covariance matrix was used as input and the Satorra-Bentler Scaled chi-square test was

inspected (SBS-χ², Sattorra & Bentler, 1994). To evaluate model goodness of fit, the

Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA) and

the Standardized Root Mean Square Residuals (SRMR) were selected. According to Hu and

Bentler (1999), the combined cut-off values close to .95 for CFI, below .08 for RMSEA and

below .09 for SRMR indicate good model fit. SBS-χ² difference tests were used in

comparisons between nested models.

Results

Data reduction

As reported in study 1 and in Boets et al. (2006a), the exploratory factor analysis

convincingly confirmed the traditional three-fold phonological structure. Consequently, for

every phonological factor summarizing composite scores were calculated averaging the

standardised scores of their constituent tests (AWARENESS, RAN and VSTM). Similarly,

averaging the Z-scores of the six reading tests created a reading composite (READ), and

averaging the Z-scores of the spelling and the pseudohomophone task constituted a writing

composite (WRITE). Although both of these literacy composites were highly correlated

(r = .72, p < .0001), it was thought this subdivision might be conceptually valid. Further, also

135

for the speech-in-noise perception data a SPEECH composite was calculated averaging the

Z-scores of the proportion correctly perceived words for the -4 dB and -7 dB SNR conditions.

Finally, for the auditory and visual measures (FM and CM), the average of the best and

second best threshold was used as an indicator of a child’s sensory sensitivity.

Construction of a preschool measure for orthographic ability

In accordance with the theoretical model, visual magnocellular processing is

postulated to determine literacy development by means of orthographic ability. Since it is

impossible to administer a pure orthographic test at this preschool age, we considered letter

knowledge as the best approximate measure to give an indication about orthographic ability.

After all, resolving a letter knowledge task relies on recognizing the visual features of the

written symbol on the one hand, and retrieving the corresponding linguistic information on

the other. As such, letter knowledge might be regarded as a measure that reflects both

orthographic and phonological skills. To create a more ‘pure’ preschool orthographic

measure, we extracted all the phonological aspects out of the letter knowledge task by

statistically removing all the variance due to differences in phonological awareness, rapid

automatic naming and verbal short-term memory. As a result, the newly created variable

ORTHOGRAPHY represented the letter knowledge scores residualized on AWARENESS,

RAN and VSTM using simultaneous regression.

Correlations among sensory processing, speech perception, orthographic and phonological

ability and literacy skills

Correlations between the (composite) variables are shown in Table 5. Bivariate

correlations are shown below the diagonal and partial correlations controlling for non-verbal

intelligence are shown above the diagonal. As hypothesised, both dynamic sensory processing

measures were indeed significantly related. Furthermore, whereas visual processing was

related to orthographic ability, auditory processing was related to speech perception and

phonological awareness. Regarding the phonological sub skills, phonological awareness was

significantly related to RAN and VSTM, who themselves were unrelated. Finally, reading

ability was significantly predicted by FM and CM sensitivity, orthographic ability, speech

perception, phonological awareness, RAN and VSTM. Writing ability was significantly

predicted by orthographic ability, speech perception, phonological awareness and VSTM.

Table 5

Pearson (partial) correlations between measures for sensory processing, speech perception, orthographic and phonological ability and literacy skills.

RCPM

CM

ORTHO-

GRAPHY

FM

SPEECH

AWARE-

NESS

RAN

VSTM

READ

WRITE

CM

ORTHOGRAPHY

FM

SPEECH

AWARENESS

RAN

VSTM

READ

WRITE

.25*

.17

.16

-.10

.17

-.14

.25*

.12

.10

-

.29*

.30*

.09

.00

-.08

.13

.34**

.23

.26*

-

.20

.28*

.00

.00

.00

.32**

.38**

.27*

.17

-

.33**

.45***

.05

.09

.44***

.23

.12

.30*

.35**

-

.35**

.15

.10

.48****

.33**

-.04

-.03

.43***

.37**

-

.35**

.33**

.52****

.40***

-.05

.02

.07

.14

.38**

-

.08

.24*

.10

.07

-.04

.05

.13

.30*

.12

-

.35**

.32**

.33**

.31*

.43***

.49****

.51****

.25*

.34**

-

.72****

.22

.37**

.22

.34**

.39**

.12

.31*

.71****

-

Note: Coefficients above the diagonal are partial correlations after removing variance attributable to individual differences in nonverbal intelligence (RCPM).

* p < .05, ** p < .01, *** p < .001, **** p < .0001

137

Noteworthy, interrelations were generally found to be larger in the LR group

compared to the total or HR group, indicating that the total group correlations were not

inflated because of aggregating data over extreme groups.

Path model for reading development

Testing of the theoretical model depicted in Fig. 5 resulted in a moderate global fit

(SBS-χ²(15) = 25.02, p < .05; CFI = .90; RMSEA = .11; SRMR = .12) with all the path

coefficients being significant except for the SPEECH → RAN path, the SPEECH → VSTM

path, the RAN → READ path and the correlation between RAN and VSTM. Since

elimination of these non-significant paths resulted in a statistically equivalent model, this

reduced model formed the basis for further model building.

Because FM sensitivity was significantly related to phonological awareness, we tested

whether adding a direct path would improve the model compared to the fully speech-

mediated model. This yielded a significant improvement (∆SBS-χ² (1) = 7.05, p < .01) and

resulted in an excellently fitting model (SBS-χ² (13) = 17.46, p = ns; CFI = .96;

RMSEA = .08; SRMR = .09). Interestingly, as a consequence of adding the direct path, the

SPEECH → AWARENESS path became insignificant (β = .17, p > .05), indicating that the

influence of auditory processing on phonological awareness happens in a direct way and is

not or only very marginally mediated by speech perception (see Baron & Kenny, 1986, and

Holmbeck, 1997 for a clear conceptual and statistical definition of mediation8).

Similarly, because speech perception was significantly related to reading, we explored

whether adding a direct path would improve the model compared to the fully phonologically-

mediated model. Although the added direct path was highly significant (β = .27, p < .01), the

global model improvement was not (∆SBS-χ² (1) = 2.70, p > .05). Besides the significant

direct path, also the indirect SPEECH → AWARENESS → READ path was retained

(β = .29, p < .005 and β = .34, p < .001, respectively). This suggests that phonological

awareness indeed partially mediates the relation between speech perception and reading

development.

Likewise, in order to verify whether the relation between visual magnocellular

processing and reading would be fully mediated by orthography, we compared the fully

orthography-mediated model with one where we added the direct CM → READ path. Again

the added direct path was highly significant (β = .25, p < .01), but the global model

138

improvement was not (∆SBS-χ² (1) = 2.56, p > .05). Moreover, since both path coefficients

from the indirect path were retained (β = .29, p < .05 and β = .25, p < .01, respectively), this

indicated that the relation between visual magnocellular processing and reading development

is at least partially mediated by orthographic ability.

Fig. 6: Path analysis predicting first grade reading achievement from preschool measures of dynamic sensory

processing, speech perception, phonological and orthographic ability.

* p < .05, ** p < .01, *** p < .005, **** p < .001

Ultimately, addition of the three abovementioned significant direct paths resulted in

the excellently fitting final structural model depicted in Fig. 6 (SBS-χ² (11) = 8.76, p = ns;

CFI = 1.00; RMSEA = 0.00; SRMR = .06)9. According to this estimated model 51 % of the

observed variance in reading ability could be predicted. Noteworthy, applying a model

trimming strategy where we initially allowed any possible relations and systematically

omitted all non-significant paths, resulted in exactly the same model. Finally, in order to

verify the stability of the model, we repeated the analysis upon the data partialed out for

individual differences in non-verbal intelligence (i.e. all variables were regressed upon the

scores for Raven Coloured Progressive Matrices and the residualized scores were used to

calculate the covariance matrix). The fit indices and path coefficients of this model were

139

virtually identical to the ones presented in Fig. 6, with the only difference that the SPEECH

→ AWARENESS path became significant again (β = .19, p < .05).

Path model for writing development

Testing of the structural model for writing development was executed in a similar way

as we did for reading development. In contrast to the reading model, testing of the theoretical

writing model depicted in Fig. 5 already resulted in a fairly satisfying global fit (SBS-χ²(15) =

19.73, p = ns; CFI = .93; RMSEA = .07; SRMR = .04). Again, all the postulated paths were

significant apart from the correlation between RAN and VSTM and the SPEECH → RAN

path, the SPEECH → VSTM path and the RAN → WRITE path. Since elimination of these

non-significant paths resulted in a statistically equivalent model, again this reduced model

was used as a basis for further model building.

Exploration whether the model could be improved by adding the same direct paths as

mentioned above, indicated that only the addition of the direct FM → AWARENESS path

resulted in a significant path coefficient (β = .36, p < .001) and a significant improvement of

model fit (∆SBS-χ² (1) = 9.08, p < .005). Consecutive addition of the SPEECH → WRITE

path and CM → WRITE path did not only fail to yield a global model improvement (∆SBS-χ²

(1) = 1.24, p > .05 and ∆SBS-χ² (1) = 1.48, p > .05, respectively), but did neither result in a

significant path coefficient (β = .11, p > .05 and β = .10, p > .05, respectively). Consequently,

this indicated that whereas the path from auditory processing towards phonological awareness

is only marginally mediated by speech perception, the path from speech perception towards

writing development is fully mediated by phonological awareness, as well as the path from

visual magnocellular processing towards writing is fully mediated by orthographic skills. In

conclusion, the revised writing model with the additional FM → AWARENESS path resulted

in the excellently fitting model depicted in Fig. 7 (SBS-χ² (13) = 10.23, p = ns; CFI = 1.00;

RMSEA = 0.00; SRMR = .07). According to this model 35 % of the variance in writing skills

could be predicted. Again, applying an empirically guided model trimming strategy resulted

in exactly the same model. Also if we retested the model for the scores residualized on non-

verbal intelligence, fit indices and path coefficients were virtually identical, with the only

difference being the significant SPEECH → AWARENESS path (β = .19, p < .05).

140

Fig. 7: Path analysis predicting first grade writing achievement from preschool measures of dynamic sensory

processing, speech perception, phonological and orthographic ability.

* p < .05, ** p < .01, *** p < .005, **** p < .001

Discussion

In this study we investigated the interrelations between preschool measures of

dynamic sensory processing, speech perception, orthographic and phonological ability and

first grade measures of reading and writing achievement. In particular we evaluated whether

the hypothesized causal path, as suggested by the general magnocellular theory, could be

validated empirically. To eliminate plausible rival hypotheses, several competing

hierarchically nested models were statistically compared.

Inspection of the empirically deduced models depicted in Fig. 6 and Fig. 7 indicates

that the basic causal assumptions of the general magnocellular theory could be validated fairly

well. In both models dynamic auditory processing was related to speech perception, which on

its turn was related to phonological awareness. In the same way, dynamic visual processing

was related to orthographic ability. Subsequently, phonological awareness and orthographic

ability –together with verbal short-term memory- were unique predictors of literacy

development. Somehow unexpectedly, the model fit could significantly be improved by

141

allowing a direct influence of dynamic auditory processing upon phonological awareness,

indicating that this determination happened mostly in a direct way and was only marginally

mediated by speech perception. Similarly, for the reading model there was also an additional

direct influence of speech perception upon reading development, besides the considerable

influence mediated by phonological awareness. Likewise, besides the influence mediated by

orthography, visual magnocellular processing also executed a direct influence upon reading

development. For the writing model in contrast, these two additional direct paths were not

retained. A possible explanation for the retention of this additional direct path from visual

magnocellular processing towards reading and not towards writing achievement, might be the

relatively larger involvement of parietal processes like eye movement control and peripheral

vision in reading compared to writing.

Comparing our data to the SEM studies of McBride-Chang (1996), Watson and Miller

(1993) and Schulte-Körne and colleagues (1999), both correspondences and differences can

be observed. The three studies as well as ours converge in demonstrating a significant relation

between speech perception and phonological processing. However, where we only

demonstrated a significant influence of speech perception upon phonological awareness,

McBride-Chang and Watson and Miller also observed an influence upon respectively rapid

automatic naming and verbal (short- and long-term) memory. Further in contrast with their

results, we also observed a direct influence of speech perception upon reading besides the

phonology-mediated influence. As reported, in the studies of Watson and Miller (1993) and

Schulte-Körne and colleagues (1999) auditory temporal measures were also administered.

However, since Schulte-Körne et al. did not observe significant group differences on any of

these measures comparing literacy disabled versus control children, they did not integrate

them into the structural model. Conversely, in the study of Watson and Miller a latent

nonverbal temporal processing factor comprising four tasks was estimated (with only one of

the tasks showing a significant difference comparing control versus literacy disabled

subjects). In their model fitting they demonstrated that auditory temporal processing did not

additionally predict phonological processing when the influence of speech perception was

already taken into account. However, they did not explore the theoretically more relevant

hypothesis whether speech perception would be determined by auditory processing.

Obviously, another major difference of our study compared to these three studies is the

fact that we did not only investigate the auditory pathway to literacy development, but also

included the orthography-mediated visual pathway. Considering the results of this combined

142

perspective, our preschool data confirmed previous results from adults and school-aged

children demonstrating that dynamic auditory sensitivity seems to be uniquely related to

phonological skills, whereas dynamic visual sensitivity seems to be specifically related to

orthographic skills (Talcott, Hansen, et al., 2000; Talcott, Witton, et al., 2000; Talcott &

Witton, 2002).

Regarding the relations between preschool phonological processing and early literacy

development, our data basically corroborated numerous previous prospective studies (see e.g.

Scarborough, 1998, for an overview of 61 studies). Each of the three phonological sub skills

individually was significantly associated with reading. For phonological awareness and verbal

short-term memory the magnitude of these relations was well in line with other prediction

studies, for rapid automatic naming this magnitude was a bit reduced. When all three skills

were included simultaneously in an analysis to predict literacy achievement, only

phonological awareness and verbal short-term memory emerged as unique predictors. This

was somehow unexpected since generally phonological awareness and verbal short-term

memory tend to share more common variance, with rapid automatic naming being more

distinct when predicting reading achievement (e.g. Bowers & Ishaik, 2003). However, as

mentioned by Elbro and Scarborough (2003), the relative importance of a predictor depends

on the level of reading proficiency we aim to predict as well as on the specific reading skill

assessed. Indeed, it has been demonstrated that phonological awareness is mostly important in

the initial phase of reading development when reading mainly depends upon phonological

decoding (Elbro & Scarborough, 2003). Conversely, during a later phase the development of

decoding speed gains more importance and then rapid automatic naming becomes a more

important predictor10 11(see also van den Bos, Zijlstra & lutje Spelberg, 2002). Since we

assessed reading skills at the end of first grade when children hardly received nine months of

reading instruction, it is evident that their reading was not yet automated and still called

mainly upon phonological decoding and thus upon phonological awareness. Furthermore, it

has also been suggested that phonological awareness is most closely related to reading

accuracy and non-word reading, whereas rapid automatic naming is specifically related to

reading speed (de Jong & Wolters, 2002; Savage & Frederikson, 2005). Since our reading

composite showed a preponderance of non-word reading and reading accuracy tasks, this is

probably an additional explanation for the reduced predictive power of rapid automatic

naming. Indeed, when correlating rapid automatic naming scores with the reading test that

specifically measured word reading speed an increased correlation was observed.

143

A specific problem of SEM studies is the existence of several plausible alternative

models that are mathematically equivalent though substantively different (MacCallum,

Wegener, Uchino & Fabrigar, 1993). Indeed, there are many models of associations among

sensory processing, speech perception, orthographic/phonological ability and literacy

achievement that could have been tested. The best approach to manage this problem is the

specification and systematic elimination of plausible rival models, as we did in the current

study. Moreover, the fact that both the theoretically guided model building and the

empirically guided model trimming strategy eventuated in the same final structural model,

provides further validity to our results.

A somehow related problem concerns the issue of causality. It is well known that in a

strict sense causality can only be demonstrated in an experimental and not in a correlational

study. However, by applying a longitudinal design we intended to clarify at least the direction

of some of the observed relations. Accordingly, in view of the fact that all measures for

sensory processing, speech perception, orthographic and phonological ability were collected

before children received any formal reading instruction, it is evident that any relation with

later literacy development is unidirectional. On the contrary, the nature of the relations within

this group of concurrently administered preschool measures is much less straightforward.

Indeed, similar to the established observation that phonological awareness and literacy

development influence each other in a bidirectional way (e.g. Morais, Bertelson, Cary &

Alegria, 1979; Perfetti, Beck, Bell & Hughes, 1987; Read, Zhang, Nie & Ding, 1986), there is

also growing evidence that auditory processing, speech perception and phonological ability

influence each other reciprocally.

In two intervention studies it was convincingly demonstrated that speech

discrimination is causally related to development in phonological skills. Hurford (1990) found

that disabled readers in second and third grade who received speech discrimination training

improved their segmentation skills significantly compared to those disabled readers who had

not received training. Similarly, Moore, Rosenberg and Coleman (2005) trained 8- to 10-year-

old mainstream school children presenting eleven phonemic contrast continua. Trained

children significantly improved phonological awareness and word listening skills by about

two years, although they only showed weak and variable improvement on the trained

discriminations. No improvements were found in the control children. However, contrary to

the theoretically hypothesised direction, there is also some evidence that development in

phonological awareness seems to stimulate (or at least precede) development in auditory

144

processing and speech perception. For example, Mayo and colleagues (2003) demonstrated

that phonemic awareness skills tended to improve before cue weighting strategies in speech

perception changed, and that early phonemic awareness ability predicted later cue weighting

strategies and not the other way around. In another research tradition, Warrier et al. (2004)

demonstrated that a phonological/language training program given to a group of learning-

impaired children not only enhanced their perceptual encoding of speech but also significantly

improved the timing of their cortical responses to speech-in-noise. Moreover, it is interesting

to note that the timing of this cortical response was altered independently of the brainstem

onset responses (Hayes et al, 2003), suggesting there might be a higher-order compensation

mechanism for altering cortical stimulus encoding even when the input from the brainstem is

still deficient. With respect to the causality issue, these latter studies indicate that the relation

between auditory processing, speech perception and phonological awareness is not as

straightforward as has been hypothesized and should probably be conceptualized as being a

bidirectional one. Nevertheless, the reciprocal character of these relations does not essentially

conflict with the basic assumptions of the general magnocellular theory. Actually, it is very

well possible that minor initial problems in sensory processing will multiply up to more

serious deficits exactly because of the interactive nature of these relations.

In sum, although we do not exclude that there might be other viable models to explain

the observed relations between sensory processing, speech perception, orthographic and

phonological ability and literacy achievement, the current study demonstrates that the causal

model proposed by the general magnocellular theory is at least a plausible one. Hence, this is

the first longitudinal study investigating preschool children throughout early literacy

development that offers empirical evidence for the validity of the causal relationships

postulated by the general magnocellular theory.

145

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Notes

1 In the Belgian school system, formal instruction starts in grade 1 at the age of six. This

means in kindergarten no reading instruction is offered. 2 The reading accuracy tests for words and nonwords consisted both of 40 items gradually

increasing in difficulty. For these tests there was no time limit. To assess reading speed we

developed a test yielding almost 100 % accuracy, consequently reflecting merely variation in

reading speed and not in accuracy. In order to obtain a minimal error score, the word reading

speed test consisted exclusively of high-frequent one-syllable words, known by 90 % or more

of Belgian six-year-olds (Schaerlaekens, Kohnstamm & Lejaeghere, 1999). By decomposing

and recombining these words, the items for the nonword reading speed test were constructed.

Both speed tests consisted of 150 items, to be read within a two-minute time limit. 3 All MMA’s used the Kenward-Roger degrees of freedom estimation method that is more

robust against violations to assumptions of normality (Kowalchuk, Keselman, Angina &

Wolfinger, 2004). 4 For parental educational level the correspondence in frequency distribution of the different

educational categories was also confirmed using Fisher’s Exact Test. 5 For 8 subjects, the phoneme boundary was estimated below stimulus 1, indicating that they

even perceived the original /bAk/ stimulus as /dAk/ in more than 50 % of the presentations.

Data of these subjects were not included in the identification analyses. 6 Discriminability equals zero when performance is at chance. It increases with increasing

discrimination accuracy without influences of bias to respond ‘same’ or ‘different’.

Discriminability is maximal at the -ln eta value of 4.6; this value is obtained when the

probabilities for the correct ‘difference’ (= Hit) and correct ‘same’ responses (= Correct

Rejection) are both 0.99, the value assigned (for computational purposes) when the actual

probabilities were 1.00. 7 Although the sample represented a rather heterogeneous group with some children showing

serious literacy problems, data were normally distributed for the Reading and Writing

composite, as was the case for most other variables too. 8 According to Holmbeck (1997) and Baron and Kenny (1986), mediation is shown when the

addition of a direct path from the independent variable to the dependent variable does not

improve model fit compared to the fully indirect model. 9 In figure 6 and figure 7, the standardized solution with standardized regression coefficients

(β’s) is depicted. As the reader might notice, the significance level of a coefficient is not

exactly in proportion to its magnitude. This is due to two reasons: (a) mathematically SEM

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calculates the significance levels for the unstandardized regression coefficients and (b) the

significance test reflects not only the absolute magnitude of a path coefficient but is also

determined by intercorrelations among variables (Kline, 1998). An alternative for interpreting

the results of statistical testing is the immediate interpretation of the absolute magnitudes of

path coefficients. In line with recommendations by Cohen (1988) about effect size

interpretations of correlations, Kline (1998) suggests the following guidelines: standardized

path coefficients with absolute values less than .10 may indicate a ‘small’ effect; values

around .30 a ‘medium’ effect; and those greater than .50 a ‘large’ effect. 10 This is particularly the case in languages with a more transparent orthography. In those

languages phonological demands are more easily met than in English due to the higher

regularity of grapheme-to-phoneme correspondences. Consequently, almost all children – also

those with literacy problems- tend to perform well on phonological awareness tasks and tend

to read highly accurately after some years of reading instruction (e.g. Landerl, Wimmer &

Frith, 1997; Wimmer, 1993). Therefore, in these languages reading speed -and as a result also

rapid automatic naming- is a more sensitive measure to differentiate between good and poor

readers. 11 However, other researchers reported different results regarding the changing relations

between phonological sub skills and reading development. In the longitudinal study of

Wagner and colleagues (1994, 1997) phonological awareness appeared to have a continuous

impetus on reading acquisition from kindergarten through 4th grade. In contrast, the rapid

automatic naming effects were found to be rather small and limited to the first three years. In

a similar longitudinal study examining phonological abilities in Dutch children from

kindergarten through 2nd grade de Jong and van der Leij (1999) reported that in kindergarten

only rapid automatic naming was specifically related to subsequent reading achievement, with

specific contributions of phonological awareness and verbal short-term memory appearing

only after a few months of reading instruction. However, as they concluded themselves, the

absence of a specific predictive influence of kindergarten phonological awareness and verbal

short-term memory was probably due to the use of an over-restrictive design controlling for

general cognitive abilities that were extremely highly correlated with these phonological sub

skills. Indeed, without taking intelligence into account, both phonological awareness and

verbal short-term memory were also found to exert significant additional influences on

reading achievement.

Appendix: Individual Z-scores for literacy, phonological ability, gap detection, tone-in-noise detection, FM detection, motion coherence detection, speech-in-noise perception (SRT) and categorical perception (slope of phoneme boundary). Deviant scores are depicted in bold.

Group Subject LITERACY PHONOLOGY GAP TN FM CM SRT

Slope Phoneme Boundary

HR-LD 11 -1.40 -1.07 1.32 0.91 0.76 1.76 1.96 25 -2.17 -2.45 1.37 1.54 -1.05 1.10 29 -2.28 -3.33 2.83 0.06 1.32 0.19 0.72 1.24 35 -1.58 -5.17 2.88 4.11 3.23 1.46 1.32 -0.70 41 -1.55 -1.66 1.19 1.62 0.34 0.75 1.07 -0.01 43 -1.46 -0.66 -0.68 -1.40 0.28 4.99 2.16 -0.69 49 -1.90 -7.31 3.98 0.93 2.83 0.43 -0.13 -2.19 55 -1.42 -2.82 0.18 0.93 2.88 1.24 0.46 -1.67 57 -2.40 -6.16 -0.75 1.37 2.05 1.83 2.40 -3.47 HR-LN 1 1.31 1.08 -0.01 -0.03 0.98 -0.64 -0.29 -0.93 3 -0.10 -1.89 -1.03 0.75 -1.53 0.83 1.32 5 0.77 -0.68 -1.05 0.49 1.55 1.41 0.78 -1.45 7 0.74 -1.59 -0.68 0.75 0.36 2.97 -0.35 -1.35 9 0.59 -0.65 -1.48 -1.75 -1.34 -1.00 0.10 -0.34 13 -1.17 0.35 -0.22 0.41 -0.09 -0.41 1.55 -0.65 15 -0.09 0.59 -0.71 -2.80 0.65 -0.23 0.26 -1.32 17 -0.57 -3.11 2.39 -0.03 1.29 0.67 2.52 19 -1.02 -3.70 0.09 0.57 -0.35 1.25 0.55 -3.33 21 -0.73 -1.13 -0.70 -0.81 0.42 0.34 0.10 0.51 23 0.00 1.61 0.31 0.58 -0.25 1.24 0.35 27 -0.49 -2.38 -0.43 1.01 2.53 0.87 -0.73 0.55 31 -0.63 -4.57 -0.16 0.58 0.87 -0.86 1.16 -3.86 33 -0.41 -1.39 1.95 -2.27 -1.08 0.07 1.58 -0.20 37 0.03 -0.99 0.35 -2.36 -1.76 0.56 -1.95 1.18 39 0.11 -4.12 -1.34 -2.10 -2.61 0.24 0.34 -0.60 45 -0.44 -0.43 4.92 1.02 1.96 -0.40 -1.07 -1.69 47 -0.80 -0.79 -1.00 2.22 0.63 -0.08 0.54 -1.80 51 -0.41 -4.47 1.81 2.90 2.10 0.95 0.57 53 -0.40 -7.30 -0.02 1.10 0.61 -2.32 0.95 -1.32 59 0.22 -3.06 0.25 -0.63 0.82 -0.71 -0.31 -0.27 61 0.52 -4.26 2.08 -1.35 -0.69 0.53 -0.77 -0.74 LR-LN 2 -0.05 0.89 -0.74 0.76 -0.09 -0.29 0.67 4 -0.76 -1.13 0.31 0.84 1.18 2.12 1.02 -1.12 6 1.12 0.16 -0.69 -0.65 -0.17 0.52 -0.50 -0.70 8 -0.32 -5.44 0.44 2.05 1.17 0.09 0.52 0.59 10 -0.57 0.56 0.87 1.62 -0.52 -1.12 0.95 -1.48 12 -1.28 -6.06 0.44 -0.11 0.64 0.90 0.63 14 0.01 0.36 -0.52 -0.29 0.58 1.04 0.23 -1.58 16 0.01 0.21 0.24 -2.02 0.11 0.96 -0.35 2.38 18 -0.19 -0.51 -0.02 -0.40 0.93 -0.15 0.73 -0.60 20 -1.13 -0.66 0.62 -0.97 0.72 -0.15 2.07 22 -0.36 0.74 0.54 -1.09 0.20 -0.57 0.15 -0.32 26 -0.43 0.11 3.59 1.10 2.84 3.72 -0.48 -0.30 28 0.76 0.67 -0.58 -1.52 0.25 1.21 -1.70 -0.51 32 0.42 -0.95 -2.02 -0.74 -1.99 -1.02 -1.71 0.63 34 -0.34 0.11 1.96 1.10 0.53 2.65 0.66 36 0.24 0.95 0.08 -0.54 -1.28 1.28 -0.82 0.65 38 1.05 1.48 -0.42 0.67 -0.35 -0.77 -0.31 -0.28 40 1.68 1.40 -1.33 -1.52 -2.80 -0.95 -0.74 0.86 44 0.30 -0.52 0.90 0.41 3.20 0.70 1.04 0.61 46 -0.13 -2.21 -0.64 1.02 1.47 0.29 0.34 48 0.95 1.37 0.99 0.67 -0.52 -0.03 0.74 -0.46 50 -0.29 0.15 2.55 1.36 -0.64 0.20 -1.98 -1.64 52 1.14 -0.30 0.09 -0.55 -0.87 -1.69 1.19 1.62 54 0.11 -1.36 -0.31 0.49 1.15 -1.05 1.53 -1.11 56 0.72 -0.29 4.61 0.93 -0.05 -0.69 -0.56 -1.82 58 0.28 1.54 -1.25 0.67 0.86 1.04 0.06 -0.45 60 0.58 -2.43 -0.79 -1.23 -0.26 -1.98 1.35 -2.29 62 1.21 -0.36 -0.73 -0.02 -0.27 0.10 -0.48 Note. Except for LITERACY, all Z-scores have been calculated relatively against the mean and SD of the ‘restricted’ LR-LN group; +/- 1.65 SD was chosen as the deviance criterion. For LITERACY Z-scores have been calculated relatively against population average; -1.3 SD was chosen as the deviance criterion. Note. For LITERACY, PHONOLOGY and the Phoneme Boundary Slope a higher Z-score indicates better processing; in contrast for all other measures a higher Z-score indicates reduced sensory sensitivity.

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General conclusions and future perspectives

In this last chapter we discuss the theoretical, methodological and practical relevance

of the doctoral dissertation. Further, we consider some limitations of the study and provide

some general guidelines for future research.

Theoretical relevance

In this study we empirically evaluated some assumptions of the general magnocellular

theory of dyslexia. In short, the general magnocellular theory postulates that developmental

dyslexia is the consequence of a cross-modal sensory deficit in the processing of transient and

dynamic stimuli (e.g. Stein & Talcott, 1999; Stein & Walsh, 1997; Talcott et al., 2002; Talcott

& Witton, 2002; Witton et al., 1998). In the visual domain, this magnocellular deficit has been

hypothesized to provoke binocular instability, poor visual attention, poor visual search and

uncertainty about letter position, and as such it might affect the development of orthographic

skills and subsequent reading and spelling skills. In the auditory modality, an analogous

temporal processing deficit has been hypothesized to interfere with the accurate detection of

the acoustical changes in speech. Consequently, this speech perception problem causes a

cascade of effects, starting with the disruption of normal development of the phonological

system and resulting in problems learning to read and spell. Recently, it has also been

suggested that both the auditory and the visual deficit might be the result of one common

biological cause: a specific deficit in the neural pathways involved in the fast transmission

and processing of sensory information (Stein, 2001).

Based upon the theoretical assumptions of the general magnocellular theory two

straightforward and verifiable predictions can be formulated: (1) compared to controls,

subjects with dyslexia are hypothesized to demonstrate auditory and visual temporal

processing deficits, speech perception problems and orthographic/phonological problems, and

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(2) we expect to observe significant relations between auditory temporal processing, speech

perception, phonological ability and literacy achievement on the one hand, and between visual

temporal processing, orthographic ability and literacy on the other. Moreover, assuming that

the same processes are involved in the development of normal as well as deficient literacy

ability, we do not only expect to observe these interrelations in dyslexic subjects, but also in

the general population as well. Numerous studies have been conducted exploring these

hypotheses. Most of them have been carried out on adult subjects, a minority on school-aged

children and very few on preschool children. However, besides the confusing aspect that they

often yielded very variable successes in demonstrating the hypothesized deficits and

interrelations, most of these studies only have a limited theoretical value since they do not

offer any indication about the direction of the studied relations. Indeed, the mere observation

of a sensory deficit in adult or school-aged dyslexic subjects does not provide insight into

whether the deficit is (a) at the basis of the reading problem, (b) a consequence of the reading

problem, or (c) a non-causally related marker of literacy problems.

In order to take a first step towards exploring the nature of the sensory deficits and

their relation with subsequent literacy development, we studied preschool children and

followed them up through early literacy development. Hence, the first data were collected in

kindergarten - before children received any formal reading instruction - and were

retrospectively reanalysed comparing children categorised in groups according to family risk

status and first grade literacy achievement.

Based upon a global inspection of the data, our results are quite favourable for the

general magnocellular theory. Apparently, children that become dyslexic1 do already

demonstrate significant preschool deficits in dynamic auditory and visual sensory processing,

speech perception, phonological ability and orthographic ability (viz. letter knowledge).

Apparently, dynamic auditory processing is related to speech perception, which on its turn is

related to phonological awareness and subsequent literacy development. In addition, dynamic

visual processing seems to be related to orthographic ability and subsequent reading and

writing development. Finally, integration of all the preschool and first grade data within one

structural model yields a fairly satisfying fit of the theoretically postulated interrelations.

Moreover, since the measures for sensory processing, speech perception and

phonological and orthographic ability have been administered before children received any

formal reading instruction, it is clear that the observed deficits do precede the literacy problem

and are not merely the result of lacking reading experience. Likewise, the same is true for the

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observed interrelations: it is evident that these preschool measures have a predictive relation

with later literacy development. Therefore, our prospective study is one of the first to

convincingly demonstrate that the typically observed sensory deficits in dyslexia are not a

consequence of the literacy problem. Hence, option (b) of the abovementioned alternatives

can already be eliminated, leaving over the hypotheses (a) and (c), namely that the sensory

deficit is at the basis of the literacy problem versus that it is rather a non-causally related

marker of literacy problems. Currently, our data do not offer enough information to infer the

causal status of the sensory deficit and discern between these competing hypotheses.

Therefore, by further following up these children and integrating the current data within a

more elaborated developmental perspective, we hope to gain more insight into the nature of

the observed sensory–literacy relation.

So, from a global perspective our data appear to be quite in favour of the general

magnocellular theory. However, if we inspect the data in a more detailed way, we notice

several findings that are not in line with the theory. First, it is clear that not all dyslexic

subjects demonstrate a (temporal) sensory deficit. Of course, it could be argued that we did

not assess all crucial aspects of sensory processing or that our psychophysical tasks might

have lacked differentiating sensitivity. Yet, we would like to emphasize that the test battery

that we administered is among the broadest applied in this research tradition, including

several tasks that have proven to differentiate reliably between dyslexic and normal reading

subjects. The observation that not all dyslexic subjects demonstrate a sensory deficit indicates

that deficient sensory processing is not a necessary condition to develop dyslexia – a

conclusion that clearly conflicts with the assumption that the sensory problem would be at the

basis of the reading problem (see Bishop, Carlyon, Deeks & Bishop, 1999 for a similar

conclusion regarding specific language impairment).

Second, some normal reading subjects do also show sensory problems. This indicates

that deficient sensory processing is neither a sufficient condition to develop dyslexia (again

see Bishop et al., 1999 for a similar conclusion regarding specific language impairment).

Apparently, children growing up with quite severe low-level sensory problems can still

develop normal literacy skills. This could imply that either low-level sensory processing only

plays a limited role in the development of literacy ability or that some of these sensory-

impaired children can rely upon compensatory mechanisms to overcome their basic

impairments.

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Third, it is not possible to demonstrate a consistent pattern of deficiencies across

sensory processing, speech perception and phonological abilities, neither for individual

subjects of the dyslexic group, nor for individual subjects of the normal reading groups.

Actually, even at a group level it is not possible to demonstrate such a consistent pattern. For

instance, if we focus upon the group results of the children at family risk for dyslexia that do

not (yet) present an actual literacy problem, it becomes clear that these children do present a

mild phonological deficit but they do not present the sensory deficit. This suggests that

whereas the occurrence of a phonological problem seems to be largely genetically determined

and partly irrespective of actual literacy achievement, the presence of a sensory problem

seems to be restricted to children demonstrating a real literacy deficit. Obviously, the

observation of this partial dissociation between phonological and sensory deficits poses a

problem for the general magnocellular theory, since it demonstrates that phonological

impairments are not necessarily secondary to sensory problems.

It is evident that the aforementioned objections question the validity of the general

magnocellular theory. However, it is important to note that the same objections also apply to

the phonological theory. Indeed, also with regard to phonology some dyslexic subjects present

relatively preserved skills, whereas some normal reading subjects demonstrate extremely

severe phonological deficits (even within the low risk group). This implies that even the more

established phonological theory is not able to fully explain the whole story of dyslexia.

Combining the results of the ‘global’ and the ‘in depth’ analyses, we can conclude that

preschool children that will ultimately become dyslexic present a reduced ability in dynamic

sensory processing, speech perception, letter knowledge and phonological ability. Moreover,

we observe a significant relation between dynamic auditory processing, speech perception,

phonological ability and subsequent literacy development on the one hand, and between

dynamic visual processing, orthographic ability and reading and spelling development on the

other. However, at the individual level, these deficits and relations tend to be not as absolute

as assumed, with many dyslexics showing isolated or only partially overlapping deficits. Even

more surprising is the observation that a substantial number of normal reading subjects also

presents quite severe preschool deficits in sensory processing, speech perception or

phonological processing and nevertheless is still able to develop normal literacy skills. Thus,

although deficient sensory processing and in particular deficient phonological processing

tends to be a risk factor to develop literacy problems, neither of both is a sufficient nor a

165

necessary condition to cause these problems. This suggests that whether one develops

dyslexia or not, still depends upon additional factors executing an aggravating or protective

influence. Actually, recent work has suggested that general language ability might be an

important moderator variable in the process of literacy achievement. For instance, Snowling

and colleagues demonstrated that children at risk for dyslexia because of speech difficulties

(Nathan, Stackhouse, Goulandris & Snowling, 2004) or because of a family history of

dyslexia (Snowling, Gallagher & Frith, 2003) can overcome their emerging phonological

problems and yet develop normal literacy skills under condition that they can rely upon strong

compensatory language abilities.

To conclude, although the general magnocellular theory provides a solid unifying

theoretical framework, it seems not able to fully account for all the various manifestations of

dyslexia. However, the same conclusion applies just as much to the more established

phonological theory. Bearing these conclusions in mind, one might recall the citation

mentioned at the beginning of this thesis:

“The greater becomes the volume of our sphere of knowledge,

the greater also becomes its surface of contact with the unknown.”

(J. Sageret)

Indeed, together with the growing awareness that there does not exist one uniform

manifestation of dyslexia, we also start to realise that no single all-embracing cause or theory

will be found. Reading and writing is a complex multifaceted activity. It involves a dynamic

interplay of multiple sensory and cognitive-linguistic processes, moderated by various

unspecified environmental or higher-order cognitive influences. Deficits at any level might

interfere with normal development. Comprehensive theories like the general magnocellular

theory are nevertheless important and necessary to guide and stimulate scientific research, but

it is an illusion to expect them to explain the full complexity of dyslexia.

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Methodological relevance

Besides the theoretical relevance, an important merit of this dissertation has been the

development and composition of a series of instruments to test auditory and visual sensory

processing, speech perception, orthographic and phonological ability and early literacy

development in very young children.

In the first place, the adaptation of the psychophysical tests for the use in very young

children is noteworthy. In general, children - and especially preschoolers - are much harder to

test because of the cognitive demands and the dull repetitive structure of psychophysical

tasks. In order to make the relatively tedious psychophysical tests more interesting and child-

friendly, we integrated them in an interactive videogame (for the auditory test platform see

Laneau, Boets, Moonen, van Wieringen, & Wouters, 2005; Boets, Wouters, van Wieringen, &

Ghesquière, 2006). Because the most important condition for successful psychophysical

testing of young children is the transformation of the abstract meaningless signal into a

concrete and well known ‘daily life signal’, several stories were invented to give a

psychological and naturally fitting meaning to the particular stimuli at hand. Consequently,

every story was worked out through a collection of animated cartoons, with an introductory

movie introducing the specific target stimulus in a game-like manner and a closing movie

rounding off the experiment and rewarding the child for its achievement. Further, the use of a

touch screen and an extensive reinforcement system made the whole experiment really

interactive. Finally, the application of a child friendly forced-choice paradigm, the manual

control over stimulus presentation and the limited test duration by using optimised step sizes,

allowed us to control for fluctuations in attention in order to get reliable threshold estimates.

As a consequence of applying this concept of testing, the preschool children did not only

perform surprisingly accurately, but they also really enjoyed the psychophysical tasks2.

At the moment, the software for the auditory psychophysical testing of young children

is also used in other studies at the Laboratory for Experimental ORL (KULeuven) and in

foreign auditory research centres (e.g. K. Neijenhuis, Rotterdam). Similarly, the coherent

motion task is currently in use for testing children with cerebral visual impairments (P. Stiers,

Laboratory of Neuropsychology, KULeuven) and unilateral hemi neglect patients (K.

Verfaillie, Laboratory of Experimental Psychology, KULeuven).

In addition to the psychophysical tests, an extensive phonological and orthographic

test battery was composed based upon existing and self-developed material. Likewise, this

test battery is currently used in an analogous prospective study examining preschool children

167

with specific language impairment (E. Vandewalle, Laboratory for Experimental ORL,

KULeuven).

In this doctoral dissertation we introduced and applied some statistical procedures that

are relatively uncommon in the social sciences.

First, not only were the risk and control group matched at the individual level, data

were also analysed at the level of the matched pairs. Indeed, by entering pair number as a

random variable in a Mixed Model Analysis, the dependence at the subject level could be

taken into account without loosing additional degrees of freedom. Consequently, the statistical

differentiating power could be considerably increased. In this way, the Mixed Model Analysis

is statistically identical to a paired t-test, but it has some important advantages: (a) it is much

more robust in analysing semi-normally distributed data (Verbeke & Lesaffre, 1997), (b) it

allows repeated measures analysis, (c) it allows to incorporate covariates and weight

variables, (d) conclusions can be generalised to any group of matched pairs since they

represent a random selection of a larger population, and (e) it can handle missing or non-

balanced data. For a more detailed description of Mixed Model Analysis, the reader is

referred to Aerts, Geys, Molenberghs, & Ryan (2002), Littell et al. (1996) and Verbeke &

Molenberghs (1997).

A second statistical advantage concerns the two-stage analysis of the speech data.

Here, we first estimated the relevant parameters by fitting a logistic function to the raw data

and subsequently compared both groups on the resulting parameters. Moreover, in order to

take into account the variable quality of the fits, the inverse standard error of the estimated

parameters was added to the model as a weight variable. Actually, the addition of such a

weight variable is rather unique in the current research tradition and allows comparing groups

while correcting for differences in the reliability of the data.

Finally, the doctoral research project documents that the selection and longitudinal

follow-up of children at family risk is a valuable method to study the developmental pathway

of both normally and aberrantly developing children. In particular in situations where it is not

practicable to follow up a very numerous sample, this is a useful method to increase the

number of clinical subjects in the final research group.

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Practical relevance

In this thesis we mainly analyzed and interpreted the data from a theoretical

perspective and less from a clinical point of view. However, our data could also offer valuable

information regarding the early prediction and even prevention of literacy problems. Although

the results of the deviance analysis demonstrated that currently none of the individual

preschool measures was able to predict future literacy failure in a satisfying way, the

predictive possibilities of the preschool measures might be enhanced by combining groups of

indicators. This would require a reanalysis of the data in order to determine adequate cut-off

criteria and possibly an estimation of appropriate weight coefficients. In the near future, we

plan to carry out such an analysis in order to evaluate the predictive and discriminative power

of our entire battery of preschool tests.

Limitations of the study

A possible limitation of the doctoral project is the relatively limited number of

subjects. Yet, this is a natural consequence of the specific design of the study with its highly

intensive and time-consuming psychophysical testing of carefully selected preschool children

attending schools scattered all over the Flanders. This restricted sample size might reduce the

generalizibility of the study. For instance, the conclusion that preschool ‘dyslexic’ children

present sensory, speech perception, phonological and orthographic problems, is ultimately

only based upon the data of nine subjects that eventually turned out to be dyslexic. Moreover,

although our definition of the dyslexic group was based upon the co-occurrence of family risk

and actual literacy delay, further follow up of these children is recommended to verify

whether they will also comply with the resistance criterion (Gersons-Wolfensberger &

Ruijssenaars, 1997; Fletcher, Francis, Morris & Lyon, 2005).

In particular, the restricted sample size might also affect the stability of the parameter

estimates in the SEM analyses (Kline, 1998). Therefore, caution is required while interpreting

the results. Furthermore, a larger population sample would have allowed us to run the SEM

analyses separately for the control versus clinical group (i.e. ‘dyslexia status’ would have

been entered into the model as a moderator variable). This highly interesting additional

analysis would have informed us whether the same causal path leading to adequate literacy

169

development in the normal reading group, in a similar way leads to impaired literacy

development in the dyslexic group.

Another limitation of the study is that, although we were able to demonstrate that

dyslexics’ sensory problems precede the literacy problem and are significantly related to it,

we were not yet able to discern whether they are at the basis of the reading problem or rather a

non-causally related marker of it. We acknowledge that the verification of this causality

matter is a tricky issue, in particular in a correlation study where no experimental intervention

is applied. Nevertheless, we hope that further follow up of these children at least could shed

some light upon the direction of the observed relations.

A minor drawback of the study is the exclusive use of the Raven Coloured Progressive

Matrices (Raven, Court, & Raven, 1984) as a measure for general intelligence. Since it can be

administered collectively and because of its excellent psychometric qualities (Evers, van

Vliet-Mulder, Groot, 2000), this test is frequently used in scientific studies. Although it

provides a good indication of general cognitive ability (Spearman’s g factor) and in particular

of non-verbal analogous reasoning, assessment of full-scale IQ (e.g. WPPSI-R, Vandersteene

& Bos, 1997) would have been preferential but was not feasible because of practical

constraints.

One might argue that a more serious limitation of the study is the lack of an instrument

to assess attention. Indeed, since psychophysical testing calls a lot on sustained attention, it

has been suggested that dyslexics’ sensory deficits could be a side effect of the generally

increased incidence of attention-related disorders (ADHD/ADD) in the reading disabled

population (Breier, Fletcher, Foorman, Klaas, & Gray, 2003; Hulslander et al., 2004; Willcutt

& Pennington, 2000). Although we were aware about this concern, we decided not to exclude

risk subjects on a basis of showing AD(H)D symptoms. Hence no information regarding

attention deficits was collected at the time of selecting subjects. This strategy was motivated

by the intention to maintain a representative sample of dyslexic subjects, including those with

the inherent attention problems.

Nevertheless, at the end of first grade for every child an ADHD rating scale assessing

inattentive, hyperactive and impulsive behaviour was completed by the teachers (Scholte &

van der Ploeg, 1998). Although psychometrically valid and reliable, this questionnaire only

gives a rudimentary and probably over-inclusive indication of ADHD-related problem

170

behaviour as perceived by the teacher. Results of the rating scale demonstrated that two of the

nine dyslexic subjects scored in the clinical range (i.e. subject 11 and 49). In the HR-LN as

well as in the LR-LN group there were another two subjects in the clinical range (i.e. subject

17 and 51, and 18 and 44 respectively), with another one in the LR-LD group (i.e. subject 42).

So far, scores upon the attention scale have not yet been entered as covariates in the analyses.

However, inspection of the individual data (see Manuscript 4, Appendix A) already reveals

that those subjects showing ADHD-related behaviour do not particularly fail in the

psychophysical tests. This indicates that the observed sensory deficits in subjects with

dyslexia are not merely a consequence of comorbid attention problems.

Finally, we would like to emphasise that we do not aim to make any claim regarding

the biological deficit that has been hypothesized to be at the basis of the general

magnocellular theory. We merely observe that psychophysical tasks measuring dynamic

auditory and visual processing differentiate between preschool dyslexic versus preschool

normal reading subjects, and that sensory thresholds are related to respectively phonological

and orthographic processing and future literacy development. Of course, it is only to the

extent that these tests provide a correct indication of underlying neurological magnocellular

functioning, that they also provide indirect evidence for a biological magnocellular deficit.

Within the visual modality, there is quite convincing evidence that coherent motion detection

is one of the best psychophysical markers to asses visual magnocellular processing3 (Britten,

Shalden, Newsome & Movshon, 1992), hence suggesting that our dyslexic subjects (as a

group) might indeed present a real neurological deficit in visual magnocellular processing. On

the contrary, in the auditory modality no analogous magnocellular neural pathway has yet

been identified, let alone that FM detection would have been demonstrated to be a good

indicator of it. As a consequence, we do not intend to make any inferences regarding a

possible neurological or biological auditory deficit. Moreover, we would not even

characterize the behavioural/psychophysical auditory deficit as specific to rapid or temporal

processing (as postulated by the general magnocellular theory), since it only seems to hold for

dynamic auditory processing. Therefore, a further investigation of the neurological

mechanisms involved in dynamic auditory processing is required to investigate whether a

specific neurological deficit can be demonstrated.

171

Future perspectives

As aforementioned, we hope to be able to follow up this group of children and

administer all sensory, speech perception, orthographic, phonological and literacy tasks

several times again, in order to further explore the exact nature of the relation between

sensory processing and literacy development. In particular, we aim to verify the direction of

this relation and whether it also comprises a causal aspect.

So far we only used behavioural measures in our research design. We have related the

cognitive deficits of dyslexia with low-level auditory and visual processing using

psychophysical threshold estimates. The use of ‘objective’ neurophysiologic measures could

be a next step in the search for further evidence of sensory processing deficits in persons with

dyslexia. In general, neurophysiologic measures have more discriminative power than

psychophysical measures (Bishop & McArthur, 2004). Objective neurophysiologic techniques

could not only enable us to verify the reliability of the psychophysical tests, but could also

offer us a better insight in the underlying neurological problems of dyslexia.

In the visual domain, our psychophysical results concerning coherent motion detection

could be evaluated using fMRI, especially of the cortical region V5/MT. Eden and colleagues

(1996) have shown less activity in that cortical region after stimulation with coherent moving

stimuli in dyslexic compared to normal reading adults. Demb, Boynton en Heeger (1997)

were even able to show a significant relationship between reading abilities of adults and the

magnitude of hemodynamic BOLD-responses in region V5/MT.

For the auditory domain we could make use of the recently developed technique of

auditory steady-state responses (ASSR). For many years, ASSR has been extensively studied

as a double-objective technique to estimate hearing thresholds. ASSR’s are periodic electrical

responses of the brain that can be evoked by amplitude- and/or frequency-modulated signals.

Depending on the modulation frequency, responses can originate from brainstem or auditory

cortex. In the Laboratory for Experimental ORL (KULeuven) this technique has been further

elaborated and adapted for newborns and infants at risk for hearing loss (Luts, Desloovere &

Wouters, 2006). The ASSR technique has a number of interesting features. The objectivity of

these measures and the possibility to use them to evaluate temporal aspects of auditory

processing (not possible with fMRI) makes this technique an interesting instrument for our

research interest.

172

To conclude, there is still a lot of work to do to unravel the mystery of dyslexia. And

while research progresses, not only answers but also many new questions will be encountered.

We hope this dissertation was able to answer some of these questions.

173

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Notes

1 For the sake of clarity, in this conclusion we will refer to dyslexic versus non-dyslexic

children, although we recognize that in our study the diagnosis did not comply with the

‘resistence to intervention’ criterion (Fletcher, Francis, Morris & Lyon, 2005; Gersons-

Wolfensberger & Ruijssenaars, 1997). 2 For the visual as well as for the auditory psychophysical tests, reliability was fairly

satisfying (based upon inspection of the individual staircases and intra-subject correlations).

However, for the categorical perception task, reliability appeared to be less. Yet, we do not

think this is merely the result of the exigent cognitive demands of the applied procedure, but it

might also be an intrinsic consequence of measuring a premature and still emerging

perceptual skill. 3 However, see Skottun and Skoyles (in press) for some highly recent counterevidence

regarding the validity of coherent motion detection as an indicator of visual magnocellular

processing.