Sarah Elizabeth Alix Kuppen St. John s College Cambridge University. April, 2010

Transcription

1 Sarah Elizabeth Alix Kuppen St. John s College Cambridge University April, 2010 Basic auditory processing skills and phonological awareness in low IQ good and poor readers and typically-developing controls This thesis is submitted for the degree of Doctor of Philosophy

2 Dedication This thesis is dedicated to my mother Rebecca, my father Peter and my husband Stefan all of whom contributed to making this PhD possible. I would also like to thank my supervisor, Usha Goswami for all of her patient guidance and the many children, parents and schools who volunteered their time and who believed in the value of this research. 2

3 Statement of Originality This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. 3

4 Summary This thesis profiles the strengths and weaknesses of low IQ poor readers on tasks of auditory processing, language, reading and working memory. Performances were compared to younger children reading at the same level, age-matched peers and a small group of low IQ matched children who were reading well for their age. The present study examines the utility of an auditory deficit approach in identifying factors which may underlie poor reading in low IQ children. This investigation provides valuable data, conclusions and general insights into a reader group frequently neglected by reading disability research. The present investigation presents the findings from two test batteries presented over the course of two years, with a minimum of six months inter-battery interval. In summary, children with low IQ, reading and language skills were significantly impaired compared to age matched controls on tasks of auditory and phonological processing and in most other areas tested. However, children with low IQ and preserved decoding skills, termed in this thesis low IQ good readers, showed similar auditory thresholds and phonological abilities to age matched controls. Significant predictive relationships between auditory processing and single word reading skills were observed in two matched group analyses. These findings suggested that poor auditory processing may be linked to poor decoding in low IQ poor readers. This outcome cannot be explained as a result of low IQ given the age-appropriate performance on some aspects of auditory performance in the low IQ good readers. 4

5 Table of Contents General Introduction 9 Chapter 1. Reading disability in children of all abilities Introduction to reading disability The importance of phonology Classification procedures in the formation of dyslexia and low achieving 16 groups 1.4 Phonological tasks in low achieving poor readers and individuals with 17 dyslexia Nonword reading Phonological segmentation Word and non-word spelling Single word reading Reading comprehension in low achieving poor readers and individuals with 23 dyslexia 1.6 Reading intervention in low achieving poor readers Language performance in low achieving poor readers and individuals with 30 dyslexia 1.8 Summary 33 Chapter 2. The auditory deficit hypothesis of reading disability Introduction The rapid auditory deficit hypothesis The amplitude rise time deficit hypothesis Amplitude rise time discrimination in adults Backward masking Frequency discrimination Literacy interventions based on auditory deficit approaches Summary 53 5

6 Chapter 3. The development of phonological knowledge in children Development of phonological representations Environmental cues for speech segmentation Word recognition models Development of phonological awareness Early development of phonology Phonological awareness in early childhood Development of phonological working memory Introduction to the Baddeley and Hitch model of working memory The role of long term memory in the phonological store Phonological memory and learning to read Summary Research goals 67 Chapter 4. Phonological awareness, auditory processing and decoding in 69 low IQ children and controls (Project Phase 1) 4.1 Introduction Research Predictions Phase 1 Methods Participants Experimental tasks and procedure Results Standardised tests Phonological processing tasks Auditory tasks Summary and discussion 99 Chapter 5. Language skill in low IQ poor readers and controls (Project 102 Phase 2) 5.1 Introduction Phase 2 research questions 104 6

7 5.3 Phase 2 research predictions Phase 2 methods Participants Experimental tasks and procedure Phase 2 results General standardised tests Standardised language ability measures Non-standardised language ability measures Phonological awareness tasks Measures of verbal working memory Auditory tasks Language impaired subanalysis Introduction Selection of language impaired subgroups Research predictions Phonological processing ability Verbal working memory Auditory tasks Phase 2 summary 152 Chapter 6. Longitudinal relationships Introduction Research predictions Longitudinal matched groups assessment Longitudinal assessment of standardised variables and PSTM Longitudinal assessment of the language impaired matched groups Longitudinal assessment summary 177 Chapter 7. Project summary and discussion Introduction to summary and discussion Areas of performance which differ between low IQ good and poor readers 180 7

8 7.2.1 Auditory processing Language skills Digit span and working memory Characterising low IQ readers in regards to the existing literature Low IQ poor readers are similar to children with dyslexia in 189 demonstrating an auditory deficit Low IQ good readers are not best described as having hyperlexia Low IQ good readers are not best described as poor comprehenders Pragmatic language ability may be a relevant variable which has 192 not yet been assessed Low IQ readers do not demonstrate Matthew effects Overall conclusion Further research 195 References 198 Appendix Phonological tasks Non-standardised language tasks Auditory tasks Non-parametric statistics Additional tables 250 List of tables and figures 252 8

9 General introduction An inequality has long existed between children with what has historically been termed dyslexia and children who have general learning difficulties in addition to poor reading. Specifically, children with dyslexia have been given special status and access to further support barred to generally low achieving children. For example, to gain a statement of Special Educational Needs (SEN), the majority of Local Education Authorities (LEAs) in the UK have stipulated that a student must demonstrate poor reading in combination with an average or above average non-verbal IQ (see Dyslexia, Literacy and Psychological Assessment, a working party document from the British Psychological Society [BPS], 1999, 2005). The application of these exclusionary criteria, known as a discrepancy definition, bars access for many poor readers to the legally binding, individually tailored, supplementary support guaranteed by a SEN statement. Such a stance could only be ethically justifiable through an unequivocal demonstration of a qualitative difference between poor readers of low and typical IQ. Specifically relevant would be evidence that low IQ poor readers respond less well to intervention than those of average and above IQ. However, such evidence has not been found. As a careful review of the literature will illustrate, few differences between discrepancy defined individuals with dyslexia and poor readers with low IQ have been demonstrated. This includes response to word level remediation (see Fuchs & Young, 2006). Decoding, the process of translating written to spoken language, has been shown to function sufficiently outside of IQ as to be considered an independent skill. As this skill is a fundamental first step in successful reading, it is perhaps not surprising that poor readers of all abilities demonstrate similarly diminished reading and reading-related abilities, including reading comprehension (see review provided in Chapter 1). Given the demise of the discrepancy definition of dyslexia and the necessity for inclusion of all poor readers in definitions of reading disability, it is important to consider the ramifications of this change of viewpoint. Although a consensus now exists regarding the inappropriateness of IQ in defining reading disability, children with low IQ are still consistently excluded from research examining causal hypotheses. Practically this means 9

10 that we know very little about the underlying factors in poor reading in low IQ children. We also know very little about the general profile of the low IQ poor readers readingrelated strengths and weaknesses. Research conducted in America suggests that a far greater proportion of children with reading difficulties are Garden-Variety poor readers (children with low IQ and poor reading) than discrepancy defined dyslexics (Kieffer et al., 2007). If a similar situation is also the case in the UK (unfortunately, given the limitation of records this cannot currently be determined), low IQ poor readers could make up a large proportion of poor readers in our classrooms. With regards to the government s consistent priority to bring as many children as possible up to age appropriate reading levels (Jan., 2010, researching and preparing strategies to aid low achieving poor readers should be a priority. This thesis will go some way in preparing the ground for this goal by (1) profiling the reading and language related strengths and weaknesses of children who have low IQ and poor reading (2) investigating the relationship between auditory processing ability and these reading and language factors. As a behavioural approach was adopted for this thesis it is not possible to determine underlying biological similarities and differences between readers. Additionally, although significant relationships were identified, causal explanations can only be suggested and cannot fully be supported without further longitudinal evidence. However, despite these limitations, this thesis contributes valuable and novel findings. It represents the first (as far as it is known) investigation of auditory processing in children with low IQ and poor reading. Although certain cautions are necessary in the interpretation of group data, as individual children will vary in their behaviour, the comparative profiles presented here provide valuable information for both educators and researchers alike. For ease of reading a review of the structure of this thesis may be helpful. The thesis begins with a review of the relevant fields of literature starting with an overview of studies demonstrating similar reading related behaviours between low IQ poor readers and individuals with dyslexia. This section is key to the thesis as it lays out the rationale for approaching reading disability as a learning difference across children of all IQs. A number of studies are reviewed which demonstrate no significant differences between the 10

11 behaviour of individuals with dyslexia and low achieving poor readers. As such, this first section provides the rationale for approaching all poor readers with similar causal hypotheses. It also supports the wider theoretical stance, that children with low IQ and poor reading are an appropriate subject for reading disability investigations. Following the reading disability chapter, a second literature review concerning the auditory deficit hypothesis is presented. It is important to note that this chapter does not review any studies concerning auditory deficit hypotheses in low IQ children with poor reading as studies of this type do not exist, an oversight rectified in this thesis. In this second chapter, a summary of the mixed findings concerning auditory processing as a causal contributor to reading and language disability is presented. Currently, no consensus concerning the relationship between auditory processing skills and the development of appropriate decoding skills exists. It has been suggested that the mixed results demonstrated on this topic may be due to the differences in auditory tasks administered and the fact that many studies have measured only one auditory variable increases the variability in outcomes. Alternatively, mixed results could be due to the lack of regard for sample variations in IQ and/or poor stringency in the characterisation of the reading disabled sample (Kuppen et al., in press). This second chapter also includes an outline of the amplitude envelope deficit hypothesis which suggests children with reading difficulties may have auditory problems related to the processing of rhythmic timing. Rhythmic timing is related to changes in the amplitude envelope, further characterised by differences in amplitude rise time, a concept which is explained further in Chapter 2. This version of an auditory hypothesis is of particular interest, as it motivates the experimental research underlying the current thesis. Although a deficit tied to the processing of the amplitude envelope is a relatively new area of research. It appears a very promising avenue for understanding the foundations of reading disability. A recent meta-analysis of all auditory non-speech studies tied to reading in adults and children (Hämäläinen et al., in press), identified significant differences between individuals with dyslexia and controls in studies investigating amplitude rise time (believed to be a key factor in the processing of rhythmic timing in speech). Additionally, significant further group differences were found on auditory tasks related to 11

12 the processing of the amplitude envelope. Seventy-three percent of studies investigating frequency discrimination, plus 67 percent of duration studies and 10 percent of intensity studies also found a group difference. To further underline how an auditory deficit hypothesis may impact upon reading and language over development, the third chapter summarises the literature concerning how knowledge and representations of the sounds in language are acquired. Developmental studies demonstrate that phonological processing ability is key to reading and language skill. Thus children who possess an auditory deficit may have difficulties with the development of phonological awareness which in turn may impact upon reading acquisition and language development. Although it may appear that a significant amount of research exists concerning the low IQ poor reader, this project responds to a prominent gap in the current literature. To reiterate, although children with poor reading and low IQ have similar reading related behaviours to children with typical and above IQ, the low IQ group are currently excluded from reading disability research. Again this is despite the demonstration that poor reading cannot be fully explained by low IQ alone. This thesis aims to rectify this oversight and provides an assessment of the relationship between reading and language factors and underlying auditory processing abilities in the low IQ poor reader as compared to controls. The remaining chapters of this thesis outline the procedures and outcomes for the administration of the two task batteries (Phase 1 [Chapter 4] & Phase 2 [Chapter 5]). Included in Chapter 5 is a sub-analysis of children identified from the project as being language impaired. In this analysis the language impaired children are compared to other children from the project who are matched on vocabulary age in addition to a chronologically age matched group. Longitudinal assessments of these language impaired matched groups are presented in Chapter 6 in addition to the primary longitudinal assessments for the project as a whole. It is worth noting here that the primary longitudinal analyses presented in Chapter 6 utilise the matched groups 12

13 presented for Phase 2 (Chapter 5). Although the Phase 2 matched groups set represents a smaller group of children than presented in Phase 1 (Chapter 4), this procedure was necessary due to attrition rates and differing inter-battery intervals. For further details of this approach, please see Chapter 6. The final chapter (Chapter 7) presents a general overview of the key findings. Here the most important issues emanating from the research are discussed and are related to a further discussion of the most relevant literature. This chapter is completed by an overview of the plans currently underway for future research. An outline of a follow-up project with this doctoral cohort is provided. 13

14 Chapter 1. Reading disability in children of all abilities 1.1 Introduction to reading disability Unlike learning to talk, reading is a skill which must be explicitly taught. In most countries this process begins in the primary classroom, where children are first introduced to letter and sound correspondences. However, despite receiving adequate tuition and opportunity, some children are not able to master reading and spelling at the level of their peers. These children, who demonstrate no obvious contributing disorders, are known as having a reading impairment or dyslexia. The Research Group on Developmental Dyslexia of the World Federation of Neurology defines dyslexia as a disorder in children who, despite conventional classroom experience, fail to attain the language skills of reading, writing and spelling commensurate with their intellectual abilities (1968). More recently the American Psychiatric Association has published this definition of reading disability in its Diagnostic and Statistical Manual of Mental Disorders: Reading disability is diagnosed when Reading achievement, as measured by individually administered standardized tests of reading accuracy or comprehension, is substantially below that as expected, given the person s chronological age, measured intelligence and age appropriate education. (DSM-IV, American Psychiatric Association [APA], 1994, p.74) Essentially, the terms reading disability and dyslexia can be used interchangeably. The key factor is whether a discrepancy between intellectual capacity as measured by IQ and reading ability is in place. As the common usage of the term dyslexia implies a discrepancy definition, such usage will be adopted for this thesis. However, as it is now clear that a phonological deficit, unrelated to IQ, is at the heart of decoding written to spoken language, discrepancy definitions are no longer in favour. Reading disability or dyslexia can now be diagnosed simply when accurate and fluent word reading and/or 14

15 spelling develops very incompletely or with great difficulty (Dyslexia, Literacy and Psychological Assessment, BPS, 1999). Although a sea change in educational conceptions of reading disability is taking place, research has been slow to include low IQ poor readers in causal investigations. It is thus important to review the large amount of research which demonstrates similar reading related behaviours in low achieving poor readers and individuals with dyslexia. This research underlines the importance of including low achieving poor readers in reading disability research and supports the choice of this group as the subject of the present investigation. Additionally, in investigating poor phonology as the key deficit in low IQ poor readers, it is also essential to present tasks which have differentiated children with reading and language problems in the past literature. This review provides the rationale for the choice of tasks, the composition of which are presented and discussed in the experimental chapters of this thesis. 1.2 The importance of phonology A large amount of converging evidence now supports the relationship between the ability to represent and manipulate the sounds in spoken language, known as phonological awareness, and learning to read. Segmentation skills are good predictors for later decoding achievement (Bradley & Bryant, 1983; Goswami & Bryant, 1990 for a review) and phonological training for readers (e.g. Bradley & Bryant, 1985) and pre-readers (Lundberg, Frost & Peterson, 1988) and is related to superior decoding outcomes. Phonological awareness is a better predictor than IQ of individual variance in reading attainment (e.g. Siegel, 1993) and this statement is equally true when evaluating individual responses to single word reading remediation (e.g. Vellutino et al., 1996 discussed further in section 2.6). These findings are encapsulated by the phonological deficit hypothesis. This hypothesis places poor phonology at the heart of the deficits observed in short-term memory, sound segmentation, sound categorization, sound blending and consequently reading and spelling in poor readers (Rack, Snowling & Olson, 1992). The Phonological Core Variable Difference (PCVD) model (Stanovich, 1988) is a variation of the phonological 15

16 deficit hypothesis and a conceptualisation of the specificity hypothesis of reading disability. In this framework two types of reader are described. The first is the reader with dyslexia who has an impairment limited to the phonological core. The second is the garden variety poor readers whose deficits include and extend beyond the phonological core. Following this model, the prototypical individual with dyslexia can be described as being of average to above average IQ with a specific reading disability, while the garden variety poor reader suffers from more general deficits and a below average IQ. Despite these differences both poor reader groups show poor word decoding and demonstrate a diminished performance on all phonological tasks. Such a conceptualisation places the phonological deficit at the heart of all poor reading. It also supports the inclusion of low IQ poor readers in reading disability research. A large amount of research now supports the PCVD model. Selected studies are presented below. 1.3 Classification procedures in the formation of dyslexia and low achieving groups In the comparison of reader group behaviour, the criteria used for classification will be fundamental to the outcomes achieved. In the case of low achieving poor readers and dyslexia groups it is important to assess cut-off criteria for group classification. It is also essential to look at the type of intelligence test which has been used. At a basic level, intelligence tests measure two types of cognitive functioning; verbal and non-verbal ability, with the latter also known as performance-based intelligence. As a full scale IQ is a combination of these measures, language ability comprises a large component of full scale IQ and may be depressed in children with poor reading ability. To address this issue some researchers have used non-verbal IQ measures to categorise poor reader types. However, the appropriateness of this measure has been called into question (e.g. Siegel & Heaven, 1986). Siegel, amongst others, claims many performance-based IQ measures (e.g. Wechsler Intelligence Scale for Children [WISC-R] Wechsler, 1974) still possess a strong language element and thus may still be unfair to disabled children. Others offer alternatives such as a definition of reading disability defined as a discrepancy between listening and reading comprehension (Badian, 1999). The choice of reading measure in the classification of reading disability is also quite variable. Although some studies choose to use measures of reading comprehension in addition to word reading measures, 16

17 it is likely that the latter provides an adequate measure for definitional purposes. An individual who is reading significantly below the mean on a test of single word reading is unlikely to possess good levels of reading comprehension. Additionally, individuals with good word reading and poor reading comprehension are known as poor comprehenders and do not represent the appropriate profile for individuals with reading disability. In all cases where possible in this thesis, an indication of the classification parameters for individual studies has been given. 1.4 Phonological tasks in low achieving poor readers and individuals with dyslexia Nonword reading. A popular task to assess the quality of children s ability to apply letter to sound correspondences is the reading of nonwords, also known as nonsense or pseudowords. In the dual route conceptualisation of word recognition, a direct route for decoding allows an irregular word such as yacht to be recognised and pronounced as a whole unit, while unfamiliar words or nonwords such as drepnort can be sounded out using an indirect phonological route (Coltheart, 1978). A large number of studies have demonstrated a particular difficulty for poor readers of all intellectual abilities to pronounce nonsense or pseudowords, suggesting an impaired phonological route. For example, Felton and Wood (1992, references which are discussed in detail are bolded for ease of consultation) used a reading age match design to test the hypothesis that children with dyslexia would read nonwords below the level of younger decoding matched children, while low achieving poor readers would read nonwords at a similar level to these controls. The authors tested 93 third grade and 54 fifth grade children who were selected from a larger group of children presented as poor readers by their teachers. The selected children demonstrated low percentile scores on the California Achievement Test reading test (no reference given) and were compared to 147 typically reading first graders. In summary, Felton and Wood were unable to confirm their hypothesis. They found that all poor readers were significantly impaired on pseudoword reading compared to the grade 1 reading age controls. Performance on this nonword reading task was unrelated to verbal IQ within the poor reading group. However, it is important to note that ideally a 17

18 chronological age control group should also be included in reading age matched designs. In the Felton and Wood study it is not clear that poor readers would be worse in their performance on nonword reading than other children their own age. The discrepancy in grade related performances could in theory be due to other factors such as differences in educational provision. Further evidence for a shared pseudoword reading deficit amongst all poor readers comes from a longitudinal study by Share (1996), who followed 543 children from school entry to grade two. At the end of grade two he identified 19 specific reading disabled children (word recognition in context [Neale] and/or Neale reading comprehension 1.5 SE below that predicted by age and IQ [Columbia Mental Maturity Scale]) and 13 low achieving poor readers (accuracy or comprehension scores 1.5 SD below the sample mean but not included in the first group) matched on age, sex and word recognition. Poor reading groups were not teacher-referred and were defined only by scoring poorly on word recognition or reading comprehension or both. From this group 11 children from each group were followed up to grade three (age eight) and individually matched on age and reading age. It is not clear why children were identified in year two and tested in year three but it is likely due to the practicalities of the larger study from which they were selected. Both groups were found to be equally poor on 25 out of the 26 measures of reading and spelling which were administered, these included reading regular, exception, strange and pseudowords. The only statistically reliable difference between the two groups came from reading errors where the target word was substituted for a regular high frequency word (MUST most). Here, specific reading disabled children made a significantly higher proportion of errors. However, it must be noted that these findings relate to only a very small sample of children (22). Also, no control group performances were included in the comparison. Finally, Stanovich and Siegel (1994) provided further support for the view that children with dyslexia and low achieving poor readers do not differ. In a meta-analysis with 907 children reading between approximately school grade years one through six (as judged by grade equivalency scores on the Reading subtest of the Wide Range Achievement Test, 18

19 WRAT-R, Jastak & Jastak, 1978) participants were put into three groups; Group 1 had percentile scores on the WRAT-R than 30 (normal reader group), Group 2 had percentile scores on the WRAT-R to 25 with IQs greater than 90 (dyslexia group) and Group 3 had percentile scores on the WRAT-R 25 with IQs 90 (low achieving poor reader group). Children were then grouped according to reading grade level, allowing for a regression based reading age match control design to be used. Analysing the outcomes of seven different phonological tasks including pseudoword spelling, pseudoword reading and word decoding tasks, the children with dyslexia were found to slightly outperform the low IQ poor readers on one pseudoword reading measure. The effect size was small (0.09) and explained only a small amount of unique variance and therefore was considered theoretically insignificant by the authors. Overall, there was a consistent tendency for groups 2 and 3 to perform less well than group 1 at all reading levels on all of the tasks. Additionally, both of the poor reader groups generally performed below the reading age controls as assessed through the use of regressions. Overall, this study provides strong support for the existence of few if any systematic differences between the group of children with dyslexia and the low achieving poor reader group. In this study the participant group is large and the statistical techniques are robust Phonological segmentation. Another popular measure for assessing phonological awareness is the phonological segmentation task. At the most basic level, these tasks require a child to tap out, count or identify the phonemes within a word. For example with the word hat the child would tap, count or identify the three phonemes within the word (/h/ /a/ /t/). In more advanced segmentation tasks the child might be required to hold the sound representation of the word in his or her head and perform a phonemic substitution, addition or deletion. A number of researchers have compared phonological segmentation skills in children with dyslexia and low achieving poor reader groups. Fletcher et al. (1994) for example, used the deletion task with 199 children aged 7 years 5 months to 9 years 5 months. Participants were selected from a larger sample of children involved in a study on learning, attention and behavioural disorders. These children were recruited through schools, parent groups and media announcements and were not teacher referred. Participants were selected on the basis of two definitions of 19

20 learning disability. One definition was the discrepancy definition and identified a child as having dyslexia if he or she had a regression discrepancy of 1.5 standard errors between achievement and IQ. The measures were the Woodcock-Johnson Psychoeducational Test Battery (Woodcock & Johnson, 1977) decoding subtest (real words and pseudowords) and the IQ test used was either the WISC-R Verbal IQ or the WISC-R Performance IQ. It is supposed that this choice allowed for one aspect of IQ without disqualifying the individual from the discrepancy definition. The second definition was the low achievement definition and identified children as learning disabled if they scored below the 15 th percentile on the Woodcock-Johnson reading decoding subtest. This definition required either a WISC-R Verbal IQ or Performance IQ above 79. A typical control group was also recruited. Fletcher et al. administered nine tasks, one of which was a phoneme deletion task (Auditory Analysis Test, Rosner & Simon, 1971). These authors found that although differences between poor reader groups and controls were large, no differences between the two types of poor reader groups were found. In another example, Hurford et al. (1994) tested 171 first graders on reading-related variables over the course of two years. In addition to other phonological processing tasks, Hurford et al. administered a phonemic segmentation task which consisted of 12 consonant-vowel-consonant (CVC) words and 12 CVC pseudowords. The task began with the experimenter pronouncing the word and asking the participant to repeat it. The participant was then asked to repeat the word again without a particular consonant (e.g. Now say bug with the /b/ sound). At the final testing point three groups were identified (controls: standard score [SS] > 1 SD below the mean on a reading composite score [average of the SS for Word Identification and Word Attack subtests of the Woodcock Reading Mastery Test {WRMT-R, 1987}] dyslexia group: SS 1 SD below the mean on the reading composite with 1 SD below the mean on Picture Vocabulary Test Revised [PPVT-R, Dunn & Dunn, 1981] and low verbal IQ poor readers: SS 1 SD below on the reading composite and PPVT-R ). A multivariate analysis of variance (MANOVA) found that children who were Garden-Variety poor readers were significantly older than the students in the other two reading groups. The control group performed better than the 20

21 two poor reader groups on all of the reading related tasks including the phoneme deletion task. The two poor reader groups were not significantly different on this task. However, it must be remembered that the ages were not matched between the two groups and the superior age of the low verbal IQ poor readers may have affected the outcomes on these tasks Word and non-word spelling. As learning to spell is the process of learning to symbolise oral language, it is highly dependent on the quasi-regular sound to letter correspondences found in the English language. For this reason, poor spelling is one of the hallmarks of reading disability and according to the phonological core deficit model should be impaired in all poor readers. Confirmatory evidence was found by Siegel (1992) who performed a meta-analysis of a number of her previous studies covering 1,657 children age seven to 16. Children were defined as reading disabled on the basis of a Wide Range Achievement Test (as before) reading score less than or equal to the 25 th percentile. The remainder of the children were identified as nondisabled readers if their percentile scores on the WRAT reading tests were at the 30 th percentile and above and if their IQ test was at 80 or above. IQ tests were either the WISC-R (as before), or the Peabody Picture Vocabulary Test (Dunn, 1965). The children with reading disability were divided into two groups based on the difference, or the lack of difference, between their scores on the reading subtest and the IQ test. Children were identified as having dyslexia if their reading standard score was more than 15 points lower than their IQ score. Comparing children with dyslexia to low IQ poor readers and controls on a selection of phonological and linguistic tasks administered, Siegel found very few significant differences between the two poor reader groups. Specifically, on measures of word (PIAT spelling test) and nonword spelling (Spelling of Symbols Subtest Goldman, Fristoe & Woodcock sound-symbol Test) poor reader groups were not significantly different in performance from each other but were significantly different from the age matched typical readers. Similar results were reported (reviewed above) for Stanovich and Siegel,

22 1.4.4 Single word reading. The evidence presented so far overwhelmingly supports a lack of differentiation between individuals with dyslexia and low achieving poor readers on phonological tasks, nonword reading and spelling. These findings are equally true when assessing potential differences in word reading strategies, i.e. phonological versus whole word approaches. The dual route model of reading assumes that two strategies are used for reading regular and irregular words, a whole word approach for irregular words and a phonological approach for regular items. To test the hypothesis that children with dyslexia may have an underlying phonological disability which requires them to use whole word strategies, Fredman and Stevenson (1988) compared children with dyslexia to low achieving poor readers. For this study, single members of twin pairs were selected from an epidemiological twin study investigating the genetics of reading disability. It is not clear how this original sample was recruited. From this larger pool, Fredman and Stevenson selected 38 thirteen-year-olds with dyslexia who were compared to 63 low achieving poor readers. Individuals with dyslexia were identified by attainment of either Neale reading accuracy or comprehension scores two years (two standard errors) or more below the level predicted on the basis of age and full scale WISC IQ. Individuals who were low achieving poor readers were again two years or more below the mean reading age for the original twin sample on the reading accuracy or comprehension tests. This experimental design was slightly unusual in allowing individuals with poor reading comprehension and good reading to be classified as reading disabled. Nevertheless, this study found no difference in irregular and regular word reading performance by poor reader group. Development of word reading skills therefore also appears to be similar in the two types of poor reading group. Francis et al. (1996) identified 32 children with dyslexia (Reading-cluster score from Woodcock-Johnson [composite of Word Identification, Word Attack and Passage Comprehension subtests] 1.5 SE below predicted by WISC-R), 37 low achieving poor readers (Reading cluster Woodcock-Johnson < 25 th percentile and falling outside dyslexia group) and 334 not reading impaired children from the Connecticut Longitudinal Study (CLS). The children in the CLS represent two randomly chosen kindergarten classes within each of 12 communities in Connecticut and therefore represent all ability levels. 22

23 Growth models were fitted for each child over the nine year period. Individual children were found to vary in their rate of growth, in their final level of performance and in the age at which they reached that level. However, there were no significant differences between the two poor reading groups on any of the variables related to the word reading measures. 1.5 Reading comprehension in low achieving poor readers and individuals with dyslexia In accordance with the simple view of reading, poor decoding constrains reading comprehension. When decoding is slow and effortful resources are consumed by word level processes and comprehension suffers (Perfetti, 1985). In modelling the role of language as espoused in the simple view, the triangle model, popular with connectionist approaches to reading development (e.g. Seidenberg & McClelland, 1989), is often invoked. The triangle is formed by three main skills; orthography (written words), phonology (sounds of words) and semantics (meaning of words). Orthography and phonology form the base of the triangle and are linked by the phonological pathway, which maps letters to sounds. The semantic pathway provides a more indirect route to sound, with semantics linking orthography to phonology. In other words, this route maps words to sounds through meaning. It is theorised that once an efficient reading system has been developed, the phonological pathway is used to read new words, while the semantic route is activated by the reading of familiar exception words. According to this model the low IQ poor reader has disruption to both routes, while individuals with dyslexia maintain a functional semantic pathway and have difficulties restricted to the phonological pathway. The existence of children with a poorly functioning semantic pathway, while maintaining a preserved phonological pathway, provides some support for the hypothesised separation of systems within the model. These children are known as poor comprehenders and have been characterised by poor semantic and vocabulary skills (Nation & Snowling, 1998) and low levels of verbal working memory (Marshall & Nation, 2003). In accordance with the simple view of reading, poor comprehenders may generally be characterised as having difficulty with linguistic comprehension rather than decoding. Indeed oral language appears to be weak in this group, evidence which also 23

24 supports the simple view of reading as representing the product of decoding and oral comprehension in children. However, it is important to note that these children should be differentiated from individuals with hyperlexia. Although individuals with hyperlexia demonstrate poor reading comprehension with good decoding they also often additionally suffer from learning disabilities such as autism (Grigorenko et al., 2003) and exhibit particular behavioural characteristics discussed further in Chapter 7 of this thesis. The example provided by poor comprehenders suggests that children with semantic and vocabulary problems will have difficulty with reading comprehension (Snowling & Hulme, 2007). This is indeed the case in children who have extensive linguistic difficulties, particularly in oral language, while maintaining normal non-verbal IQ. Children with this cognitive profile are known as having Specific Language Impairment (SLI) and are often found to display word reading problems, have difficulties with reading comprehension and also have poor spelling (Stothard et al., 1998). To describe such children, Bishop and Snowing (2004) have extended the Seidenberg and McClelland (1989) triangle model to reflect the role played by language in reading development. Grammar and Discourse are added as branches to Phonology, reflecting the role grammar and context play in activating semantic and thus phonological representations in successful reading. It is believed by authors such as Bishop and Snowling (2004) that an impairment of such linguistic resources may influence the development of reading in children with language impairments. The practical outcomes of variations in phonological and language abilities have been effectively modelled by these authors. A modified version of the model, which includes the reader groups of interest to this project, is provided in Figure 1.1 (Good readers with low IQ [GR-LIQ] and poor readers with low IQ [PR-LIQ] will be fully discussed later in this thesis). 24

25 Figure 1.1 Model of oral language and phonological skills in the reader groups of interest (modelled after Bishop & Snowling, 2004). Very few experimental comparisons exist which look at reading comprehension in agematched low achieving poor readers and individuals with dyslexia. This may be due to a belief that reading comprehension is too closely linked to the single word reading matching variable, used for classification, for any differences to be found. In fact in some studies, reading comprehension itself has been used as a classification variable, essentially producing two poor reader groups with similarly impaired levels of reading comprehension and thus making this variable unsuitable for analysis. An exception to this generalisation is provided by Siegel (1988; 1992). Siegel (1988) provided a metaanalysis of her previous studies on measures of reading, spelling, arithmetic, memory and language in poor and typical readers from age seven to 16. To be considered reading disabled, children had to demonstrate scores of 25 th percentile on the reading subtest of the Wide Range Achievement Test (WRAT, Jastak & Jastak, 1978). Non-reading disabled children had a score of 30 th percentile on this test. FSIQ WISC scores were used. After having categorised children according to age and IQ, certain patterns were found. Although reading disabled children performed at significantly lower levels on most tests given, no significant differences were evident between IQ bands within this reading disabled group. This conclusion extended to a measure of reading comprehension level as assessed by the Gilmore Oral Reading Test (Brace and World, 1968). Siegel (1992), in a further meta-analysis of her past studies (likely involving some of the same children from the 1988 study, but not stated), investigated a total of 1,657 25

26 children again aged seven to 16 and found similar results. Children were classified as reading disabled or as typical readers according to the criteria in the previously discussed meta-analysis. Again little variation was found according to FSIQ group within children who were reading disabled. This finding once again extended to the Gilmore comprehension test. Although Siegel found no differences between the two poor reader groups on measures of reading comprehension, it is important to remember that IQ is related to reading comprehension when considering readers in general. Research has found that IQ, especially verbal IQ, is strongly related to reading comprehension in the later primary and secondary school years. For example, De Jong & van der Liej (2002) found verbal IQ was significantly related to the development of reading comprehension over grades two to five in Dutch children. A longitudinal study evaluating reading growth from second to fourth and third to fifth graders, found that performance on the Vocabulary subtest of the Stanford-Binet intelligence test increased over development. While Grade two vocabulary explained 24 percent of the variance in Grade four vocabulary, Grade three vocabulary explained 43 percent of the variance in Grade five vocabulary (Torgesen et al., 1997). It is therefore likely that the similar levels of reading comprehension in low achieving poor readers and children with dyslexia in the Siegel studies was due to the bottleneck presented by poor decoding. Deficits in word reading may have been blocking access to comprehension for both groups and thus the potential vocabulary advantage provided by higher FSIQ for the individuals with dyslexia did not come into play. A relationship between IQ and reading comprehension has also been found in intervention studies assessing all reader types with typical levels of IQ. Although IQ does not predict growth in reading decoding skills within this group (similar to the results from Francis et al discussed previously, which included children with low IQ) it is a significant unique predictor when assessing reading comprehension over development. Hatcher and Hulme (1999), utilising data gathered from a previous study (Hatcher, Hulme & Ellis, 1994), examined the relationship between a number of phonological tasks and measures of IQ as predictors of reading skill remediation. One hundred and twenty- 26

27 seven, seven-year-olds with standardised reading quotients of less than 86 (Word Recognition Test, Carver, 1970) and a 25 th percentile or higher on the Ravens Coloured Progressive Matrices (Raven, 1956) were divided into four groups. Matched on IQ (WISC-R), reading ability (BAS word reading) and age, children were randomly assigned to four conditions (a) Reading and phonology (b) Reading alone (c) Phonology alone (d) Control. Children were administered a battery of cognitive, reading and phonological measures before beginning a 20 week intervention consisting of 15 minutes targeted teaching according to condition. Children were tested again at the completion of the intervention for the purposes of measuring progress. General decoding ability after remediation was found to be related to group membership (i.e. variation of intervention) but not to IQ. However, individual differences in verbal IQ were related to responsiveness in terms of gains in reading comprehension ability. These findings further underline the relationship between IQ and reading comprehension, although it is important to note that children with very poor reading and significantly reduced IQ (i.e. below the 25 th percentile) were removed from this sample In a review of the reading intervention literature Fuchs and Young (2006) assessed 13 studies involving 1,542 children who were at risk for poor reading or reading disabled to assess whether IQ predicted response level to reading intervention. Of the 13 investigations reviewed, six involved interventions consisting primarily of phonological awareness training while seven included a more extensive intervention involving remediation of reading comprehension. The authors found that IQ became an increasingly robust predictor of responsiveness to intervention as the instruction programs became more complex. In fact in every study in which reading comprehension was measured, IQ was a statistically significant predictor of growth. This topic is discussed in more detail in the section on reading intervention which follows. In summary, although evidence is somewhat mixed, it is reasonable to expect variations in reading comprehension in line with IQ. FSIQ measures are heavily dependent on vocabulary, which is an essential component in understanding spoken or written text. The relationship between non-verbal IQ and reading comprehension is less clear. It is likely 27

28 however that individual variation in short term memory and test taking strategies (Siegel & Heaven, 1986) known to influence reading comprehension scores, will play a role in both areas. Overall, in relation to the evidence available, there is no support for the view that groups of individuals with dyslexia and groups of low achieving poor readers will show a significant difference in mean performance on measures of reading comprehension. However, a relationship between IQ and reading comprehension has been supported. 1.6 Reading intervention in low achieving poor readers It is now acknowledged that the best approach for children with literacy learning difficulties is early intervention. Literacy interventions for struggling readers are a component of school provision and form part of the national educational policy (DfES 2003). Studies demonstrating the short term (e.g. Wasik & Slavin, 1993; or for a review Teaching children to read, National Institute of Child Health and Human Development, 2000) and long term (e.g. Hurry & Holliman, 2009) benefits of reading tutoring programs support this approach. Reflecting the previously presented literature, it has also become evident that all poor readers can gain from remediation programs. As discussed in section 1.5, Fuchs and Young (2006), in their review of 13 intervention studies of low achieving and discrepancy defined poor readers, found that the two groups benefitted equally from decoding based reading interventions. However, when response to intervention included a comprehension element, poor readers with a higher IQ demonstrated better overall outcomes. Indeed, one of the few large scale intervention studies to include low achieving poor readers and demonstrate clear long term success was decoding rather than comprehension based. This study was recently reported by the Institute of Education ( 2010) and concerned the longitudinal reading outcomes for an intervention program known as Reading Recovery. The administration of Reading Recovery (RR) was part of the Every Child a Reader strategy which attempts to give children a boost in their early reading skills. In 2005, a 10 million pound program addressing the lowest performing readers in 10 disadvantaged London boroughs was launched. Out of the 235 total children, 147 were administered 28

29 one-to-one 30 minute daily sessions of individualised reading remediation (RR) while the other poor readers continued with their various reading programs in school (controls). In this study, significantly different gains in decoding for the experimental versus control group both one year and three years after intervention were demonstrated to have taken place (Hurry & Holliman, 2009). More specifically, children who had been administered a Reading Recovery program demonstrated a 13 month gain in reading age (BAS single word reading) over controls at a one year follow up point. At a three year re-assessment point (mean age nine) only National Curriculum Assessment levels were available for comparison purposes (correlated with BAS at r =.83). For ease of interpretation, Level 2 represents the expected attainment level for a seven-year-old while Level 4 is the expected attainment at age 11. Letters are used to indicate sublevels with a representing a strong level, b a sound level and c a weak level. At the end of Year 4, control children (N = 120) were at national curriculum level 2a while RR children (N = 73) were at level 3b (indicated by Hurry & Holliman as on track for level four at age 11). Although IQ tests were not administered as part of this project, it is likely given the demographics of the sample that a significant proportion of low achieving poor readers were included. Specific socio-economic demographics were provided across the school groupings rather than for the particular groups tested. However, from the data available it is evident that in both RR and control schools a high proportion of children were receiving free school meals (39.6 percent RR and 44.2 percent controls). Free school meals are typically used as an acceptable proxy for socio-economic status (SES), however it is worth noting that discrepancies between the two measures have been identified (Hobbs & Vignoles, 2007). Potentially more reliably, a relationship has also been drawn between IQ and SES, with children from low income families demonstrating a more generally depressed IQ score (Siegel & Norman, 1998). Given the free school meals data, it may therefore be suspected that many of the RR children demonstrating significant gains from intervention represent low achieving poor readers. However, with only mean outcomes presented and no IQ information available, it is possible that low IQ poor readers did in fact respond less well than their peers but that these data are simply not visible in the output provided by this report. 29

30 Further evidence demonstrating an irrelevance of IQ levels in reading remediation is provided by Vellutino et al. (1996). This study divided first grade poor readers (reading below the 15 th percentile on the Word Identification or Word Attack subtest of the Woodcock Reading Mastery Test, WISC-R [non-verbal or verbal] score of 90 or above) into readily remediated and difficult to remediate students. Response to remediation was judged following a comprehensive one to one tutoring program consisting of 15 weeks of thirty minute daily sessions. This intervention was composed of the reading of connected text, the practicing of word identification strategies, developing a sight vocabulary, practicing phoneme awareness and developing the alphabetic principle and writing skills. Reading achievement was measured through a word identification task, oral reading of connected text and silent reading comprehension. Vellutino et al. found children who were difficult to remediate performed below both children who were readily remediated and normal readers on phonological skills. However, despite the comprehension element, IQ scores did not differentiate the two groups (find further discussion of this study in relation to the discrepancy definition of reading disability in Vellutino, Scanlon & Lyon. 2000). These findings could be related to the specific techniques taught in the tutorial sessions. Although no specific evidence is readily available to support this suggestion, helpful tips in word identification, particularly how to use the context of the text effectively and a concentration on building vocabulary, an essential component in reading comprehension, may be equally helpful to all poor readers. 1.7 Language performance in low achieving poor readers and individuals with dyslexia As has been summarised to this point, to learn to read children must have an understanding of the alphabetic system. They must learn that letters of printed words correspond in a systematic fashion to phonological segments of a spoken word. As discussed, children who read poorly have difficulty producing, holding and manipulating the sounds of speech. The phonological core variable difference model suggests that this difficulty may be causally related to all poor reading. However, according to the simple view of reading (Hoover & Gough, 1990), reading with understanding requires linguistic comprehension in addition to the decoding of letters into speech sounds. This proposition 30

31 appears logical as it is difficult to comprehend a text in which many words can not be identified. Just as difficult is to comprehend decoded words when you have limited linguistic knowledge. Also supportive is the demonstration that if measures of word reading and listening comprehension are taken separately on the same group of children and then multiplied, resulting scores are strongly correlated with measures of reading comprehension (Hoover & Gough, 1990). Using this simple model, Hoover & Gough characterise the individual with dyslexia as skilled in linguistic comprehension but poor in decoding, while the low achieving poor reader is described as having difficulties in both areas. Experimental evidence however does not really support this model. Although low achieving poor readers do perform poorly in both areas, many studies find that despite good vocabulary, individuals with dyslexia can also show low language skills. For example McArthur et al. (2000) found that more than half of their sample of 110 discrepancy defined children with dyslexia was 1 SD below the mean on a battery of oral language skills (Clinical Evaluation of Language Fundamentals Revised [CELF-R] Semel, Wiig & Secord, 1989). Although higher level language tasks may appear peripheral to phonological processing, deficits in morphological manipulation may be directly related to phonological skill. For example, Fowler and Liberman (1995) found that seven to 10-year-olds (IQ levels unknown) who had poor reading and spelling also had particular difficulty in producing morphological forms which involve a phonological change (e.g. courage/courageous) as compared to producing forms which did not (e.g. danger/dangerous). A number of studies comparing individuals with dyslexia and low achieving poor readers are available which assess measures of grammar, syntax and oral comprehension. For example, using a retrospective predictive method, Jorm et al. (1986) investigated reading and language deficits in a cohort of 453 Australian kindergarten children, measured in their first three years of schooling (mean start age 5 years 4 months to mean finishing age 7 years 11 months). All children were assessed on a range of cognitive tasks at the start of their first term at school. Children were then followed up in year three and administered the Neale reading test and a non-verbal intelligence test, the Columbia Mental Maturity Scale (Burgemeister, Blum & Lorge, 1972). A regression equation to 31

32 predict Neale reading score from a combination of age and Columbia score allowed children to be classified at age 11 into a dyslexia group (Neale principal component score more than 1.5 standard errors below that predicted), or a low non-verbal (NV) IQ poor reading group (children more than 1.5 standard errors below prediction using age only and outside of dyslexia group mean = 76.93). All other 414 children were classified as normal readers. Jorm et al. (1986) found both poor reader groups to be significantly diminished compared to controls on measures of syntax (Northwestern Syntax Screening Test), vocabulary (Peabody), non-confusable sentence memory and picture/colour naming. However, low non-verbal IQ poor readers performed at a significantly lower level than individuals with dyslexia on all of these measures with the exception of picture/colour naming. Thus, the Jorm et al. study reports poor vocabulary and syntax for all poor readers, but a more profound and wider ranging deficit in low non-verbal IQ poor readers. In contrast to the Jorm et al. (1986) findings, Siegel (1988) as presented earlier, found performance on a number of language measures to be largely unrelated to IQ. Siegel performed a meta-analysis of a number of her previous studies which included a total of 120 reading disabled ( 25 th percentile on the reading subtest of the Wide Range Achievement Test,[as before]) and 719 control children aged seven to 16 years old. Children were then divided into groups on the basis of their IQ scores and age. Three types of IQ test were possible depending on the origin of the data; they were either a full scale WISC-R score, an estimated WISC-R score from the Vocabulary and Block Design subtest or Peabody Picture Vocabulary Test Scores (PPVT, Dunn, 1965). Three-way ANOVAs with reading group, IQ level and age found few significant differences on the language tasks within the poor reading group. At the age of nine, higher IQ poor readers performed better than those of lower IQ on tasks of grammatic closure (Here the man is planting a tree. Here the tree has been ) and oral cloze ( Four the boys were eating their lunch) however, this was not the case at other ages. Although only small numbers of children were available for comparison on 32

33 tasks of sentence correction (The moon is very big and bright in the morning) and sentence repetition (The cat that the bird sees is in the tree), children who did perform these tasks did not demonstrate an IQ related difference. This was also the case for the phonological short term memory task (the child has to write down in the correct order 5 rhyming or non-rhyming letters presented for 3 s), working memory (the child must supply the missing word for all sentences in a set e.g. In summer it is very, People go to see monkeys in a, With dinner we sometimes eat bread and ) and number working memory (counting of dots and then recalling of counts for each set). Siegel found few differences among the IQ groups within the reading disabled participants. Reading disabled children at all IQ levels had significantly lower scores than typical children and the variation amongst the IQ groups was in the whole part nonsignificant. More specifically, Siegel found that children with reading disability were poor on syntax compared to non-disabled readers regardless of IQ. Siegel (1992, reported above) found similar results with poor readers of all IQs performing at a similarly diminished state on grammatical closure tasks (irregular items) as well as on a test of text reading accuracy, reading comprehension, oral cloze and sentence repetition. However, it is clear that Siegel has used varying methods in her studies to identify low achieving poor readers. One of these, using low picture vocabulary as the sole indicator of poor achievement, is unorthodox and not as stringent as the low non-verbal IQ measure used by Jorm et al. (1986). Additionally, Siegel s conclusions are based on small sample sizes, in some cases only a small number of children have been tested with individual measures. 1.8 Summary Although not exhaustive, this literature review has sought to be representative of research comparing low achieving poor readers and discrepancy defined individuals with dyslexia. Strong support has now been demonstrated for the phonological core variable difference model and it has been shown that it is difficult to differentiate performance according to IQ when poor readers are given tasks of nonword reading, phonological segmentation, 33

34 spelling and single word reading. Some demonstration of group differences on language tasks and language related variables outside of the phonological core have been demonstrated between low achieving poor readers and individuals with dyslexia, for example performance in reading comprehension. A number of meta-analyses also provide further support for a lack of differentiation. For example, Steubing et al. (2002) surveyed 46 studies which contained group comparisons of individuals with dyslexia and low achieving poor readers. Aggregate effect sizes for differences between groups when considering an achievement composite (spelling, reading, writing, mathematics) and a cognitive composite (somatosensory, language, memory, attention, motor, executive function, spatial and IQ) were found to be small. Low achieving poor readers underperformed individuals with dyslexia (effect size ) on spelling. However, in areas of reading, writing and maths, effect sizes were negligible. The phonological core deficit model thus appears to provide an appropriate conceptualisation of poor reading in all children. This review has now demonstrated that low achieving poor readers cannot be consistently differentiated from individuals with dyslexia on measures of phonological awareness. Thus this chapter supports the view that poor phonological awareness underlies poor decoding in poor readers of all IQ levels. Testing this hypothesis in relationship to age matched and reading level matched peers will be a goal of this thesis. Additionally, it is now clear that low achieving poor readers should be included in all forms of reading disability research. The exclusion of children with low IQ in reading disability studies marginalises these children and artificially truncates the variation in IQ within a given sample. The next chapter addresses basic sensory deficits which may underlie the phonological problems of poor readers. This chapter will review some of the relevant literature concerning auditory deficit hypotheses. It will motivate the second goal in this thesis which is to ascertain whether the auditory difficulties which have been identified in dyslexics may also exist in low IQ poor readers. This same chapter will also provide evidence to support the focus on the rise time deficit hypothesis in this dissertation. 34

35 Chapter 2. The auditory deficit hypothesis of reading disability 2.1 Introduction As Chapter 1 made clear, the majority of children with reading difficulties demonstrate a deficit in phonological awareness irrespective of IQ. One proposal for the biological basis of these deficits, suggests atypical cell formation and migration in the cortical areas directly related to the processes of literacy (Galaburda, Sherman et al., 1985). Alternatively, auditory and visual deficit theories suggest more basic sensory problems as a possible underlying cause of poor phonological development (see Ramus, 2003 for a review). Specifically, a large body of literature explores the possibility of an auditory deficit in poor readers. However, it is important to note that in the studies which follow, participants with reading disability or dyslexia have been identified using a discrepancy definition, thus indicating typical or above levels of nonverbal or full-scale IQ. The auditory deficit hypothesis is as yet unexplored in children with low IQ and poor reading a large gap in the literature which this thesis aims to address. This chapter provides a review of the auditory deficit hypothesis research and focuses on one aspect of auditory ability in particular, the processing of amplitude rise time. A long controversy surrounds the auditory deficit hypothesis of reading disability. One of the strongest arguments supporting the key role of audition in language and reading development is the disruption of literacy development in individuals with auditory impairments. Effective language learning appears to require appropriate perception of the speech stream and an ability to discriminate spectral shape and to perceive amplitude, fundamental frequency and spectral frequency modulation. Both slow and fast temporal resolution is also necessary. Slow modulations and their discrimination enable a processing of the longer temporal patterns of entire words and phrases while fast temporal processing is essential in discriminating consonants through very rapid changes in fundamental frequencies. Both are extremely important for reading and language development. 35

36 2.2 The rapid auditory deficit hypothesis Tallal was one of the first researchers to suggest that problems with temporal auditory processing could underlie language and reading deficits. An initial series of studies using the Auditory Repetition Task (ART) found that children with specific language impairment (SLI) had difficulty perceiving the order of tones when spaced with short inter-stimulus intervals (Tallal & Piercy, 1973). This non-speech rapid auditory processing deficit was thought to impact directly on the ability to discriminate rapid spectral-temporal changes in the speech stream. It was proposed that this impaired discrimination was related to impaired phonological representations leading to impaired language development in children with SLI. However, a number of replications of the experimental paradigm subsequently found conflicting results. Bishop et al. (1999) for example, did find performance on an ART was significantly impaired in an SLI versus control group. However, group differences were more prominent at slower rates of presentation. In addition, a number of control children with impaired auditory processing but typical language development were also observed, thus challenging poor rapid auditory processing as a model of sufficient causality for all children with language impairment. When extended to children with dyslexia, Tallal (1980) found nine out of 20, eight to 12- year-olds were impaired on two variations of the Repetition Temporal Order Judgement (TOJ) Test. In this paradigm children were required to learn associations between auditory stimuli and the pushing of individual buttons. In the Rapid Perception Test children were presented with two stimuli with varied inter-stimulus intervals (ISI) and required to identify the presentation of each stimulus with a button push. In the Same- Different discrimination paradigm children were presented with a similar paradigm but were required to identify two rapidly presented tones as either same or different. Tallal found no significant differences between performances on the two tasks. She did find however that nine of the 20 children made significantly more errors than controls when stimuli were presented rapidly (8-305 ms ISI) rather than with longer ISI (428 ms ISI). Again, it was Tallal s argument that this interaction between short and long ISIs was 36

37 related to a selective impairment of stop consonants but not vowel discrimination in disabled readers. The rapid auditory deficit hypothesis in children with dyslexia has also faced heavy criticism. Subsequent discussion of Tallal s (1980) experiment has highlighted the normal nonword reading of her participants with dyslexia, a characteristic which indicates her participants may not have been typical children with reading impairment. Again, replications of the experimental paradigm have found little common ground. De Weirdt (1988) for example, compared five-year-olds on tone discrimination using 10 tones with frequencies varying in five percent steps with a tone duration of 130 ms and ISI of 900 ms. Poor readers were found to have an overall auditory processing deficit on this task. However, a later replication by Heath et al. (1999) found normal tone processing in seven to 10-year-old poor readers with impaired language skills and in age matched peers with typical reading levels. A deficit was evidenced however in the TOJ task in the reading and language diminished group. A longitudinal examination by Share et al. (2002) found reading disabled children were impaired at school entry age on Tallal s repetition test; however this impairment was for variations with long ISIs only. Share et al. also found that these poor readers were not impaired compared to reading level controls and that individual children with temporal deficits were not poor on tasks of phonological processing or various reading measures. Finally, Tallal s causal hypothesis was not upheld as concurrent relationships between temporal processing difficulties and phonological impairments were evident at school entry. Temporal deficits were not predictive of various types of reading and phonological processing outcomes. 2.3 The amplitude rise time deficit hypothesis An alternative auditory deficit hypothesis has been suggested concerning the discrimination of amplitude rise time. Detection of changes in amplitude over time, for which rise time describes the rate of change, facilitates perception of the most basic aspects of rhythm in speech. These variations in amplitude, known as the amplitude envelope, reflect the presence of syllables and are fundamental to the clarity with which speech is perceived (Smith et al., 2002). The amplitude rise time indicates how quickly 37

38 the maximum amplitude is reached in a given sample and variation in this characteristic carries strong linguistic implications. For example, rise time occurring at the onset of the syllable is considered to be particularly important in the creation of rhythm in speech (Scott, 1998) and as the peak of acoustic energy occurs with the production of the vowel, rise time also functions as a cue to vowel onset. These changes in amplitude over time are particularly distinct for vowels which are stressed as compared to vowels in unstressed syllables. For example in Figure 2.1 with the word quickly, the rise time preceding the first syllable is much sharper than that for the second syllable, despite both being associated with the same phoneme /k/. Variations in amplitude rise time also act as a segmental cue to the manner of articulation. For example the voiceless affricate-fricate distinction /tσ/ and /Σ/ as presented in the initial consonants of chop and shop (presented in Howell & Rosen, 1983 and Thomson, 2004). Difficulties in the discrimination of rise time could therefore affect accurate speech perception, particularly through difficulties with syllabic segmentation and the accurate perception of syllable components such as syllable onset and nucleus (e.g. Greenberg, 1999). Alternatively, phonological difficulties could be the result of problems with acquiring literacy. Challengers to the existence of a universal phonetic inventory suggest that the letter-like symbolic units, or phonemes which we appear naturally to discriminate in the speech stream are in fact a result of becoming literate (Ziegler & Goswami, 2005). In further support against the rapid auditory hypothesis, phonemic discrimination may in fact not be heavily dependent on formant transitions as previously thought but may rely on amplitude structure. Remez, Rubin et al. (1981) for example demonstrated that speech intelligibility in adults is quite accurate without the benefit of any formant structure. In addition, adult listeners can recognise speech with amplitude envelope information from only a few (three to four) frequency bands (Shannon et al., 1995). These modulations are commonly found to be in the lower frequency regions (one to 16 Hz) of the speech signal (Drullman, Festen & Plomp, 1994). 38

39 Figure 2.1 Schematic representation of a speech utterance, illustrating local changes in features like duration (length of segments) pitch (drawn lines) intensity (doted line) and rise and fall times (changes in dotted line). Figure with thanks to Tim Fosker (2008). A growing body of research supports a link between poor discrimination of amplitude rise time and poor literacy. Lorenzi, Dumont and Fullgrabe (2000) for example, reported that children with dyslexia and typical children were impaired in comparison to adults when performing amplitude envelope detection in synthetically modified speech. These researchers recorded detection of amplitude modulation in children aged eight to 14 years with a diagnosis of dyslexia and compared them to typical age matched controls and adults. The children with dyslexia were found to have much higher detection thresholds at 4 Hz and 1024 Hz than both the age matched children and the adults. However, there was no difference in discrimination thresholds at the intermediate values - 16, 64 or 256 Hz. Additionally, typical children and adults performed at a similar level on these tasks suggesting discrimination of amplitude modulation may reach adult-like levels early in development. In an additional experiment presented in the same paper, Lorenzi et al. presented degraded synthetic speech, where only the amplitude envelope could be used for interpretation, to the same three groups. In this manipulation, both younger groups performed significantly below the adults. However, the performance of the typically reading children improved with practice which was not the case with the children with dyslexia. These findings could suggest that although amplitude envelope 39

40 information is used for speech comprehension early in development, this process undergoes refinement into adulthood. The children with dyslexia may have particular difficulties with the speech detection task due to their potentially underdeveloped amplitude discrimination skills as demonstrated in the first amplitude modulation paradigm used in the previous experiment. Rocheron et al. (2002) furthered this research by investigating 10 children with dyslexia aged years old. In this study the researchers attempted to further evaluate their previous findings and to assess amplitude modulation discrimination in addition to the detection procedure used previously. The detection tasks used require an identification of modulation versus no modulation in the presentation of amplitude envelope information. The discrimination tasks however present amplitude modulation at supra-threshold rates and require the listener to distinguish changes in depth or rate of amplitude change (Thomson, 2004). In this second study, Rocheron et al. found that discrimination was poorer in the group with dyslexia at both slow and fast rates of presentation (four and 128 Hz) as compared to age matched controls. These two studies are consistent in presenting a deficit in the processing of the amplitude envelope at four Hz in children with dyslexia. These are the lower frequency regions identified as critical to speech intelligibility by Drullman et al. (1994). However, studies are not consistent with regards to the alternative rates of presentation. Specifically, the second study demonstrates a group difference at the presentation rate of 128 Hz while the first study does not. Although this difference may be related to the differing paradigms, i.e. detection versus discrimination, it is not clear why this may be. The authors have suggested that poor detection may result in a smoothing of the auditory stream while poor discrimination is related to the amount of information which can be carried within the envelope. However, this description does not adequately explain the discrepancy in results. Despite these inconsistencies, a deficit in the processing of slow rate amplitude information was found in both studies. Four Hz is characteristic of the rate of speech and thus may be a crucial presentation rate for the development of phonological representations in young people. 40

41 Further support for an amplitude rise time deficit in reading disability is provided by Goswami et al. (2002) who gave 101 children with dyslexia and typically reading eleven year-olds an amplitude modulation beat perception task, a rapid frequency detection task and a temporal order judgement task. The beat detection task required children to judge whether an amplitude modulated sound was perceived as one element with intensity fluctuations or as two elements: distinct beat with a background sound. The shorter the rise time the more likely the stimulus would be perceived as a beat with a background sound. Children with dyslexia were found to differ significantly from controls in the slope of the categorization function for amplitude onset detection in the beat perception and temporal order judgement task. Furthermore, individual differences in sensitivity to beat detection accounted for 25 percent of the variation in decoding and spelling measures after controlling for age, nonverbal IQ and vocabulary. Findings from this study were further supported by Muneaux et al. (2004) who performed the same beat detection task with French children, specifically 18 with dyslexia, 18 controls matched on single word decoding ability (reading age) and 20 chronological age controls. Muneaux et al. found French children with dyslexia exhibited a beat perception deficit similar to that observed by Goswami et al. (2002). For English children these authors also found that beat perception detection levels predicted unique variance in word and nonword reading. Subsequent research building on these findings is provided by Richardson, Thomson, Scott and Goswami (2004) who administered an extended battery of auditory discrimination tasks from their earlier study. In this instance an investigation was made into the relationships between decoding and phonological awareness and amplitude rise time, duration, intensity, pitch and temporal order judgement. Children with dyslexia as a group demonstrated deficits in rise time, duration and temporal order judgement. In confirmation of their previous research, individual differences in rise time tasks were found to be related to individual differences in phonological processing. These findings were also replicated using the same tasks with adults with dyslexia (Thomson, Fryer et al., 2006). Investigations concerning rise time perception in Finnish nine-year-olds undertaken by Hämäläinen et al. (2009) found slightly different results with reading disabled children 41

42 demonstrating a group deficit in phoneme duration discrimination but not in amplitude modulation and rise time perception. This finding may be related to the higher discrimination thresholds demonstrated by Finnish control children on the rise discrimination tasks. Although children with dyslexia performed at the same level in both English and Finnish, the elevated thresholds in the Finnish control children resulted in a group difference which was non-significant. Additionally, the phonotactic and prosodic differences of the languages, where English can have consonant clusters at the beginnings of words while Finnish cannot (thus the syllable nucleus always occurs relatively early), may have contributed to the different outcomes. As syllable onsets which are sonorant such as /w/ have a more gradual rise time slope than syllables that begin with a plosive sound such as /b/, it has been suggested that impaired sensitivity to rise time may be associated with difficulty in syllable differentiation. For example Goswami, Fosker et al. (under review) demonstrated that impaired sensitivity to amplitude rise time is related to the ability to discriminate /ba/ from /wa/. Goswami, Fosker et al. created two continua of 20 speech syllables which changed from ba to wa either by changing rise time or fundamental frequency. The ba syllable in both was the standard sound and was presented twice in each trial in addition to an adaptively selected stimulus. In the amplitude envelope manipulated condition the ba was compared with a wa with a 125 ms amplitude rise which became shorter in accordance with an adaptive staircase procedure (see methods section in Chapter 4 for details of this procedure). In the frequency transition (formant duration) manipulated condition, the wa began with a 120 ms frequency rise that again became shorter in accordance with the adaptive staircase procedure. Goswami, Fosker et al. found children with dyslexia had excellent phonetic discrimination based on formant transition duration, but were significantly worse compared to age matched controls on phonetic discrimination based on amplitude envelope cues Amplitude rise time discrimination in adults. Further evidence for a rise time deficit has also been found in adults with dyslexia. Pasquini, Corriveau & Goswami (2007) administered five auditory tasks in addition to measures of decoding and 42

43 phonological awareness to adults with and without dyslexia. In the Intensity discrimination task individuals were asked to pick out the louder tone from a standard at 73 db and a second adaptively selected tone from a stimulus set of 31 pure tones ranging from 73 db to 81.1 db. In the single ramp rise task (see Appendix 3 for examples of these types of tasks), individuals were asked to discriminate the sharper rising sound, an adaptively selected stimulus from a set of 40 stimuli varying from 15 to 300 ms, from a 300 ms standard. In the two ramp rise task a standard tone with two 300 ms rise ramps was paired with an adaptively selected tone from a set of tones with rise times varying logarithmically from 15 to 300 ms. Participants were asked to identify the stimulus with the sharper beat, essentially the shorter rise time. In the beat perception task a five ramp variation of the two ramp rise task was presented with the exception that the stimuli were of longer duration in this task. Finally, a temporal order judgement task required individuals to judge which sound came first, a sound of a car or a sound of a dog. Both sounds were 115 ms in length and were presented with a variable stimulus onset asynchrony ranging from -405ms (i.e. car first with an interval of 290 ms) to 405 ms (i.e. dog first with an interval of 290 ms) with step sizes of approximately 20 ms. Pasquini, Corriveau & Goswami (2007) found the one rise and temporal order judgement task accounted for significant variance in predicting decoding attainment and phonological skills (phonological awareness battery, spoonerisms and phoneme deletion) This also extends to Finnish adults, Hämäläinen, Leppänen et al. (2005) administered an auditory rise time detection task to a group of 42 adults, 19 individuals with dyslexia, nine with compensated dyslexia and 14 age controls. Participants were presented with a number of sound pairs and were asked to detect whether the two sounds differed. Three variations were presented, with 10, 30 and 80 ms rise time stimuli. Although, both individuals with dyslexia and controls found discrimination of the 10 and 30 ms rise time stimuli difficult and performed at chance on these variations, individuals with dyslexia performed at a significantly lower level than controls in the final 80 ms category. Hämäläinen, Leppänen et al. also found a correlation between discrimination ability and phonological skills as measured by phoneme deletion, rhyme recognition and syllable reversal tasks. 43

44 In order to summarise the findings of the amplitude envelope investigations (using tasks which emanate primarily from those developed at the Goswami lab), Table 2.1 presents each study discussed here with an indication of the outcomes. As can be seen the most consistently replicated tasks which demonstrate a group difference are the amplitude modulation task, the temporal order judgement task, the rise time tasks and the duration task. As low IQ poor readers have not been included in any study to date which tests these measures, these findings support the decision to adopt an amplitude envelope discrimination hypothesis for this thesis. 44

45 Table 2.1 Summary of auditory outcomes A.M A.R.T T.O.J. Int. Int. Rise Freq. Rise Dur. 2 Rise BA/WA (AXB) (2IFC) Time (2IFC) Time (2IFC) (2IFC) (AXB) (2IFC) Goswami et al. (2002) Muneaux et al. (2004) Richardson et al. (2004) Hämäläinen et al. (2009) Goswami et al. (In press) Corriveau et al. (2007) Pasquini et al. (2007) Hämäläinen et al. (2005) Significant group difference non significant group difference. Blank were not addressed in the individual study. A/M = amplitude modulation (Eeyore/Winnie or Witton), ART = rapid frequency detection, TOJ = Temporal Order Judgement task 45

46 2.4 Backward masking Although not fully formed hypotheses, two further deficits in auditory discrimination have been reported in the literature. The first is a deficit in backward masking. Wright et al. (1997) for example, found normal thresholds in children with SLI in simultaneous masking, where a target co-occurs with a sound masker, but elevated thresholds in backward masking, where the target precedes the masking sound. More specifically, children with SLI were impaired in their ability to separate a brief sound from a sound of a similar frequency rapidly following the target, thus supporting a rapid processing auditory deficit. In an extension of this study targeting children with reading disability Wright et al. also found that just under half of the experimental group demonstrated high thresholds in backward masking. However, subsequent research has not supported these findings. In fact backward masking tasks have failed to distinguish SLI/dyslexia groups from controls where other auditory tasks have succeeded (Bishop et al., 1999). It has now been suggested that the greater attentional resources required by backward as compared to simultaneous masking tasks may have been responsible for the initial group differences observed (Rosen, 2003). For this reason backward masking has not been pursued in this project. 2.5 Frequency discrimination A number of investigations into potential group differences in frequency discrimination have also been made. For example, in the study discussed previously, De Weirdt (1988) found that young poor readers were significantly worse than young good readers at identifying pairs of 130 ms pure tones as same or different (as reported in Mengler et al., 2005). However, significant findings using non-adaptive paradigms such as that used in the De Weirdt study are relatively rare. In a review of auditory processing in dyslexia, Hämäläinen, Salminen et al. (in press) found 73 percent of frequency discrimination studies reported a significant group difference. Gibson, Hogben & Fletcher (2006) provide one example of a positive result found using an adaptive paradigm. These authors presented 44 children with dyslexia and 44 controls (both aged between eight and 12) with three 100 ms 83 db SPL tones separated by 300 ms. The first tone (A) was a 1,000 Hz standard, the third tone (B) was the adaptive selected comparison, and the 46

47 second tone (X), matched either A or B on a random basis. Gibson et al. found that children with dyslexia possessed a significantly higher threshold than the control children on this task. Also, in controls, frequency detection was correlated with irregular word decoding (Castles Word/ Nonword lists, 1993). However, this was not the case for the dyslexia group. A potential explanation for the conflicting findings regarding frequency discrimination deficits and reading disability is provided by Banai and Ahissar (2004). These authors recruited 108 native Hebrew speakers, 48 of whom were identified as having dyslexia (typical non verbal IQ with single pseudoword reading more than 1 SD below the mean) and 60 who were normal readers. All participants were administered a two tone frequency discrimination task. When the authors sorted the participants into groups according to whether or not they could perform the task, they found two clearly separated clusters. These two groups (could/could not perform successful frequency discrimination) did not differ in their reading abilities but presented different cognitive profiles and different patterns in their psychoacoustic performances. Individuals identified as having dyslexia with poor frequency discrimination also had poor duration discrimination scores and poor verbal working memory. The authors suggested that successful performance in the auditory task required auditory information to be transferred and held in areas of the brain where comparison could then take place. Therefore poor working memory may have been related to both the reading impairment and to the generally poor psychoacoustic abilities (frequency and duration discrimination). Specifically, it was proposed that these individuals had a reading deficit related to poor working memory and not to a phonological core deficit. Banai and Ahissar (2004) present an interesting interpretation of the conflicting results in auditory discrimination within poor readers. However, it is potentially not valid to separate a working memory deficit from a phonological deficit as the two are likely strongly linked. The findings do indicate however that individuals with poor working memory and poor reading may perform below the levels of controls on duration and frequency tasks. This has implications for low IQ poor readers who will on average not 47

48 perform well on working memory tasks. The question of whether IQ is related to auditory processing is also of high relevance to this project. Specifically, a number of studies have investigated the relationship between tasks of frequency discrimination and IQ. Deary (1994) administered three auditory tests in addition to the Raven s standard progressive matrices (Raven, 1958) and Mill Hill Vocabulary Test (Raven, Raven & Court, 1982). He found that all three auditory tests were significantly correlated with the IQ related tests. Factor analysis of the frequency tasks found two underlying factors which were speed of processing and pitch discrimination. Deary suggests that these two factors were both significantly associated with nonverbal and verbal IQ and that speed was the most prominent influencing factor. The relationship between IQ and frequency discrimination has also been replicated in adults in the general literature. For example, Raz et al. (1987) found that discrimination of two 20 millisecond tones varying in frequency was significantly correlated with Cattell s Culture Fair Intelligence Test (range from -.42 to -.54) in university students. However, it was not the case that more intelligent individuals performed better on all auditory tasks. For example, no aptitude related differences were found on a signal detection task which involved the identification of a target from background noise. In comparing the outcomes from these discrimination and detection tasks, Raz et al. differentiated signal identification from frequency discrimination procedures. While signal detection can be performed by individuals with lesions to the auditory cortex, frequency discrimination requires the extraction of salient features from the stimulus which can be supplemented by features which may be stored in memory. A timedependent decision may then be made when the number of features reaches a cut-off value for recognition. Raz et al. suggested that high IQ individuals might have faster feature extraction, better sensory representation and/or faster reaction time, resulting in better discrimination on frequency tasks. Although studies such as these have explored the relationship between IQ and auditory processing ability, it is not known how individuals with below average IQ would fare. The current project will provide an important opportunity to observe a low IQ sample, a group typically excluded from research of this type. 48

49 2.6 Literacy interventions based on auditory deficit hypotheses As discussed in section 1.6, reading intervention for children facing literacy difficulties is now standard in education. Escalating levels of intervention have been incorporated into the national curriculum and thus must be provided by all schools. Additionally discussed in section 1.5, was the observation that phonological difficulties, established as the core deficit in poor reading, are not unique to children identified as possessing a specific reading disability. A large proportion of children with language impairment, estimated in some cases to be as much as 50 percent (e.g. Catts, 1993), demonstrate similar difficulties in phonological processing. An American meta-analysis (NICHD, 2000) of 96 studies reporting effects of phonological awareness instruction in children found an overall effect size on phonological outcomes to be large (Cohen s d =.86) while the effect size for reading and spelling outcomes was moderate ( d =.53 and.59 respectively). When this analysis was restricted to children with reading disability smaller gains in phonological awareness were reported (d =.62). This was also the case for reading (d =.45) and spelling (d =.15). Given these findings it is not surprising that the majority of intervention programs have included or focused on improving phonological awareness skills. A number of interventions target specific phonological skills. Two of the most popular and most commonly cited in research publications are the Lindamood Phonemic Sequencing Program, (LiPS; Lindamood & Lindamood, 1998), previously known as the Auditory Discrimination in Depth (ADD) Program (Lindamood & Lindamood, 1984) and Fast ForWord (FFW; Scientific Learning Corporation, 1999). The LiPS is delivered by a trained administrator and has six main components: (1) Introducing the concept of selective listening; (2) Setting the climate (understanding method and rationale) for students; (3) Identifying and classifying speech sounds by place and manner of articulation (identifying brother, cousin and borrower sounds based on their shared sounds with other words); (4) Introducing, practicing and tracking consonants; (5) introducing, practicing and tracking vowels and (6) Tracking, spelling and reading simple syllables and words (Lindamood & Lindamood, 1998). The LiPS uses an articulatory approach in that the listener s attention is drawn to how speech is produced for 49

50 identification purposes. Success for this program was demonstrated by Torgesen et al. (2001) who found large improvements in reading skills in 60 eight to ten-year-olds who participated in two 50 minute LiPS sessions for eight weeks. Findings have been mixed however. In their study of 60 children of mean age eight, Pokorni, Worthington and Jamison (2004) found progress with LiPS was limited to phonological awareness skills which were not transferred to general reading ability. This was despite a short intensive program of 20 days of three one hour intervention periods. More closely related to this thesis, in that it adopts a non-speech auditory deficit approach, is the Fast ForWord program. This computer software based on the research of Merzenich et al. (1996) and Tallal et al. (1996) utilises manipulated speech stimuli and phonological tasks to improve literacy learning in children. The family of Fast ForWord programs are very popular in the U.S. and in 2004 were quoted as having been used by over 120,000 students (Rouse & Kruger, 2004). The suite of programs is designed so that as a child progress in ability he or she should move from Fast ForWord language to Fast ForWord language to reading and finally to Fast ForWord reading as their literacy develops. Fast ForWord language focuses on developing oral language skills that are thought to create a foundation for reading. This program focuses on four areas: phonological awareness, listening comprehension, language structures and sustained focus and attention. Fast ForWord language to reading focuses on making the connection between spoken and written language through sound/letter recognition, decoding, vocabulary, syntax and grammar as well as listening comprehension and word recognition. Fast ForWord reading focuses on building reading skills such as word recognition, fluency, decoding, spelling, vocabulary and passage comprehension. The Fast ForWord products were based on the proposition that auditory inputs which have been perceived in rapid succession may be processed as a single entity in the poor literacy learner, thus arriving to the individual as an undiscriminated unit. It was the proposition of Merzenich and Tallal that remediation in the auditory discrimination of speech sounds would thus improve both oral language and as an extension, reading ability. Based on this premise Fast ForWord targets the training of rapidly changing 50

51 speech transitions with the end goal of improved receptive language learning related to better reading outcomes. Some examples of tasks included in the Fast ForWord products are: Old MacDonald s Flying Farm. The user clicks and holds the flying animal to hear a repeated sound and then releases the animal when the sound changes. This task targets phoneme discrimination, sustained and focused attention and processing speed. In the Circus Sequence the user identifies a sequence of sounds by clicking buttons that correspond to individual sound sweeps. This task targets working memory, sound sequencing ability and processing speed (Scientific Learning Corporation, 2000). Additionally, a number of tasks are incorporated which modify normal speech to extend rapidly changing components by 50 percent and also amplify the speech up to 20 db (Tallal et al., 1996). Increases in duration and volume in speech are intended to enhance the ease with which words can be perceived. Over the training program (normally six weeks in duration) the speed and volume gradually returned to that of normal speech. This aimed to leave the user with enhanced skills in auditory perception. In support of the product which they had developed, Tallal and colleagues (Temple et al., 2003) found an improvement in reading in children with dyslexia after administration of the Fast ForWord programs). Temple et al. reported that a group of children with dyslexia aged eight to 12 years old improved on measures of Word Attack (pseudoword reading), Word Identification (decoding), Passage Comprehension on the Woodcock-Johnson Psychoeducational Battery (Woodcock & Johnson, 1989) and measures of receptive and expressive language and rapid naming compared to age matched controls. However, these gains did not apply to all individuals. Fifty percent of the poor reading group made no progress in oral language skills and another 50 percent made no progress in decoding ability. The authors did not report whether these two groups represented the same group of children. Independent studies investigating the Fast ForWord products have not been so positive. A number of studies report no direct benefit from Fast ForWord products for reading skills. For example, Valentine, Hendrick and Swanson (2006) investigated amelioration in auditory temporal processing following Fast ForWord training, as tapped by the 51

52 backward masking task. They found that children did improve on this task. Specifically, 26 low and moderately low readers aged seven to 10 were found to improve on backward masking in addition to language and phonological awareness skills after six weeks of the Fast ForWord language program. However, against product claims, no improvement in reading was made. At a six month follow-up auditory improvements were still evident on the backward masking task but again reading skills had not improved. Similar findings were reported by Agnew, Dorn and Eden (2004). These authors tested seven children of average age eight in their ability to accurately judge relative durations of auditory stimuli before and after receiving the Fast ForWord program. Children showed an improvement in accuracy on a test of judgement of auditory duration subsequent to training. However, no improvements were demonstrated on a pre and post test of a Phoneme Deletion task (Robertson & Salter, 1997) and on the Word Attack subtest of the Woodcock Diagnostic Reading Battery (Woodcock, 1997). Therefore, the improvement of auditory skills, in this case the discrimination of duration, was not found to be related to improved phonological and reading skills. Most likely due to the nature of the target group being studied, the research on Fast ForWord products does not appear to cover Fast ForWord reading. However, Rouse and Krueger (2004) recorded that a small number of the poor readers in their study did progress to Fast ForWord language to reading. The Rouse and Kruger study investigated the reading performance of the bottom 20 percent of third sixth grade readers in a large US urban school system. Children randomly assigned to the experimental group were given six to eight weeks to complete the training during which time students worked for 90 to100 minutes per day, five days a week. All students began at the basic level in each game and progressed to more advanced levels once they achieved a pre-set level of ability. Successful completion occurred after 20 days and/or after the completion of at least 80 percent of the majority of the games offered. Control children simply attended their regular classes and reading remediation sessions as normal. Children in the Fast ForWord group performed the computer based sessions in addition to their normal classes and their normal reading supplementary sessions. Rouse and Krueger found only a small gain on a general language measure (CELF-3-RP, Semel, Wiig & 52

53 Secord, 1995) after intervention and negligible gains on reading skills as assessed by the state reading assessment (a further description of this measure was not provided). In summary, the evidence presented here suggests that training in non-speech auditory based tasks can be related to improvements in tasks which directly assess these skills. Evidence does not currently support the hypothesis that a trained improvement in aspects of auditory processing will be tied to an improvement in literacy outcomes. 2.7 Summary It is clear from this review of the auditory deficit studies that not all individuals with reading and/or language difficulties suffer from auditory problems (Ramus, 2003). These findings allow two possible interpretations. Firstly, a subset of children may have reading disability related to underlying poor auditory processing. Alternatively, all children may have difficulty with auditory processing related to poor reading, some of whom manage to resolve or compensate for this difficulty over development. It is also clear that a large amount of variability exists when considering which component of auditory processing may be compromised. The repetition task appears to have reasonable success in separating children with literacy difficulties from matched controls. However, it is not clear whether it is rapid or slow presentations which cause the most difficulty for reading disabled children. Given the evidence which exists thus far, the most promising approach appears to be the investigation of a slow temporal processing deficit related to poor discrimination of the amplitude envelope. The current project therefore uses a number of tasks which tap related abilities, particularly those identified as being impaired in poor reader groups (Amplitude Rise Time, Duration, Frequency & Rhythm). In the next chapter a review of how phonological knowledge develops in children will be provided. This summary will provide the background literature to ascertain how poor auditory and poor phonological development could be linked to poor literacy outcomes over development. 53

54 Chapter 3. The development of phonological knowledge in children In order to evaluate the evidence that poor phonological awareness underlies decoding deficits in poor readers, it is important to review the processes that contribute to the development of phonological knowledge in children. In undertaking this task, this chapter outlines how we come to store the sound representations of language, known as phonological representations, which must then be related to written representations when reading is acquired. Phonological awareness, which concerns sensitivity to the manipulation of phonological representations, is also discussed. Finally, the role of memory, both working memory and long term memory and its key role in the development of phonological knowledge is outlined. Discussion of these developmental factors and relationships will help set out the rationale behind the choice of many of the tasks for the current project. Specifically, the literature which introduces the relationship between working memory tasks and phonological knowledge supports the inclusion of these tasks. 3.1 Development of phonological representations Environmental cues for speech segmentation. In the pursuit of effective communication, the cataloguing of speech sounds by the brain is an essential first step. Brain mapping studies have demonstrated that the complex organising of the auditory cortex is highly responsive to environmental input in the critical periods of development (Tallal, 2004). By the time a child comes to reading, he or she will have gathered a large store of phonological representations which are constantly being retained and organised in the mental lexicon for efficient retrieval and production. To create these representations the child must learn to identify the units of language such as phonemes and syllables and be able to combine them in an appropriate manner. Children can perform the first of these skills from very early on. Infants have been shown to distinguish individual words from the speech stream using a statistical metric known as phonotactic probability. Phonotactic probability refers to the constrained nature of the sequencing of sounds within words. For example, the sequence str in English can only 54

55 occur in a word-initial position while rst can only occur in a word-final position (e.g. in string and worst). A specific example of phonotactics is provided by transitional probability. Saffran et al. (1996) have shown that eight-month-olds use transitional probability (the rule that some sounds are more likely to follow each other within words than across word boundaries) in learning nonsense words in a preferential listening paradigm. These authors presented infants with a continuous speech stream consisting of three syllable nonwords (equal to the learning of new long words) with no pauses, stress differences, acoustic or prosodic boundaries e.g. bidaku-padoti. As syllable transitions within words were much more likely to occur (probability 1.0) than syllable transitions across boundaries (probability 0.33), infants were able to correctly segment the stream into nonsense words. Demonstration of learning of the nonsense words took place in a subsequent habituation paradigm where infants showed more attention to new as compared to previously presented three syllable nonwords. A second experiment by this same group demonstrated the ability for eight-month-olds to distinguish word-internal pairs from word-external pairs. An analogous example in English would be the ability to extract pretty from pretty baby as opposed to ty ba. In performing this task successfully infants demonstrated a sufficient specification of nonword learning that sequences across word boundaries were recognised as unfamiliar. In addition to the statistical phonological probability patterns inherent in spoken language, a number of other environmental language cues facilitate speech segmentation. For example, mothers often intuitively speak to their children using Infant Directed Speech or Motherese (also known as Parentese), which has been described as either exaggerating pitch at the end of sentences to highlight target words (Fernald & Mazzie, 1991) or as extending in time and amplitude the acoustic changes which differentiate phonemes, syllables and words (Tallal, 2004). Also, the typical strong-weak syllable patterns found most often in English, can guide word identification. For example Jusczyk et al. (1999) found seven and a half month old infants could extract words with strong-weak syllable patterns (e.g. doctor) while sentences with words with weak-strong 55

56 patterns (e.g. guitar) were sometimes incorrectly segmented. This tendency persisted in sentence segmentation where if a baby heard her guitar is too fancy, infants would often mis-segment taris as a possible word. By 10.5 months children were no longer making these mistakes. Further cues to appropriate speech segmentation are also provided by prosodic and rhythmic patterns. These inputs feed into neurons selective to the temporal features of sound (as seen in animal studies e.g. Blake et al., 2002) and through the features and statistical probabilities of these inputs, shape the auditory cortex early in development. Mehler et al. (1986) found four day old French babies preferred a French storybook recording to the same story recorded in Russian. However, when the tape was played backwards thus, according to Mehler et al., preserving pitch but removing rhythm and prosodic cues, the same infants could no longer tell the two versions apart. Mehler et al. presented these findings as evidence supporting the importance of prosody and rhythm in very early speech discrimination. These findings also indicated that the extraction of language specific prosodic information requires experience of a language, including prenatally Word recognition models. In addition to external factors, the existence of an internal predisposition to language learning has also been discussed. Jusczyk (1992) for example, has proposed the Word Recognition and Phonetic Structure Acquisition (WRAPSA) model which discusses how infant speech perception capacities evolve to support word recognition. In this model, infants begin to weigh available information in terms of the usefulness it presents for word recognition. Jusczyk labels this an interpretive scheme, one which is heavily weighted towards prioritising prosodic information. He proposes that a system of this type would aid word segmentation and allow a more enduring syllable-based acoustic representation to be formed. The system would be most useful in highlighting the properties relevant to distinguishing words, for example the fact that aspiration is relevant for stops in word-initial but not word-final placement. According to Jusczyk, this scheme appears around the age of one year when it is thought that children begin to attach meaning to individually spoken words in a 56

57 consistent manner. However, the WRAPSA model is a heavily theoretical proposition and has little experimental evidence which can support it as distinct from other word recognition models. Further models of this type, built for adults but applicable to grade school children, have also been proposed. In summary, these models suggest that the speed and accuracy with which a spoken word is recognised is a function of the word s acoustic properties (intelligibility) and its lexical properties (neighbourhood density, familiarity). Most word recognition models assume that several lexical representations related to the phonology of the stimulus word (known as the phonological neighbourhood) are activated from which a single candidate is selected. The Neighbourhood Activation Model (NAM, Luce et al., 1990) addresses the effects of intelligibility, the number of similar sounding words (known as phonological neighbourhood density) and the familiarity of the target and neighbourhood words upon the lexical selection. NAM is a mathematical model which more specifically addresses the process of a speech sound activating a set of acoustic-phonetic patterns in memory. These memory patterns are activated in proportion to their perceptual similarity to the stimulus. Once the decision units connected to the input patterns have been activated, the NAM equation is then applied to achieve word selection. The NAM equation is calculated as the probability of support for a stimulus word based on its constituent segments x word s frequency of occurrence. These variables are placed over the probability of support for a stimulus word based on its constituent segments x word s frequency of occurrence plus the probability of support for the neighbour word n based on the stimulus word s constituent segments x neighbour word s frequency of occurrence. Essentially, the NAM equation predicts that a word s recognition accuracy will be determined by the intelligibility of its segments and by the number of words which sound similar to it in the lexicon. This means that given an equal level of intelligibility, words from dense phonological neighbourhoods will be more difficult to recognise. Additionally, the model predicts that although words with high frequency will be facilitated in terms of recognition, words with many high frequency neighbours will be 57

58 difficult to recognise. The authors present a number of studies which support the model. Goldinger, Luce & Pisoni, (1989) for example, demonstrated that priming with phonetically related words inhibits target recognition. Further related experimentation by this group has generally supported inhibition for the dense words in word recognition paradigms. Interestingly, Vitevich and colleagues have found the exact opposite results with experiments in speech production. Utilising a tip-of-the-tongue (TOT) elicitation task, where children are required to provide a word after being given its definition, Vitevich and Sommers (2003) looked at neighbourhood density in addition to other lexical factors impacting on speech production. These authors found that the number and the frequency of a word s neighbours impacted lexical retrieval for speech production. Adults found it more difficult to find and produce, or in other words had TOT experiences, with words from low word frequency and from sparse neighbourhoods. Similar findings in undergraduates have been reported by Andrews who reported a shorter naming latency for low frequency (1989) and high frequency (1992) words which have larger phonological neighbourhoods. Developmental theories, such as the Lexical Restructuring Hypothesis (Walley, 1993) help to explain lexical effects on word recognition and production. This particular hypothesis rests on the large accumulation of vocabulary over development which starts slowly but increases in the rate of acquisition at around a year and a half. From the age of one to 18 months a child s vocabulary grows at a singular rate but from around 18 months to three years there is typically an exponential increase in vocabulary size, with vocabulary typically doubling within a month (e.g. Nelson, 1973). During this period the child is rapidly learning to segment words from speech, a process which, as discussed later, suffers from under-segmentation, over-segmentation and mis-segmentation (Peters, 1983) and undergoes refinement over development. According to Walley s (1993) Lexical Restructuring Hypothesis, analysis of the correspondences between recurring speech patterns, assessing non-linguistic events, discovering the semantic referents for words and translating sound patterns into articulatory sequences means that the child is 58

59 kept extremely busy. For this reason Walley suggests words are stored and retrieved as unanalysed wholes. However, as many words begin to overlap in terms of shared sounds, children begin to refine their phonological representations and organise words according to shared morphology and phonology (e.g. cat and cats, Walley 1993). Support for the vocabulary burst as the basis for global to more specified phonological representations is provided by Charles-Luce and Luce (1990) who measured lexicons for five-year-olds, seven-year-olds and adults. This study found that an increase in neighbourhood density was related to increasing vocabulary levels, suggesting a learning of a high number of similar sounding words and thus a need for detailed phonological representations. Estimates of continued vocabulary increases place average vocabularies for four-year-olds at 2500 to 3000 words, five-years-olds at 7000 to 10,000 words and ten-year-olds at 39,000 to 46,000 words (Anglin, 1989). It may thus be that refinement of phonological representations continues in line with vocabulary development into middle childhood. 3.2 Development of phonological awareness Early development of phonology. In its most basic form, speech is a varying wave of acoustic energy. From a spectrogram, which we use to look at the change in frequency and amplitude over time, we cannot see the individual sounds which make up a word (for example the three sounds which make up the word bat). Therefore, as stated by Wagner and Torgesen (1987), the segmentation of the speech stream is a cognitive percept and not intrinsic to the nature of the acoustic stimulus. As such, precise explanations of the basic elements of phonology, such as phones, phonemes, consonants and vowels remain elusive and hotly contested. At the most detailed level, speech is traditionally described as being composed of phones, which represent the complete set of speech sounds. For example the t in ten and the t in stop are slightly different sounds and thus represent two different phones. However, these differences are not necessarily perceived in everyday speech. Sound distinctions which are processed linguistically are known as phonemes. A phoneme is a group of phones 59

60 which speakers consider to be variations of the same sound. Phonemes are responsive to the physical nature of the sounds which surround them and are not invariable units. As such, phonemes provide a useful shorthand (Goswami, 2008) for identifying speech sounds and loosely correspond to the letters in the alphabet. However, as demonstrated previously, phonemes do vary slightly according to the sounds that surround them (e.g. t in ten and stop) and hence are abstractions from the basic sensory signal. It is well understood that phonemes are perceived categorically. To measure categorical perception, a synthetic continuum of consonant-vowel or syllables varying in one acoustic parameter is most often used. Experiments using this paradigm have found that, for example, at a measureable point, stimuli that are consistently being perceived as /b/ will switch categorically to be consistently perceived as /p/. Such categorical judgements appear to be present in a rudimentary form from early infancy. For example, Eimas et al. (1971) used a sucking paradigm to ascertain that one month and four month old infants can successfully discriminate between the synthetic speech sounds ba and pa. In this experiment, a nipple on which the infant was sucking was connected to a transducer which provided auditory feedback. The stimuli were synthetic speech sounds and were three variations of the bilabial voiced stop /b/ and three variations of the voiceless counterpart /p/. The variations between all stimuli were represented by voice onset time which can be accomplished through the manipulation of formant transitions. In sucking paradigms such as this one, a presentation of an auditory stimulus results in an increase to a baseline rate of sucking but with continued presentation of the stimulus, due to decreased novelty, the response rate lowers. For the infants in Eimas et al. s experimental group, after a minimum of 20 percent decrement in sucking rate for at least two minutes duration, a second auditory stimulus was presented. For the control condition these parameters were replicated but only one stimulus was used. Eimas et al. compared mean response rate during the two minutes after the presentation of a new stimulus to the two minutes prior to the shift. The authors found differences were greatest when these occurred across a phonemic category, indicating a discrimination of categorical boundary at this very early age. 60

61 Infants also appear to attune themselves very quickly to language input, losing the ability to discriminate non-native language contrasts and becoming exclusively interested in language sounds relevant to their social interactions (Kuhl et al., 2003). Werker and Tees (1984) have shown for example, that six to eight-month-old infants discriminate nonnative contrasts but that this ability declines. After the age of one year infants respond to speech sounds in a language specific way. They discriminate acoustic differences between phoneme categories of their own language but no longer within these categories. It has been suggested that these early perception abilities are an innate system analogous to the proposal of a language acquisition device as proposed by Chomsky (1965) concerning the acquisition of syntax. Early discrimination ability is only possible due to a well developed auditory system, which provides appropriate temporal and frequency resolutions for speech perception (Diehl et al., 2004) Phonological awareness in early childhood. Phonological awareness, as briefly outlined in Chapter 1, is the ability to isolate and manipulate the component sounds of words. For children this is most easily done at the level of the syllable. However, similar to the phoneme, the syllable is not an invariable entity. On many occasions the exact beginning and ending of this unit can be difficult to determine (Wagner & Torgesen, 1987). However, the syllable is always constructed around the peak of energy provided by the vowel and always follows with a restriction of the vocal tract provided by the consonant(s). Liberman et al. (1974) demonstrated the ability for pre-readers (46 percent of four-year-olds, 48 percent of five-year-olds) and new readers (90 percent of six-yearolds) to successfully tap out the number of syllables in a given word following a strict criterion of six consecutive correct responses. After segmenting words at the level of the syllable, children also have a relative facility for dividing syllables into onset and rime. The onset in each syllable is represented by the phonemes leading up to the vowel while the rime includes the vowel and all remaining phonemes. The oddity task, which asks individuals to select a word from groups of three or four words, varying by an onset, vowel or coda, can be used to assess onset-rime awareness. Judgements of beginning sounds can be formed using onset discrimination 61

62 while vowel or coda discrimination can be performed with segmentation of the rime. Bradley and Bryant (1983) demonstrated that pre-school children aged four and five were 56 percent correct on the Onset Oddity task and 71 percent correct on the rime oddity task. Additionally, when these children were visited approximately four years later, oddity task performance at time one and reading and spelling performance at time two, were correlated. The oddity task also accounted for 10 percent of unique variation in reading outcomes after controlling for individual differences in memory and IQ. In contrast to syllable and onset-rime awareness, it is thought that phonemic awareness largely occurs during the early processes of learning to read an alphabetic script and can form a reciprocal relationship with reading instruction. For example, Perfetti et al. (1987) found in a longitudinal examination of first grade children that phonemic knowledge developed as children learned to read. In this study 82 first grade children learning to read were given four tests throughout the school year. These tasks were a synthesis task, which required the child to synthesise a word or pseudoword spoken in segments by the experimenter, a tapping task where a child had to tap a pencil for each sound in a word and a deletion task where the child was required to remove a segment of a spoken word and produce the appropriate response. Having run partial time-lag correlations, the authors found that the deletion task in particular tapped phonemic knowledge that developed in a reciprocal manner in relationship with reading. However, not mentioned in the article is that the speed of development of these relationships is dependent on orthographic consistency. Children using transparent orthographies, where spelling systems have a one letter to one phoneme correspondence, find phonemic segmentation tasks much easier. For example, Porpodas (1999) found that in Greek, a transparent language, almost all first grade children could perform a phoneme deletion task. This finding is in stark contrast to Goswami and East (2000) who found than none of the five-year-olds whom they tested in English could perform a phoneme segmentation task in which the child was asked to say a CCVC or CVCC (C = consonant V = vowel) target word slowly so that each of the phonemes (four in this experiment) could be heard. 62

63 3.3 Development of phonological working memory Introduction to the Baddeley and Hitch model of working memory. Many of the tasks purporting to look primarily at phonological awareness, for example the word oddity task, also tax the phonological memory system. To understand the functioning of verbal memory it is useful to refer to a conceptual model. Baddeley and Hitch (1974) as explained in Gathercole and Baddeley (1990a) propose a three component system of working memory. This model comprises an attentional system known as the central executive and two subsidiary slave systems, the visual-spatial sketchpad and the articulatory loop. The articulatory loop includes a phonological store and a rehearsal loop where information can be retained for approximately two seconds or longer through audition or sub-vocal repetition. It is generally suggested that young children rely on the visual-spatial sketchpad for recall prior to the age of four or five. Strong support for this assumption was provided by Conrad (1971) who gave three 11-year-old children a serial recall task for pictures of common objects. Conrad found that up to the age of five the acoustic similarity of the names of the pictured objects made no difference to levels of recall. This finding was in contrast to adults who repeatedly demonstrate a difficulty in recalling word lists of homophones (e.g. way-weigh) compared to lists of different sounding control words (Conrad, 1963, as discussed in Conrad, 1971). However, after the age of five Conrad did find that children followed the adult model with a systematic advantage for picture series with different sounding names. Further research supporting Conrad s findings has been provided by Hitch et al. (1988). These authors found that a set of visually similar images (nail, bat, key, spade, comb, saw, fork and pen) were more difficult for five-year-olds to recall than a set of visually dissimilar images (doll, bath, glove, spoon, belt, cake, leaf and pig) and a set of images with long names (elephant, kangaroo, airplane, banana, piano, policeman, butterfly and umbrella). Ten-year-olds however demonstrated the use of a phonological strategy by finding the picture set with the long names the most difficult group to remember. A reliance on phonologically based systems in middle childhood is further supported by research performed by Salame and Baddeley (1982). Children aged four and above were again found to be responsive to the physical 63

64 characteristics of words in recall tasks. These authors suggest their observation of poor recall of phonologically similar words may have been related to the confusability of decaying traces in the phonological store. In addition to a move from visually to phonologically based working memory, an increase in working memory span is also observed over development. It has been suggested that increases in digit span, a typical measure of phonological working memory capacity, are related to the employment of strategies for the encoding of verbal information to facilitate manipulation and retrieval. For example, when children are taught to use techniques such as verbal rehearsal, their ability for recall reaches levels similar to those observed in adults (Keeney et al., 1967). It has also been suggested that schoolroom activities such as the requirement to recall lists of unrelated items may improve memory strategies. However, an alternative explanation of increase in memory span is presented by Hulme et al. (1984). This paper presented two experiments which examined the effects of word duration on the memory span of individuals of different ages. In the first experiment, 36 participants were used with a group of three and fouryear-olds, a group of seven and eight-year-olds and group of nine and eleven-year-olds and nine adults. A number of different word lists were read out and as soon as the last word had been produced, the child attempted to recall the words in the order presented. After the final serial recall task, children were given pairs of words of a particular length and asked to repeat each pair 10 times as fast as possible. The authors found that the same linear function relating recall to speech rate fitted the results from the youngest children to the adults. In the second experiment, 18 eight-year-olds and 18 ten-year-olds were tested with a similar paradigm but in this case groups of three words were repeated to assess speech rate. The findings from this experiment replicated those from the first experiment and demonstrated again that increases in speech rate with age reflect increases in the speed of word articulation. Furthermore, fewer longer words and a greater number of shorter words were remembered from each category. Hulme et al. (1984) suggest that the increase in phonological short-term memory span from three words at around three years of age to seven words for adults could best be explained by an increase in speech rate over development. The theory suggests that as children age 64

65 their speed of articulation increases, allowing more words to be retained on a fixed length rehearsal loop. Hulme et al. concluded that individuals from age four to adulthood can remember as much as they can say for the duration of the phonological loop, suggested from this experiment to be approximately one and a half seconds The role of long term memory for the phonological store. Although generally supporting the results found by Hulme et al. (1984), a role for long term memory in the functioning of the phonological store has additionally been suggested. Henry and Millar (1991) found that although articulation rate went some way in explaining better memory span in older children, effects of word familiarity were also important in this group. It has been suggested that when phonological representations are stored temporarily they must be continually refreshed to enable production. In turn this redintegration process may be supported by long-term memory representations. Henry and Millar propose that words that are familiar in long-term memory are more easily retrieved, and can boost recognition of items in the phonological store which are partly decayed. The example provided by Henry and Millar (1993) is that a less clear representation for a highly familiar long word such as elephant can be better tolerated than a less clear representation for a highly unfamiliar nonword such as flugerstan. It is generally accepted that short term memory familiarity effects operate through an item by item process of redintegration. Redintegration is the mechanism by which phonological representations which have been partially degraded can be reconstructed with the retrieval of long term phonological memories. In most models, the partial representation in short term memory is used as a retrieval cue to locate the closest match in long-term memory. The highlighted item is the word used for output production in the recall task (Woodward et al., 2008). For example, short term memory facilitation for words in high density neighbourhoods is thought to be related to processes of redintegration. Thomson et al. (2005) demonstrated this effect when they compared recall levels in children aged seven and nine for lists of four words. These authors found that words such as bone, pail, king, gum which have many phonological neighbours were better remembered than words which were from phonologically less populated neighbourhoods such as wipe, bird, hook, leg. 65

66 3.3.3 Phonological working memory and learning to read. It is not surprising given the role phonological memory plays in word selection and word production that a large amount of research links phonological short term memory (PSTM) to early reading skill. As Wagner and Torgesen (1987) explain, effective phonetic coding is essential for beginner readers as the new reader has to (a) decode a series of letters (b) store the sounds of the letters and (c) blend the contents of the store to form words. Additionally, the child may search long term memory for similar phonological representations while holding an initial sound blend in mind. Given the complex undertakings, it is logical that efficient storage of phonological representations allows the maximum amount of remaining resources to be devoted to the formation of words. Experimental evidence supports this contention with the demonstration that PSTM for children entering school is significantly correlated with reading achievement one year later (Mann & Liberman, 1984). Furthermore, poor reading in young children is related to poor levels of phonological working memory and impinges on vocabulary development which may also be detrimental to successful reading. Gathercole and Baddeley (1989) for example, found measures of phonological short term memory were correlated with receptive vocabulary and were a good predictor of vocabulary development. In a longitudinal design, the vocabulary skills of 104 children between the ages of four and five were tested and then tested again a year later. Phonological memory was investigated by asking the child to repeat nonwords varying in length and complexity. Gathercole and Baddeley found that phonological memory score was highly correlated with vocabulary at both ages. Phonological memory scores at age four also accounted for significant variance in phonological memory at age five. A further study by these authors found a low PSTM group took significantly longer to learn new names for a set of toys than an intellectually matched higher memory group (Gathercole and Baddeley, 1990a). 3.4 Summary By the time a child comes to learning to read, he or she possesses a vast number of phonological representations. These lexical entries have been segmented from speech, a process which is facilitated by a number of environmental and internal factors. Environmental factors include phonotactic probability as well as the natural rhythm, 66

67 intonation and pitch of speech, these last of which are exaggerated in Motherese. Internal systems are thought to provide the adaptive function of fast language acquisition. Models such as WRAPSA propose a scheme by which the most distinct features of language are highlighted, encouraging effective learning. The ability to segment a word into smaller phonological units requires good verbal working memory and a well specified phonological representation. Due to lexical pressures, words which must be distinguished from many other similar sounding words are typically very well represented. These groups of well specified words exert lexical effects which can be observed in word processing tasks. For example, neighbourhood density is related to an inhibitory effect in word recognition and a facilitative effect in word production. Furthermore, phonological awareness at the syllable, onset-rime and phoneme level predicts good decoding ability. Good decoding is also related to good verbal working memory which in turn correlates with high levels of word learning. Altogether, the well specified phonological representation appears to be the key to successful literacy acquisition. In summary, this chapter demonstrates the key role played by phonological skills, language and verbal working memory in successful literacy acquisition. Therefore, in combination with Chapter 2, the literature provided here supports the hypothesis that poor auditory processing skills may underlie poor phonology and thus poor decoding ability in the low IQ poor reader. The present discussion also provides the rationale for research goal three which aims to investigate the relationships between the variables of interest. 3.5 Research goals For clarity the research goals which have been introduced over the course of the first three chapters are listed here. 67

68 Goals 1. To profile the strengths and weaknesses of the low IQ poor reader in comparison to theoretically driven groups. 2. To assess whether low IQ poor readers demonstrate an auditory deficit related to the processing of the features of the amplitude envelope. 3. To assess whether individual differences in auditory processing are related to individual differences in aspects of phonological processing, decoding, language and verbal working memory. 68

69 Chapter 4. Phonological awareness, auditory processing and decoding in low IQ children and controls (Project Phase 1) 4.1 Introduction As discussed in the general introduction, Chapters 4 and 5 present the outcomes from the administration of the Phase 1 and Phase 2 test batteries. To provide the background for the current project a short introduction is hereby provided. The impetus for the current project was a five year Medical Research Council project (MRC ) awarded to the Centre for Neuroscience in Education at Cambridge University, investigating auditory processing in children with dyslexia. As a part of the MRC project a large number of primary school children were identified by teachers, educational psychologists and special needs coordinators as being poor readers and as such possibly having dyslexia. However, subsequent to the administration of reading and IQ tests, a significant number of these children were found to have low IQ and were therefore not suitable for the MRC project. Given the explosion of research presenting low IQ poor readers as behaviourally similar to individuals with dyslexia, it was thought important to investigate these children further. It was at this point that the current study was devised and the recruitment of appropriate controls began. As discussed previously, the investigation of an auditory deficit hypothesis for dyslexia has met with mixed success. However, the amplitude rise time hypothesis has demonstrated promise. Given the differences between individuals with dyslexia and controls on amplitude rise time related tasks, it was decided that the auditory processing skills of low IQ children should be investigated. Such an undertaking represented a completely new undertaking. The current research aimed to gain further understanding of the low IQ poor reader group and to identify whether individual differences in auditory processing ability were related to literacy outcomes. With this goal in mind, a battery of standardised and bespoke tasks including auditory measures was administered to the low IQ children. These tasks were also administered to a large group of children recruited as either reading and age matched controls. 69

70 4.2 Research predictions Given the research already reviewed in this area it was predicted that low IQ poor readers would perform either below or at the same level as reading age matched peers on the phonological processing and language measures that were selected (described in detail later). The performance of the low IQ poor readers on the auditory parameters could not be predicted as this undertaking was completely novel. 4.3 Phase 1 Methods Participants. Twenty-three children excluded from the MRC project were identified as low IQ poor readers. Children were judged as low IQ if they possessed a FSIQ score of 84 (one SD below the mean) or below. Full scale IQ was used in order to identify generally low achieving children. This criterion follows the definition of the Garden-Variety poor reader as defined by Stanovich (1988). Additionally, children were identified as poor readers if their decoding ability was more than six months behind their chronological age. Measures of reading comprehension were not taken in Phase 1. To provide chronological and reading age matched controls for these 23 existing low IQ poor readers, six Cambridge schools were approached where a total of 104 children were selected as participants. Children were aged from six to 10 and possessed appropriate levels of English and no known diagnoses of Autism and Attention Deficit Hyperactivity Disorder (ADHD). After the administration of the decoding test and reading ages had been calculated, it became evident that the sample did not contain sufficient reading age controls. For this reason the same schools were approached again, this time targeting children aged six to eight. A number of new children were included from this second recruitment process and the final cohort comprised 127 children in total. After IQ and reading measures had been undertaken children were allocated into reader groups. Reading age (RA) controls were children who had been identified as typical readers by their teachers and/or parents, were aged 92 months or below and possessed a minimum FSIQ of 85. Chronological age (CA) controls again were identified as typical readers by parents or teachers, were aged 93 months and above and possessed a minimum 70

71 IQ of 85. Of these children recruited as controls, nine were found to be low IQ poor readers and were added to the MRC group. All children were additionally required to pass an auditory screening using an audiometer. Sounds were presented in both the left and right ear at a range of frequencies (250, 500, 1000, 2000, 4000, 8000Hz). All subjects were sensitive to sounds within the 20dB HL range. Given the previous discussion of the separation between IQ and decoding ability it should be evident that a fourth category of reader may also have been present within the sample. Although very little has been written about the low IQ good reader, there were 10 children recommended as controls, or in a few cases as poor readers, who were in fact reading at a level above that expected for their age (minimum five months ahead to maximum 41 months ahead) with an IQ at least one standard deviation or more below the mean. Although these children may not necessarily be evident as a group in the classroom, they are a very interesting sample for comparison purposes and their potential as such was immediately obvious. These children we called low IQ good readers (GR- LIQ). To provide an overview of the composition of the complete recruited sample for Phase 1, ages, reading levels and IQ are provided in Table

72 Table 4.1 Means and SD for age, reading age, IQ and SD for entire recruited sample for Phase 1 PR LIQ (N=32) GR LIQ (N=10) CA Conts (N=43) RA Conts (N=42) Age (mos) SD (13.17) (16.06) (7.70) (7.94) BAS read age (mos) SD (12.01) (18.16) (20.79) (20.81) FSIQ (SS) SD (6.76) (6.77) (12.82) (14.19) Matching colours in each row indicate values which are statistically equivalent (i.e not significantly different using a one-way ANOVA) SS = standardised score For the current project it was important that a matched groups analysis take place. Thus the low IQ poor readers were compared to children of their own age (CA controls) and also to younger children reading at the same level (RA controls). A design of this type has historically been helpful in identifying particular areas which may be related to reading impairments. For example, if a poor reader s performance on a certain variable is below that demonstrated by a younger reading age control, the variable may be particularly related to the reading difficulty. Additionally, if poor readers perform at the level of reading age controls on certain skills there may be evidence for a developmental lag. In other words poor readers may potentially just be slow in developing this skill but may eventually catch up to their peers. In the current project both controls are simply used as comparison points for the performance of the low IQ poor reader. 72

73 As is evident from Table 4.1, when all children were included, the low IQ groups and the CA controls were not matched on age. Also absent is a reading match between the poor reading low IQ group and the RA controls. To facilitate matching of these variables some children falling outside of the matching boundaries were excluded. The entire sample was nevertheless retained at Phase 2 of this longitudinal study. The matched groups are shown in Table 4.2 Table 4.2 Means and SD for age, reading age and IQ for matched groups Phase 1 PR LIQ (N=30) GR LIQ (N=10) CA conts (N=29) RA conts (N=26) Age (mos) SD (12.35) (16.06) (5.42) (5.39) BAS read age (mos) SD (11.26) (18.16) (17.98) (15.37) FSIQ SS SD (6.57) (6.77) (13.23) (14.11) Experimental tasks and procedure. For the Phase 1 test battery, 21 tasks were delivered to each child in one of six presentation orders. The use of multiple conditions allowed three phonological task orders to be administered with two overall running sequences. Visits made to children in school typically resulted in approximately seven sessions of twenty minutes. Children who were visited at home, particularly where visits involved long travel distances, were administered the 21 tasks over a minimum of two visits. 73

74 Standardised psychometric tests. Following a procedure outlined by Sattler (1981) four subsets of the Wechsler Intelligence Scale for Children (WISC III, 1992) were administered to achieve a FSIQ; Block Design, Picture Arrangement, Similarities and Vocabulary. This tetrad short-form is composed of two Verbal (Similarities & Vocabulary) and two Performance Scale (Block Design & Picture Arrangement) tests and demonstrates a high validity coefficient (r =.95) when compared with standardised FSIQ scores (Sattler, 1981). To provide a short description of each test, the Block Design is a perceptual reasoning test which requires the child to use red and white blocks to reconstruct a two dimensional design as quickly as possible. The test has been designed to assess the child s ability to analyse and synthesise abstract visual stimuli but also involves aspects of nonverbal concept formation, visual perception and organization, simultaneous processing, visual-motor coordination, learning and the ability to separate figure and ground in visual stimuli (WISC IV manual). Picture arrangement is a second perceptual reasoning test and requires children to arrange a series of pictures into a coherent story. Picture arrangement assesses the child s ability to demonstrate logical organisation of thought and demonstrate elements of processing speed. However, this subtest has been criticised due to its reliance on an overly large number of disparate abilities. The Similarities subtest is a measure of verbal reasoning and concept formation. In this test children are presented with two words that represent common objects or concepts. The child is then asked how these two items are similar. Finally, the Vocabulary subtest requires children to provide definitions for words read aloud by the examiner. The vocabulary test is designed to measure the child s word knowledge and verbal concept formation. It also measures general knowledge, learning ability, longterm memory and to some extent language development (WISC IV manual). The British Ability Scales (BAS II, Elliot, Smith & McCulloch, 1997) reading test and the Sight Word Efficiency and Phonetic Decoding Efficiency from the Test of Word Reading Efficiency (TOWRE, Torgesen et al., 1999) were also administered to achieve standardised reading measures. The BAS spelling and maths tests and the British Picture Vocabulary Scale (Dunn et al., 1982) completed the standardised tests for this Phase 1 battery. All tasks were delivered according to published guidelines. Standard scores were 74

75 used to minimise individual variation and to allow for differences in ages when performing regression analyses on the dataset as a whole. Phonological awareness tasks. The measures of nonword reading and spelling administered as standardised tasks provide one indication of phonological ability. However, a bespoke measure of onset discrimination (oddity) which manipulated aspects of sonority and phonological short term memory (PSTM) and rapid automatic naming (RAN) tasks which manipulated phonological density were also administered. Phonological neighbourhood density is an important factor known to affect typical development of phonological awareness (De Cara & Goswami, 2003; Storkel, 2001). The term is used to describe similarities between words according to the numbers of shared phonemes. For example, phonological neighbours to the word cup would include cusp, up, cap and cut. Studies have shown that target words which have a large number of phonological neighbours, or exist within a dense phonological neighbourhood are acquired earlier and are easier for children to manipulate in phonological awareness tasks (e.g. Metsala, 1999). It is worth noting however that these findings are in some way contradictory to the NAM model which predicts that words from dense neighbourhoods may be more difficult to recognise than those from neighbourhoods which are sparsely populated (see section 3.1.2). Although neighbourhood density effects have been demonstrated in children with dyslexia in a PSTM task (Thomson et al., 2005) the current battery aimed to establish whether phonological neighbourhood effects would also be found in low IQ poor readers. Phonological neighbourhood density effects would indicate that phonological awareness is affected by structural lexical factors in low IQ poor readers. In addition to phonological density, levels of sonority were also manipulated. The sonority profile refers to the sound composition within a word or syllable. Sonority describes the resonance of a sound in relation to other sounds. Vowels are the most sonorant, followed decreasingly by glides (e.g., /w/), liquids (e.g., /l/), nasals (e.g., /n/), and obstruents or plosive sounds which are the least sonorant (e.g., /p/, /d/, /t/). Theoretically, an optimal sonority profile for articulation presents more sonorant sounds 75

76 closer to the vowel, for example tra rather than rta (e.g. Clements, 1990). Most syllables in English end with obstruents or plosive sounds, such as big, kick, cat. This is not true for other languages, for example half of syllables in French either end in liquids or have no final consonant phoneme. It has been found that children find it easier to segment and therefore to spell syllables that end in plosives compared to syllables that end in liquids, for example pit is easier to segment than pill (see De Cara, Goswami & Fayol, 2001). For the purposes of this study, monosyllable words with good sonority were considered to be those words which began with a more sonorous sound (e.g. lamb, ran, rang) while monosyllables beginning with less sonorous sounds (e.g. cap, cat, pack) were considered to have poor sonority. Given previous research using the same tasks (Richardson, 2003, unpublished work) it was hypothesised that children may find phonological awareness tasks easier which use less sonorant sounds compared to those using more sonorant sounds. However, it was unclear whether low IQ poor readers, or readers in general from this sample, would follow this expected pattern. Onset Oddity task. The oddity task used was developed by Richardson. This task used digitised speech created by a native female speaker of standard Southern British English. Children were randomly administered one of three presentations varying in trial order. Each presentation consisted of 20 trials of three words, 10 using good sonority words (e.g. rain, name, nail) and 10 using poor sonority words (e.g. cope, poke, coat). The two sets of 10 words were controlled for word familiarity and frequency as much as possible in addition to matching of rime neighbourhood. In each trial children were asked to listen to the three words and repeat the word that began with a different sound (see Appendix 1A for stimulus list). Phonological short term memory (PSTM) task. The PSTM measure utilised was developed by Thomson, Richardson and Goswami (2005). This task was again administered using digitised speech by the same female speaker. The child was given 16 trials each with four unique monosyllabic consonant-vowel-consonant (CVC) words with no phoneme occurring more than once in each trial. Three orders of trial presentation were used which were counterbalanced across participants. Dense versus sparse sets 76

77 were randomly varied within each order. Half of the trials were composed of words with dense rimes (e.g. knit, laid, rack, pub; fed, tub, shake, lip) the other half used words from sparsely populated rime neighbourhoods (e.g. hem, dull, join, song; fib, road, peg, shook). The mean number of rime neighbours for dense stimuli according to the phonological density database complied by DeCara and Goswami (2002) was 18 (SD 3.3) and for sparse stimuli was seven (SD 2.7). Density conditions were balanced for lead neighbours (words possessing the same onset and vowel), spoken frequencies or familiarities. Children were required to listen through sound attenuating headphones. Responses were captured by DAT recorder (see Appendix 1C for PSTM stimulus list). Rapid automatised naming (RAN) task. The RAN task used was developed by Thomson (2004, unpublished thesis) and consisted of the rapid naming of line drawings of familiar objects drawn from sparse and dense phonological neighbourhoods. The mean rime neighbourhood density for dense stimuli according to the DeCara and Goswami database (DeCara & Goswami, 2002) was 19.5 (SD 2.38) and for the sparse stimuli was 8.5 (SD 1.73). Conditions were matched on phonotactic probability, spoken frequency, written frequency and familiarity. Children were first introduced to the names of four pictures and then shown a page with the same pictures repeated 40 times in different orders. Children were asked to name all of the pictures as quickly as possible. Performance was timed and errors were counted. The presentation order of dense trial conditions (Gate, Wheel, Shop, Tie) and sparse trial conditions (Fire, Cup, Bird, Leaf) was counterbalanced (see Appendix for 1D RAN stimuli). Auditory processing tasks. The battery of auditory processing tasks was based on measures expected to be related to the processing of amplitude envelope structure in the speech stream. Three rise time discrimination measures were used (1 ramp rise, 2 ramp rise, 1 ramp rise with intensity roving). These tasks had been used in slightly different formats, specifically using a different adaptation paradigm and with a different standard, in previously published research (e.g. Richardson et al., 2004) and as such were known to be difficult for children with dyslexia. In addition to the discrimination of rise time measures of sensitivity to duration, to periodicity (recurrence of tones at regular intervals) 77

78 and to rises and falls in frequency (as in Thomson & Goswami, 2008) were also administered. Looking at these tasks as a whole, previous research has found group differences between children with dyslexia and control children in sensitivity to rise time and duration but not to intensity. Discrimination of frequency also appears difficult for some children at all levels of reading ability. Although a deficit in perception of rhythm has previously been found in children with literacy difficulties (tempi task, Thomson & Goswami, 2008), the periodicity task used here was novel. Psychoacoustic stimuli were presented to both ears through headphones at 75 db. Earphone sensitivity was calculated using a Zwislocki coupler in one ear of a KEMAR manikin (Burkhard & Sachs, 1975). All tasks were based on a child friendly threshold estimation paradigm. The Dinosaur program (see Figure 4.1), originally created by Dorothy Bishop (Oxford University) and reprogrammed by Martina Huss, used a paradigm where sounds were presented by animals jumping on coloured boxes. In this setup, tasks were presented in either an AXB or 2IFC presentation format. In the AXB format, X presented the standard while A or B differed from X in one direction. Children were asked to select the different stimulus and responses were recorded by the computer according to mouse click. As a part of the program auditory feedback was given after each trial as to the accuracy of individual trial performance and with regards to the task overall. Figure 4.1. Dinosaur program delivering auditory measures 78

79 For each auditory task, the child was given five practice trials. During this period further verbal confirmation of the desired discrimination was given and reinforcement was provided by the researcher. Through the adaptation of the program by Martina Huss the Dinosaur program used an adaptive staircase procedure (Levitt, 1971) using a combined 2-up 1-down and 3-up 1-down procedure, where after two reversals, the 2-up 1-down staircase procedure changes into 3-up 1-down. After the fourth and sixth reversal the step size was halved at each occasion. A trial was typically terminated after eight response reversals or after 40 trials, whichever was shorter. The adaptive method is used to identify the appropriate region for discrimination and places the test stimuli as close to the individual threshold of the child as quickly as possible. Catch trials, presenting the easiest discrimination, were presented a maximum of four times to assess attention levels in each child. A threshold score was calculated using the last four reversal points. A Probit function was then fitted using SPSS, which allowed the point at which the child was able to maintain 79.4 percent correct to be calculated. A threshold value was then obtained which indicated the smallest difference between stimuli at which the participant could still discriminate with a 79.4 percent accuracy rate. Thresholds were reported on a scale from zero to 40 with 40 indicating the least sensitivity to the targeted discrimination. Schematic depictions of the auditory stimuli can be found in Appendix 3. One amplitude rise time task- AXB format. (1 Rise task). Three 800 ms tones were presented, two of these were standard tones with a 15 ms linear rise time envelope, 735 ms steady state and 50 ms linear fall time. The third tone, adaptively selected, presented a logarithmically varied rise time with 15 ms representing the shortest rise time and 300 ms the longest. The AXB label was used to indicate the presentation of the adaptively selected stimulus as either the first or the third tone. Children were asked Which dinosaur has a softer rising sound the yellow or the red one? The concept of rising sound was reinforced with the use of hand motions which contrasted brushing contact of hand to table (soft rising sound) to a hard hitting of table with the hand (sharper rising sound). 79

80 Two amplitude rise time task- 2IFC format (2 Rise task). Forty stimuli of 3573 ms (2.5 cycles) in duration were created using a sinusoidal carrier at 500 Hz amplitude modulated at the rate of 0.7 Hz (depth of 50 percent). A square wave was the basis of the underlying envelope modulation. Rise time was again varied logarithmically from 15ms to 300 ms with a fixed linear fall time of 50 ms. Children were asked to choose the dinosaur making the sharpest beat. This required the shorter rise time tone to be discriminated from the standard which represented the longest possible rise time (300 ms) in this task. One amplitude rise time task with intensity roving- 2IFC format. (1 Rise Rove). For this task the stimuli were the same as used in the AXB measure, but with a randomly varied intensity level for each stimulus. As the shortening of rise time is perceived as an increase in intensity, the random pairing in this measure prevented intensity functioning as an additional cue to rise time. Children were again asked to choose the dinosaur making the softer rising sound. Frequency discrimination task 2IFC format. Children were presented with two sequences of five tones. In each sequence five 25ms sine tones were used with 10ms rise time, 10ms fall time and inter-stimuli intervals of 100ms. In one sequence the tones were all the same (AAAAA). In the second sequence, alternate tones had a higher frequency (ABABA). B tones were adaptively selected from a continuum of 60 stimuli which increased in frequency at a constant 2.6 Hz from the standard 600Hz task. Children were asked to identify the bird making a mixture of high pitch and low pitch sounds, accompanied by vocal examples of high and low sounds from the experimenter. Rhythm discrimination task 2IFC format. Children were again presented with two sequences of five tones. In one sequence a 500 Hz sinusoid 1600 ms in duration was manipulated to present five identical tones with equal 150 ms ISIs. The second sequence had a linearly modified interval between the third and fourth tones ranging from 150 ms to 15 ms. Children were told that one penguin walked in a steady rhythm while one walked in an unsteady rhythm and had a skip in its step. This distinction was supported 80

81 by the experimenter tapping examples of each on the desk. Children were asked to identify the penguin with the unsteady rhythm. Intensity discrimination - 21FC format. Again this task used two sequences of five tones. In each sequence five 200 ms sine tones were presented with 50ms rise time, 50 ms fall time and ISIs of 100ms. In one sequence tones were of a constant intensity at 75 db (AAAAA). In the second sequence alternate tones had a reduced intensity (ABABA) and were adaptively selected from a continuum logarithmically manipulated to reach a final state of half the original amplitude. This resulted in 40 stimuli which decreased in intensity at constant 1.7 percent steps from the standard 75dB tone. Children were asked to choose which monkey was making a mixture of loud and soft sounds. Duration discrimination task AXB format. A continuum of 40 stimuli was created using 500 Hz pure tones with a 50ms rise and fall ranging in duration from 400ms to 600ms. The duration of the standard tone was 400 ms. Children were asked to choose the dinosaur which was making the longest sound, but again were told that the dinosaur making the second standard sound was not in the game and not to be chosen. 4.4 Results Phase 1 results are reported for the statistically matched groups only. Due to the unequal group sizes all parametric statistical assessments were checked using non-parametric versions of the same tests. As these tests in general supported the findings of the parametric assessments they are not presented in the main text but may be referenced in Appendix Standardised tests. Outcomes for the standardised tests are provided in Table 4.3. A series of box plots performed on the data did not find any outliers by group; therefore no data points were removed. As expected the PR-LIQ group did poorly on all of these tasks, with the GR-LIQ group performing at a significantly higher level in decoding and spelling but not in receptive vocabulary. A number of one-way ANOVAs were used to explore significance. The ANOVAs for BAS reading found a significant main effect of 81

82 group [F(3,91) = 24.01, p< 1], with post hoc tests indicating significantly better decoding in the GR-LIQ (p< 1), RA (p< 1) and CA (p< 1) groups compared to the PR-LIQ group. This was also true for TOWRE word reading [F(3,91) = 26.19, p< 1] and for TOWRE nonword [F(3,91) = 25.89, p< 1]. For BAS spelling a significant group difference was found [F(3,91) = 28.06, p< 1], with post hoc tests indicating a significantly better performance in spelling by the GR-LIQ (p< 1) RA (p< 1) and CA (p< 1) groups compared to the PR-LIQ group. In regards to BAS maths another significant group difference was found [F (3,91) = 8.27, p< 1]. Post hoc tests indicated more typical performance by RAs (p<.01) and CAs (p< 1) compared to the PR-LIQ group in mathematical ability. Finally another significant group difference was found for receptive vocabulary (BPVS) [F(3, 91) = 10.52, p< 1]. RAs and CAs performed significantly better than the PR-LIQ and GR-LIQ groups on this task (RA > PR-LIQ, p<.01, CA > PR-LIQ, p<.01; all others at p< 1). 82

83 Table 4.3 Means and SD for standardised tasks in Phase 1 matched groups (N= 95) PR LIQ GR LIQ CA conts RA conts BAS Read SS A A A SD (8.29) (8.99) (11.26) (15.37) TOWRE word SS A A A SD (11.66) (9.30) (11.31) (11.97) TOWRE nonword SS A A A SD (9.96) (9) (14.75) (10.96) BAS Spelling SS A A 109 A SD (10.24) (11.63) (13.46) (14.77) BAS Maths SS A A SD (11.89) (10.59) (15.26) (19.29) BPVS SS B A SD (8.99) (6.92) (13.61) (12.46) A significantly different from PR-LIQ B significantly different from the PR-LIQ & GR- LIQ group SS = standard score. Coloured cells are statistically below at least one other group Phonological processing tasks. The mean performances on the phonological measures by group after the removal of outliers (Oddity 1 outlier, PSTM 0 and RAN 4) are shown in Table 4.4. As was expected the PR-LIQ group performed poorly on all measures. In contrast, the GR-LIQ group appear to perform at the level of the CA controls on most tasks. 83

84 Onset Oddity Task An exploration of the Onset Oddity data uncovered one outlier in the CA group for the good sonority items (Sub 47 score = 1) and this score was thus removed. To assess the role of sonority and performance on the Onset Oddity task a 4 (reading group) x 2 (sonority profile) factorial ANOVA was run with group as the between participants factor and trials correct as the within groups factor. No significant difference for sonority was found F (1,73) = 0.81, p =.78. A main effect for group was found F (3,73) = 4.92, p<.01, with CAs performing better than RAs and the PR-LIQ group. When the total trials correct were examined, two outliers were found and removed (RA sub 86 score = 3; CA sub 47 score = 3). A one way ANOVA was run on the total trials correct, adding across sonority conditions, a significant group effect was found F(3,89) = 5.72 p = 1 with post hoc tests demonstrating a significantly better performance by the CA compared to the PR LIQ group (p< 1). 84

85 Table 4.4 Means and SD for phonological measures (N= 95) Standard deviation in parentheses PR LIQ GR LIQ CA conts RA conts Onset Oddity task 5.77 (2.68) 7.50 (2.22) 8.07 B (2.11) 6.35 (2.62) Good sonority (max 10) Onset Oddity task 5.31 (2.56) 7.60 (2.01) 7.86 B (2.01) 6.46 (2.87) Poor sonority (max 10) Onset Oddity task (4.48) (4.07) A (3.22) (5.20) (max 20) PSTM Dense items (6.21) 22 (8.27) A (5.99) (7.44) (max 32) PSTM Sparse items (5.74) (7.69) A (5.98) (7.87) (max 32) PSTM words correct (10.95) A (15.58) A (9.82) 37 (13.78) (max 64) RAN dense naming time (16.11) (11.13) B (5.41) (12.77) (secs) RAN sparse naming time 41.43(10.10) (10.98) B (4.09) (7.55) (secs) A significantly different from PR LIQ group, B significantly different from the PR-LIQ group & RAs. Bold significantly different from sparse condition. Coloured cells are statistically below at least one other group. PSTM task An inspection of the data for the PSTM task found no individual outliers. To assess the influence of phonological neighbourhood density a 4 (reading group) x 2 (sonority profile) ANOVA with group as the between participants factor and sonority (good/poor) 85

86 as the within participants repeated measure was run. This analysis found a significant effect of density F(1,79) = p = 1 with words from dense conditions being better recalled. Overall, there was an effect of group F (3, 79) = 7.25, p< 1 with post hoc tests indicating a significantly better performance in CAs compared to the PR LIQ (p< 1) group. The lack of interaction effect between group and phonological neighbourhood density indicates that structural lexical factors were affecting the development of phonological awareness in the same way for the low IQ children despite their smaller receptive vocabularies. RAN task Given previous findings that adults are slower to name words from dense neighbourhoods because of lexical competition effects (Vitevitch & Luce, 1999) it was expected that an effect of density would be observed in the RAN task. An inspection of means identified four outlier data points. One RA child was removed from the sparse condition (Sub 84 = 116) and was also removed from the dense condition (Sub 84 = 76). Another RA score (Sub 89 = 77) and CA score (Sub 85 = 77) were additionally removed from the sparse condition only. A 4 (reading group) x 2 (neighbourhood density) factorial ANOVA was run and demonstrated a density effect, F (1,88) = 54.03, p< 1, with naming in dense conditions taking significantly longer. A main effect of group was also significant F (3,88) = 4.85, p<.01, with the CA performing significantly better than RA (p<.05) and PR LIQ (p<.01) groups. The GR-LIQ group performed at an approximately similar level to CAs on this task. Overall, these results demonstrated that the effect of density was found for all participant groups, even though the low IQ poor readers had slower naming overall. 86

87 4.4.3 Auditory tasks. As a first step the auditory discrimination data were checked for outliers using the boxplot method. This analysis revealed five outlier data points which were removed from the dataset (One Rise GR-LIQ Sub 271 = 40, Two Rise Sub 271 thresh = 40, Intensity PR-LIQ Sub 138 thresh = 5, Rhythm RA Sub 51 thresh = 40, GR- LIQ Sub 271 thresh = 40). Additionally, a small number of children did not reach the minimum threshold level on some tasks, hence the software did not generate thresholds in these instances (Values missing - Rove RA Sub 43, Intensity RA Sub 2; PR-LIQ Sub 70 Frequency RA Sub 43, Rhythm RA Sub 99). A visual inspection of the means and standard deviations provided in Table 4.5 and the same data in Figures 4.2 and 4.3 indicated generally poorer performance (higher thresholds) by the PR-LIQ group who demonstrated consistently higher mean values than CAs on all of the auditory tasks. Furthermore, the GR-LIQ group appeared to be performing at a similar level to the CAs on most measures. A series of one-way ANOVAs on the four groups indicated a significant group difference for the following tasks: One Rise (F[3,90] = 7.45, p< 1) with CA (p = 1) and GR-LIQ groups (p<.05) performing better than the PR-LIQ group, Intensity (F [3,88] = 5.55, p = 2) with the CA (p<.01) and GR-LIQ. groups (p<.05) performing better than the PR-LIQ group, Frequency (F[3,90] = 4.49 p<.01) with the CA performing better than the RA (p<.05) and PR-LIQ groups (p<.01) and Rhythm [F (3, 88) = 2.97, p<.05] with the CA group performing better than the PR-LIQ group (p<.05). The remaining tasks although not reaching significance followed a similar trend, with higher thresholds in the PR-LIQ versus the CA group (Two Rise F[3,91] = 2.27, p =.09, Duration F[3,91] = 1.33 p =.27 and Rove F [3,90] = 1.32 p =.27]). 87

88 Table 4.5 Mean and SD for auditory tasks (N= 95) PR LIQ GR LIQ CA conts RA conts Intensity A A SD (8.51) (12.41) (12.36) (10.79) Rhythm A SD (12.43) (4.39) (6.07) (10.29) Two Rise SD (11.40) (11.30) (10.09) (12.10) One Rise A A SD (13.41) (5.93) (7.77) (12.11) Duration SD (11.29) (9.71) (10.18) (10.82) Rove SD (12.23) (11.79) (10.98) (12.63) Frequency B SD (8.99) (11.95) (12.92) (12.56) A significantly different from the PR LIQ group B significantly different from the PR- LIQ & RA groups. Coloured cells are statistically below at least one other group. Higher threshold values equal decreased sensitivity 88

89 Figure 4.2. Graph of rise time measures * Significantly different from PR-LIQ. Higher threshold values equal decreased sensitivity Figure 4.3 Graph of related auditory measures * Significantly different from PR-LIQ * Significantly different from PR-LIQ and RA. Higher threshold values equal decreased sensitivity 89

90 Correlations To explore the relationships between auditory tasks, phonological variables and literacy development, two sets of partial correlations were run. The first was on the whole sample, controlling for nonverbal IQ and age (see Table 4.6). A small but significant relationship between many of the key variables was demonstrated. For example, all of the auditory tasks were correlated with nonword reading and decoding, with the exception of the Two Rise task and Rhythm. 90

91 Table 4.6 Partial correlations controlling for nonverbal IQ (blocks) and age on entire Phase 1 data (N=127) Int. Rhythm Two Rise One Rise Dur. Rove Freq. BAS Read age (mos) SD TOWRE Read SS SD TOWRE nword SS SD BAS spell SS SD BAS maths SS SD BPVS SS SD RAN dense SD Oddity Onset SD PSTM SD Coloured boxes indicate significant correlations Secondly, to assess the relationships present within the matched groups, a set of partial correlations controlling for Group membership was run. By controlling for reader group the effects of decoding ability, age and IQ were partialled out. When considered by 91

92 individual group, i.e. when taking the low IQ poor readers on their own, significant correlations disappeared. This result was likely due to the small amount of variation amongst this group, an issue which is discussed further in Chapter 5 (see Figures 5.3 & 5.4). 92

93 Table 4.7 Partial correlations controlling for reader group, Phase 1 matched groups only (N= 95) Intens. Rhythm Two One Dur. Rove Freq. Rise Rise BAS Read age SD.03 TOWRE Read SS SD TOWRE nword SS SD BAS spell SS SD BAS maths SS SD BPVS SS SD RAN dense SD Oddity Onset SD PSTM SD Coloured boxes indicate significant correlations With reference to Table 4.7, all auditory variables were at least weakly correlated with decoding ability (reading age), while the majority of the auditory tasks were correlated with phonological short term memory and more than half with Onset Oddity. The rapid 93

94 naming task was also correlated with many of the auditory tasks. The correlations between single ramp onset amplitude discrimination (1 rise task) and literacy and Duration and literacy are consistent with previous findings (Richardson et al., 2004). However, the correlations between PSTM and the majority of the auditory tasks were a new finding. Ongoing research using the same tasks with children with dyslexia has not found a significant relationship between phonological memory and auditory performance. As such, the present positive correlation may indicate an important role for short term acoustic memory in auditory performance for low IQ children. It is also interesting to compare the differences between Tables 4.6 and 4.7. Table 4.6 shows more significant correlations between the auditory and literacy measures and unlike Table 4.7 it does not demonstrate a correlation between the auditory measures and mathematics. Arguably, Table 4.6 may provide a more representative account given the larger size of the sample utilised, however it is worth noting that the correlations are weaker than those presented in Table 4.7. To explore whether individual differences in the auditory tasks predicted individual differences in decoding, a number of fixed entry, two-step multiple regressions were run. BAS reading age was used for these analyses as this variable was better distributed than BAS reading standard score in this phase of the project. As a first consideration, an analysis of kurtosis and skew tested the normal distribution of the key variables (Table 4.8). These analyses found that the distributions were reasonably normal as kurtosis and skew statistics were broadly within the normal range (near to +/- 2 times the standard error as recommended by the Psychology Department of the University of New England). Although all of the auditory tasks slightly exceeded this mark on at least one variable, they did so by a small margin and were thus deemed to be normally distributed for the purposes of this project. However, it is important to mention that unlike the other tasks, Rhythm outcomes were particularly skewed and peaked in distribution. As can be seen in Figure 4.4, a large number of children did well on this task and achieved a reasonably low threshold. As a log transformation (recommended for skewed data Dawson & Trapp, 2004) did not help to normalise the auditory outcomes (including Rhythm), it was 94

95 decided that the data would remain unaltered. Outcomes for the Rhythm task may thus be interpreted with slight reservations. Table 4.8 Kurtosis and skew with standard error values for regression variables, Phase 1 matched groups (N= 95) Variable Kurtosis Skew Reading age -.35 (.49).43 (.25) Intensity (.50) -.63 (.25) Rhythm 2.77 (.51) 1.39 (.26) 2 Rise (.49) -.18 (.25) 1 Rise -.77 (.50).70 (.25) Duration 1.1 (.49).03 (.25) Rove (.49) -.55 (.25) Frequency -.99 (49) -.66 (.25) * p<.05 ** p<.01 *** p< 1 95

96 Figure 4.4 Histogram of Rhythm task outcomes for Phase 1 matched groups (N = 95) 96

97 Multiple regressions predicting BAS reading age for the entire phase 1 sample data are presented in Table 4.9 As can be seen, a fixed step regression with step 1 as age, step 2 as non-verbal IQ (blocks), and step 3 as each individual auditory variable, identifies a number of significant predictors. As demonstrated in the relevant correlation table (Table 4.6), all of the auditory variables are significant predictors of variation in BAS reading age, with the exception of the Two Rise task. Table 4.9 Fixed step multiple regressions predicting BAS reading age, entire Phase 1 data (N= 127) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Age.55.31*** NV IQ.48.22*** Intensity * Rhythm * Rise Rise ** Duration * Rove ** Frequency **.58 * p<.05 ** p<.01 *** p< 1 97

98 Multiple regressions for the Phase 1 matched groups with group as step 1 and each individual auditory measure as step 2 are presented in Table As can be seen, all auditory measures functioned as significant predictors of decoding ability. Table 4.10 Fixed step multiple regressions predicting BAS reading age, Phase 1 matched groups (N= 95) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Group * Intensity *** Rhythm * Rise ** Rise *** Duration *** Rove ** Frequency ***.18 * p<.05 ** p<.01 *** p< 1 As the main research question was the investigation of the relationship between auditory variables and progress in literacy, it was also of interest to see whether any of the auditory variables were predictive of nonword reading (PWE decoding) in this first phase of testing. The correlation table indicated a significant relationship between Duration and 98

99 Frequency and nonword reading outcomes. As Table 4.11 demonstrates, both Duration and Frequency thresholds were significant predictors of nonword reading. Table 4.11 Fixed entry multiple regressions predicting TOWRE nonword, Phase 1 matched groups (N= 95) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Group *** Intensity Rhythm Rise Rise Duration * Rove (p =.07) Frequency *.27 * p<.05 ** p<.01 *** p< Summary and discussion The findings presented here suggest that poor auditory processing and poor phonological skills are related to poor decoding in low IQ poor readers. Low IQ poor readers demonstrated a perceptual deficit in auditory tasks and these auditory measures predicted decoding skill even after controlling for reader group in multiple regression equations. Low IQ good readers however demonstrated age appropriate auditory processing skills, 99

100 despite their low IQs and low levels of vocabulary. These data suggest a link between phonology and auditory processing across all levels of IQ. The current findings also confirm the presence of a phonological deficit in low IQ poor readers (see Chapter 1). Additionally, we are now aware through the phonological density and sonority manipulations that phonological awareness is developing according to the same language based factors in all of these children. Similar to their peers, low IQ poor readers remember more words from dense phonological neighbourhoods and are slower to produce the names of objects from dense neighbourhoods. Again, these findings decrease the likelihood that poor phonological awareness in this group arises from a different linguistic trajectory. The low IQ good readers however demonstrate typical development of phonological awareness in addition to age appropriate skills in all areas, receptive vocabulary excepted. Although beyond the data gathered for this project, it may be suggested that good auditory skills enable good decoding skills in this group. Previous research has found that rise time discrimination measured in pre-readers is predictive of both phonological and literacy outcomes a year later (Corriveau et al., 2008). Correlational analyses supported the relationship between auditory and literacy variables, even after controlling for age and non-verbal IQ. Only the Two Rise task, which all children seemed to have difficulty with, did not reach significance as a predictor for Phase 1 data. Additionally, all of the auditory measures accounted for unique variation in predicting BAS reading skill in multiple regression equations using the matched groups sample. Unusually, a relationship between intensity and decoding was demonstrated in these results. This result may have some relation to the Intensity task being a new task utilising an ABABA format. Also new was the demonstration of a relationship between discrimination of periodicity or rhythmic timing and decoding. However, the relationship between rhythm and literacy has ample foundation, with evidence that both children with developmental dyslexia (Thomson & Goswami, 2008) and children with specific language impairment (Corriveau & Goswami, 2009) have difficulty keeping time to a 100

101 beat. This deficit is particularly prominent at slow rates such as two and one and a half Hz, speeds close to the syllable rate in speech. In relation to the project goals it has been demonstrated that low IQ poor readers perform at a generally low level on all tasks that were administered. These children perform below same aged and reading age controls on spelling, maths and vocabulary and at a similar level to the younger matches, on oddity and rapid naming tasks. However, they were worse than this younger group on the phonological short term memory task. As regards the auditory processing skills, the low IQ poor readers generally perform below the chronological age controls and perform at a similar level to the reading age matches. However, this is not the case on the Frequency task where the younger and low IQ children all perform below the chronological age controls. In summary, low IQ poor readers have a phonological deficit and poor auditory processing. Phase 1 findings suggest that their poor decoding ability may be related to an underlying difficulty in discriminating aspects of the amplitude envelope. However, these relationships were based on all reader groups together not in low IQ readers individually. Further testing of the auditory hypothesis in Phase 2 and the longitudinal regression analyses presented in future chapters, will be used to test the robustness of these conclusions. 101

102 Chapter 5. Language skill in low IQ poor readers and controls (Project Phase 2) 5.1 Introduction Phase 1 enabled a number of conclusions to be made. Firstly, low IQ poor readers were found to have phonological, auditory and vocabulary deficits. Secondly, low IQ good readers showed good phonology linked with good auditory processing and poor receptive vocabulary. Together, these findings suggested that low IQ poor readers possessed poor phonology related to poor auditory processing and not poor phonology related to poor receptive vocabulary (as suggested by an alternative hypothesis, see Swan & Goswami, 1997). Phase 1 was thus key in establishing the relationship between phonology and auditory processing in low IQ poor readers. However, this first test battery did not provide a full assessment of the language ability of the cohort. As vocabulary is one of the subtests of the tetrad FSIQ short-form (see Chapter 3), a low receptive language performance was fairly well expected. However, group performances on other aspects of language were unknown. Therefore, one additional research goal in Phase 2 was to assess language ability from a broader perspective. This was of interest for two reasons. Firstly, although the GR-LIQ group and the PR-LIQ group were both impaired in vocabulary, they differed in phonological ability. Thus by testing more language skills it was hoped to ascertain which aspects of language were constrained by low IQ and which were preserved in combination with good phonology. Secondly, some children within our sample may have had significant language impairment. To fully understand the relationship between literacy variables and auditory processing it was important to identify these children as it seemed possible that children with language impairments could demonstrate a different relationship with auditory processing. For identification purposes, measures which have been linked to language deficits in children with SLI, such as immediate verbal memory for sentences and measures of past tense inflection (Conti-Ramsden & Botting, 2001), were employed. This latter measure, known to be related to phonological awareness (see Chapter 1), was of particular interest in regards to its potential capacity to differentiate low IQ good and poor readers. Additionally, the relationship between auditory processing and wider language skill was of theoretical interest. Importantly, children with language deficits (e.g. SLI) have shown difficulties in 102

103 discriminating frequency and in distinguishing tones with short inter-stimulus-intervals (see Chapter 2). Children in this group have also demonstrated difficulty in discriminating variations in amplitude envelope rise time and differences in tone duration (Corriveau, Pasquini & Goswami, 2007). Thus in addition to enabling longitudinal measurement of the variables assessed in Phase 1, Phase 2 added a broader language perspective to the examination of the relationship between auditory processing and literacy. In addition to a wider consideration of language skill, Phase 2 also included assessments of verbal working memory. As discussed in Chapter 3, a number of prominent models of language processing have suggested the importance of working memory which provides the mental work space, to perform online computations related to verbal processing (e.g. Baddeley, 1986). In accordance with this model success in comprehending and producing language should be related to the ability to actively maintain and integrate linguistic material within the working memory system. Working memory is correlated with measures of intelligence in addition to reading and reading comprehension in school age children (Swanson, 1996). Not surprisingly, children with SLI may have particular difficulties with working memory, specifically in the processing and storage of verbal information (Bishop, 1992). Supporting this suggestion are demonstrations of reduced phonological storage capacity in children with SLI (Montgomery, 1995) and twin studies of children with SLI suggesting nonword repetition as a phenotypic marker for developmental language impairment (Bishop et al., 1996). Children with low IQ and poor reading also typically demonstrate poor phonological and more general working memory deficits (Gathercole, 2006). As mentioned previously, the language and working memory data gathered also enabled the identification of any language impaired (LI) children in the sample. LI children were identified as those functioning at at least one standard deviation below the mean on a minimum of two of the clinical language assessments (CELF). In general, Phase 2 used the additional data to identify key predictor variables to be identified with the use of 103

104 regression techniques. It also allowed conclusions from Phase 1 to be tested longitudinally, allowing more robust conclusions to be made. 5.2 Phase 2 research questions An extended set of research questions motivated Phase 2. These are listed below: As related to Phase 1 research goal as outlined in 3.5 (to profile the strengths and weaknesses of the PR-LIQ group) 1. What are the language and memory strengths and weaknesses of the PR-LIQ group? Is the pattern of strength and weaknesses found for Phase 1 variables upheld in Phase 2? 2. Who amongst the sample is language impaired (how many language impaired also fall into the PR-LIQ group?) 3. How do language impaired children perform on auditory processing tasks and phonological measures compared to appropriate controls? Can predictive relationships within this language impaired matched groups set be demonstrated as with the PR-LIQs? 4. Which tasks differentiate low IQ good from poor readers? Are these tasks related to the better phonological processing and word reading skills of the GR-LIQ group? To further explore research goals 2 and 3 as outlined in 3.5 (to assess whether the PR- LIQ group demonstrate an auditory processing deficit and whether individual differences in auditory processing are related to individual differences in reading and language related abilities) 5. Does the PR-LIQ group still demonstrate an auditory deficit in Phase 2? 6. Are individual differences in auditory performance related to individual literacy outcomes in Phase 2 both cross-sectionally and longitudinally? 5.3 Phase 2 research predictions Based on the Phase 1 research, in addition to studies reviewed to this point, a number of expected outcomes could be predicted. Firstly, it was anticipated that once again low IQ poor readers would demonstrate a phonological deficit related to poor auditory processing. It was also predicted that the GR-LIQ group would again perform similarly to 104

105 CA controls on the majority of the auditory tasks. Additionally, mainly due to their low levels of vocabulary, all low IQ children were predicted to be impaired in reading comprehension and on the broader language measures in general. The past tense elicitation task, a grammatical morphology task thought to be tied to phonological awareness skills, provided the exception to this prediction. On this task it was expected that low IQ good readers would perform at a higher level than low IQ poor readers given their good phonological awareness. Also, given the performance of the GR-LIQ group on the PSTM task in Phase 1, it was predicted that this group would perform better than the PR-LIQ group but below CA controls on the measures of verbal working memory. The performance of the language impaired group was largely unknown Phase 2 Methods Participants. From the 127 children tested in Phase 1 (Table 4.1 Chapter. 4), the project retained 103 children. The children who remained are presented in Table 5.1 (PR-LIQ: 7 lost, GR-LIQ group: fully retained, CA: 9 lost, RA: 8 lost). 105

106 Table 5.1 Age, reading age and IQ for entire retained sample for Phase 2 PR LIQ (N=25) GR LIQ (N=10) CA Conts (N=34) RA Conts (N=34) Age (mos) SD (15.12) (17.91) (6.87) (10.31) BAS read age (mos) SD (14.49) (18.73) (20.98) (23.84) FSIQ SS SD (6.76) (6.77) (12.82) (14.19) Matching colours in each row indicate values which are statistically equivalent (i.e. not significantly different using a one-way ANOVA) SS= standard score As is evident from Table 5.1, the desired match between the reading age controls and the low IQ poor readers was not in place when all children were included. To create a matched groups set for Phase 2, a new grouping of children was made using as many of the individuals from the Phase 1 matched groups sample as possible. Descriptive statistics for the new Phase 2 matched groups are presented in Table 5.2. The Phase 1 means presented in this table are the same as those included in Chapter 4 and are for reference purposes only. 106

107 Table 5.2 Age, reading age and IQ for matched groups for Phase 1 and Phase 2 PR-LIQ GR-LIQ CA controls RA controls Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 (30) (19) (10) (10) (29) (22) (26) (17) Age (mos) SD (12.35) (13.35) (16.06) (17.91) (5.42) (5.62) (5.39) (7) BAS read (mos) SD (11.26) (13.62) (18.16) (18.73) (17.98) (19.20) (15.37) (11.66) FSIQ SS SD (6.57) (5.38) (6.77) (6.77) (13.23) (10.94) (14.11) (14.54) Matching colours in each row indicate values which are statistically equivalent within each Phase (i.e. not significantly different using a one-way ANOVA). The total number of children available for the Phase 2 matched group analysis (N= 68) was much smaller than Phase 1 (N= 95). This was an unfortunate side effect of differing inter-battery intervals and participant attrition. A reduction in numbers was necessary to enable matching while allowing children to remain assigned to the same group throughout the course of the project. However, longitudinal analyses using correlations and multiple regression statistics were carried out using the entire retained sample (N= 103; see Chapter 6 for longitudinal outcomes) Experimental tasks and procedure. The procedure for Phase 2 was identical to Phase 1. In most cases children who had been seen at home in Phase 1 continued to be visited at home, while children tested in school also continued to be visited in school. 107

108 Standardised psychometric tests The full short form IQ (see Chapter 4) was not administered in Phase 2. Due to the purported stability of IQ measures across short periods of development in childhood (Hanson, 1975) and to save administration time, the WISC III blocks measure (see Chapter 4) was the only subtest administered in Phase 2. A simple correlation run on Blocks Phase 1 and Blocks Phase 2 was 0.77, indicating a stable measure of a onedimensional construct. For this reason the Blocks measure was considered a reliable indicator of non-verbal IQ. The British Ability Scales (BAS) reading test and the Sight Word Efficiency and Phonetic Decoding Efficiency tasks from the Test of Word Reading Efficiency (TOWRE) were also re-administered to achieve a second set of standardised reading measures. The BAS spelling and maths tests and the British Picture Vocabulary Scale were additionally used again. As mentioned previously, a number of new standardised language and working memory measures were introduced which will be outlined below. New standardised general language measures Four subtests from the Clinical Evaluation of Language Fundamentals (CELF, Semel, Wiig & Secord, 1989), a diagnostic measure for speech and language disorders in young people, assessed productive and receptive language. The Recalling Sentences subtest evaluated the child s ability to recall and produce sentences which varied in length and syntactic complexity. All deviations from exemplar word meaning, inflection, derivation, morphology and sentence structure were recorded. The second productive language subtest, Formulated Sentences, evaluated the child s ability to formulate complete, semantically and grammatically correct sentences using given words and within the contextual constraints provided by accompanying illustrations. The Semantic Relationships, a receptive language subtest, is a task targeted to older ages (10-17) which evaluated the child s ability to understand and interpret sentences which make comparisons, identify location or direction, specify time relationships, specify a serial order or are expressed in a passive voice. Finally, the Sentence Structure subtest is a task targeted at younger ages (6-10) which evaluated the child s ability to understand and interpret spoken sentences increasing in length and complexity by selecting the 108

109 appropriate matching illustration. Raw scores were converted to an age appropriate scaled score with a mean of 10 and a standard deviation of 3. As the final two measures were only applicable to certain age ranges, the appropriate measure was used for each individual child and was presented as an overall receptive language score. It is important to note that young children reported finding the Sentence Structure task easy as this measure tapped very basic syntactical knowledge, while the older children reported finding the Semantic Relationships difficult as it required advanced conceptual knowledge. Although both were measures of receptive language it was apparent after administration that comparison across tasks may not have been appropriate. The Wechsler Individual Achievement Test (WIAT II) Reading Comprehension measure was administered to assess aspects of reading comprehension. In the early test items younger children are asked to match a written word with a representative picture. In later items, the child reads different types of written passages and answers questions which assess the ability to comprehend content (identifying the main idea and specific ideas within the passage), make inferences (a conclusion made from given premises) and define vocabulary with the use of contextual cues. The Wechsler Intelligence Scale for Children (WISC IV) Digit Span test was administered as a measure of working memory. The test is composed of the Digit Span Forward task and the Digit Span Backward test. Digit span forward required the child to repeat numbers in the same order as provided by the examiner while digit span backward required the child to repeat the numbers in the reverse order. The digit span task overall is a measure of auditory short term memory, sequencing skills, attention and concentration (WISC IV manual). Specifically, digit span forward involves rote learning and memory, encoding and auditory processing while digit span backward involves working memory, transformation of information and mental manipulation of verbal information (WISC IV manual). The WISC IV Letter-Number Sequencing task was administered as an additional measure of working memory. In this task a sequence of numbers and letters was orally presented 109

110 by the experimenter. Children were then asked to recall and manipulate the sequence so that the numbers were presented in ascending order and the letters were presented in alphabetical order. The tasks assessed the ability to sequence, perform mental manipulation and maintain attention in addition to assessing levels of attention, shortterm memory, visual-spatial imaging and processing speed (WISC IV manual). Non-standardised general language measures The Test of Reception of Grammar (TROG, Bishop 1989) was administered as a test of linguistic comprehension. In this measure children are presented with four pictures and must select the picture which matches an experimenter s verbal cue. The inventory has 80 items, however presentation is discontinued after an extended period of errors, therefore fewer items may be administered. The TROG is intended as a pure measure of the comprehension of grammatical contrasts, although poor working memory or attention may also contribute to a low score on this measure. Although percentile rankings are available for TROG, raw scores were used as a more sensitive measure of performance on this task. Two bespoke measures of grammatical morphology were additionally administered. A sentence correction task adapted from Kamhi and Catts (1986) presented a series of 16 sentences, 12 of which contained errors. The child was asked to identify whether the sentence formulation was right or wrong and if wrong to present the correct formulation. Both grammatical and syntactical errors were presented, with only one error per incorrect sentence. A past tense elicitation task was also administered. In this task children were shown an illustration in combination with a verbal prompt by the experimenter. For example, the experimenter showed a picture of a boy walking and said This boy is walking. He walks everyday. Yesterday he? The child was then asked to complete the final sentence. This task was adapted from Marchman et al. (1999), who found prompts using a third person singular noun in subject position provided the most straightforward past tense elicitation measure. The current task selected the 14 regular (7 high frequency, 7 low frequency) and 10 irregular (5 high frequency, 5 low frequency) verbs which 110

111 language impaired children in the Marchman et al. study found most difficult to inflect (see Appendix for stimuli). Phonological processing tests The Phonological Short Term Memory (PSTM) task from Phase 1 was re-administered in Phase 2. In addition a new measure of phonological awareness, the Rime Oddity task was also delivered. Rime oddity task. The rime oddity task was taken from Thomson (2004, unpublished thesis) and delivered using digitised speech created by a native speaker of standard Southern British English. Children were presented with one of three randomly selected presentation orders. Similar to the PSTM task, phonological neighbourhood density was manipulated with 10 trials presenting words from dense neighbourhoods (e.g. that, wag, nag) and 10 trials presenting words from sparse neighbourhoods (e.g. dutch, budge, hutch). Children were required to repeat the word which ended with a different sound e.g. that, wag, nag that. Using words again selected from the DeCara and Goswami database (2002) the mean rime neighbourhood density for the dense condition was (SD 2.91) and the sparse condition was 6.83 (SD 3.35). Conditions were again matched for phonotactic probability, spoken word frequency and written word frequency (see Appendix 2A for all items). Auditory processing tasks The auditory processing tasks from Phase 1 were re-administered, with two exceptions. The Rove task which appeared to be difficult for all children was not included and a new intensity discrimination task was presented in addition to the Intensity task from Phase 1. This new task was intended to minimise the cognitive processing demands inherent in the Phase 1 Intensity task and to provide a simpler measure of intensity discrimination. This task is outlined below. Intensity discrimination (Intensity 2) 21FC format. Children were presented with two tones. The standard was a pure tone with a frequency of 500Hz presented at 75 db SPL 111

112 with a duration of 200ms and a 50 ms rise and fall. The adaptively selected tone ranged from 55 to 75 db SPL and represented one of 40 steps on a logarithmic scale. Children were asked to pick the softer sound. 5.5 Phase 2 results General standardised tests. An examination of the standardised test outcomes for the matched groups using the boxplot technique identified one score extending three times beyond the interquartile range. As such this score was labelled as an outlier and removed (BPVS PR-LIQ SUB 164 score = 48), all other values were retained. Final outcomes, with the one outlier removed, are presented in Table

113 Table 5.3 Means and SD for the general standardised tasks for Phase 2 matched groups (N= 68) PR LIQ GR LIQ CA conts RA conts BAS Read SS A A A SD (9.75) (11.14) (12.10) (13.50) TOWRE SS A A A SD (10.80) (10.72) (8.37) (13.68) TOWRE nonword SS A A A SD (10.76) (9) (11.73) (8.63) Reading Comp SS B A SD (17.70) (11.59) (7.86) (16.39) BAS Spelling SS A A A SD (7.06) (14.10) (10.90) (11.05) BAS Maths SS A A A SD (10.56) (11.93) (13.12) (15.65) BPVS (SS) B B SD (9.41) (7.63) (8.07) (5.85) A significantly different from PR-LIQ B significantly different from the PR-LIQ group & the GR-LIQ group. Coloured cells are statistically below at least one other group As observed in Phase 1, the low IQ poor readers performed poorly on all of these tasks in comparison to the chronological age and reading age controls. Additionally, the low IQ good readers demonstrated preserved decoding and spelling as expected from the Phase 1 results. A number of one-way ANOVAs were again computed. The ANOVA for BAS reading found significant group differences (F[3,64] = 22.42, p< 1) with post hoc tests indicating a significantly better performance by the GR-LIQ (p< 1) RA (p< 1) and CA (p< 1) groups compared to the PR-LIQ group. On the TOWRE word reading measure similar results were demonstrated (F[3,64] = 15.66, p< 1) again with the GR- LIQ (p< 1) RA (p< 1) and CA (p< 1) groups performing significantly better 113

114 than the PR-LIQ group and on TOWRE nonword (F[3,64] = 20.22, p< 1) identical results were demonstrated with the GR-LIQ (p< 1), RA (p< 1) and CA (p<1) groups who were significantly better than the PR-LIQ group. For WIAT reading comprehension (F[3,64] = 6.68, p = 1) CAs were significantly better than the GR-LIQ (p<.05) and the PR-LIQ groups (p = 1) and on the BAS spelling measure a significant group difference was observed (F[3,64] = p< 1) again with the GR-LIQ (p< 1) RA (p< 1) and CA (p< 1) groups performing significantly better than the PR- LIQ group. On BAS maths a significant group difference was also observed (F[3,64] = 15.56,) p< 1 with GR-LIQ (p<.01), RA (p< 1) and CA (p< 1) groups performing significantly better than the PR-LIQ group. Finally on the measure of receptive vocabulary, a significant group difference was again evident (F[3,63] = 18.34, p< 1 with RAs performing significantly better than the GR-LIQ (p< 1) and the PR- LIQ (p< 1) groups and with CAs performing significantly better than the GR-LIQ (p<.01) and the PR-LIQ (p< 1) groups Standardised language ability measures. Having assessed the general standardised measures, a boxplot inspection was made of the standardised language outcomes for the matched groups. No outliers were found. The full means and standard deviations for these measures are presented in Table 5.4. As the CELF standardised measures have a mean of 10, we can see the low IQ groups are diminished on the productive language measures (Recalling Sentences & Formulated Sentences) and the receptive language measure. A higher performance by the RA controls on the receptive language task may be due to the difference in difficulty between the receptive measure for older and younger children. 114

115 Table 5.4 Means and SD for CELF standardised language measures (N= 68) PR LIQ GR LIQ CA conts RA conts Recal. Sent (mean 10) B B SD (2.63) (2.91) (2.50) (2.52) Form. Sent (mean 10) B 8.41 SD (2.11) (2.41) (3.60) (3.16) Recep. Measures (mean 10) A SD (2.14) (2.45) (3.18) (3.11) A significantly different from PR-LIQ B significantly different from the PR-LIQ & GR-LIQ groups. Coloured cells are statistically below at least one other group A series of one-way ANOVAs were run to assess group performance on the standardised language measures. These tests identified a main effect of group for Recalling Sentences (F [3,64] = 16.06, p< 1), Formulated Sentences (F[3,64] = 10.23, p< 1) and the receptive language measure( F[3,64] = 7.56, p< 1). Bonferroni post hoc tests for the Recalling Sentences measure showed that RAs and CAs (p = 1) were significantly better than the GR-LIQ group, and RAs (p< 1) and CAs (p< 1) were also significantly better than the PR-LIQ group on this task. On the Formulated Sentences measure the CAs (p<.01) were significantly better than the GR-LIQ group, the CAs (p< 1) were also significantly better on this task than the PR-LIQ group. On the receptive language measure the RAs (p< 1) were significantly better than the PR-LIQ group. To establish the relationships between the new language standardised measures and the general standardised measures a series of correlations were run on for the entire Phase 2 sample of 103 children (Table 5.5). With reference to Table 5.5 it is evident that the majority of the variables were intercorrelated even after controlling for age and nonverbal IQ. Although this would be expected for the reading, spelling, phonological and 115

116 language measures, it is surprising that maths would also be so strongly associated with language. This may be due to a relationship between these variables and working memory (presented later in this chapter). Table 5.5 Correlations between language and general standardised measures controlling for age and NVIQ, complete retained sample (N= 103) BAS TOW. TOW. BAS BAS BPVS Read. Read Read Nword Spell Math Comp Recalling Sentences p value Formulated Sentences p value.01 Receptive Language p value Coloured boxes indicate significant correlations Non-standardised language ability measures. As a first step in assessing the outcomes from the non-standardised language outcomes, a boxplot inspection found a number of outliers. For the sentence correction task, two data points were removed (RA SUB 89 score = 1; CA SUB 57 score = 8) and an additional two were also removed in the past tense elicitation task (GR-LIQ SUB 271 score = 6; CA SUB 56 score = 1). The mean scores by group after removal of these outliers are presented in Table

117 Table 5.6 Means and SD for non-standardised general language measures, Phase 2 matched groups (N= 68) PR LIQ GR LIQ CA conts RA conts TROG (max 20) SD (2.58) (2.72) (2.43) (1.66) Sent. Corr. (max 16) A B SD (2.50) (2.35) (1.29) (1.85) Past tense El. (max 24) A A SD (6.05) (2.18) (1.85) (3.37) A significantly different from PR-LIQ B significantly different from the PR-LIQ group & RAs. Coloured cells are statistically below at least one other group As the sentence correction and past tense elicitation measures are raw scores, it is evident that younger children found this task reasonably difficult compared to their older peers. This was particularly true on the sentence correction task where RAs performed under CAs and the GR-LIQ group despite their preserved IQ and good vocabulary levels. It is also evident from looking at Table 5.6 that there does not appear to be any group difference on the test of reception of grammar. Two one-way ANOVAs confirmed this, finding no significant main effect for group on the TROG (F[3,64] = 2.55, p =.063) but identifying significant main effects of group on the sentence correction task (F[3,62] = 7.40, p< 1) and the past tense elicitation task (F[3,62] = 7.47 p< 1). Bonferroni post hoc tests found that CAs were significantly better than RAs (p<.05) and the PR-LIQ group (p< 1) and the GR-LIQ group were significantly better than the PR-LIQ group (p <.05) on the sentence correction task, while on the past tense elicitation task the GR- LIQ group (p<.05) and CAs (p< 1) were significantly better than the PR-LIQ group. Again to confirm the relationship between the non-standardised language measures and the general standardised measures, correlations controlling for age and non-verbal IQ 117

118 were run on the complete retained sample (N= 103). Outcomes for these correlations can be found in Table 5.7. Once again we can see that as expected the tasks are positively inter-correlated (please note that RAN presents a negative correlation as high scores indicate poorer performance on this task). Table 5.7 Correlations between non standardised language tasks and general standardised measures controlling for age and NVIQ, complete retained sample (N= 103) BAS read TOW. read TOW. nword BAS spell BAS math BPVS Rime Odd PSTM RAN dense Read comp Sent. Cor p value Past T Elicit p value Coloured boxes indicate significant correlations Phonological awareness tasks. When considering phonological processing ability in Phase 2 for the matched groups, a boxplot analysis found one outlier score on the PSTM total combined items measure (GR-LIQ SUB 87, score = 21). After the removal of this score the final means and standard deviations for the matched groups on these tasks were as presented in Table

119 Table 5.8 Means and SD (in brackets) for phonological awareness tasks, Phase 2 matched groups (N= 68) PR LIQ GR LIQ CA conts RA conts Rime Oddity 4.37 (1.95) 7.20 A (2.35) 7.45 A (1.95) 6.13 (2.33) Dense items (Max 10) Rime Oddity 3.79 (1.55) 6 A (2.54) 7.05 A (2.21) 5.25 (2.24) Sparse items (Max 10) Rime Oddity 8.16 (3.06) A (4.71) B (3.88) (4.87) Total items (Max 20) PSTM (8.03) A (6.03) A (3.28) 22 A (7.43) Dense items (Max 32) PSTM (7.33) A (4.26) A (5.13) 21 A (6.40) Sparse items (Max 32) PSTM (14.93) A (5.24) A (7.86) 43 A (13.10) Total items (Max 64) RAN sparse (seconds) (10.51) (10.98) A (9.59) (10.51) RAN dense (seconds) (16.62) (11.13) A (5.08) (11.61) A significantly different from PR-LIQ B significantly different from the PR-LIQ group & RAs Bold = Significantly different to sparse condition Coloured cells are statistically below at least one other group 119

120 Similar to the outcomes for Phase 1, the PR-LIQ group in Phase 2 were diminished on phonological awareness tasks compared to reading age and chronological aged matched controls. The GR-LIQ group on the other hand performed closer to the level of the CAs. To assess performance by group and the role of phonological neighbourhood density on the Rime Oddity task, a 4 (reading group) x 2 (density) mixed model ANOVA with group as the between participants factor and density as the within participants factor was run. A significant effect of density was found (F[1,63] = 12.94, p = 1) with, on average, all children performing better on dense trials. An effect of group was also significant, with Bonferroni post hoc tests indicating significantly better performance by the GR-LIQ group (p<.01) and CAs (p< 1) compared to the PR-LIQ group. There was no significant interaction between these factors. A one-way ANOVA adding across density conditions supported these conclusions showing a significant main effect of group on the total number of items correct (F[3,64] = 9.02, p< 1) with Bonferroni post hoc tests indicating a significantly better performance by the GR-LIQ group (p<.05) and CAs (p< 1) compared to the PR-LIQ group. In addition the CA group was also significantly better than RAs (p<.05) on this measure. To assess the role of phonological neighbourhood density and reader group on the PSTM task, another mixed model 4x2 ANOVA was run with reading as the between participants measure and phonological density as the within participants measure. A significant main effect of density was present (F[1,62] = 8.59, p<.01) with children remembering significantly more items for words from the dense condition. A significant group difference was also found (F[3,62] = 8.93, p< 1), with post hoc tests indicating a significantly better performance by the GR-LIQ, RA (p<.01) and CA (p< 1) groups compared to the PR-LIQ group. No significant interaction effects were present. A oneway ANOVA run on PSTM total items correct also found a significant main effect of group (F[3, 61] = 9.99, p< 1), with post hoc tests indicating significantly better performance by the GR-LIQ (p<.01), RA (p< 1) and CA (p< 1) groups compared to the PR-LIQ group. 120

121 A final 4 x 2 mixed model ANOVA was run on the RAN tasks. A significant main effect of density was again present [F(1,64) = 35.53, p< 1], with, on average, all children taking longer to complete the rapid naming task in the dense condition. The main effect of group was also significant [F(3,64) = 4.32, p< 1], with the CA group performing significantly better than the PR-LIQ group (p<.01) in both conditions. Finally, to assess the relationships between the phonological awareness tasks and the language measures the correlations presented in Table 5.9 were run using the entire sample. From this table it is evident that using the entire Phase 2 data and controlling for age and non-verbal IQ, the phonological measures are correlated with all of the standardised and language measures. Again maths is also significantly correlated with the phonological variables. 121

122 Table 5.9 Correlations with phonological awareness measures after controlling for age and non-verbal IQ, complete retained sample (N= 103) Rime PSTM RAN Oddity dense BAS reading p value BAS spelling p value.01 BAS maths p value BPVS p value.02 Recalling Sentences p value Formulated Sentences p value Receptive Language p value TROG p value.02 Sentence correction p value.11 Past tense elicitation p value Coloured boxes indicate significant correlations 122

123 5.5.5 Measures of verbal working memory. In considering the verbal working memory measures for the matched groups, a boxplot analysis of the working memory measures found no outliers. Means and standard deviations for these tasks are presented in Table Table 5.10 Means and SD for the verbal working memory tasks, Phase 2 matched groups (N= 68) PR LIQ GR LIQ CA conts RA conts Digit span A A A SD (2.42) (2.95) (2.51) (2.86) Letter number sequencing A 8.06 A SD (3.08) (4.88) (2.50) (4.41) A significantly different from the PR-LIQ group. Coloured cells are statistically below at least one other group As is evident from Table 5.10 the PR-LIQ group were impaired compared to CA and RA controls on the working memory measures. The GR-LIQ group on the other hand appear to be performing at the level of CA controls on the digit span task but have a mean performance half-way between the CA controls and the PR-LIQ group on the letter number sequencing task. A series of One-Way ANOVAs identified a significant main effect of group for the Digit Span task (F[3,64] = 7.11, p< 1), with post hoc tests indicating that the GR-LIQ (p<.01), RA (p<.01) and CA (p = 1) groups were significantly better than the PR-LIQ group. On the Letter-Number sequencing task a significant main effect of group was also present (F[3,64] = 8.72 p<.01), with CAs (p< 1) and RAs (p<.05) performing significantly better than the PR-LIQ group. Again, to assess the relationship between verbal working memory and the other variables, a series of correlations were run with digit span and letter-number sequencing and the phonological awareness and standardised tasks using the whole sample. As Table

124 demonstrates, an analysis on the complete project data controlling for age and non-verbal IQ found the digit span task was significantly correlated with all of the measures. The letter-number sequencing task also demonstrated significant correlations with the majority of the measures. Although both tasks were intended as measures of verbal working memory, many children found the sequencing task a more complicated task to understand and execute. 124

125 Table 5.11 Correlations between working memory and literacy variables controlling for age and non-verbal IQ, complete retained sample (N= 103) BAS Read p value BAS Maths p value BPVS P value Recal. Sent. p value Form. Sent. p value Recep. Meas. p value TROG p value Sent. Correct. p value Past tense Elicit. p value Rime oddity p value PSTM p value Digit Span Let. Numb. Seq Coloured boxes indicate significant correlations 125

126 5.5.6 Auditory tasks. To begin the assessment of the auditory outcomes for Phase 2 a boxplot check identified four outliers (Intensity 2 RA Sub 102 score = 27, Sub 51 Score = 23; Rhythm PR-LIQ Sub 150 Score = 40; One Rise CA Sub 46 score = 40) which were removed from the data set. A second inspection identified one additional outlier which was also removed (One Rise CA Sub 85 score = 28). The mean thresholds, which are in relation to a maximum threshold of 40 and a minimum score of 0, and standard deviations after the removal of outliers are presented in Table Table 5.12 Mean thresholds and SD for auditory tasks, Phase 2 matched groups (N= 68) PR LIQ GR LIQ CA conts RA conts Intensity SD (11.21) (9.62) (10.15) (12.31) Intensity SD (3.26) (3.95) (3.40) (2.16) Rhythm SD (5.80) (2.53) (3.72) (5.90) Two Rise A SD (14.22) (11.75) (8.46) (12.74) One Rise A SD (13.54) (8.53) (4.50) (9.69) Duration A A SD (11.06) (10.39) (7.78) (8.10) Frequency A A SD (8.66) (13.53) (11.69) (11.54) A significantly different from the PR-LIQ group. Coloured cells are statistically below at least one other group. Higher threshold values equal decreased sensitivity 126

127 A series of One-Way ANOVAs found significant main effects of group for the Two Rise [F(3,64) = 3.95, p<.05], One Rise [F (3,61) = 4.15 p =.01], Duration [F (3,64) = 6.08, p = 1] and Frequency tasks [F (3,64) = 5.63, p<.01] tasks. For Intensity [F (3,61) = 1.65 p =.19], Intensity 2 [F(3,61) = 1.11 p =.35] and Rhythm [F (3,62) = 1.47 p =.23] the main effect of group was not significant. Bonferroni post-hoc tests for the Two Rise task found CAs were performing significantly better than the PR-LIQ group (p = 0.1), this was also true for the One Rise task (p<.01). For the Duration task RAs (p<.05) and CAs (p = 1) were performing significantly better than the PR-LIQ group, with the same pattern repeated for the Frequency task, where RAs (p =.05) and CAs (p<.01) were performing significantly better than the PR-LIQ group. The GR-LIQ and CA groups were not significantly different for the Two Rise, One Rise, Duration or Frequency tasks. For ease of comparison bar charts of this same data is presented in Figures 5.1 and 5.2. Figure 5.1 Rise time measure outcomes for Phase 2 matched groups (N= 68) * Significantly different from the PR-LIQ group. Higher threshold values equal decreased sensitivity 127

128 Figure 5.2 Related auditory measures for Phase 2 matched groups (N= 68) * Significantly different from the PR-LIQ group. Higher threshold values equal decreased sensitivity Correlations To explore the relationships between the auditory variables and the literacy, phonological and language measures, correlations were run on the entire project data for Phase 2. The general standardised task and auditory variables are presented in Table These are followed by correlation tables for the non-standardised language measures (Table 5.14), and the standardised language and verbal working memory measures (Table 5.15). 128

129 Table 5.13 Correlations between auditory variables and general standardised measures with nonverbal IQ (blocks) and age controlled, complete retained sample (N= 103) Int. Int. 2 Rhythm Two One Dur. Freq. Rise Rise BAS Read age SD BAS Read SS SD TOWRE Read SS SD TOWRE Nword SS SD BAS Spell SS SD BAS Maths SS SD BPVS SS SD Coloured boxes indicate significant correlations It is evident from Table 5.13 that reading age was consistently weakly correlated with all of the Phase 2 auditory variables with the exception of the Rhythm task. In general, the One Rise, Duration and Frequency tasks appear to be related to the spelling and nonword tasks. 129

130 Table 5.14 Correlations between auditory variables and non-standardised language measures with non-verbal IQ (blocks) and age controlled, complete retained sample (N= 103) Int. Int. 2 Rhythm Two One Dur. Freq. Rise Rise Rime Oddity p value PSTM p value Past tense el p value Sent. correct p value TROG p value Coloured boxes indicate significant correlations From Table 5.14, a small but consistent relationship between the auditory variables and the Rime Oddity task is clear. The majority of the variables were also again weakly correlated to PSTM, a similar finding to that presented in the Phase 1 data. In addition, past tense elicitation is not related to the auditory variables while TROG is significantly correlated to five of the seven measures. The lack of relationship between sentence correction and the auditory variables suggests the former measure may in fact not be entirely related to phonological processing skills per se. It could be suggested that past tense elicitation may be related to small grain phonological ability not directly to the auditory measures presented here. 130

131 Table 5.15 Correlations between auditory variable and standardised language and verbal working memory measures with non-verbal (blocks) and age controlled, complete retained sample (N= 103) Intens. Intens. Rhythm Two One Dur. Freq. 2 Rise Rise Reading Comp p value Recalling sent p value Form. sentences p value Receptive meas p value Digit Span p value Let Numb. Seq p value Coloured boxes indicate significant correlations As is evident from Table 5.15, the majority of the auditory variables were weakly correlated with reading comprehension. Interestingly, the Formulated Sentences task was also related to the majority of the auditory tasks, the Intensity and One Rise task providing the only exceptions. There were no significant relationships between the receptive language measure, the letter number sequencing task and the auditory measures. Overall, the correlation tables do not provide one coherent message. For example while grammar as tapped in the test of receptive grammar (TROG) appears reasonably strongly 131

132 related to the auditory variables, the grammatical morphology required for the past tense elicitation and sentence correction tasks does not demonstrate such a relationship. Equally, while Rime Oddity and PSTM are reasonably strongly related to the auditory variables, nonword reading and spelling, also dependent on phonology, are not. As discussed in Chapter 4, it was also of interest to look at the correlations within the low IQ poor reader group only. However, when a correlation table was created for the auditory and literacy variables for this group (not presented), none of the relationships were significant. When isolating this group it was clear that very little variation in decoding ability and auditory performance remained. To illustrate, two scatterplots are provided which show the individual data points for one of the auditory tasks. Figure 5.3 plots reading age against the One Rise task using the Phase 2 matched groups. It is evident in this scatterplot that a relationship exists between a higher auditory threshold (i.e. poor discrimination performance) and a lower reading age. However, Figure 5.4 demonstrates what happens when the PR-LIQ group are isolated from this group. The restricted range of reading scores means a linear relationship is no longer evident. 132

133 Figure 5.3 Reading age against One Rise, Phase 2 matched groups (N= 68) 133

134 Figure 5.4 Reading age against One Rise with Phase 2, the PR-LIQ group only Regression analyses To further assess the predictive nature of the auditory variables in relation to literacy and language variables, a number of fixed-step multiple regression equations were run. In the Phase 2 matched group regressions, BAS standard score was used. This variable was more normally distributed than BAS reading age for the Phase 2 matched groups sample. However, in the analysis of the entire retained sample, regressions onto BAS reading age were also reported (see Table 5.19). To demonstrate the appropriate normal distribution, Table 5.16 provides kurtosis and skew values for each variable. Kurtosis output was within the normal range for all tasks (within +/- 2x standard error) as was skew (again within +/- 2x standard error). Similar to Phase 1 however, Rhythm provided the exception to this rule and was slightly outside the normal range. Once again the Rhythm task outcomes were deemed adequately normally distributed to be included in the regression analyses (similar to Chapter 4, boundaries for determining normal distribution 134

135 have been adopted from common research practice such as that recommended by the Psychology Department of the University of New England). Table 5.16 Kurtosis and skew with standard error values for regression variables, Phase 2 matched groups (N= 68) Variable Kurtosis Skew Reading SS Phase (.57) -.03 (.29) Intensity (.59).34 (.30) Intensity (.59).38 (.30) Rhythm 1.49 (.58) 1.13 (.30) 2 Rise (.57).39 (.29) 1 Rise 1.01 (.59) 1.29 (1.29) Duration -.67 (.57).53 (2.9) Frequency (.57) -.24 (2.9) * p<.05 ** p<.01 *** p< 1 As demonstrated in Tables 5.17 & 5.18, after controlling for age and non-verbal IQ (as not all children fit into an appropriate reader grouping) all tasks with the exception of Rhythm (and Intensity for BAS SS) predicted significant variance in BAS reading standardised scores. As would be expected these findings directly mirror the correlation table presented in Table Additionally, after controlling for reader group membership, Table 5.19 demonstrates that Intensity 2, 1 rise, Duration and Frequency functioned as significant predictors of BAS reading standardised scores for the Phase 2 matched groups. However, this particular analysis must be viewed with some reservation as the proportion between predictors and sample size is not ideal. Brace, Kemp and 135

136 Snelgar (2006) advocate a bare minimum of five participants for every predictor in multiple regressions, which would indicate a minimum sample size of 35 children. However, they also advocate a best practice minimum of 10 participants per predictor which would stipulate a minimum sample size of 70. As the matched groups sample in Phase 2 is 68, this is slightly under the desirable level and should thus be cautioned. Table 5.17 Fixed step multiple regressions predicting BAS reading SS, complete retained sample (N= 103) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Age NVIQ.49.23*** Intensity Intensity * Rhythm Rise * Rise ** Duration *** Frequency **.29 * p<.05 ** p<.01 *** p< 1 136

137 Table 5.18 Fixed step multiple regressions predicting BAS reading age, complete retained sample (N= 103) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Age.45.20*** NVIQ.42.17*** Intensity * Intensity * Rhythm Rise * Rise ** Duration ** Frequency **.40 * p<.05 ** p<.01 *** p< 1 137

138 Table 5.19 Fixed step multiple regressions predicting BAS reading SS, Phase 2 matched groups (N= 68) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Group *** Intensity Intensity * Rhythm Rise (p =.067) Rise * Duration *** Frequency **.26 * p<.05 ** p<.01 *** p< 1 138

139 5.6 Language impaired sub-analysis Introduction. Individuals with dyslexia are typically defined as having phonological processing and decoding deficits. Children with SLI on the other hand are described as having difficulties with syntax, semantics and discourse (Catts et al., 2005). Practically speaking the two groups of children overlap. Children with reading impairment can show deficits in semantics and syntax (e.g. Gallagher, Frith & Snowling, 2000) while children with SLI are often found to have phonological processing deficits and problems in decoding and comprehending written text (Snowling, Bishop & Sothard, 2000). For this reason it was thought important to identify children who were language impaired within low IQ poor readers, low IQ good readers and in fact within the project cohort as a whole. After identification had taken place, it was clear that many of the language impaired children also had low IQ. To separate the more general influence of low IQ from the specific effects of language impairment, it was thought helpful to additionally identify a group of children with language impairment who possessed average and above average IQ (SLI). A matched LI and SLI group comparison allowed the two groups to be compared in terms of mean performance on the key variables. However, it is important to recognise that the LI and SLI children were identified on the basis of psychometric test performance only. Ideally, for educational relevance, this study would have identified a language impaired group through classroom based assessment Selection of language impaired subgroups. To identify children within the project who were language impaired, a standard selection method from the literature was modified and employed. For this particular procedure, the first step was to establish measures which covered the three domains of language - vocabulary, grammar and narration and to address the two modalities of language receptive and expressive. Under this method, children who are generally language impaired, fall at least 1.25 SD below the mean on a minimum of two of the five areas mentioned (Bishop, McDonald et al., 2009; Catts et al., 2005). Children are commonly considered to have a specific 139

140 language impairment if they demonstrate these characteristics in the presence of an average and above (greater than one SD below the mean) nonverbal IQ. Functioning within the limitations of the battery, it was decided that children would receive a working label of language impairment if they functioned at one SD (as 1.25 fell between scores six and seven on the CELF tests) or below on two of the three CELF language measures. Additionally, they were also required to be at least one SD or more below the mean on either the past tense elicitation (score of 18 or below) or the sentence correction (13 or below) task. Together these measures broadly covered the three domains and two modalities of language as outlined above. Children were identified as being SLI if they possessed the same deficits but presented at least one WISC non-verbal IQ measure at or above the mean value of 10. Using these criteria, 18 low IQ language impaired (LI) children were identified (16 from the PR-LIQ group and 2 from the GR- LIQ group). Seven children with SLI (previously 2 RAs and 5 CAs) were identified who were matched to children form the LI group on chronological age. Additionally, 14 vocabulary age (VA) typical children were identified who were matched to the LI children on BPVS age (previously 12 RAs and 2 CAs), in addition to 20 chronological age (CA) matched controls. Descriptive statistics for the key matching variables are presented in Table As is evident from the Table, the SLI groups were matched to the CAs on vocabulary age, past tense elicitation, WISC vocab., WISC similarities, WISC blocks, WISC picture arranging and FSIQ. The SLI and LI groups were matched on age, the three CELF measures, past tense elicitation and sentence correction. 140

141 Table 5.20 Descriptive statistics for language impaired matched groups analysis Age (mos) SD LI (18) SLI (7) VA (14) CA (20) (16.24) BPVS age (mos)sd (18.14) Recal. sent. SS SD Form. sent. SS SD Recep. vocab. SS SD Past tense el. SD Sent. corr. SD WISC vocab. SS SD WISC sim. SS SD WISC blocks SS SD 5.22 (2.51) 5.50 (2.98) 5.39 (2.28) 14 (6.07) (2.50) 5.72 (1.93) 6 (2.45) 5.78 (2.39) WISC pict. arr. SSSD 6 (1.91) (9.84) (20.96) 7.86 (2.73) 7.14 (2.12) 5.86 (1.68) (1.37) (1.51) 9.71 (2.43) (3.02) (4.35) (2.94) (8.73) (4.90) (2.17) 8.93 (2.73) (3.50) (3.51) (1.73) 9.50 (3.76) (3.65) (5.11) 8.79 (2.72) (8.13) (22.71) (2.76) (3.35) 9.45 (3.52) (2.63) (1.15) (3.44) (2.36) (4.71) (3.59) FSIQ SD (6.04) (9.95) (11.16) (13.23) Cells of the same colour or cells containing bolded values are statistically equal Research predictions. Having used the majority of the language tasks to create the language impaired group, it was the focus of this analysis to look at three remaining areas of performance (1) phonological processing (2) verbal working memory and (3) auditory discrimination. Given the high percentage of the PR-LIQ group who were also language impaired, the predictions for this latter group were based on the Phase 1 and 2 outcomes. Thus, it was generally anticipated that children from the LI group would demonstrate a general literacy, verbal working memory and auditory deficit. Of more theoretical interest was a comparative analysis of the SLI and LI groups. A number of 141

142 reports have described children with SLI as possessing both problems with phonological awareness (e.g. using a rhyming picture matching task, Briscoe, Bishop & Norbury, 2001) and phonological short term memory (e.g. Gathercole & Baddeley, 1990b). Given these findings it was anticipated that the children from the SLI group here would perform similarly and also have difficulty with the digit span task, as it is heavily related to verbal short term memory. Finally, an expectation was in place that phonological ability in the children with SLI would be predictive of auditory task performance. Mean SLI performance on the auditory tasks was unknown, although weakness was anticipated on Frequency, Duration and amplitude rise time tasks, given previous findings which have been discussed earlier Phonological processing ability. As a first step in assessing the relative abilities of the language impaired groups, a comparison of the phonological measures took place. For this purpose performances on the TOWRE nonword measures and Rime Oddity task were assessed. An outlier search found no outlying values on the TOWRE nonword and the Rime Oddity task, resulting in the means presented in Table One-way ANOVAs found a significant main effect of group for reading age (F[3,55] = 14.89, p< 1) with the SLI group (p<.01) and CA group (p< 1), significantly above the LI group. For the nonword measure a significant main effect of group was again found (F[3,55] = 11.87, p< 1) with the CA (p<1), VA (p< 1) and SLI (p<.01) groups performing significantly better than the LI group. On reading comprehension a main group effect was again demonstrated (F[3,55] = 11.17, p< 1), again with SLI (p<1), VA (p<.01) and CA (p< 1) groups performing better than the LI group. Additionally, a main effect of group was also present for Rime Oddity (F[3,58] = 9.05, p< 1), with the CA (p< 1) and SLI (p<.05) groups performing significantly better than the LI group. The CA group was also significantly better than the VA (p<.05) group. 142

143 Table 5.21 Mean performance on nonword reading and Rime Oddity, language impaired matched groups (N= 59) LI SLI VA conts CA conts Reading age (months) A A SD (13.75) (29.11) (17.91) (26.84) TOWRE nonword SS A A A SD (11.82) (16.05) (13.01) (13.96) WIAT reading comp SS A A 106 A SD (13.45) (9.84) (15.13) (10.80) Rime Oddity (max 20) A B SD (2.99) (2.94) (5.36) (3.85) A significantly different from LI B significantly different from LI and VA Coloured cells are statistically below at least one other group RAN task As the RAN task was not run in Phase 2, the Phase 1 outcomes were evaluated for this group comparison. A search identified one outlier in the SLI group on the RAN sparse condition (SUB 85 score = 77) which was removed. A One-way ANOVA on the group means found a significant main effect of group for both RAN dense (F[3,55] = 6.6, p = 1) and RAN sparse (F[3,54] = 6.18, p = 1), with the CA group performing significantly better than the LI (p<1) and VA (p<.05) groups on the RAN in both dense and sparse conditions. It is clear from Table 5.22 that the children with SLI perform well on this task. This finding would support the Bishop et al. (2009) position in that the children with SLI appear to have good decoding, reading comprehension and RAN in spite of poor language. 143

144 Table 5.22 Performance on RAN task, language impaired matched groups (N= 59) LI SLI VA conts CA conts RAN sparse (secs) A SD (11.30) (4.49) (11.39) (5.05) RAN dense (secs) A SD (16.59) (7.57) (12.39) (6.49) A significantly different from LI and VA Coloured cells are statistically below at least one other group Verbal working memory. An outlier search performed on both the Digit Span and PSTM tasks found no outlying values. A One-way ANOVA run on the values presented in Table 5.23 found a significant main effect of group (F[3,55] = 12.50, p< 1) with the VA (p<1) and CA (p<1) groups performing significantly better than the LI group, and with the CA (p<.05) group again performing significantly better than the children with SLI. There was also a significant main effect for group on the PSTM task (F[3,56] = 9.76, p< 1), with the CA group performing significantly better than the VA (p<1) and LI (p<1) groups. These findings suggest all language impaired children have poor verbal working memory for numbers and for words. Given these results it could be suggested that, on average, the children with SLI in this project have good phonological processing skills when the memory load is low (TOWRE nonword & Rime Oddity) but are poor when the memory load is more demanding (PSTM). Also, these results again underline the importance of good phonological awareness. Although poor working memory does not appear to limit decoding ability, once again phonological processing ability appears to be an essential element in the acquisition of this skill. 144

145 Table 5.23 Mean performance on verbal working memory tasks, language impaired matched groups (N= 59) LI SLI VA conts CA conts Digit Span task (mean 10) A B SD (3.02) (2.64) (2.62) (2.59) PSTM (max 64) C SD (16.09) (10.32) (12.97) (9.16) A significantly different from LI B significantly different from LI and SLI C significantly different from LI and VA Coloured cells are statistically below at least one other group Auditory tasks. An outlier search on the SLI matched group scores for both Phase 1 and Phase 2 found nine values to be removed in addition to those previously removed in the analyses reported in this chapter (Intensity Phase 1 LI SUB 315 score = 15; Intensity Phase 2 SLI SUB 72 score = 31; Intensity 2 Phase 2 SUB 93 score = 17; Rhythm Phase 1 LI SUB 239 score = 40, LI SUB 150 score = 40; One Rise Phase 1 CA SUB 58 score = 38; One Rise Phase 2 CA SUB 58 score = 36, CA SUB 55 score = 25; Duration Phase 1 CA SUB 27 score = 39). Having removed these outliers, a second inspection found three further outliers which were also removed (Intensity Phase 1 LI SUB 143 score = 29; Intensity 2 Phase 2 SLI SUB 57 score = 14; Rhythm Phase 1 LI SUB 154 score = 40) resulting in the means presented in Table A series of one-way ANOVAs found a significant main effect of group for Intensity Phase 1 (F[3,48] = 9.09, p< 1), Intensity Phase 2 (F[3,53] = 2.81, p<.05) Intensity 2 Phase 2 (F[3,49] = 11.19, p<1), Rhythm Phase 2 (F[3,53] = 3.22, p<.05), One Rise Phase 1 (F[3,51] = 6.60, p = 1), One Rise Phase 2 (F[3,52] = 4.60, p<.01), Duration Phase 1 (F[3,52] = 6.47, p = 1), Duration Phase 2 (F[3,55] = 3.87, p<.05) and Frequency Phase 2 (F[3,55] = 3.20, p<.05). In all cases where there was a significant group difference, with the exception of the Rhythm task in Phase 2, the CAs performed significantly better than the LI group (Intensity Phase 1 and Intensity 2 Phase 2 at p< 1, One Rise Phase 1, One Rise Phase 145

146 2 and Duration Phase 2 at p<.01 and Duration Phase 2 and Frequency Phase 2 at p<.05). On Intensity Phase 1 (p<.05) and One Rise Phase 1 (p<.05) the children with SLI performed better than the LI group. On the Intensity 2 Phase 2 task, the VA performed significantly better than the LI (p< 1) group and the VA (p<.05) and the CA ( p= 1) groups both performed significantly better than the SLI group. On the One Rise Phase 1 task, the CAs performed additionally significantly better than the VA group. In summary the CAs performed significantly better on the Intensity 2 task alone. While the CAs performed significantly better than the LI group for both Phases on Intensity and Duration and on Intensity 2, One Rise Phase 1 and Frequency Phase

147 Table 5.24 Auditory means, language impaired matched groups (N= 59) Intensity Phase 1 Intensity Phase 2 Intensity 2 Phase 2 Rhythm Phase 1 Rhythm Phase 2 Two Rise Phase 1 Two Rise Phase 2 One Rise Phase 1 One Rise Phase 2 Duration Phase 1 Duration Phase 2 Rove Phase 1 Frequency Phase 1 Frequency LI SLI CA VA 39 (1.66) (12.72) 9.12 (3.97) (5.34) (4.31) (11.43) (13.89) (12.30) (13.84) (10.40) (11.94) (12.18) (9.47) A (12.95) (2.34) 9.86 (1.22) 9.86 (3.81) (3.85) 26 (10.15) 21 (14.11) 8.57 A (4.16) 5.67 (4.89) (4.06) (8.98) (14.34) (12.28) A (11.42) A (10) 4.85 B (2.11) (3.92) 9.25 (3.60) (11.01) (10.94) C (6.12) 6.17 (3.15) A (6.47) A (9.10) (11.57) (13.23) A (11.54) (12.31) 5.44 B (1.42) (8.13) 14 (6.05) (11.69) (11.92) 21 (12.14) (8.05) (12.03) (8.77) (11.06) (12.74) 19 (10.76) Phase 2 (11.46) (12.63) (11.07) A significantly different from LI B significantly different from LI & SLI C significantly different from LI & VA. Coloured cells indicate values that are statistically equivalent according to individual parametric tests. Higher threshold values equal decreased sensitivity 147

148 In summary, it is evident that the LI group performed poorly on all of the auditory tasks as expected. The children with SLI however performed broadly at the level of the CAs on Intensity, One Rise, Rove and Frequency. In comparison, the children with SLI performed at the level of the LI group on the Intensity 2 task and at the level of the VAs on the Duration task. For the remaining tasks the relative performance of the children with SLI is not clearly defined. Overall, these results indicate fairly typical auditory discrimination ability for the seven children with SLI in this sample. To assess whether, as expected, phonological ability predicted literacy outcomes in these matched groups, a series of correlation matrices and fixed step regression equations were produced. Table 5.25 demonstrates the positive correlation between decoding and auditory variables for Phase 1. It is also evident from this table that a number of positive correlations existed amongst the auditory variables and the phonological measures, with Intensity, One Rise, Duration and Frequency related to concurrent nonword reading. 148

149 Table 5.25 Partial correlations between Phase 1 auditory and literacy variables controlling for Group membership, language impaired matched groups (N= 59) BAS TOWRE Onset Odd PSTM R Age P1 Nword P1 P1 P1 Intensity P Rhythm P Two Rise P One Rise P Rove Duration P Frequency P Coloured boxes indicate significant correlations Phase 2 correlations as presented in Table 5.26, indicate once again the positive relationship between reading age and auditory discrimination performance. In Phase 2 the majority of the auditory measures were correlated with Rime Oddity and PSTM. 149

150 Table 5.26 Partial correlations between Phase 2 auditory and literacy variables controlling for Group membership, language impaired matched groups (N= 59) BAS TOWRE Rime PSTM P2 R Age P2 Nword P2 Odd P2 Intensity P p value Mouse p value Rhythm P p value Two Rise P p value One Rise P p value Duration P p value Frequency P p value Coloured boxes indicate significant correlations To further explore the predictive nature of these variables, a number of fixed step regressions were run with group membership as the first step and each auditory task as a second variable. Due to the small sample size one auditory task has been removed from each table. In Table 5.27 this was the Rove task as it was close in function to the One Rise task and in Table 5.28 it was Intensity 2, again as it was close in function to the Intensity task. Following the correlation tables, Table 5.27 demonstrates that all of the auditory tasks with the exception of the Rhythm task, predicted significant variation in BAS reading age for phase 1. Table 5.28 again reflects the corresponding correlation 150

151 table and here demonstrates a predictive relationship between Phase 2 Intensity, Two Rise and One Rise with Phase 2 BAS reading age. Rhythm and Duration are close to significance. Table 5.27 Fixed step multiple regression predicting BAS reading age Phase 1 using Phase 1 variables, LI matched groups (N= 59) Step Beta R 2 change Adjusted R 2 (final model fit) 1. LI Group.59.34*** Intensity Ph ** Rhythm Ph Two Rise Ph * One Rise Ph * Duration Ph * Frequency Ph *.38 * p<.05 ** p<.01 *** p< 1 151

152 Table 5.28 Fixed step multiple regression predicting BAS reading age using Phase 2 variables, LI matched groups (N= 59) Step Beta R 2 change Adjusted R 2 (final model fit) 1. LI Group.56.32*** Intensity Ph * Rhythm Ph (p =.08) Two Rise Ph * One Rise Ph * Duration Ph (p =.06) Frequency Ph * p<.05 ** p<.01 *** p< Phase 2 summary To summarise the findings from Phase 2 it is helpful to return to the points outlined in the Phase 2 research questions. In addressing these questions, firstly, the PR-LIQ group again performed at a lower level than the CA and RA controls on spelling, maths and vocabulary and on the reading comprehension. They were again at the level of the RAs on phonological awareness tasks but performed below the chronological aged controls on these tasks. The PR-LIQ group was additionally generally poor on all aspects of language, with the exception of the Test of Reception of Grammar which appeared to be a task of similar ease for all children tested. Again they were below chronological age controls on the two measures of working memory. 152

153 A large number of the PR-LIQ group was identified as language impaired. This language impaired analysis in general found the LI group to be low on decoding, nonword reading, reading comprehension, phonological awareness, rapid naming and working memory in comparison to chronological aged controls. The LI group also typically performed below the SLI group on the majority of the tasks, this was significant for the reading, nonword reading, comprehension and rime oddity tasks. Only on the digit span task did both groups perform equally below the chronological age controls. On the auditory tasks the LI group demonstrated generally less sensitive discrimination as compared to the chronological aged controls and performed at a similar level to the vocabulary matched younger children on most measures. Again individual variation in auditory discrimination ability predicted significant variance in reading outcomes for both Phase 1 and Phase 2 for this language impaired matched set. This was also the case for the larger Phase 2 matched groups analysis. In response to the Phase 2 research questions, the PR- LIQ phonological deficit was again demonstrated and once again poor auditory processing appeared to be related to poor decoding in this group. 153

154 Chapter 6. Longitudinal relationships 6.1 Introduction Now that the outcomes for each battery have been discussed, it is appropriate for an analysis of the changes from Phase 1 to Phase 2 to take place. The aim of the longitudinal analysis was to compare development over time for each group and to assess the predictors of development for these groups. To fully tap the potential offered by the dataset, analyses restricted to the longitudinal sample (N = 68, as presented in Chapter 5) were performed in addition to analyses which included all project data. 6.2 Research predictions In general, it was predicted that children would improve in performance from Phase 1 to Phase 2. Participants in Phase 2 were on average one year older and had gained experience with the nature of the battery through testing in Phase 1. Although little fluctuation was expected on the standardised tests, gains were predicted for the nonstandardised variables, particularly the auditory measures, where given the results presented in Chapters 4 and 5, an improvement in line with an increased ability in phonological processing was expected. It was also thought likely that a relationship between auditory processing and decoding would exist longitudinally. Specifically, individual variation in auditory variables in Phase 1 would predict decoding outcomes in Phase 2. Differences in change according to group were not expected, although outcomes in this area were relatively unknown. It was thought possible that the PR-LIQ group might show a decline in WISC block performance (non-verbal IQ) and in decoding and reading comprehension given discussions of Matthew effects for poor readers (see Chapter 7 for a more thorough discussion of this topic). Additionally, it was anticipated that the GR-LIQ group would show less positive change in decoding given their unusually high ability in this area in relation to age and IQ in Phase

155 6.3 Longitudinal matched groups assessment As was evident from the results presented in Chapter 5, fewer children were able to be matched in Phase 2 as compared to Phase 1 of the project. As mentioned previously, this was due to participant attrition and differing inter-test intervals. To assess change in a matched groups sample present throughout the project, the matched groups discussed in Chapter 5, known here as the longitudinal sample, was re-employed. Essentially, the Phase 2 matched groups have retrospectively been assessed on their Phase 1 and Phase 2 performances to identify any relative changes in skill. Table 6.1 presents the Phase 1 and Phase 2 means for the longitudinal sample (means for Phase 2 are identical to those presented in Chapter 5 and appear for reference purposes only). 155

156 Table 6.1 Descriptive statistics for the longitudinal sample PR LIQ (N19) GR LIQ (N10) CA Conts (N22) RA Conts (N17) Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Age (months) (10.76) (13.35) (16.06) (17.91) (5) (5.62) (5.08) (7) Interval (months) (5.36) 13 (5.72) (3.20) (4.67) BAS read (months) (11.06) (13.62) (18.16) (18.73) (15.49) (19.20) (13.82) (11.66) R age change (8.95) 5.6 (7.93) (14.07) 8.7 (6.84) Discrep. (R age age) (7.28) (13.10) (10.41) (16.49) (14) (16.37) (12.60) (11.79) FSIQ (SS) No No No No (5.38) measure (6.77) measure (10.94) measure (14.54) measure Colours indicate matched variables respective to each row As is evident from Table 6.1, there was a mean difference in the time elapsed between Phase 1 and Phase 2 for some of the groups. A one-way ANOVA found the mean intervals significantly different (F[3,64] = 8.12, p< 1), with a significantly longer interval for the PR-LIQ group compared to the RAs (p<1). Despite this difference the matched variables, established in Phase 2, were still matched in Phase 1. Therefore, group performance on measures which had been administered at both phases of the study were compared. To assess the relative progress made in decoding, measured as reading age at Phase 1 and 2 minus chronological age at these time points, a repeated measures 156

157 factorial ANCOVA was run. As repeated throughout this chapter, Phase was the withinparticipants variable, group was the between participants variable and interval was run as the covariate. An interaction between Phase and interval was found to be just nonsignificant (F[1,63] = 3.83, p =.055) with no main effect of Phase and no interaction between Phase and group, although this was near the significance level and thus worth mentioning (F[1,63] = 2.26, p =.09). As would be expected, a group difference was significant, with the GR-LIQ (p< 1), RA (p<1) and CA (p<1) groups showing greater positive change than the PR-LIQ group, and CAs showing greater positive change than RAs (p<.05). It can be seen from Table 6.2 that the PR-LIQ group made reasonably good progress in decoding over the course of the interval, with a mean gain of 14 reading months over an 18 month interval. This progress is in stark contrast to the GR-LIQ group, who made only an approximate 6 month gain over 13 months. This may be in some part due to the reading rehabilitation which was ongoing in the participants schools for the PR-LIQ group, and regression to mean performance for the GR-LIQ group Longitudinal assessment of standardised variables and PSTM. Although little change was expected, it was still thought important to summarise the movement of means from Phase 1 to Phase 2 for the standardised variables. Standardised variables rather than raw scores were utilised to minimise individual variation in task performance. An outlier search identified one value (BAS reading, Phase 1 PR-LIQ SUB 138 score = 60) which was removed, resulting in the mean scores presented in Table 6.2. The same information but highlighting the change from Phase 1 to Phase 2 is represented in Table 6.3 (which presents the full measures as compared to the matching variables presented in Table 6.1) and presented in chart form in Figure 6.3. Included in both tables is the measure WISC blocks. The blocks measure, which was run in both Phase 1 and 2, requires the child to copy paper-based designs using four to nine coloured blocks. This task requires visual spatial capabilities and as such, functions as an indicator of non-verbal intelligence. As is evidenced from a quick survey of Table 6.3, little change occurred for the majority of the standardised variables. The exceptions to this summary are the change in BAS reading standardised score for the GR-LIQ group and the change in maths for the RAs and the PR-LIQ group. 157

158 Table 6.2 Mean outcomes for standardised variable in Phase 1 and Phase 2, longitudinal sample (N= 68) PR LIQ GR LIQ CA controls RA controls Phase 1 Phase 2 Phase 1 Phase 2 Phase 2 Phase 2 Phase 1 Phase 2 BAS Read SS A A A A A A SD (5.02) (9.75) (8.99) (11.14) (13.16) (13.50) (10.69) (12.10) TOWRE word SS A A A A A A SD (12.42) (10.80) (9.30) (10.72) (9.42) (13.68) (9.71) (8.37) TOWRE nword SS A A A A A A SD (10.76) (11.54) (9) (9.91) (8.63) (14.19) (11.73) (11.73) WISC blocks SS B B 9.09 A A SD (3.03) (2.67) (2.55) (3.39) (4.41) (3.63) (3.84) (2.98) BAS Spelling SS A A A A A SD (11.34) (7.06) (11.63) (14.10) (11.23) (11.05) (13.35) (10.90) BAS Maths SS A 103 A A A A A SD (12.28) (10.56) (10.59) (11.93) (19.93) (15.65) (13.55) (13.12) BPVS SS B B B B SD (10.32) (9.41) (6.92) (7.63) (9.08) (5.85) (11.99) (8.07) A significantly different from PR-LIQ B significantly different from PR-LIQ and GR-LIQ coloured cells are statistically below at least one other group 158

159 Figure 6.1 Change on standardised variables according to group (Phase 1 to Phase 2) Change from Phase 1 to Phase 2 standard score change PR-LIQ GR-LIQ CA Conts RA Conts BAS TOWRE TOWRE WISC BAS BAS maths BPVS reading word nword blocks spelling Table 6.3 Change between means for Phase 1 and Phase 2 with SD in brackets, longitudinal sample (N= 68) PR-LIQ GR-LIQ CA Conts RA Conts BAS Read SS -1 (7.56) (6.66).27 (12.67) -1 (6.98) TOWRE word SS -.95 (11.85) (10.29) -2 (9.41) -.88 (10.86) TOWRE nword SS.53 (8).80 (11.58) -.86 (7.39) (10.98) WISC blocks SS -.63 (3.15).70 (4.27) 1.14 (3.03).88 (3.69) BAS Spelling SS (8.03) (13.27).59 (8.99) (5.22) BAS Maths SS (11.95) -.90 (16.46) 1.18 (14.44) 7.41 (15.52) A BPVS SS -.67 (8.85).60 (9.62) (10.26) 4.82 (10.10) A Significantly different from PR-LIQ. Coloured cells indicate a large change (more than seven SS units) 159

160 To formally assess differences in change, a series of repeated measures factorial ANCOVAs was run on the mean values presented in Table 6.2. Again, each standardised variable in Phase 1 and Phase 2 was the repeated measure, group was the between measures factor and interval was the covariate. These analyses found in the majority no main effects for Phase, indicating no significant difference in Phase 2 versus Phase 1 performance. There was also no interaction between group and Phase, indicating a similar change in performance from Phase 1 to Phase 2 for all groups. One exception was provided by the maths measure, where a significant interaction was found between Phase and group (F[3,64] = 3.13, p<.05). Supporting this finding, a series of one-way ANOVAs run on the delta values from Table 6.3 confirmed maths to be the only significantly different change by group (F[3,64] = 3.13, p<.05), with RAs demonstrating a greater positive change than the PR-LIQ group (p<.05). Although no main effects for interval or Phase and no interactions were present for the remaining variables, group difference indicating higher performance across both time points for individual groups were present in each measure. As previously mentioned, it was not expected that the control groups would show significant variation in performance on the standardised variables. However, the stability of the low IQ groups was unknown and of theoretical interest. Previous research on socalled Matthew effects has suggested that children who are already diminished on reading (Stanovich, 1986) or IQ (Shaywitz et al., 1995) may develop a deeper deficit over time. With reference to Table 6.2 it is evident that the PR-LIQ group demonstrated consistently lower mean scores in Phase 2 compared to their Phase 1 performance (with the exception of TOWRE nonword reading). The GR-LIQ group also showed some decline, most significantly in decoding. Interestingly however, the GR-LIQ group showed a slight improvement on WISC blocks over time, while the PR-LIQ group showed a slight decline. Therefore, a further assessment of whether good phonology plays a role in maintaining IQ levels over time could be of great research interest. To specifically address the possibility of the PR-LIQ group increasingly falling behind age norms, BAS spelling, maths and BPVS ages were calculated, in addition to BAS 160

161 reading age, as presented earlier. Table 6.4 presents mean ages for these variables and calculates the change in age between Phase 1 and Phase 2. As can be seen, the PR-LIQ group are performing consistently below their chronological age, a characteristic which worsens over time. To assess whether performance at Phase 1 and Phase 2 is significantly different by group, a series of repeated measures factorial ANCOVAs was run. For these analyses Phase was the within participants factor, group was the between participants factor and interval was the covariate. The ANCOVA for Spelling found no significant effects of Phase or interval and found no significant interactions. However, as expected, a main effect of group was present (F[3,63] = 22.16, p< 1) with the GR-LIQ (p<1), CA (p<1) and RA (p<1) groups performing at a significantly higher level than the PR-LIQ group at both Phase 1 and Phase 2 on this task. On Maths there was a significant interaction between Phase and interval (F[1,63] = 19.18, p<1) reflecting the longer interval for the PR-LIQ group and the little improvement made from Phase 1 to Phase2. However, there were no main effects for Phase or interval. Again a significant main effect of group was found for Maths (F[3,63] = 9.11, p< 1) with RAs (p<1) and CAs (p<1) demonstrating a significantly better performance than the PR-LIQ group at both Phase 1 and Phase 2 on this task. Finally, for BPVS no effects of Phase or interval were significant. There was a significant effect of group (F[3,61] = 8.86, p< 1) with RAs (p<.01) and CAs (p<.01) demonstrating more positive change than the GR-LIQ and the PR-LIQ groups. 161

162 Table 6.4 Discrepancies between spelling, maths and vocabulary and chronological ages at Phase 1 and 2, longitudinal sample (N= 68) PR LIQ GR LIQ CA Conts RA Conts Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Age (months) (10.76) (13.35) (16.06) (17.91) (5) (5.62) (5.08) (7) Interval (mos) (5.36) 13 (5.72) (3.20) (4.67) Spelling age (mos) A A A A A A (11.74) (8.30) (23.42) (18.57) (22.45) (22.39) (19.22) (10.44) Spelling age 9.84 (8.66) 9.80 (16.67) (14.11) 3.35 (15.74) Maths age (mos) A A A A (16.39) (13.02) (20.01) (17.89) (14.58) (30.20) (16.45) (18.05) Maths age 9.53 (16.48) (17.28) (23.22) 6.24 (13.04) BPVS age (mos) B B B B (17.41) (19.17) (18.07) (18.80) (21.56) (16.60) (19.93) (12.47) BPVS age (16.22) 12 (15.66) 9.29 (18.66) (19.98) A significantly different from PR-LIQ B significantly different from the GR-LIQ group & the PR-LIQ group coloured cells are statistically below at least one other group PSTM task To assess individual progress made on phonological processing ability, the PSTM task was compared in Phase 1 and 2. The Oddity task was not appropriate for longitudinal comparison as it varied between an Onset Oddity measure in Phase 1 and a Rime Oddity 162

163 measure in Phase 2. To begin the statistical analysis, a boxplot inspection identified two outlier values which were removed from the dataset for PSTM Phase 1 (PR-LIQ 1 SUB 101 score = 55; CA SUB 85 score = 14) and one outlier for Phase 2 (GR-LIQ SUB 87 score = 1). Table 6.5 presents the mean values for the PSTM task after the removal of these outliers (additional CA score missing). It can be seen from this table that previous findings in Chapters 4 and 5 have been confirmed, with both low IQ groups performing at a lower level than the CA group at both time points. Also, as would be expected, all groups increased in the number of items remembered from Phase 1 to Phase 2. However, in the PR-LIQ group where the mean inter-phase interval was seventeen months, this increase was very slight, indicating only a small improvement with age. To assess change between Phase 1 and Phase 2 on the PSTM task, a factorial ANCOVA with PSTM as the repeated measure, group as the between participants variable and inter-phase interval as the covariate was run. A significant interaction was present between the covariate (interphase interval) and Phase (F[1,59] = 5.99 p<.05) indicating a relationship between the difference in Phase 1 and 2 outcomes and months elapsed. However, there was no difference in change by group. There were, as expected, group differences with the GR- LIQ (p< 1), RA (p<.01) and CA (p< 1) groups performing significantly better on both Phase 1 and Phase 2 as compared to the PR-LIQ group. Table 6.5 Phase 1 and Phase 2 PSTM performance, longitudinal sample (N= 68) PR LIQ GR LIQ CA Conts RA Conts Phase 1 Phase 2 Phase 1 Phase 2 Phase 2 Phase 2 Phase 1 Phase 2 PSTM A A A A 43 A SD (9.96) (14.93) (15.58) (5.24) (10) (7.86) (15.55) (13.10) PSTM 3.67 (10.96) 4 (9.12) 2.10 (5.67) 6.47 (11.56) A Significantly different from the PR-LIQ group. NB change computations include outliers and thus do not equal phase 2-1 as presented in the table. Coloured cells are statistically below at least one other group 163

164 Auditory tasks As a first step in analysing the auditory tasks, an outlier search identified three values to be removed: Intensity1 PR-LIQ SUB 138 score = 5; Rhythm 2 PR-LIQ SUB 150 score = 40 and One Rise 2 CA SUB 46 score = 40. To assess change between Phase 1 and Phase 2 on the auditory tasks, the same factorial ANCOVAs with Phase as the within participants factor, group as the between participants factor and inter-phase interval as the covariate were run on the means from Table 6.6. For the Intensity task, the effect of Phase was not significant nor was inter-phase interval. The group means, as expected, were significantly different (F[3,58] = 3.63, p<.05), with the GR-LIQ group appearing distinctly better than the PR-LIQ group although this was not significant (p=.08). On the Rhythm task, the effect of Phase was significant (F[1,59] = 4.45, p<.05), with groups generally performing significantly better at Phase 2 (with the exception of the CAs), but with no interaction with group. The effect of inter-phase interval was also significant (F[1,3] = 4.60, p<.01), as was group (F[3,59] = 4.60, p<.01), with the CAs performing better than the PR-LIQ group, again not quite at a significant level (p =.07). On the Two Rise task the effect of Phase was significantly different (F[1,63] = p = 1), again with all groups performing significantly better in Phase 2, but again with no significant group interaction. The effect of interval was non-significant, but a significant group difference was present (F[3,63] = 3.98, p<.05), with CAs performing significantly better than the PR-LIQ group. On the One Rise task, the effect of Phase was nonsignificant, with no significant group interaction, and the effect of interval was also nonsignificant. However, a group difference was significant (F[1,60] = 1.07, p< 1), with CAs performing significantly better than RAs (p<.05) and the PR-LIQ group (p< 1). On the Duration task, a significant effect of Phase was present (F[1,63] = 5.53, p<.05), with no effect of interval, but a significant group difference (F[3,63] = 2.96, p<.05), with CAs performing significantly better than the PR-LIQ group. Finally, on the Frequency task, there was no significant effect of Phase or interval; however, there was a significant effect of group (F[3,62] = 4.34, p<.01), with CAs performing significantly better than the PR-LIQ group (p<.01). Table 6.6 also makes clear the similar performance between the GR-LIQ and CA groups, who are not statistically different on any of the measures. 164

165 For the purposes of monitoring consistency, it is important to report that the means presented in Table 6.6 are very similar to those presented in the Table 4.5. A set of oneway ANOVAs confirms the similarity of these relationships. In Table 6.6 Phase 1, the GR-LIQ and CA groups are again performing better than the PR-LIQ group on the Intensity and One Rise Task and again the CAs are superior to the PR-LIQ group on the Rhythm measure. Only on the Frequency measure does the subset differ slightly from the larger sample in Table 4.5 as in the second analysis Frequency is no longer significantly different by group (for statistical output and mean table please see Appendix 5). 165

166 Table 6.6 Phase 1 and Phase 2 auditory task outcomes, longitudinal sample (N= 68) PR-LIQ GR LIQ CA Conts RA Conts Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 One Rise B 6.84 B SD (13.84) (13.54) (10.53) (8.53) (7.72) (4.50) (12.45) (9.69) Two Rise A A SD (10.31) (14.22) (11.30) (11.75) (10.43) (8.46) (8.59) (12.74) Intensity SD (8.46) (10.91) (12.97) (9.62) (11.74) (10.15) (10.78) (11.71) Frequency A A SD (9.46) (8.66) (11.95) (13.53) (13.08) (11.69) (9.85) (11.49) Rhythm SD (9.99) (5.80) (4.39) (2.53) (5.44) (3.72) (12.07) (5.95) Duration A A SD (11.05) (11.06) (9.71) (10.39) (10.59) (7.78) (9.81) (8.10) A B significantly different from PR-LIQ significantly different from RAs and the PR-LIQ group. Bold significant effect of Phase. Coloured cells are statistically below at least one other group. Higher threshold values equal decreased sensitivity. A table isolating the mean change for each group is provided as Table 6.7. As can be seen, all children made progress on most of the tasks, as expected. Also as expected, the GR-LIQ and CA groups made a similar amount of progress on the majority of the tasks. 166

167 The exception to this summary is the Frequency task, where controls made a greater level of improvement. Table 6.7 Mean change from Phase 1 to Phase 2 for auditory tasks, longitudinal sample (N= 68) PR-LIQ GR-LIQ CA conts RA conts Intensity SD (12.57) (13.37) (12.82) (12.04) Rhythm SD (7.38) (3.68) (6.80) (10.33) Two Rise SD (12.16) (9.72) (10.31) (13.60) One Rise SD (14.76) (12.80) (9.11) (13.50) Duration SD (13.34) (8.09) (8.59) (9.51) Frequency SD (11.90) (7.20) (11.59) (13.62) Coloured cells indicate a negative change (i.e improvement from Phase 1 to Phase 2) 167

168 Predictive relationships To assess the theoretically relevant relationship between the auditory measures from Phase 1 and the literacy measures from Phase 2, a correlation matrix was run controlling for age and non-verbal IQ (blocks) on all project data (N= 127). As a first step, after a boxplot analysis on the project data as a whole, six outliers were identified and removed from the Rhythm task (PR-LIQ group SUB 239 score = 40, SUB 150 score = 40, SUB 173 score = 35, SUB 1011 score = 40; SUB 100 score = 37; SUB 31 score = 34). Outcomes may be found in Table

169 Table 6.8 Correlations between auditory variables from Phase 1 and literacy variables from Phase 2, controlling for age and non verbal IQ (WISC blocks), all project data (N=127) Intens. Rove Rhythm Two One Dur. Freq. Rise Rise BAS Reading age p value TOWRE word SS p value TOWRE nword SS p value BAS Spelling age p value BAS Maths age p value BPVS age p value Oddity p value PSTM p value Coloured boxes indicate significant correlations As is evident from Table 6.8, after controlling for age and nonverbal IQ (blocks), all of the auditory variables in Phase 1 are correlated with BAS reading age in Phase 2 and the majority are also correlated with TOWRE SS Phase 2. It may be helpful to note, that as this correlation table includes many children who have not been used in the matched 169

170 group analyses, controlling for age and IQ is more appropriate than controlling for group. A number of the auditory variables from Phase 1 are also significantly correlated with the phonological measures in Year 2, such as TOWRE nonword reading and the PSTM task. To look at the predictive nature of these relationships, fixed step regressions were run on BAS reading age and TOWRE nonword reading standard score, with Step 1 as age in Phase 1, Step 2 as WISC blocks Phase 1 (non-verbal IQ) and Step 3 as an auditory measure. The outcomes from these regressions are presented in Table 6.9 and Table

171 Table 6.9 Fixed step regressions of Phase 1 variables on to BAS reading age Phase 2, all project data (N= 127) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Age Ph *** WISC blocks Ph *** Intensity Ph *** Rhythm Ph Two Rise Ph * One Rise Ph * Duration Ph * Rove Ph *** Frequency Ph *.45 * p<.05 ** p<.01 *** p< 1 As can be seen in Table 6.9, each of the auditory variables in Phase 1 is a significant predictor of variation in BAS reading age in Phase 2, with the exception of Rhythm which is reasonably near to significance (p =.08). This is true after accounting for variations in age and non-verbal IQ. However, when isolating the longitudinal matched groups sample (see Table 6.10) it is evident that fewer predictive relationships remain, potentially due to the smaller sample 171

172 size. Please note that age and nonverbal IQ have been controlled here as group membership is not a significant predictor in this case. Also, caution is once again necessary in the interpretation of these results. Similar to Table 5.17, using seven predictor variables with a sample of 68 children is not ideal but passes the minimum standards as advocated by Brace, Kemp and Sneglar (2006). Table 6.10 Fixed step regressions of Phase 1 variables on to BAS reading age Phase 2, longitudinal sample only (N = 68) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Age Ph *** NVIQ.21.05* Intensity P ** Rhythm P * Two Rise P One Rise P * Duration P ** Rove P * Frequency **.46 * p<.05 ** p<.01 *** p< 1 Table 6.11 demonstrates that individual auditory tasks in Phase 1, also account for variation in TOWRE nonword reading in Phase 2, after controlling for age and non- 172

173 verbal IQ. Only Phase 1 Intensity, Phase 1 Two Rise and Phase 1 Frequency are not significant predictors. Table 6.11 Fixed step regressions on to TOWRE nonword reading SS Phase 2, all project data (N= 127) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Age Ph WISC blocks Ph *** Intensity Ph Rhythm Ph * Two Rise Ph One Rise Ph * Duration Ph ** Rove Ph *** Frequency Ph * p<.05 ** p<.01 *** p< Longitudinal assessment of the language impaired matched groups. As an extension of the assessment from Chapter 5, a longitudinal assessment of the language impaired matched groups was also undertaken. Similar to the main matched groups analysis, it was hypothesised that a predictive relationship between Phase 1 auditory tasks 173

174 and Phase 2 literacy outcomes would be in effect. To explore this possibility, Table 6.12 presents a correlation matrix for the relevant variables. As can be seen, after controlling for group membership, the majority of the auditory tasks from Phase 1, are related to the majority of the reading and phonological variables in Phase 2. Table 6.12 Partial correlations between Phase 1 auditory and literacy variables controlling for Group membership, language impaired matched groups (N= 59) BAS TOWRE Rime PSTM P2 R Age P2 Nword P2 Odd P2 Intensity P SD Rhythm P SD Two Rise P SD One Rise P SD.06 Rove P SD.01 Duration P SD Frequency P SD Coloured boxes indicate significant correlations Once again, when a single group was isolated, here the LI group, the correlations became non-significant. Scatterplots show the relationship between Phase 1 Duration and Phase 2 nonword reading for the whole group (Figure 6.2) and for the LIs alone (Figure 6.3). 174

175 Figure 6.2 Correlations for Duration Phase 1 and Nonword reading Phase 2, LI matched groups (N= 59) 175

176 Figure 6.3 Correlations for Duration Phase1 and Nonword reading Phase 2 LI group only To further explore the nature of these predictive relationships, a fixed step multiple regression was run, controlling for group and adding each auditory measure as a second step (see Table 6.13). As in Table 5.27, Rove has been removed as a predictor variable due to the small sample size. As reflects the correlation table, all of the auditory variables with the exception of One Rise and the Rhythm task predict significant variation in TOWRE nonword reading in Phase 2. Although not presented for briefness, this was also the case for BAS reading age. In this analysis all Phase 1 auditory tasks with the exception of Rhythm (p =.07) and Duration (p =.08), were significant predictors. 176

177 Table 6.13 Fixed step regressions for Phase 1 auditory variables on TOWRE nonword reading in Phase 2, LI matched groups (N = 59) Step Beta R 2 change Adjusted R 2 (final model fit) 1. Group *** Intensity P Rhythm P Two Rise P One Rise P Duration Frequency * p<.05 ** p<.01 *** p< Longitudinal assessment summary The main goal of the current assessment was to ascertain whether a relationship between auditory variables and phonology existed longitudinally. This analysis has clearly shown that auditory variables in Phase 1 predict decoding and phonological outcomes in Phase 2 even after controlling for age and nonverbal IQ. Additionally, these relationships existed in the language impaired analyses, as well as the main matched groups assessment. Further support for this predictive relationship was provided by the longitudinal analysis at the beginning of this chapter. Children who made the most progress with decoding and phonological awareness, the reading age controls, also made the most progress on the auditory variables. This is despite this group having the shortest inter-phase interval, and gaining only an average of 10 months in age between the two testing sessions. The low 177

178 IQ poor readers also showed gains in phonological processing skills and a general improvement in auditory discrimination (with the exception of Duration and Frequency). In further considering the auditory outcomes, the differences in group performance discussed in Chapters 4 and 5 are supported. The PR-LIQ group demonstrate a higher threshold on all of the auditory tasks compared to the GR-LIQ and CA groups, but perform on a similar level to the RAs on these measures. Also supported is the approximately equivalent performance by the GR-LIQ and CA groups on the auditory tasks. In terms of the impact of diminished IQ, the PR-LIQ group remain relatively stable in decoding ability, while the GR-LIQ group show decline in this ability over time. The drop in BAS reading standard score by the GR-LIQ group was anticipated, as these children were remarkable in their decoding skill in relation to their peers and IQ. When referencing the age norms, it was also apparent that although both groups got slightly better on their vocabulary and the GR-LIQ group progressed well on spelling and maths, this was not the case for the PR-LIQ group. Low IQ poor readers fell an additional eight months behind their peers in math, while their change in spelling performance was not much better. It is therefore evident, that if Matthew effects are applicable to the present cohort, they do not apply to all low IQ children. 178

179 Chapter 7. Project summary and discussion 7.1 Introduction to summary and discussion According to government statistics, six to seven percent of children leave primary school with very poor literacy. These children have an English National Curriculum of level three or below, a standard appropriate for a child three to four years younger. Despite targeted government initiatives the situation has not greatly improved. Statistics demonstrate that literacy levels remain largely static over time. Furthermore, a poor start in literacy is related to reduced employment opportunities and an increased likelihood of antisocial behaviour later in life (although it is not clear whether SES has been controlled in this equation, all statistics from January 2010). These statistics demonstrate that improving literacy and investigating the causes of poor literacy learning must remain a national priority. As such the profiling of the low IQ poor reader is an important undertaking. This chapter aims to provide a summary of the project as a whole and to discuss in reference to the appropriate literature, the main findings of the present research. In this undertaking it is helpful to return to the research goals presented in Chapter 3. These are replicated below: 1. To profile the strengths and weaknesses of the low IQ poor reader in comparison to theoretically driven control groups. 2. To assess whether low IQ poor readers demonstrate an auditory deficit related to the auditory processing of features of the amplitude envelope. 3. To assess whether individual differences in auditory processing are related to individual differences in aspects of phonological processing and development, decoding, language and verbal working memory. Goals two and three have been thoroughly addressed in Chapters 4, 5 and 6. To reiterate these chapters found that the PR-LIQ group performed below the level of their chronological age matched controls on the majority of auditory and phonological processing tasks as well as the majority of the literacy measures. Additionally, as a general rule, the low IQ poor readers performed below (on standardised tasks) or at the 179

180 same level (on non-standardised tasks) as the younger reading age matched controls. The profile of the PR-LIQ group was however different to that of the children with low IQ who were good readers. Although the latter group of children was small and therefore conclusions were tentative, two areas of performance distinguished the two low IQ reader groups. These are discussed below. 7.2 Areas of performance which differ between low IQ good and poor readers Auditory processing. The AP tasks administered at Phases 1 and 2 demonstrated that the low IQ poor reader group have demonstrated a difficulty in performing auditory discrimination tasks in general. However, this was not the case for the low IQ good reader group. These children performed at an approximately similar level to the chronological aged controls on the majority of the auditory tasks (with the exception of the Frequency task). As such, these outcomes were representative of the larger findings from the project, specifically that skill in auditory processing was related to decoding and to phonological processing abilities. As has been discussed previously, phonological deficits provide the key to understanding literacy difficulties. Not only are difficulties in processing the sounds in language characteristic of children with delayed language development (Leitao et al., 1997), they can also predict which children will go on to have difficulty learning to read (Castles & Coltheart, 2004). Thus understanding potential contributors to poor phonology is a key area for investigation. Auditory processing deficits provide one way of explaining the phonological deficits experienced by low IQ poor readers. Factors involved in the development of phonological specification were discussed in Chapter 3. The data presented here support a longitudinally predictive relationship between auditory processing and phonological awareness for all readers. Factors which characterise the processing of the amplitude envelope, such as amplitude rise time, duration and periodicity were found in this thesis to predict concurrent and longitudinal outcomes on literacy variables. However, it is must be remembered that given the mean values provided by group data, assumptions cannot necessarily be made for the individual child. 180

181 The auditory tasks presented as part of this thesis were developed out of previous research demonstrating an auditory processing deficit in children with dyslexia. As will be recalled, a rise time perception deficit has been demonstrated in children with dyslexia (Goswami, Thomson et al., 2002; Richarson et al., 2004; Thomson & Goswami, 2008), in children with SLI (Corriveau et al., 2007) and in children with phonological difficulties in a number of languages other than English (French - Muneaux et al., 2004; Finnish - Hämäläinen et al., 2005, Hungarian Suranyi et al., 2009). Additionally, difficulties in discriminating amplitude rise time and amplitude modulation have been found to be related to reading outcomes (Hämäläinen et al., in press). This thesis extends existing work demonstrating auditory impairments in children with reading disability by showing that a relationship between rise time related discrimination and reading ability is evident in groups containing children with poor reading and low IQ. The data presented here suggest that reading difficulties in the PR-LIQ group may be associated with poor phonological awareness and amplitude rise related processing skills, rather than poor vocabulary or low IQ. Correlation and regression analyses performed in this thesis have demonstrated that the single ramp rise time task (both with roving amplitude and without) predicts significant unique variance in nonword reading after a mean interval of 14 months. These contributions have been demonstrated after having removed the influences of age and IQ. In addition to rise time discrimination, tasks of duration and periodicity or rhythm were also significant predictors of literacy outcomes in this project. These factors are believed to be important for the processing and segmentation of the speech stream and may be important in enabling younger children developing awareness of the syllable structure of language. Reports of reduced duration discrimination in Finnish infants at risk of dyslexia support the first of these findings (Richardson et al., 2003). Similarly supportive are related reports of English children with dyslexia (approximate mean age eight) who demonstrated difficulties in discrimination duration in the manipulated medial phoneme in the Finnish non-words ata versus atta (Richardson et al., 2004). As concerns periodicity, a relationship between rhythmic timing and literacy has previously been documented in children with developmental dyslexia (Thomson & Goswami, 2008) and children with specific 181

182 language impairments (Corriveau & Goswami, 2009). These reports found that both children with SLI and children with dyslexia were impaired when asked to tap in time with a rhythmic beat. This was despite the fact that manual dexterity as assessed by the pegboard task showed no group differences. Overall, we may suggest that the discrimination of amplitude rise time, duration and periodicity are particularly important in the development of phonological representations, thought to underlie phonological processing. Early deficits in these areas may lead to poor segmentation of speech at the syllable level, which may be related to a poorly specified phonological lexicon and ultimately underlie poor decoding and poor overall reading ability. However, low IQ good and poor readers were not distinguishable on all aspects of auditory processing. Both groups had difficulty with the Frequency task presented here. This task consisted of two sequences presented with five tones each. One sequence represented the standard, where all tones were the same frequency (AAAAA). The second presented a sequence of the same standard tone, alternated with one of differing frequency which had been adaptively selected (ABABA). Low IQ good and poor readers performed below the level of chronological controls and in Phase 2 were significantly below the younger reading age matched controls. Frequency discrimination is a difficult task for many groups. For example, subsets of children and adults with language impairment and dyslexia struggle with this task (see Chapter 2), as well as some children and adults who are typically developing. A relationship between frequency discrimination and IQ has also been demonstrated. Banai and Ahissar (2004) presented adults with a two stimuli task. Here a tone of 1000 Hz 70 db SPL and 50 ms in duration was compared to a tone with the same duration and intensity, but with an adaptively selected frequency. All individuals were able to perform the most easily distinguishable comparison (adaptive tone at 1200 Hz), ruling out any auditory disabilities. However, the individuals with dyslexia showed bimodal discrimination performance, with distinct groupings of those who could and could not 182

183 perform the task at typical levels. Additionally, when good and poor frequency detection groups were created for all participants, adults with dyslexia who were poor detectors were significantly lower on the Raven s matrices test, Digit Span and the block design test. As this was also the case for the adult control group, an association between low overall IQ and frequency discrimination amongst all adults was demonstrated. In children, research evidence has demonstrated mixed outcomes. McArthur and Bishop (2004) for example, who investigated typically developing and children with SLI and young adults, found no significant non-verbal IQ differences between good and poor frequency groups. However, Deary (1994) who administered three tasks to thirteen-yearolds which varied in the degree to which they emphasised speed of processing and pitch, found all three related to verbal and non-verbal IQ. Deary also found that speed of processing was the factor most closely related to both forms of IQ. Although speed of processing and poor sensory representation may be linking IQ and frequency discrimination in this project, it is not clear why the GR-LIQ group struggled with this task in particular. One suggestion may be that the GR-LIQ group found the sequencing of the five tone serial structure difficult to process. It is possible that the increased demands of the sequencing presentation combined with the slower feature extraction and poor sensory representation related to low IQ, may make these sequencing tasks difficult. However, against this interpretation is their performance on the five tone Intensity version of this task, which was at the level of age matched peers. However, all children demonstrated reasonably high thresholds, at least in Phase 1, on the Intensity task. A more compelling explanation is provided by factors outside of auditory processing. Despite vocal and verbal reinforcement, it may be that the concept of pitch is demanding for young children to understand. Research demonstrates that children, aged seven, may struggle to attach up and down labels to musical notes or scales (Mills, 2001). It is therefore a sensible suggestion, that the low IQ groups, who possess poor vocabulary and reasoning skills, may have particular difficulty with concepts of pitch Language skills. In developing the Phase 2 assessment battery, one intention was to better understand potential linguistic factors related to more age typical word decoding 183

184 in the GR-LIQ as compared to the PR-LIQ group. Although Phase 1 results demonstrated poor levels of receptive language for both low IQ groups, measures to ascertain further language strengths and weaknesses of low IQ poor readers had not been undertaken previously (e.g. not mentioned in the summary of research in the field provided by Stanovich, 1998). A large amount of literature now supports an overlap in behaviours between individuals with language disability and reading impairment. For example, longitudinal studies have shown that more than 50 percent of children who are diagnosed with SLI also meet the criteria for dyslexia. Additionally, many people with dyslexia also show some degree of oral language impairment (Tallal, 2004). It has also been suggested that given the similarity of deficits found in each group, differences between SLI and dyslexia may exist as points on a continuum rather than as representing two qualitatively different disorders. To acknowledge these findings and to adopt this theoretical viewpoint, many researchers now adopt inclusive terms such as language learning impaired. Given the prominence of this issue, it was considered appropriate to investigate whether in general low IQ poor readers may also be language impaired. With this goal in mind, a number of language measures reported as difficult for children with SLI were administered in Phase 2. Additionally, some general clinical standardised measures for the assessment of language attainment (i.e. CELF) were included. With respect to the GR-LIQ and PR-LIQ groups, only two low IQ good readers were language impaired while the majority of the latter children fell into this classification in addition to demonstrating a phonological deficit. However, these impairments did not appear to affect the structural development of their phonological lexicons. The PR-LIQ group was similar to the GR-LIQ group and all other children in demonstrating typical effects of structural factors of lexical organisation in phonological tasks. The demonstration of neighbourhood density effects indicated a typically organised phonological lexicon for all children. If findings can be so directly interpreted, all children including the low IQ poor readers appeared to possess the typical phonological advantage for words from dense neighbourhoods. In other words they had a better phonological specification for words with common sound patterns. Such a profile has been discussed previously concerning density effects in PSTM tasks in children with 184

185 dyslexia. Thomson, Richardson and Goswami (2005) found significant rime density effects for serial recall of words and nonwords in typical children and children with dyslexia. Using the current PSTM task, plus a nonword PSTM task, the children with dyslexia and typical children studied by Thomson et al. were more successful in recalling items that had rimes from dense phonological neighbourhoods. However, an interesting finding from Thomson et al. was that children with dyslexia were more likely to substitute real words for nonwords, an effect not seen in the typically-developing children. As the quality of lexical entries is thought to support word and nonword recall, Thomson et al. suggested that a poorly specified phonological lexicon was responsible for the substitution effect. Using this explanation, an inefficiency in segmenting lexical representations at the level of the rime, resulting in poor lexical support for redintegration could lead to guessing behaviour. Given these findings, it would be a potentially fruitful avenue to perform a similar specific analysis of the quality of phonological representations in the GR-LIQ and PR-LIQ group tested here. Although the PR-LIQ group were uniformly impaired across most of the language tasks, this was not the case for the GR-LIQ group. The latter children appeared to have a mixed language profile, with weaknesses in areas which relied on vocabulary, such as immediate verbal memory for sentences and sentence formulation. Two strengths also emerged in these children, thus differentiating the two low IQ groups. The first to be discussed is the sentence correction task. Sentence correction The sentence correction task is a measure of grammatical morphology. The GR-LIQ group performed well on this task, likely due to their good phonological and decoding ability. Specifically, this task tested the child s ability to mark the possessive case ( s), insert an infinitive segment (to), insert auxiliary verbs (has, do, are), provide the past tense (saw), mark a comparative form (er), correctly produce an adverb (quiet + ly), correct an auxillary inversion, mark a plural (s) and insert an article (a). Children with literacy difficulties have been found to struggle on this task. For example, when administered the same set of sentences, six to nine-year-old reading or language impaired 185

186 children were found to perform significantly more poorly than chronological age controls (Kahmi & Catts, 1986). Although the PR-LIQ group performed below all other groups on this task, the good readers with low IQ performed at the level of the controls. Good performance by the GR-LIQ group on the sentence correction task indicated an adequate level of verbal memory for sentences (interesting given their poor performance on the Recalling Sentences task, yet intact digit span) and a good understanding of grammatical rules and how to apply them. As the GR-LIQ mean score was above that of the RA controls, it may be that their extended experience of text through successful decoding and rule learning in the classroom, was contributing to their good performance. In other words, it may be that age in combination with successful decoding was playing a role in performance on this task. At a more detailed level, sentence correction tasks are thought to involve the employment of attention, recognition of syntactic structure, recognition of deviations from known syntactical rules and the ability to create new syntactical structures (Deutsch & Bentin, 1996). It is interesting to note that although the GR-LIQ group may be able to create new syntactical structures using morphemes, this group struggled with the freeform sentence generation necessary in the CELF Formulated Sentences task. It is further evident that good grammatical morphology is not dependent on high levels of vocabulary. It may be that these skills can be boosted through good decoding. Additionally, given the significant correlations between the sentence correction task and nonword reading (r =.43, p< 1), rime oddity ( r =.38, p< 1) and PSTM ( r =.42 p< 1; all correlations for entire Phase 2 sample), it is likely that the ability to apply morphemes in a grammatically successful manner is heavily reliant on phonological processing skills. Past tense elicitation The second language measure to differentiate the GR-LIQ from the PR-LIQ group was the past tense elicitation task. Here, a superior mean performance was demonstrated by the first compared to the second group. Again, past tense elicitation is a form of grammatical morphology and is suggested to be related to phonological factors. For 186

187 example Marshall and van der Lely (2007) analysed the role of phonological complexity in a past tense elicitation task. These authors used three varieties of verbs (1) verbs with no consonant cluster at the verb end (e.g. sewed), (2) verbs with a two consonant cluster at verb end (e.g. wrapped) and (3) verbs with a three consonant cluster at verb end (e.g. munched). Children with SLI and a mean age of 12 years and 3 months were significantly better with verb group 1 versus verb group 2 and in verb group 2 versus verb group 3. These results suggested an influence of phonological complexity in correct past tense inflection. Children with SLI also performed significantly below two control groups matched on either sentence comprehension or receptive vocabulary, with a mean age of six years and nine years and six months respectively. One account for why this might be relates past tense inflection to limitations of storage and retrieval of phonological forms in verbal short-term memory (Leonard, 1992, from Marchman, 1999). This interpretation would be consistent with the finding here that the GR-LIQ group demonstrate good verbal working memory as measured by the digit span task. Given the findings discussed previously from Thomson et al. (2005), it may also be suggested that children with poor past tense inflection are relying on poorly specified phonological representations. An alternative explanation suggests that difficulties in inflected morphology may be due to delays in lexical development and slower vocabulary growth, particularly with respect to verbs (Jones & Conti-Ramsden, 1997). This hypothesis may be tentatively refuted due to the poor vocabulary and good inflectional ability demonstrated by the GR-LIQ group. However, the GR-LIQ group s specific verb knowledge is unknown. In summary the good performance by the GR-LIQ group on the sentence correction and past tense elicitation tasks suggests a good grasp of the rules and practical application of morphology related to phonological processing ability. With reference to converging research, these findings also point to well-specified phonological representations in this good decoding, low IQ group Digit span and working memory. In addition to elements of language development, specifically grammatical morphology, the GR-LIQ group was also distinguished from the PR-LIQ group by good verbal working memory performance. As discussed in Chapter 3, Baddeley has proposed a model of working memory consisting of 187

188 three components, a central executive, a visual-spatial sketchpad and a phonological loop (Baddeley & Hitch, 1974). The digit span task is generally presented as measuring the capacity of the verbal working memory system by assessing the phonological loop. However, when addressed more closely, WISC digit span consists of two components, a forward and backward serial recall task. The prominence of the phonological loop is evident when considering forward digit recall. However, when considering the backward component, it is likely that backward digit span calls on components of the central executive in addition to making use of phonological representations in the short term store. The current project has not assessed forward and backward span individually. Therefore, it is not clear whether the GR-LIQ group and the PR-LIQ group differ on elements of executive functioning as measured by the backward digit span task. It is possible, although unlikely, that the GR-LIQ group maintain good forward digit recall while demonstrating a selective deficit in backward digit span. However, this appears improbable when the two groups maths performances are taken into consideration. As discussed previously, the GR-LIQ group performed at a higher level than the PR-LIQ group on BAS maths, significantly so in Phase 2. Although the BAS maths test is a written assessment, it has been observed in this project that children perform the required calculations mentally, using the paper solely for answering. Despite the precise function of the central executive being under-specified in the literature, arithmetic is often called up as an example of a task which is heavily reliant on this working memory component. Andersson (2008) for example, found that three central executive tasks (counting span, verbal fluency and trail making) and digit span accounted for 59 percent in variance on a written arithmetic test. Andersson further concluded that the monitoring and coordinating of multiple processes and accessing arithmetical knowledge from long-term stores are important central executive functions, fundamental to success with maths. Verbal coding strategies were also found to be important in nine to 10-year-olds, as children at this age were thought to be using the phonological loop for the processing of numerical information. Given the evidence for a strong role for working memory in maths, it may be suggested that the GR-LIQ group are likely to be generally better on all components of 188

189 the digit span task and may, therefore, have a better general verbal working memory system than the PR-LIQ group. Before leaving this topic, it may also be useful to mention the second measure of verbal working memory which was administered, the letter number sequencing task. To quickly recall the relative group performances, RAs and CAs were significantly better than the PR-LIQ group on this task. The GR-LIQ group were also reasonably close to being significantly better than the PR-LIQ group according to a Mann-Whitney test (U = 58 N1 = 10 N2 = 19, p =.09). The letter number sequencing task requires children to repeat back numbers and letters with the letters in alphabetical order followed by the numbers in numerical order. As such, this task taps similar phonological and executive function components to the digit span task. However, an assessment of these two tasks found that, while much of the variance on the letter number sequencing task was explained by digit span, unique contributions were also made by processing speed and visual-spatial working memory (Crowe, 2000). Again, these findings support the conclusion that the GR-LIQ group perform better than PR-LIQ group on measures of verbal working memory, perhaps including a visual-spatial component. 7.3 Characterising low IQ readers in regards to the existing literature A number of topics which are highly relevant to the reading disability and language impairment literature have emanated from the characterisation of the low IQ poor reader in this project. These issues are discussed here Low IQ poor readers are similar to children with dyslexia in demonstrating an auditory deficit. In early conceptualisations, poor reading in low IQ children was thought to be qualitatively different to poor reading in more typical children. For example, investigations of what was known at the time as congenital word blindness (i.e. developmental dyslexia) were explicitly differentiated from poor reading in children with generally low cognitive functioning (Hinshelwood, 1917 in Stanovich 1994). An assumption existed that reading deficits in children with a more general cognitive deficit were somehow expected. According to this conceptual framework, it was reading 189

190 problems in children of normal and above average IQ which were of significant educational concern and were the motivation behind research investigations (see Stanovich, 1991). This thesis and the research it draws on, fully demonstrates the misguided nature of these previous assumptions. After a careful examination of more recent literature (see Chapter 1) it was clear that children with low IQ and poor reading perform at a similar level to children with dyslexia on phonologically related tasks. Although no comparison between children with dyslexia and the low IQ poor readers has explicitly taken place in this project, a common auditory deficit related to the processing of features of the amplitude envelope has now been demonstrated (see Chapter 2 for a discussion of the auditory deficit in children with dyslexia) Low IQ good readers are not best described as having hyperlexia. The current project is not the first to highlight the existence of good readers with low cognitive ability. According to more recent sources (Grigorenko et al., 2003), children with impaired intellectual abilities and good decoding skill have been discussed in the literature since the early nineteen hundreds. However, these citations were often made in relation to clinical populations, such as in reference to children with autism (e.g. Eisenberg & Kanner, 1956; Kanner 1943 in Grigorenko et al., 2003). Silberberg and Silberberg (1967) were the first to introduce the term hyperlexia, defined as occurring when decoding is significantly above reading comprehension and general cognitive functioning. These authors described 28 children, more than half of whom had been diagnosed with developmental disability (low IQ), and many of whom had started decoding before school entry. Their final conclusion, consistent with our findings, was that a continuum of decoding skill exists which functions separately from individual variation in general verbal ability. The immediate question raised by this literature is whether low IQ good readers should be classified as having hyperlexia. Undoubtedly, the GR-LIQ group have decoding abilities equal to that of the chronological age controls, while their standardised reading 190

191 comprehension scores are significantly below chronological age controls. However, individuals with hyperlexia have been described as decoding significantly above the level of their peers. Individuals with hyperlexia often teach themselves to read from a very early age and have autistic-like traits, with restricted interests and a zealous devotion to word reading (Nation, 1999). Individuals with hyperlexia have also been described as demonstrating a distinctly better Performance IQ as compared to their Verbal IQ levels (all of these characteristics are mentioned in Grigorenko et al., 2003). By these standards, the GR-LIQ group are better characterised as we have titled them - good readers with low IQ. Despite this conclusion, the GR-LIQ group did perform similarly to individuals with hyperlexia on one particular point. As demonstrated in the summary, good readers with low IQ dropped significantly from Phase 1 to Phase 2 on their standardised decoding scores. Similar findings of a stability or slight decline in absolute performance for single word reading have also been described in individuals with hyperlexia (Grigorenko et al., 2003). These children, similar to the GR-LIQ group, demonstrate a flurry of reading activity in early development which then plateaus. Reading comprehension remains poor over time. Although not supported with research evidence, it may be suggested that once children begin to struggle with the meaning behind text and are no longer distinguished from their peers by their superior decoding ability, their attraction to reading declines. Support for this suggestion is provided by reports of a long term waning of enthusiasm for reading in individuals with hyperlexia (Sparks, 2001) Low IQ good readers are not best described as poor comprehenders. Also overlapping with the characteristics of the GR-LIQ group are poor comprehenders, a group discussed in the literature review in Chapter 1 (section 1.5). It has been suggested that approximately 10% of middle school children (Nation & Snowling, 2007) fall into this category. These children, who have poor vocabulary and who use linguistic context less efficiently, decode at the level of their peers. Therefore, on aspects of vocabulary and decoding they appear similar to the low IQ good reader. However, all studies to date of poor comprehenders have only included children with average and above average non- 191

192 verbal or full scale IQ. As such, the poor comprehender presents a different profile than the low IQ good reader described here. The low IQ good readers, it should be remembered, demonstrated good decoding ability in the presence of poor verbal and nonverbal abilities. Additionally, 10-year-old poor comprehenders have been described as having a weak verbal memory span (Nation et al., 1999) and poor morphological knowledge as measured by a past-tense inflection task (Nation & Snowling, 1998b). These characteristics further distinguish them from low IQ good readers, who performed well on tasks in both of these areas Pragmatic language ability may be a relevant variable which has not yet been assessed. In addition to the discussion of the GR-LIQ group as distinguished from individuals with hyperlexia it is also important to discuss the potential overlap between both low IQ groups and children with pragmatic language difficulties. Pragmatics can be defined as the appropriate use and interpretation of language in relation to context (Bishop, 1997). Children who have difficulties with the pragmatic aspects of language may use stereotyped language, a restricted range of conversational topics, have difficulties in using and/or understanding non-literal language and may have an inability to take the speaker s or other s perspective in communication. For example, children with high-functioning autism or Asperger syndrome often possess pragmatic language impairments. The Children s Communication Checklist (CCC) has been developed with the primary goal of identifying children who have pragmatic difficulties from those with more typical forms of specific language impairment. In this inventory the rater, who knows the person being rated well, is presented with a number of statements which he or she must indicate definitely applies, applies somewhat or does not apply to the person in question. An example is talks to anyone and everyone. In using this scale, Bishop (2000; 2001) has proposed that children with pragmatic language disorder fall somewhere between a profile traditionally associated with SLI and one normally associated with autistic disorder. Given these parameters it is possible that the low IQ readers may fall into this category. 192

193 It now appears that a significant minority of children have difficulty with the pragmatic aspects of language. For example, Botting et al. (1998) recently identified that 53 out of 235 children aged seven and eight demonstrated major pragmatic difficulties, excluding children attending language units. In further research, Botting and Ramsden (2010) assessed 10 children (ages ranging from seven years seven months to eight years nine months) identified by teachers/therapists as possessing semantic-pragmatic disorder. These children were additionally clinically identified as possessing a pragmatic disorder as well as scoring low on the CCC. A full assessment of these children found a generally high non-verbal IQ (Raven s matrices) for their age with typical levels of articulation and naming vocabulary. This description does not fit well with the low IQ readers, who have on average low performance IQs and poor receptive vocabularies. However, evaluations of pragmatic language were not undertaken and thus a true assessment is not possible. The good digit span performance combined with poor verbal memory for sentences as demonstrated by the GR-LIQ group may signal pragmatic-semantic difficulties. Thus, an investigation of aspects of pragmatic language skill in low IQ readers may offer a potentially fruitful avenue for future research (see final section on Further Research). Additionally, it is worth noting that children with official diagnoses of developmental disorders were not included in the present study, thus children known to have Autism Spectrum Disorders were excluded Low IQ readers do not demonstrate Matthew effects. When assessing reading performance over time, it is interesting to discuss Stanovich s proposal concerning Matthew effects in poor readers. As mentioned previously, Stanovich and others suggest a longitudinal fanning effect for good and poor readers, where good readers get better and poor readers diminish over time. Stanovich (1986) has proposed that early deficits in decoding ability lead to limited text exposure, poor vocabulary development and effortful reading for the poor reader. Additional effort spent in decoding text reduces the cognitive capacity otherwise available to extract meaning from text. Matthew effects have also been suggested in relation to IQ. Shaywitz et al. (1995) for example, found that a child with a mean IQ of 80 would be expected to drop by -1.1 FSIQ SS point per year over the ages of approximately five to 10. Conversely, a child with an IQ of 140 would increase 193

194 by about 4.5 points each year. It may be reasoned that as access to much of school learning takes place through books, and because vocabulary represents such a significant component of IQ, these results are not surprising. Given the prior evidence for Matthew effects in poor readers and children with low IQ it might be expected that low IQ poor readers would be particularly disadvantaged. However, it is not immediately evident that this is the case. Although low IQ good readers did change in decoding ability, demonstrating much more typical standard scores in Phase 2, this was not the case for the low IQ poor readers. Only in maths did the low IQ poor readers demonstrate a significantly lowering of relative ability over time. The stable performance in the majority of children may be due to a number of factors. Firstly, the use of standardised scores removes individual variability by design, but makes change difficult to ascertain. Secondly, the project covers only a short period of development for these children, in some cases only a couple of years. With reference to the Shaywitz et al. (1995) study example discussed previously, Matthew effects when demonstrated are subtle, with an average lowering of one standardised FSIQ point per year. Finally, any changes in IQ scores which may have taken place are difficult to ascertain, as only one WISC subtest was readministered. Because of this, any protective factor that good decoding may have had on IQ (as judged by GR-LIQ versus PR-LIQ) could only be ascertained from the WISC blocks performance which was found to be non-significant by group (repeated measures ANOVA with Phase as the between subjects measure and Group as the within subjects measure). 7.4 Overall conclusion In summary, returning to the research goals, low IQ poor readers generally perform below age and reading age matched children on the auditory and literacy tasks administered in this project. Individual differences in auditory discrimination are related to individual differences in decoding ability. However, it appears that low IQ poor readers outcomes could be better than expected on the basis of prior literature. Matthew effects, if such a concept is valid, do not appear to be aggressive in their impact, at least for the age group studied. However, it is clear that low IQ poor readers do suffer from 194

195 disadvantage in all academic areas. Research projects such as this one, can help by identifying areas for targeted support. For instance, the GR-LIQ profile suggests that with boosted phonology, low IQ poor readers could potentially learn to decode well. In addition, a boost to the PR-LIQ group s language skills, including techniques for raising levels of reading comprehension and vocabulary learning, could improve their outcomes. However, whether or not literacy targets could be achieved, it is clear that low IQ children should not be excluded from reading research. The low IQ poor reader faces the same literacy challenges as the rest of his or her peers and stands to benefit the most from our increased understanding. Although individual researchers have proposed remediation of phonological processing through auditory training, the data presented here are insufficient to decide whether currently available programmes would benefit the low IQ poor reader. As discussed previously, training programs such as Fast ForWord have met with mixed success. It is therefore not yet clear whether the training of auditory discrimination processes is necessarily helpful in the attainment of better reading and language outcomes. What has been made clear by the current project are that the weaknesses of the low IQ poor reader lie in the auditory and phonological domains. By comparison to the low IQ good reader, IQ has been removed as a confounding variable. It is now clear that low IQ impacts on frequency discrimination but is not related to the auditory variables which predict literacy, as reviewed above. In fact, low IQ is not sufficient to result in decoding disability, nor does it prevent good knowledge and application of grammatical morphology. It is also evident that low FSIQ, and the often co-occurring low vocabulary levels, do not impact upon the development of lexical organisation as measured by density factors in phonological awareness tasks. These findings have a large significance for how we address individuals with reading disability and particularly encourage us to include individuals of all IQs in remediation programs and research. 7.5 Further research An extension to the PhD project is already underway. This follow-up research will revisit as many children as possible from the original low IQ good and poor readers and 195

196 the reading and chronological aged controls. Measures of reading, IQ, vocabulary, oral language and maths will be re-administered in addition to new measures of pragmatic language and tests of word reading. As discussed within this thesis, pragmatic language ability is a variable of interest which has not yet been addressed within the PhD cohort. Ability in this area is particularly relevant as there is some possibility that the low IQ good readers may have pragmatic impairments, which may go some way to explain their good verbal memory for numbers (digit span) but poor verbal memory for sentences (Recalling Sentences). The existence of a pragmatic impairment would have important implications for how best to support these children within school, and may be relevant to their social functioning and to other aspects of their overall success within the school environment. For example, due to restricted interests and poor social abilities, some low IQ good readers could face difficulties in forming friendships or joining into classroom discussions. One to one support which targets emotional learning, for example how to identify and respond appropriately to demonstrations of feelings in others, could be beneficial for these children. The proposed project will also provide a valuable longitudinal extension to the existing dataset in many ways. Specifically of interest will be the impact of phonological awareness on long-term reading, language and IQ outcomes. The identification of potential differences between the GR-LIQ and PR-LIQ groups from a longitudinal viewpoint will be extremely valuable. Although relevant research is not currently available to support or refute this proposal, different developmental trajectories may exist for the low IQ good and poor reader groups. This hypothesis suggests that good decoding skills and some preserved language skills (as related to sentence correction and past tense inflection ability) may have wider consequences for development. Specifically, low IQ poor readers may show a decrement in IQ over time compared to low IQ good readers, who may be more stable in terms of intellectual development. Additionally, other areas such as math ability (already seen to decrease in Phase 2 from Phase 1 in the PR-LIQ but not the GR-LIQ group) may develop differently in the two 196

197 reader groups. However, whether or not these patterns are observed, it is evident that the ability to follow the same children over five years provides an excellent opportunity for insightful conclusions to be made. The benefit for our classrooms is clear, as is the need for investigations of this kind. It is the overall aim of this doctoral research to be of practical benefit to the field. Where this investigation leads it is hoped other research will follow. 197

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224 Appendix 1. Phonological tasks 1A. Words used in the Onset Oddity task (Phase 1) Sonority Word 1 Word 2 Word 3 profile poor bike tight type good laid make mate good nib rig rid poor pin pill king good mode wrote mope good rat rack map good ran rang lamb good mine rime mile poor cap cat pack poor gate take tape poor kick kit tip good rain name nail good light ripe like poor pan pal gang poor cope poke coat poor cone pole comb poor tile pine time good rim ring mill good moan roam mole poor came pail pain 224

225 1B. Words used in the Rime Oddity task (Phase 2) Density Word 1 Word 2 Word 3 sparse dutch budge hutch dense that wag nag sparse biz give fizz sparse pike like ripe sparse wig rib fib dense knock shock jot sparse bird gird shirt dense thick chit nick dense shop rod nod sparse pup bud mud dense cheek meek deed sparse rib lid bid dense wake shake date dense daze case gaze dense jack gap nap sparse pith wish fish dense wheat meek cheat sparse loss moss toff sparse dove love buzz dense zip chick nip 225

226 1C. Words used in the PSTM task (Phase 1 & 2) Density Word 1 Word 2 Word 3 Word 4 sparse type rib nook bud sparse tong curl dome gown dense rule tone boom thing sparse peg shook fib road dense jug shop hat weak dense king gum bone pale dense knob lake root map dense doom ball ping fun dense scene ring thumb hale dense shake lip fed tub sparse wool wrong home down sparse hook leg wipe bird dense rack pub knit laid sparse comb pull gong turn sparse join song hem dull sparse word league ripe nib 226

227 1D. RAN (Phase 1) Example RAN images from sparse condition 227

228 RAN (Phase 1) Example RAN images from dense condition 228

229 2A. Past tense elicitation measure 2. Non-standardised language tasks 1. This gorilla is peeling a banana. He peels a banana everyday, Yesterday he a banana. 2. This boy is riding a bicycle. He rides his bicycle everyday. Yesterday he his bicycle. 3. This boy is singing. He sings everyday. Yesterday he. 4. This boy is biting an apple. He bites an apple everyday. Yesterday he the apple. 5. This chef is tasting the soup. He tastes the soup everyday. Yesterday he it. 6. This girl is smiling. She smiles everyday. Yesterday she. 7. This clumsy girl is breaking cups. She breaks cups everyday. Yesterday she the cups. 8. The snow is falling on the rabbit. It falls on the rabbit everyday. Yesterday it on the rabbit. 9. This tower is leaning. It leans everyday. Yesterday it. 10. These twins like their clothes to match. They match everyday. Yesterday they. 11. This mother is feeding her daughter. She feeds her daughter everyday. Yesterday she her. 12. This man is carrying his horse. He carries his horse everyday. Yesterday he his horse. 13. This river is flowing. It flows everyday. Yesterday it. 14. These icebergs are melting. They melt everyday. Yesterday they. 15. This cow is eating hay. He eats hay everyday. Yesterday he hay. 16. This boy sits on his Dad s knee. He sits on his Dad s knee everyday. Yesterday he on his Dad s knee. 17. This girl is skating. She skates everyday. Yesterday she. 18. This horse is drinking. He drinks everyday. Yesterday he. 19. This boy loves his cat. He loves his cat everyday. Yesterday he his cat. 229

230 20. This boy walks to school. He walks to school everyday. Yesterday he to school. 21. This footballer is running. He runs everyday. Yesterday he. 22. This man is mending roofs. He mends roofs everyday. Yesterday he a roof. 23. This couple are dancing. They dance everyday. Yesterday they. 24. This boy is trying to write neatly. He tries hard everyday. Yesterday he hard. 2 B. Sentence correction task 1. Steven dog was lost (possessive s) 2. I tried get the book (infinitive segment to) 3. Nancy is smaller than Karen 4. He already eaten dinner (auxiliary has) 5. Yesterday he sees a movie (past tense saw) 6. Kathy has three dogs 7. John is big than Dave (comparative er) 8. She needs to go home 9. He not want to play today (auxiliary do) 10. She walked quiet into the room (adverbial ly) 11. They throwing the stick (auxiliary are) 12. Where the coat is? (auxiliary inversion) 13. Yesterday he ran to school 14. John has two book (plural s) 15. Usually they walks to school (third person sing. s) 16. The girl painted picture (article) 230

231 3. Auditory tasks One Rise task. Examples of stimuli for the AXB rise time task: Amplitude (y axis) over time (x axis) for shortest ( standard,15 ms rise time) and longest rise time (300 ms rise time) stimuli. Two Rise task. Examples of stimuli for the 2 IFC rise time task: Amplitude (y axis) over time (x axis) for the longest (standard, 300 ms rise time) and shortest (15 ms rise time) two rise time stimuli. 231

232 Intensity 1. Examples of stimuli for the 2IFC Intensity task. Amplitude (y axis) over time (x axis) for the stimuli with highest (standard) and lowest amplitude(diminished 50%) in second and fourth modulations Duration. Examples of stimuli for the AXB duration task. Amplitude (y axis) over time (x axis) for the stimuli with shortest (standard, 400 ms) and longest (595 ms) durations. 232

233 Rhythm task. Examples of stimuli for the 2IFC rhythm task. Amplitude (y axis) over time (x axis) for the stimuli with the largest (standard, ISI 150) and smallest (ISI 15ms) ISIs. Frequency. Examples of stimuli for the 2IFC frequency task. Amplitude (y axis) over time (x axis) with Frequency indicated in Hz for the standard stimuli and the largest frequency modulation. 233