2018
University of Amsterdam Liam De Meulemeester Student ID: 11363037 Supervisor: Nada Vasic MA in General Linguistics
[
Short term and working memory in
prediction of literacy ability among
children with Language impairment]
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Table of Contents
Introduction ... 2 Language Impairment and Literacy Skills ... 3-5 Working Memory and Literacy Skills ... 5-9 The Present Study ... 9-10 Predictions ... 9-10 Methodology ... 10-13 Participants ... 10 Criteria Used for Language Impairment diagnosis ... 10-11 Procedure & Measurements ... 11-13 Non-verbal Intelligence ... 11 Phonological Short-Term/Working Memory ... 11-12 Visuo-spatial Short-Term/Working Memory ... 12 Reading Ability ... 12-13 Analysis ... 13-14 Results ... 14-16 Discussion ... 17-24 Conclusion ... 24 Further Research ... 24-25 References ... 26-30
2 | P a g e Introduction
Objectives: The current study is an investigation into short-term and working memory at age (5-6) and its value in predicting literacy ability during school age (7-8) among children with Language Impairment (LI). The objective of the current study was to investigate if literacy ability is not only predicted by phonological short-term memory (STM) but also by visuospatial STM, and working memory; both phonological and visuospatial.
Design: The design consisted of longitudinal data collected across three waves with including measures of phonological STM (Digit Span Forward), visuospatial STM (Dot Matrix Forward), phonological working memory (Digit Span Backwards) and visuospatial working memory (Dot Matrix Backwards). Literacy ability was measured by way of the Klepel test and Een-Minuut-Test, measured in Wave III when the children were aged between 7 and 8 years old.
Results: The outcomes of a Multiple Linear Regression (MLR) revealed that phonological and visuo-spatial STM were significant predictors of literacy ability among language impaired children. Visuo-spatial STM proved to offer comparatively less predictive value yet seems to play a certain role. Both phonological and visuospatial working memory measures were statistically insignificant in the model.
Conclusion: Concurrent with much of the previous literature, phonological STM appears to be the most important predictor when looking at later literacy ability among LI children. Visuo-spatial STM however seems to be a separate, but important predictor. Early identification of children who might suffer from phonological and visuo-spatial short-term memory deficits might help predict which children with LI will struggle with reading in their later school years before they face the potential consequences that might accompany this.
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I. Language Impairment and Literacy Skills
Not all children go on to acquire their first language with the same level of success. About 7% of children demonstrate significant deficits in their language development which cannot be otherwise explained by hearing problems, deficient non-verbal intelligence or general neurological damage (Leonard, 2014a; Tomblin et al., 1996). Over the years, several labels have been used to identify this group, the most common of which being Specific Language Impairment (SLI) (Leonard, 2014b). Given the commonly used definition’s reference to the
presence of abnormal language development in the absence of other
nonverbal/neurobiological/sensorimotor issues, the label ‘specific’ seems appropriate when describing the language impairment seen in these children. There is however growing evidence suggesting that many children with SLI show decreased performance in areas independent to their linguistic deficits (Miller et al., 2001; Leonard, 2014a) and that non-linguistic factors might possibly underlie some of the problems seen in SLI (Bishop, 1992; 2006). The question as to how specific to language the problems seen are remains an open-ended query.
In general, many researchers have used an IQ cut-off score of 85 to diagnose children with SLI (Park, Mainela-Arnold, & Miller, 2015), yet several studies suggest children with Nonverbal IQs below 85 still show similar language and cognitive profiles (Leonard, 2006; Tomblin & Zhang, 1999) which casts reasonable doubt as to why such a seemingly arbitrary cut-off is used. Considering this similarity between profiles, many researchers choose to use a cut-off of 70 for IQ seeing as this usually refers to the limit of ‘normal’ IQ, any less being considered global intellectual disability (Gallinat & Spaulding, 2014). This group is sometimes referred to as children with Language Impairment (LI), dropping the word ‘specific’ from the moniker. Although LI tends to manifest slightly differently across languages (Leonard, 2014a), the general consensus is that it concerns oral language difficulties (Leonard, 1998). There are however several studies that have demonstrated many children with LI struggle with written language and thus experience literacy problems (Tallal et al, 1997; Snowling et al., 2000; Bishop & Adams, 1990, Catts 1993; Catts, Fey, Tomblin & Zhang, 2001).
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It is however not entirely clear which children with LI go on to struggle with reading and which do not. In general, the research on risk-factors for reading problems has identified deficits in phonology to often be central to effective prediction, especially among typically developing children (Blachman, 2000; Snowling, 2000). Studies categorising children into the ones with good or poor reading outcomes have found that those with poor reading abilities tend to have markedly more severe phonology related disorders (Larrivee & Catts, 1999). Specifically among LI children, one possible source of literacy-related vulnerabilities is poor phonological memory (Bishop & Snowling, 2004). Phonological short-term memory is often found to be deficient among LI children (Dollaghan & Campbell, 1998; Botting & Conti-Ramsden, 2001; Gathercole & Baddeley, 1990). Many of these studies use non-word repetition tasks (NWRT) to measure phonological short-term memory, finding it to predict later literacy ability among LI children. Lower scores on such tests imply impaired ability to take in verbal stimuli, manage it and verbally reproduce it.
The accurate identification and prediction of literacy ability is important, especially in the context of children already faced with the struggles associated with early LI. Children who continue to struggle in school with language and eventually reading, face potentially harsh consequences heading into adolescence (Stothard et al., 1998). Along with falling further and further behind in vocabulary development, as suggested by Stothard and colleagues, long-term consequences include poorer outcomes in social communication,
academic/educational achievement, and occupational status (Johnson et al., 2010). On top
of all this, studies have suggested early language impairment can even negatively affect various aspects of self-perception, self-concept, self-esteem and self-efficacy (Burden, 2008). Above all, it is clear the persistence of language difficulties in the school years can cause children to fall behind, and unfortunately, stay behind. Such an assertion is supported by the abundance of negative outcomes associated with literacy problems. Aside from the clear emotional and social consequences they can have outside of the classroom (Riddick, 2009; McNulty, 2003), the result of struggling with the act of reading can have far-reaching consequences in employment and higher education attainment. (Adelman & Vogel, 1990; Mortimore & Crozier, 2006; Richardson & Wydell, 2003) with strong evidence suggesting early identification is key (Blachman, 2013).
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For the children faced with the combination of LI and literacy problems the negative outcomes are potentially even worse. This is especially true given the assertion that phonological short-term memory and literacy development are likely to be reciprocal over time (Baddeley, 2003). This means that they are likely to have an effect on each other; during the early years vocabulary development might rely on short-term memory abilities while later lexical knowledge/reading ability might facilitate short-term memory related tasks. In general, investigations on LI children and their memory capacities have been based around phonological memory (Weismer, 1996), and have been focused on the recall of verbal material. However, with evidence suggesting many children with LI struggle with more than just phonology-related tasks (Johnston et al., 1988; Tallal et al., 1993), the question as to whether these additional deficits might hold predictive power when looking at later reading ability is a pertinent question. Given the possible presence of additional deficits in LI children, the question as to whether these might have an effect on literacy skills remains relatively unanswered.
II. Working Memory and Literacy Skills
Beginning with the study by Gathercole and Baddeley (1990), there has been a clear
increase in focus on the link between phonological memory and LI. The basic tenet of this
argument by Gathercole & Baddeley suggests that a deficit in the phonological loop and therefore phonological memory causes many of the difficulties children with LI face. The phonological loop is a component of the conceptualisation of Baddeley and Hitch’s (1974) working memory model. This model for working memory consists of three separate temporary memory systems; the central executive, phonological loop and visuo-spatial sketch pad. The central executive is a system with limited capacity working to coordinate storage and retrieval between working memory and long-term memory. Tasks that place concurrent demands on processing and storage have been suggested to tax the central executive (Just & Carpenter, 1992). The phonological loop specialises in temporary storage of phonological material, while the visuo-spatial sketch pad concerns the storage of visual and spatial characteristics of material. Deficits in the phonological loop have been linked to the problems seen in LI as a result of reduced capacity in short-term phonological storage causing difficulties in language acquisition such as vocabulary acquisition (Gathercole &
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Baddeley, 1989). As such, many of the oral difficulties seen in LI children might have a root in poor phonological memory.
Figure I: Working Memory (Source: Baddeley, 1986)
Poor phonological memory is however not only an effective predictor of LI, but it has also been linked to literacy ability (Mann, 1984; Kamhi & Catts, 1986; Brosnan et al., 2002; Helland & Abjornsen, 2004; Catts et al., 2005). The rationalisation behind such a link is that phonological memory plays an important role during the period in which a child learns and applies simple letter-sound correspondences (Gathercole & Baddeley, 1990). More recent studies have found longitudinal reciprocal relationships between phonological short-term memory and literacy measures (Conti-Ramsden & Durkin, 2007) and phonological working memory and literacy measures (Gathercole et al., 2006). Considering the relatively high overlap between children with LI and children with reading difficulties (Bishop & Adams, 1990; Catts, 1993; Conti-Ramsden, Simkin & Botting, 2006) and the connections made to phonological memory in both these groups of children (Catts et al., 2005), it is plausible that LI children struggle with reading, at least in part, due to their impaired phonological memory.
The conceptualization of working memory by Baddeley (1986) however makes a distinction between short-term and working memory. Short-term memory to consist of the ability to temporarily store a limited amount of information in an easily accessible state while working memory encompasses the notion of short-term memory but includes the application thereof to cognitive tasks including the updating and management of short-term memory (Cowan, 2008). This is supported by numerous studies (Archibald, 2006; Archibald
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& Gathercole, 2006; Gathercole et al., 2006) which view short-term memory as a subcomponent of working memory. As phonological STM is generally considered to be a subcomponent of working memory in general, it is often not measured as a separate construct and subsumed into the notion of working memory. Studies that have however done this have found phonological STM and phonological working memory to affect later learning outcomes differently (Gathercole & Pickering, 2000). Using the framework of memory by Baddeley and Hitch (1974), the distinction between the two types of memory is further motivated as the central executive is tasked with the simultaneous storage and manipulation of material. Tasks that measure the simple storage of material tap short-term memory, while those that require additional manipulation are thought to tap working memory. In addition to the theoretical distinction between working memory and short-term memory, there is also evidence to suggest it is possible to distinguish between the two in practice as well. Deliberate differentiation between the two is bolstered by findings that suggest short-term memory and working memory are separable constructs which load on distinct factors in factor analyses (Kai & Hall, 2001). Furthermore, studies investigating working memory and short-term memory in tandem have used tasks such as Digit span (forward and backwards). These studies have used forward recall to provide a measurement of basic phonological storage capacity while backwards recall, which requires storage and manipulation of the stored information before recalling it, to measure working memory (Bull et al. 2008; Swanson et al., 1999).
In addition to phonological working memory there is also evidence to suggest visuo-spatial working memory is of interest when examining literacy skills. Studies have shown deficits in visuospatial working memory (Smith-Spark & Fisk, 2007), and visuospatial short-term memory (Gathercole et al., 2006) among reading disabled children. Such deficits suggest these areas might potentially aid in reading ability prediction, independent of possibly co-occurring phonological deficits. Other instances have found dyslexic children to perform poorly on tasks that require visual processing and short-term memory for complex patterns (Olson & Datta, 2002). More recently, in a study by Wang and Gathercole, they found their sample of children with reading difficulties to perform worse than typically developing children on both verbal and visuospatial memory tasks (Wang & Gathercole, 2013). Although, generally visuospatial memory deficits appear less pronounced in comparison
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with phonological memory deficits; the mixed results here and the suggestion of a more general executive deficit warrants further investigation (Smith-Spark & Fisk, 2007).
In addition to children showing visuo-spatial deficits in tandem with reading disabilities, there is evidence to suggest that LI children also perform lower on visuo-spatial short-term memory in pattern recall tasks (Hick, Botting & Conti-Ramsden, 2005), show decreased accuracy in certain visuo-spatial tasks (simple recall and search-based working memory tasks) among LI children (Bavin et al., 2005), perform worse compared to TD children in visuo-spatial tasks with high executive function demands (Marton & Schwartz, 2003; Marton, 2008) and that visuo-spatial memory deficits have been shown to contribute significantly to models accounting for LI as a whole (Leonard et al., 2007), there seems to be a tenable link between working memory as a whole and LI. In a recent meta-study by Vugs et al. (2013), they found visuo-spatial working memory to be generally affected among LI children, although comparably less than phonological working memory. Although the studies are far from universal, it seems many children with LI do typically exhibit more global working memory deficits outside of phonological working memory, as typically focused on.
Given the current consensus on phonological and visuo-spatial working memory abilities among LI children and their relation to later reading ability, it is not yet clear how extensive the deficits LI children tend to exhibit are. The work done by Gathercole & Baddeley (1990) showing fairly pervasive phonological short-term memory deficits among LI children combined with the link between phonological short-term memory and literacy ability (Mann, 1984; Kamhi & Catts, 1986; Catts et al., 2005) suggests a possible deficit in the phonological loop among LI children, which might well impair literacy later on. In addition to this, evidence suggesting phonological working memory (measured by way of complex span tasks) might be impaired in LI children (Archibal & Gathercole, 2006) and be related to literacy ability (Wang & Gathercole, 2013) suggests that this deficit among LI and reading impaired children might not be limited to just phonological short-term memory but be a more global deficit of phonological working memory in general. Whether phonological working memory as a whole or just phonological short-term memory is of interest when predicting literacy ability among LI children is not clear.
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In a similar vein, impaired visuo-spatial short-term & working memory (Gathercole et al., 2006) among literacy impaired children combined with studies finding poor performance on visuo-spatial memory tasks among LI children (Hick, Botting & Conti-Ramsden, 2005) suggests that language impaired children who go on to struggle with reading might not only suffer from comprehensive phonological working memory deficits but also from visuo-spatial working memory deficits. As such, effective prediction of which children with LI go on to struggle with reading might well benefit from the inclusion of measures across the realm of working memory, thus including both phonological and visuo-spatial short-term/ working memory.
III. The Present Study
The current study aims to address the question as to how extensively the deficits in working memory among LI children predict reading difficulties later on. It might be the case that only phonological working memory effectively predicts which LI children will struggle with reading, or that visuo-spatial working memory is also important in doing so. It might also be the case that only phonological and visuo-spatial short-term memory is important while working memory as a whole (including the central executive and its role in processing/management) is not. To do this, the current study will investigate if visuo-spatial memory deficits also act as useful predictors of dyslexia among LI children. In addition to testing phonological short-term memory, the aim will be to include visuo-spatial short-term memory and see how this relates to later reading ability. The current investigation also makes a conscious distinction between short-term and working memory and therefore will also include phonological working memory and visuo-spatial working memory to determine if these act differently to phonological and visuo-spatial short-term as predictors of literacy ability.
Predictions:
The expectation for the current study is that phonological short-term memory will be the strongest predictor of reading ability among LI children. This is supported by the extensive list of studies mentioned above. Given the studies demonstrating visuo-spatial deficits among certain LI children and among children with reading disabilities, it is expected visuo-spatial short-term memory will also be a predictor of literacy ability. In line with previous
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findings, it is expected however that phonological short-term memory will be a stronger predictor when compared to visuo-spatial memory. Lastly, it is expected that short-term memory when compared to working memory as a whole will be more predictive of literacy ability. This is due to fact that the strongest links between phonological memory and literacy ability have often used tasks tapping phonological STM (such as NWRT), even when this ability is summarised as working memory. It is expected that when the two (STM and working memory) are separated and analysed side by side, that STM will be the strongest predictor. All in all, it is expected that the strongest predictor of literacy ability among LI children will be phonological STM with either phonological working memory or visuo-spatial STM as second strongest. Finally, visuo-spatial working memory, based on previous literature is expected to comparatively offer the least predictive value.
IV. Methodology a. Participants
The data used in this study was collected as part of a larger longitudinal project Cognitive Development in the context of Emerging Bilingualism (CoDEmBi) led by dr. Elma Blom at the University of Utrecht. The project included four groups, bilingual and monolingual, with and without language impairment of which a subsample was selected for the current study consisting of the monolingual language impaired children. The sample of children was tested once a year from 2014 to 2016 aged 5/6 years old in the first wave and 7/8 years old in the third and last wave. The full sample of children consisted of 94 children, 70 of which were selected for the subsample, as to minimise the amount of missing scores in the dataset. In this fashion, only the children with values on all relevant tests on Wave I and Wave III were included. The sample (N=70) consisted of 17 girls and 53 boys (an imbalance often prototypical of LI; Tomblin et al., 1997) aged between 59 and 88 months in Wave I (M= 72.3 months, SD 6.7) and between 81 and 112 months in Wave III (M=95.4, SD 6.9). The children tested in this study were monolingual speakers of Dutch diagnosed with language impairment before the onset of the study.
b. Criteria Used for Language Impairment diagnosis
The children in the monolingual language impaired subset selected for the current study were diagnosed as language impaired (LI) by licensed clinicians and diagnosed using the
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standardised criteria used in LI diagnosis in the Netherlands. This criteria requires a child to obtain a score of at least 2 standard deviations (SD) below the mean on the standardised language assessment test battery or 1.5 SD (or more) below the mean on two out of four subscales of this language assessment battery (Stichting Siméa, 2014). These batteries often consist of the Clinical Evaluation of Language Fundamentals in Dutch (CELF-4- NL; Kort et al., 2008), the Schlichting Test for Language Production and Comprehension (Schlichting and Lutje Spelberg, 2010a,b), and the Dutch Language Proficiency Test for All Children (Taaltoets Alle Kinderen (TAK); Verhoeven and Vermeer, 2001].
c. Procedure & Measurements i. Non-verbal Intelligence
Weschler Non-Verbal Scale of Ability (WNV): As a measure of non-verbal intelligence, the WNV (Wechsler & Naglieri, 2006) is a non-verbal measure of cognitive ability especially for those culturally or linguistically diverse. As such, the native language of the child taking the test is irrelevant because the test does not rely on language ability to gauge intelligence. The overall format of the tests includes four sub-tests; matrices, coding, object assembly, and recognition.
ii. Phonological Short-Term/Working Memory
Digit Span Forward (DSF): The measure of phonological short-term memory used was the Digit Span Forward condition based on the Automated Working Memory Assessment (AMWA; Alloway, 2007). Digit span tasks have been used to measure phonological short-term memory in several studies (Gathercole et al., 1997; Gathercole et al., 1999; Bull et al., 2008; Wang et al. 1994, etc.). The forward condition of the Digit Span task consisted of the child hearing numbers and being required to repeat them back in the same order they were played in. The task began with a section in which one number was played which eventually increased up to a section with eight numbers. The sections were only scored correctly if the correct order of numbers was retained by the child. The sections were scored incorrect in the case that a child forgot a number or said the series of numbers in the wrong order. The maximum score possible on the DSF is 32 (which would imply a perfect score on all 8 blocks, up to the 4th series (of 6) as a score of 4/6 series recalled correctly results in the progression to the next block.
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Digit Span Backwards (DSB): The backwards condition of the Digit Span task was selected to measure phonological working memory, again based on the Automated Working Memory Assessment (AMWA; Alloway, 2007). The backwards condition was very similar to the forward condition, except that the numbers had to be repeated back in reverse order to which they were read out in. Scoring was done identically to the forward condition, incorrect (order or faulty inclusion/exclusion of digits) or correct in the case of no mistakes. Similar to the DSF, the maximum score on the DSB was again 32.
iii. Visuo-spatial Short-Term/Working Memory
Dot Matrix Forward (DMF): The measure used for visuo-spatial short-term memory was the forward condition of the Dot Matrix task. In similar fashion to the Digit Span task, the Dot Matrix task was based on the Automated Working Memory Assessment (AMWA; Alloway, 2007). The test consisted of the child being presented a square of four frames (4x4) where a dot appeared in one of the frames. The child was told to identify which frame(s) the dot appeared in. The first block consisted of one dot while the last block consisted of a series of six consecutive dots appearing in one of the frames. An answer was marked incorrect when the child chose the wrong position or order of the series of dots. Similar to the DSF, out of 6 possible attempts, once the child scored 4 correct attempts, they progressed to the next block of the test (the highest block being 6 with 6 consecutive dots).
The maximum score for the DMF was 24, implying that the child progressed to the 6th block
(scoring 4x correctly in each block).
Dot Matrix Backwards (DMB): Visuo-spatial working memory was measured using the backwards condition of the Dot Matrix task again based on the Automated Working Memory Assessment (AMWA; Alloway, 2007). The backwards condition was similar to the forward condition but different in that the child was prompted to identify the series in reverse order by referring to the frames in which the dots appeared from last to first. Identically to the DMF, the DMB maximum score was 24.
iv. Reading Ability
EMT (Een-Minuut Test): The EMT (Brus & Voeten, 1973) is a Dutch reading test in which a consecutive list of 116 words is read out by the participant, as fast as possible, within one
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minute. As the test proceeds, the words are organised in such a way that the difficulty level increases. The amount of correctly read words in this one minute equates to the raw score on the EMT. The score is calculated as the amount of correct words read out in one minute. The target group for this test is Dutch elementary school students. The EMT measures ability to recognise real words, as a measure of reading ability.
Klepel: The Klepel (Van den Bos, Lutje Spelberg, Scheepstra, & de Vries, 1994) is another Dutch reading test in which ability to read pseudo-words is tested. In a similar fashion to the EMT, 116 pseudo-words are read off as quickly as possible. The test aims to separate two possible processes of technical reading in that it measures ability to translate letters and letter-groups into sounds. These are linked to a word instead of measuring the immediate, one-time recognition of a real word which may rely less on individual letter/letter-group recognition. This might be the case in the EMT more often than not. The test is meant for children from the 1st to the 6th grade in elementary schools in the Netherlands and is typically administered directly after the EMT.
EMT + Klepel: Both the EMT and Klepel have previously been used as measurements of reading ability when examining LI children (de Bree & Wijnen, 2008; Rispens & Been, 2008; Parriger & Rispens, 2008). The combination of the two tests used in dyslexia diagnosis is one of the recognised methods of identifying dyslexia approved by the Stichting Dylsexie Nederland (SDN). The combination of the two tests measures both the ability to read real words and that of pseudo-words.
V. Analysis
The first step taken was to select several variables from the larger battery and enter them into SPSS 23 (IBM Corp., 2013). From this larger sample, the children with LI were selected to be used in the analysis including their subject code, age and several other descriptive variables. A LiteracyTotal variable was then computed using a combination of the ‘EMT_Correct’ and ‘Klepel_Correct’ variables (from Wave III) by adding the scores of each individual variable into one score, which was selected as the dependent variable. Following this, a correlations table was produced (as shown in the Results in Table 1). Combining the theoretical expectations with the sufficient correlation with LiteracyTotal resulted in the
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choice to include Digit Span Forward, Digit Span Backwards, Dot Matrix Forward and Dot Matrix Backwards as independent variables (Table I). None of the variables correlated with each other above .7 (which might suggest multicollinearity), and for all variables, the assumptions of collinearity including Tolerance and VIF were met. The criteria for the assumption of multicollinearity required that the VIF value be under 10 and the Tolerance value be larger than .1 (Field, 2013).
With the independent variables and dependent variable selected, a multiple linear regression analysis was conducted, using Digit Span (Forward & Backwards) and Dot Matrix (Forward & Backwards) with WNIV as a control variable of non-verbal intelligence. The first block consisted of WNIV and reading ability with the remaining four variables entered in the second block. The choice to control for non-verbal intelligence was motivated in the hope to isolate the effect of the measures of interest (Digit Span tasks & Dot Matrix tasks) on literacy ability. Looking at the individual predictor variables, an analysis of standard residuals carried out revealed that the data contained no noticeable outliers (Std. Residual Min =-1.96, Std. Residual Max =2.94). According to the commonly used criteria, this is acceptable as they did not exceed +3 or -3 (Blatná, 2006). Visual inspection of the histogram and P-P plot of standardised residuals and the scatterplot of standardized residuals showed no abnormalities apart from slight deviations from the expected values. Additionally, both the standardised and unstandardized residuals were both non-significant according to the Shapiro-Wilk statistic indicating they were normally distributed. The data also met the assumption of non-zero variances. The assumption here is that the values for the variance are not 0. As a result, no notable exceptions to the assumptions of multiple linear regression were observed.
VI. Results Table I:
N Minimum Maximum Mean Std. Deviation DigitSpanForward 70 8 24 15.89 3.47
DigitSpanBackwards 70 5 17 9.19 3.36
DotMatrixForwards 70 5 25 13.29 4.16
DotMatrixBackwards 70 5 24 11 5.03
15 | P a g e Table II: Correlations DigitSpan Forward DigitSpan Backwards DotMatrix Forward DotMatrix Backwards Literacy Total DigitSpanForward Pearson Correlation Sig. (2-tailed) N 1 70 .557** .000 70 .325** .006 70 .446** .000 70 .561** .000 70 DigitSpanBackwards Pearson Correlation Sig. (2-tailed) N .557** .000 70 1 70 .404** .001 70 .643** .000 70 .482** .000 70 DotMatrixForward Pearson Correlation Sig. (2-tailed) N .325** .006 70 .404** .001 70 1 70 .559** .000 70 .509** .000 70 DotMatrixBackwards Pearson Correlation Sig. (2-tailed) N .446** .000 70 .643** .000 70 .559 .000 70 1 70 .525** .000 70 LiteracyTotal Pearson Correlation Sig. (2-tailed) N .561** .000 70 .482** .000 70 .508** .000 70 .525** .000 70 1 70 **Correlation significant at the 0.01 level (2-tailed)
*Correlation significant at the 0.05 level (2-tailed)
Correlation and multiple linear regression analyses were conducted in order to examine the relationship between several phonological and visuo-spatial memory predictor variables and literacy ability two years later. All predictor variables in both models are positively correlated with LiteracyTotal indicating that increased scores in these tests imply a higher LiteracyTotal score. The multiple linear regression model with the four predictors (in the second block; the first block being non-verbal IQ) resulted in adjusted R2 = .42, F(5, 64) = 11.171, p < .000. This result suggests the model is statistically significantly able to predict LiteracyTotal scores from the combination of the control variable and four predictor variables. Individually, DigitSpanForward significantly predicted LiteracyTotal significantly β = 3.478, p < .01 (visualised in Figure III. Below), and DotMatrixForward similarly significantly predicted LiteracyTotal β = 2.067, p < .05. This implies that those with higher scores on these tests were expected to have higher LiteracyTotal scores. DigitSpanBackwards and DotMatrixBackwards did not contribute significantly to the model.
16 | P a g e Table III.
Variable Mean std p-value Correlation with Literacy
Total
Multiple regression weights b β DigitSpanForward 15.89 3.47 .002 .56 3.03** 0.31 DigitSpanBackwards 9.19 3.36 .734 .48 0.50 0.05 DotMatrixForward 13.29 4.16 .025 .51 2.25* 0.28 DotMatrixBackwards 11.00 5.03 .286 .53 1.11 0.17 * p < .05 ** p < .01 ***p<.001 Figure II:
Figure III: Figure IV:
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VII. Discussion
The current study aimed to investigate visuo-spatial memory as a predictor of later literacy ability as an addition to the commonly considered phonological memory among LI children. In addition, the current study differentiated short-term and working memory to see if they differentially predict literacy ability. The following will be a discussion of the model as a whole and its role in predicting reading ability, a breakdown of the individual predictors, their contribution to the model in relation to the hypotheses, implications thereof, and a consideration of the LiteracyTotal score as an outcome variable. Finally, the results will be discussed in terms of larger possible theoretical considerations.
Evaluating the model based on the adjusted R2 value of .42, it appears the model was able
to account for approximately 42% of the total variance in the data. According to the F-test, there was a significant linear relationship between the four input variables and LiteracyTotal (R2 =.42, F(5, 64)= 11.17, p < .000). Just under half of the variance in LiteracyTotal scores was thus explained by this combination of DigitSpanForward, DigitSpanBackwards, DotMatrixForward & DotMatrixBackwards. This can therefore be taken as evidence that working memory (both phonological & visuo-spatial) is important, at least to an extent, in the prediction of later reading ability. It is however important to further break down this relationship and gauge which tasks were the most important and which were not. It was not the case that all individual predictors were of equal statistical significance, some were markedly more important in explaining variance, while others were statistically insignificant. As a whole, the measures of short-term memory (both phonological and visuo-spatial) were significant while the working memory measures (again both phonological and visuo-spatial) were not. Whether this reflects the role of working memory in literacy in general terms or if it reflects the literacy task chosen in this study will also be discussed below.
Although DigitSpanForward was not the only measure of phonological short-term memory available in the dataset as a whole, it was selected over Non-Word Repetition (NWRT) for several reasons. It has been suggested that, for example, response accuracy may be lower in non-word repetition tasks as the verbal output demand arguably relies more on rapid and co-articulated speech gestures which may be more difficult for certain LI children with subtle speech motor impairments to perform (Archibald & Gathercole, 2007). As such it
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makes sense that children with LI perform worse on non-word repetition tasks (Botting & Conti-Ramsden, 2001), however this does not imply that the underlying explanation is solely due to impaired short-term memory. In line with such logic, a study by Archibald & Gathercole (2007) found the LI deficit in non-word repetition to be greater than serial recall (Digit Span is an example of a serial recall task) even when differences on an independent short-term memory task were taken into account. The authors concluded the source of the poor performance in NWRT is not only due to deficits in phonological short-term memory. For the reasons listed above, the decision was made to select DigitSpanForward as the measure for phonological short-term memory in an extra effort to isolate the concept of phonological STM from other possibly distracting contributing skills. Digit Span arguably places no focus on the particular articulation skills involved in an unknown combination of sounds, but tests the phonological short-term memory ability to retain sequences of numbers.
DigitSpanForward, as a measure of phonological short-term memory, was a significant predictor of LiteracyTotal and, importantly, the most significant individual predictor in the model. This is concurrent with much of the previous literature showing phonological short-term to be an important predictor of later reading ability (Conti-Ramsden & Durkin, 2007; Dufva et al., 2001; De Jong & van der Leij, 1999; Muter & Snowling, 1998) when phonological memory is examined. Combining this with the fact that phonological STM is often found to be deficient among LI children (Gathercole & Baddeley, 1990; Dollaghan & Campbell, 1998; Botting & Conti-Ramsden, 2001), the expectation was that this would be a significant predictor of literacy ability in the model. Given phonological STM is frequently impaired among children with LI and its role in predicting reading ability; the expectation was that this would be a significant predictor. For the relatively few studies that have investigated phonological short-term memory among LI children in relation to literacy ability, phonological STM has shown to be important (Catts et al., 2005), along with other predictors such as Phonological Awareness (PA). In a more recent study by Kibby, Lee & Dyer (2014), when predicting reading performance among a mixed sample of children between 8-12 years old, they found the Digit Span Forward measure they employed to predict two subtests of the Woodcock–Johnson Tests of Achievement–Third Edition (WJ-III Form A; Woodcock et al., 2001), specifically the Decoding & Word Identification subtests.
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The relevance hereof is that that these tasks specifically reflect abilities similar to the EMT and Klepel used in this study. Both sets of tests involve the identification of real & pseudo-words at a relatively fast pace, therefore the current study can be taken as further evidence phonological short-term memory plays a certain role in predicting ability at literacy tests such as these.
The current study however did not concern separately examining phonological STM deficits in LI children and phonological STM in reading disabled children, the specific aim was to examine how phonological STM (among other predictor variables) predicted literacy ability among a sample of LI children. Therefore, this study addressed a relative lack in the literature examining the specific role of phonological STM among LI children and its role in predicting literacy ability for this specific population. Much of the current literature on the role of phonological STM in predicting literacy ability among LI children is based on investigations into phonological STM deficits in LI children and, separately, phonological STM deficits in reading impaired children. Such results on separate groups of children might imply this to also be the case among children with both LI and literacy issues, but to suggest that this can be found in longitudinal data on LI children is an assumption being made. One of the points the present study addresses is the relatively sparing literature addressing this topic in LI children.
In addition to phonological STM, the model showed that visuo-spatial STM might play an important role predicting literacy ability as well. In the model, DotMatrixForward proved to be a significant predictor, being the only other significant predictor alongside DigitSpanForward. Given certain studies showing visuo-spatial STM to be impaired among reading disable children (Gathercole et al., 2006) and visuo-spatial STM to be impaired among young LI children (Hick, Botting & Conti-Ramsden, 2005), the expectation was that visuo-spatial memory might be a statistically significant predictor of reading ability, when examining the current sample of LI children. In a similar fashion to the research on phonological STM, the larger body of literature concerning visuo-spatial STM and its relation to literacy ability is separate to that of the literature on visuo-spatial STM and LI. Not many studies have combined the two populations and measured the role of STM and/or working memory in general. As a result, although visuo-spatial STM has been shown to relate to
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literacy ability, it has not extensively been explored among LI children and if it additionally acts as a predictor of literacy ability among this sample of children.
In another more recent study by Alloway et al. (2017), they examined the cognitive profiles of children with dyslexia and children with LI and their relation to learning. Importantly, they found the cognitive profiles concerning working memory (phonological & visuo-spatial) and IQ (verbal & non-verbal) to be very similar among LI and dyslexic children. Such a result strengthens the motivation as to why working memory (both phonological & visuo-spatial) would be related to reading ability. If a child with LI displays similar general working memory ability to a child with dyslexia, it is plausible working memory will predict literacy ability in a child with language impairment. Interestingly, the authors of this paper used the Automated Working Memory Assessment (AWMA; Alloway, 2007), in which the phonological working memory test consists of a Processing Letter Recall test. In this test, the participant was visually presented a letter followed by the sound of that letter, prompting them to confirm if the visual and verbal stimulus matched. Although there is an element of management involved in this, this arguably largely taps phonological STM, especially when compared to the Digit Span Backwards task. For this reason, the argument is made that this is an example of a working memory assessment that largely also represents short-term memory.
The finding that visuo-spatial STM is impaired in children with reading difficulties has been found in studies previously (Menghini et al., 2011; Wang & Gathercole, 2013) yet, the current study is at odds with results that suggest this is due to an over-arching processing deficit causing problems issues in complex tasks, as argued by Wang & Gathercole (2013). This inconsistency is due to the clear insignificance of the backwards conditions of the Digit Span & Dot Matrix tests in the current study. To have obtained results backing this hypothesis, we would expect to see the backwards conditions of the Digit Span & Dot Matrix to also be statistically significant, or at least appear to play some role in the prediction of literacy ability. As a result, the implication hereof is that it is possible that the short-term memory deficits seen in LI children and their predictive power on literacy ability are not limited to phonological STM memory, but are reflective of a possibly more general STM deficit. The results in the current study suggest a deficit in the retention of both verbal and visuo-spatial stimuli. The results however suggest that, among the present sample of LI
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children, the deficit in phonological and visuo-spatial STM and its role in predicting literacy ability does not extend to working memory. It must however be mentioned that it is likely that working memory plays a role in predicting different types of reading ability, as discussed below.
In the model, both DigitSpanBackwards and DotMatrixBackwards were not statistically significant predictors of LiteracyTotal. Given this result, it is suggested that working memory measures are at least not as important as short-term memory measures for the current sample when looking at literacy outcomes chosen. In sum; working memory, does not significantly predict literacy ability several years later when measured through the combined score on the Klepel and EMT tests. Adhering to the deliberate distinction made between short-term memory and working memory in the current study, it seems short-term memory is a far better predictor of the LiteracyTotal measure among LI children. As such, the tasks involving the duality of storage and updating of information did not seem to relate to literacy as measured in this study as did tasks concerning the simple storage. It is however important to discuss this result in context of the literacy outcomes used in the current study. As mentioned earlier, in a study by Kibby, Lee & Dyer (2014), they found phonological STM and phonological working memory measured by way of Digit Span tasks (Forward & Backwards) to differentially predict aspects of reading. In this study, they found Digit Span Forward to predict Word Identification and Decoding and Digit Span Backwards to predict Decoding, Fluency and Comprehension but not Word Identification. These results suggest not every aspect of reading is equally predicted by Forward or Backward digit span tasks. This is something the current study also found. Kibby, Lee & Dyer (2014) concluded that when utilising the model of working memory by Baddeley (1986), it seems the phonological loop contributes to basic reading ability while the central executive seems to underlie reading fluency, comprehension & decoding.
In line with this, it must be recognised although that the Klepel and EMT are often used in tandem to diagnose dyslexia, both tests measure a very specific aspect of reading ability. Both tests concern the identification of pseudo/real words in relatively rapid succession. Neither test tap comprehension ability, processing of syntactic structure or any of the many other skills involved in reading. As such, the results cannot be simply taken as predictive of performance under all reading conditions. Consistent with the findings above by Kibby, Lee
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& Dyer (2014), it has been suggested that phonological memory might be more important in basic reading when compared to fluency or comprehension (Puolakanaho et al., 2008; Kibby & Cohen, 2008). Additionally, it seems logical that tasks (EMT and Klepel) involving relatively little mental management of material would not require an extensive amount of working memory ability. It would be a tenable assumption to assume that certain comprehension or manipulation tasks would draw upon the central executive over the phonological loop or visuo-spatial sketchpad. As such, it would be an overstatement to immediately suggest the findings in the current study point towards only STM and not working memory as a whole being reflective of reading ability. It does appear however that rapid word identification can be more efficiently predicted from phonological and visuo-spatial STM measures rather than from general phonological and visuo-visuo-spatial working memory measures, when examining LI children.
In general, the results of the current study suggest phonological and visuo-spatial short-term memory to be predictors of the chosen literacy measures. The strongest predictor was DigitSpanForward, a measure of phonological short-term memory. As for the role of phonological short-term memory in general, it has been suggested that its primary function is to support long-term acquisition of phonological structure of language (Baddeley, Gathercole, & Papagno, 1998). In addition, the significant associations between short-term memory and reading development (de Jong & van der Leij, 1999) suggest short-term memory is important in learning to read. Additionally, the current study would suggest short-term memory specifically plays a role in or is at least related to word identification (both of real words and of phonetically possible pseudo-words). Whether the predictive value of visuo-spatial memory is indicative of its importance in the act of reading or if it is simply a co-occurring example of generally deficient short-term memory is not clear. It is possible that visuo-spatial memory itself is important in learning to read and accurately identify written word, but such a statement requires additional research into the relationship.
Using the model of memory by Baddeley (1986), it is possible that LI children struggle with impaired phonological & visual short-term memory due to impairments in the phonological loop and visuo-spatial sketch pad. Considering neither DigitSpanBackwards nor DotMatrixBackwards were significant predictors, the current study would suggest
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impairments of the central executive are not as important in predicting scores on the Klepel and EMT. This is a result supported by Kibby, Lee & Dyer (2014) who suggested thecentral executive seems to underlie reading fluency, comprehension & decoding rather than basic reading ability. Other studies have suggested a more central executive deficit underlies dyslexia (Wang & Gathercole, 2013; Smith-Spark & Fisk, 2007). This is however contextualised by the fact that, as mentioned previously, the LiteracyTotal variable is not an expansive demonstration of all skills involved in reading and because it can be argued that neither DigitSpanBackwards nor DotMatrixBackwards truly capture the full extent of the central executive and all of what it is responsible for. Additionally, it is likely that the DigitSpanForward task contains certain elements of working memory, yet the contrast with the DigitSpanBackwards clearly illustrates the latter to be more processing/management oriented in comparison to the DigitSpanForward. According to the definition that the central executive is responsible for storage and retrieval and contributes to tasks placing simultaneous demands on processing & storage (Just & Carpenter, 1992), both DigitSpanBackwards and DotMatrixBackwards testing this arguably do tap into this, to a large extent. As a result, the current study cautiously suggests that, among LI children, short-term memory ability in general is more indicative of later reading difficulties (at least in word recognition) than are deficits of the central executive responsible for the management and updating of this memory.
Figure V: Working Memory (Source: Baddeley, 1986)
Additionally, the presence of both phonological and visuo-spatial short-term memory deficits lends itself to the notion that many instances of LI are characterised by more than
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just phonological short-term memory deficits. This implies LI children might struggle with a more general short-term memory deficit which in turn might cause the many deficits they face in language in general. Short-term deficits might also however be a consequence instead of the cause, but do imply a relative difficulty with short-term memory in general among LI children. Although causality is difficult to infer, the longitudinal nature, the relatively large sample of children and the results linking phonological and visuo-spatial STM with the literacy measures in the current study suggests the link between short-term memory and literacy is worthy of further investigation.
VIII. Conclusion
The straightforward interpretation of the results in the current study is that impairments in the phonological loop and visuo-spatial sketchpad (reflected by the DigitSpanForward and DotMatrixForward being statistically significant predictors) predict literacy ability among LI children. Considering the nuances of the previous research on predictors of literacy ability and the evidence showing different skills to predict the various sub-sections of reading, the conclusions cannot be drawn without caution. It is clear that although only the forward conditions of the Digit Span and Dot Matrix provide insight into predicting the LiteracyTotal score calculated in this study, that this is likely a reflection of how phonological and visuo-spatial STM affects scores on the EMT and the Klepel tests. The argument can however be made that the addition of visuo-spatial STM added predictive value to the model as DotMatrixForward was also a statistically significant predictor of the LiteracyTotal score. While it is difficult to make sweeping statements about the underlying factors causing literacy problems in LI children, it appears there is more at play in predicting literacy ability than purely just phonological STM. From the results in this study it appears there is specific value in considering phonological and visuo-spatial STM when predicting word recognition abilities later on in LI children. Early and accurate attention to such deficits could aid in early identification of which children with LI will struggle with reading and which will not.
a. Further Research
Some potential directions for further research aside from reproduction with other samples of children with LI could include more comprehensive tests of the central executive which is thought to govern and manage short-term memory. The current study cannot rule out the
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role or effect of such a system on short-term memory ability, even if both backwards conditions of the Digit Span & Dot Matrix tests were insignificant. In addition, clarifying the extent to which the possible relationship between short-term memory (phonological & visuo-spatial) and literacy ability is causally linked would be an important direction. The question as to whether impaired short-term memory causes poor literacy ability is still relatively unanswered. Finally, testing the links between short-term memory and other types of reading ability would be arguably very helpful. The measures used in the current study (EMT + Klepel) tested one specific very technical example of reading, but are perhaps not entirely indicative of other contexts in which reading occurs in. Other measures testing reading ability could include fluency, comprehension and etc. To make global statements about predictors of literacy ability in general among LI children would require a multitude of skills as outcome measures.
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