Frequency Discrimination and Reading

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Frequency Discrimination and Reading

RUNNING HEAD: Frequency Discrimination and Reading

Language Skills,but not Frequency Discrimination,Predict Reading Skills in Children At Risk of Dyslexia

Margaret J. Snowling

University of Oxford

Debbie Gooch

University College London

Genevieve McArthur

Macquarie University, Australia

Charles Hulme

University of Oxford

Abstract

This study evaluated the claim that auditory processing deficits are a cause of reading and language difficulties. We report a longitudinal study of245 children at family risk of dyslexia, children with preschool language impairments, and controls. Children with language impairmentshad poorer frequency discrimination thresholds than controls at 5½ years but children at family risk of dyslexia did not. A model assessing longitudinal relationships between frequency discrimination, reading, language, and executive skills showed that frequency discrimination was predicted by executive skills but was not a longitudinal predictor of reading or language skills. Our findingscontradict the hypothesis that frequency discrimination is causally related to dyslexia or language impairment, and suggest that individuals at-risk for dyslexia, or who have language impairments, may perform poorly on auditory processing tasksbecause of comorbid attentional difficulties.

FREQUENCY DISCRIMINATIONAUDITORY DEFICITS

RISK OF DYSLEXIALANGUAGE DISORDEREXECUTIVE SKILLS

Language Skills,but not Frequency Discrimination,Predict Reading Skills in Children At-Risk of Dyslexia

Developmental dyslexia is a learning disorderprimarily affectingthe ability to learn to read and spell. Thepredominant causal explanation for dyslexia is that it reflects a phonological deficit (Melby-Lervåg, Lyster, & Hulme, 2012; Vellutino, Fletcher, Snowling, & Scanlon, 2004).It has been suggested (e.g., Tallal, 1980) thatthis phonological deficit arises from low-level auditory impairments (auditory problems -> speech perception problems -> phonological problems -> reading and language problems;see Goswami, 2015;Schulte-Körne& Bruder, 2010 for reviews). Support for this "auditory processing deficit" theory comes from studies that compare children with dyslexia to controls on nonverbal auditory tasks, in particular tasks which tap parameters that are critical for speech perception such as frequency (pitch) discrimination and sensitivity to syllable duration and amplitude rise time. Hämäläinen, Salminen and Leppanen (2012) calculated effect sizes for group differences between dyslexic and control childrenon auditorytasks assessing frequency discrimination, frequency modulation, intensity discrimination, amplitude modulation, rise time, stimulus duration and gap detection. The largest differences between control and dyslexic children were for the perception of stimulus duration (d= .9), rise time (d= .8) and frequency discrimination (d= .7) and each of these measures correlated with reading skills.

A critical limitation of most studies that have tested the auditory processing deficit hypothesis is that they are concurrent studies employing extreme groups. That is, they simply compare auditory processing in a group of children or adults with dyslexia to a control group matched in age or reading ability. Such studies can demonstrate that poor auditory processing is associated with dyslexia, but they cannot provide any convincing support for the theory that poor auditory processing causes dyslexia. In contrast, longitudinal studies of children that start prior to reading instruction provide much stronger tests ofsuch a causal theory since they allow us to assess whether early deficits in auditory skills predict later reading and language difficulties before learning to read has exerted reciprocal effects on auditory processing (Bishop, Hardimaan & Barry, 2012).

Twolongitudinal studies assessing auditory processing very early in development in children at family risk of dyslexiaare particularly relevant here (for reviewssee Leppänen, et al., 2012; van der Leij, van Bergen, van Zuijen, de Jong, Maurits & Maassen, 2013). Both used neurophysiogical method. Leppänen et al. (2010) compared the mismatch negativity (MMN) event-related potential responses of 22 newborn children at family risk of dyslexia and 25 controlsin a task assessing sensitivity to changes in the frequency of sounds. The MMN response in newborns correlated with preschool phonological skills and letter knowledge and with Grade 2 measures of speech perception, reading and spelling (rs = .3-.4). However, while there were group differences between the ‘at risk’ children and controls in the sizeof the MMN response in the newborn period, these did not predict which of the ‘at risk‘ children would later become dyslexic.

In a similar vein, van Zuijen et al., (2012) investigated temporal processing in 17 month-oldat risk (N=12) children and control (N=12) families using the MMN response to changes in the inter-tone intervals. At 17 monthsonly the controls but not the at-risk children showed a MMN response. In this study, the amplitude of the MMN response predicted later word reading fluency (r=.52) but, surprisingly, not phonological awareness. Using children from the same longitudinal cohort, Plakas, van Zuijen, van Leeuwen, Thomson, & van der Leij(2013) assessed frequency discrimination and sensitivity to onset rise-time in children aged 41months. Correlations between MMN measures of sensitivity to rise time and frequency discrimination and later reading were weak (rs~.2-.4). Again, the correlation with phonological awareness was not significant. Combined with the findings of Leppanen et al. (2010), these results suggest that there are differences in neural responses to auditory stimuli between preschool children at family risk of dyslexia and controls. However, the sample sizes are typically small (‘at-risk’ Ns 8-34; ‘control’ Ns 11-39; Leppänen et al., 2010; Plakas et al., 2013) and evidence for associations with later reading skills is inconsistent.

A similarly mixed picture comes fromstudies investigating auditory processing in ‘at-risk’ samples at around school entry. Maurer, Bucher, Brem, and Brandeis (2003) found an attenuated MMN responseto frequency differences of 30 to 60 Hz in 6-year-olds at family risk of dyslexia, but did not follow the children’s reading at a later stage. Boets and colleagues (Boets, Ghesquière, van Wieringen, & Wouters, 2007; Boets, Wouters, van Wieringen,Ghesquière, 2006) measured thresholds for gap detection, frequency modulation and tone-in-noise detectionin 5-year-olds at family risk of dyslexia. They found no statistically significant group differences on any measure (d = .20–.36), though a higher proportion of ‘at-risk’ children scored more poorly than controls. When assessed in Grade 1,the children from this sample who were literacy-impaired (N=9) had shown poorer frequency modulation at age 5years but did not differ from controls in gap detection or tone-in-noise detection (Boets et al., 2007).

In summary, few longitudinal studies haveassessed the causal hypothesis thatearly problems in auditory processing are related to later reading and language difficulties. Mostsuffer from small sample sizes andprovide little detail of the characteristics of the children studied. This is important because dyslexia commonly co-occurs with a range of other disorders, such as language impairment and attention deficits. There is evidence that children with language impairment score poorly on the same indices of auditory processing as children with dyslexia (e.g., McArthur & Bishop, 2004; Sharma, Purdy, & Kelly, 2009). There is also evidence that auditory processing deficits may be a consequence of attentional (executive) deficits which are comorbid with dyslexia, language disorder, or conceivably, both (e.g. Gooch, Hulme, Nash, & Snowling, 2014; Henry, Messer, & Nash, 2012). Longitudinal studies with adequate sample sizesare needed to tease apart the predictive associations between auditory processing and later reading, spoken language, and attentional skills.

In the current study, we use data from a large longitudinal study of children at familyrisk of dyslexia, children with apreschool language disorder, and typical developing controls. We assessedauditory processing, reading, oral language, and attention when they were 4½, 5½, and 8-years-old. We chose a frequency discrimination task to measure auditory processing because the ability to resolve rapidly changing frequency information is critical to speech and phonological processing and deficits on such measures have been strongly associated with dyslexia in previous studies.We measured frequency discriminationin the early stages of reading development(ages 4½ and 5½ years) because,if auditory processing plays a causal role in reading acquisition (and hence dyslexia), its impact should be seen shortly after a child begins to receive formal reading instruction. We also measured executive skills since auditory tasks are attention-demanding andchildren with dyslexia might perform poorly on such tasks because of co-occurring attentional deficits rather than specific auditory problems (Breier, Fletcher, Foorman, Klaas, & Gray, 2003; Halliday, Taylor, Edmondson-Jones, & Moore, 2008; Sutcliffe, Bishop, Houghton, & Taylor, 2006).

The study had the following aims:

1. To assess whether poor frequency discrimination (FD) is associated with familial risk of dyslexia, language impairment, or both. We chose a task typical of those used to measure frequency discrimination in studies of dyslexic readers (23 studies comprising 554 control and 582 reading disabled participants; mean effect size Cohen'sd = 0.7; Hämäläinen et al., 2012). The samples of children with dyslexia, language impairment, and typically developing controls were large enough (at least 64) to detect an effect of this size with a power of 0.8.
2. To assess the longitudinal relationships between frequency discrimination, executive function, language, and reading. We examined these relationships using latent variable models to control for measurement error. The FD task has been shown to be particularly sensitive to auditory processing impairment in children with poor reading or spoken language (McArthur & Hogben, 2012). We included measures of oral language because it is plausible that anyeffect of auditory processing on reading ismediated by effects on oral language skills.
3. To investigate the possibility that top-down processes on auditory processing have a role to play in predicting performance in the frequency discrimination task (Schulte-Körne & Bruder, 2010)we included measures of executive function at 4½ (t2) as possible predictors of performance at 5½ (t3). We did not, however, expect executive skills to predict language or reading (e.g., Gooch, Thompson, Nash, Snowling & Hulme, 2016).
4. We predicted that children at family risk of dyslexia and children with language impairmentwould show poorer frequency discrimination than controls. Most critically, however, if variations in frequency discrimination are causally related to language or reading,frequency discrimination at age 4½ years should be a longitudinal predictor of languageor reading skills.

Method

Ethical permission for the study was obtained from the University of York, Department of Psychology’s Ethics Committee, and the NHS Research Ethics Committee. Informed consent was given byparents for their child’s participation in the study.

Participants

The project recruited children at family risk of dyslexia (FR), children with preschool language impairment (LI), and typically developing controls (TD) and assessed them at approximately yearly intervals: time 1 (t1; ~3½ years) time 2 (t2; ~4½ years), time 3 (t3; ~5½ years), time 4 (t4; ~6½ years), and time 5 (t5; ~8 years). At t1, 245 children entered the study; between t1 and t2, 15 children withdrew and an additional 15 children were recruited (one of whom did not fulfil criteria for FR or LI and was excluded from group comparisons). At t2(4½ years) the total sample comprised 245 children (241 at t3) (see Supplementary online materials, Figure 1 for diagram of participant flow). The sample size was determined by the practicalities of participant recruitment and is substantially larger than most earlier studies which have reported medium to large effect sizes for group differences. The subset of data used in the current study focuses on three time points: t2 (4½ years), t3 (5½ years), and t5 (8 years).

None of the children met exclusionary criteria (MZ twinning, chronic illness, deafness, English as an additional language, care provision by local authority, and known neurological disorder e.g. cerebral palsy, epilepsy, and ASD). The children were classified into groups using a two-stage process, first determining whether they were at family risk (FR) of dyslexia because they had an affected parent or sibling and then using diagnostic criteria to determine whether they had a language impairment. A child was regarded as language impaired (LI) if they obtained a below-average score on two out of four tests, namely: language comprehension, vocabulary, grammar and morphological inflection(see Nash, Hulme, Gooch, & Snowling, 2013 for details). This procedure yielded four groups: FR-only (N = 86), LI-only (N = 36), FR-LI (N = 37), TD (N = 71). Here we pool data from the FR-LI and LI-only groups because there were no significant differences between the two subgroups on preschool measures of language. This resulted in the following groups: TD (N= 74), FR (N=91; 3 withdrew at t3),orLI (N=64). We used these three groups to assess whether poor frequency discrimination is associated with familial risk of dyslexia, preschool language impairment, or both. To investigate longitudinal relationships between frequency discrimination, reading, language, and executive function – we included data from an additional 15 children who had been referred to the study by parents or therapists with concerns regarding speech and language development but who did not meet strict inclusionary criteria for LI at t1( these 15 children had weak language skills for their age; they were similar to the FR group in nonverbal IQ and on measures of receptive grammar and vocabulary, but weaker than them in sentence repetition and morphological inflection). The inclusion of data from these children is justified because language skill was a continuous measure in the latent variable models.

Procedure

At age 4½ (t2), assessments were conducted at home during two one-hour sessions with breaks as necessary. Assessments at age 5½ (t3) and 8 (t5) were conducted at school. Testers were postdoctoral and doctoral assistants who were employed throughout the study, and had substantial experience from initial assessments of the children (usually the same child) a year earlier, as well as in clinical child assessment. Training to deliver the test battery was under the supervision of the lab manager. Written protocols were prepared for each test and the lab manAger then ran through the battery. Once the testers had familiarised themselves with the test protocols, there were given individual feedback on test administration. Training for the compter-generatedFD task was intensive to ensure all testers could oversee the running of the experimental programme.

Language Measures

Grammar. At age 4½ (t2) and 5½ (t3) years, we measured receptive and expressive grammatical skills. In Sentence Structure (CELF-Preschool 2 UK; Semel, Wigg, & Secord, 2006a(t2); CELF-4 UK; Semel, Wigg, & Secord, 2006b(t3)), the child heard sentences of different syntactic structures and had to select, from a choice of four, the picture that conveyed its meaning. In aSentence Repetition test designed for the project, the child had to repeat 20 sentences varying in length (short versus long) and complexity (transitive versus ditransitive; e.g. “a lady pushed the bike to work” and “the busy teacher promised the clever boy a sticker”). The total number of sentences repeated correctly was recorded.

Vocabulary. At 4½ (t2), children completed theReceptive One Word Picture Vocabulary Test (ROWPVT; Brownell, 2000). The child heard a word and was asked to select the corresponding picture, from a choice of four.At 5½ (t3), children completed anExpressive Vocabularymeasure(CELF-4 UK; Semel et al., 2006b), where the child was asked to name objects or to describe what aperson is doing.

Reading Measures

Regular and Irregular Word Reading. At age 4½ (t2) and 5½ (t3), children completed theEarly Word Readingsubtest from the York Assessment of Reading for Comprehension(YARC; Hulme et al., 2009). The child read aloud 30 single words, graded in difficulty. Half of the words were phonemically regular (decodable), and the other half were irregular. Each correct response scored 1 point; testing was discontinued if the child made 10 consecutive reading errors.

Single Word Reading. At 5½ (t3) and 8 (t5), children completedthe YARC Single Word Readingtest (Hulme et al., 2009), which involvedreading a list of 60 words of increasing difficulty. Testing was discontinued after fiveconsecutive errors/refusals.At age 8 (t5), they completed the Exception Word subtest from the Diagnostic Test of Word Reading Processes (Forum for Research in Language and Literacy, 2012).

Nonword Reading.At age 8 (t5), children completedthe Graded Nonword ReadingTest(Snowling, Stothard & McLean, 1996) (t5) which involved reading20 nonwords (10 one-, 10 two-syllables).

Executive Function Measures

Visual Search. At age 4½ (t2), children completedthe Apples Task(Breckenridge, 2008). The child was given oneminute to search an array to identify targets (18 red apples) whilst ignoring distractors (81 red strawberries and 81 white apples). The number of targets identified and the number of commission errors made (pointing to a distractor; false alarms) were recorded. A visual search efficiency score (total targets correctly identified – commission errors)/60 seconds) was calculated; a high score reflects better selective attention.

Self regulation. At age 4½ (t2), children completed theHead Toes Knees and Shoulderstest (HTKS;Burrage et al., 2008). In this measure of behavioural inhibition,the child had to do the opposite of what the examiner said (e.g. touch their toes if asked to touch their head and vice versa). If the child was able to inhibit on 5/10 trials, they went on to complete a further block of 10 harder trials with additional commands (e.g. to touch their shoulders if asked to touch their knees and vice versa). Each correct response received twopoints. Self-corrected responses (partial inhibitions, whereby the child moved towards the incorrect, intuitive response but demonstrated the correct final response) received 1 point. (maximum score = 40).

Visuo-spatial Memory. At age 4½ (t2), children completed Block Recall (Working Memory Test Battery for Children, Pickering & Gathercole, 2001)a measure of visuo-spatial memory.The child saw the examiner tap a sequence of blocks on a board and then recalled the sequence by tapping the blocks in the same order. The task was discontinued after two consecutive failures for sequences of the same length (maximumscore 52).

Frequency DiscriminationMeasure

Frequency discrimination was measured at age 4½ (t2) and 5½ (t3) years using a task based on one shown by McArthur, Ellis, Atkinson, and Coltheart (2008) to be highly sensitive to deficits in dyslexic children. This task has good reliability across time and correlates well with other measures of frequency discrimination(McArthur & Bishop, 2004). The task is an adaptive three-interval, two-alternative forced choice AXB procedure with a maximum of 60 trials. Each trial comprised three 100ms pure tones (including 10ms offset ramps) presented at 83dB SPLand separated by an ISIof 300ms. The“standard” tone (X) set at 1000Hz was always presented as the second tone. In each trial, either the first tone (A) or third(B) tone was randomly allocated to match the frequency of the standard tone. The remaining tone became the “target” tone that was set at a higher frequency than the standard tone using a modified PEST procedure (Taylor & Creelman, 1967). There were 100 different possible target tones ranging from 1005-1500Hz in 5Hz steps. This rangeis commonly used in discrimination tasks because it represents the approximate range of the first two formants of many speech sounds (the most important formants for speech recognition). In early trials, the PEST procedure ensured that trials were relatively easy by allocating a large frequency difference between the standard and target tones (i.e., the target tone was set at 1500Hz). After two consecutive correct responses, the algorithmreduced the frequency difference in large step sizes (200 Hz) until an error was made. At this point –called a “reversal”– the algorithm decreased the step size (e.g., to 100 Hz) and made the discrimination easier by increasing the frequency of the target tone relative to the standard tone. The step size was halved progressively with each reversal. The smallest step size was 5 Hz. This final step size was chosen instead of a more typical final step size of 0.1 Hz because our sample was much younger (4½years) than those in previous studies (9+ years) and hence had less fine-grained frequency discrimination.