Attention and basic literacy and numeracy in children

Research in Autism Spectrum Disorders 9 (2015) 193–201

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Attention and basic literacy and numeracy in children with Autism Spectrum Disorder: A one-year follow-up study T. May a,b,*, N.J. Rinehart b, J. Wilding c, K. Cornish d a School of Psychological Sciences, Monash University, Building 17, Monash University Clayton Campus, Wellington Rd, Clayton, Victoria 3800, Australia b School of Psychology, Deakin University, Melbourne Burwood Campus, 221 Burwood Highway, Burwood, Victoria 3125, Australia c Royal Holloway, University of London, UK d School of Psychological Sciences & Monash Institute for Brain Development & Repair, Building 17, Monash University Clayton Campus, Wellington Rd, Clayton, Victoria 3800, Australia

A R T I C L E I N F O

A B S T R A C T

Article history: Received 26 August 2014 Accepted 13 October 2014 Available online 15 November 2014

Little is known about the link between Executive Functioning (EF) and academic performance in children with Autism Spectrum Disorder (ASD) and how such links develop over time. This study examined word reading, basic mathematics, attention switching, sustained attention and their development. Two age, gender and perceptual IQ matched groups of cognitively able 7–12 year olds (ASD N = 40; typical developing [TYP] N = 40) were assessed at baseline and one year later, completing Word Reading and Numerical Operations tests and computerized tasks tapping attention switching and sustained attention. Children with ASD had similar word reading and numerical operations performance and similar development of these skills relative to TYP children. A delay in attention switching but similar development was found in children with ASD relative to TYP children. The EF tasks were correlated with reading and mathematics in ASD children only, however, in regression analyses these factors were not significant predictors of Time 2 reading and mathematics after accounting for Time 1 reading and mathematics scores. These findings indicate similar word reading and mathematics development but atypical attention profiles in cognitively able children with ASD. Implications for educators are discussed. ß 2014 Elsevier Ltd. All rights reserved.

Keywords: Autism Spectrum Disorder Literacy Numeracy Attention switching

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition typified by social and communication difficulties and restricted interests (American Psychiatric Association, 2000, 2013). In childhood, these deficits present as significant difficulties in communicating with, and socially relating to, others. Repetitive behaviours may manifest as difficulties in changing between activities, obsessions with particularly focused interests, and rigid adherence to routines. After parents have coped with the diagnosis of ASD and associated early intervention, adjustment to primary school often becomes the next major challenge for families of cognitively able children with ASD. Understanding how their child will perform academically and whether they can cope with the organizational demands of primary school are prime concerns of parents (Attwood, 2006). Presently, there is limited empirical research in these areas in children with ASD.

* Corresponding author at: School of Psychology, Deakin University, Melbourne Burwood Campus, 221 Burwood Highway, Burwood, Victoria 3125, Australia. Tel.: +61 3 92445084. E-mail address: [email protected] (T. May). http://dx.doi.org/10.1016/j.rasd.2014.10.010 1750-9467/ß 2014 Elsevier Ltd. All rights reserved.

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T. May et al. / Research in Autism Spectrum Disorders 9 (2015) 193–201

Past studies indicate largely preserved performance for basic academic tasks in cognitively able children with ASD, however, there are uneven patterns of idiosyncratic strengths and weaknesses (Chiang & Lin, 2007; Nation, Clarke, Wright, & Williams, 2006; Whitby & Mancil, 2009). Cognitively able children with ASD generally show intact basic reading skills (Nation et al., 2006) but impaired reading comprehension (Griswold, Barnhill, Myles, Hagiwara, & Simpson, 2002; Huemer & Mann, 2010; Jones et al., 2009; Minshew, Goldstein, Taylor, & Siegel, 1994). Mathematics achievement is generally similar to typically developing children but both areas of weakness with performance lower than that expected by IQ level (Chiang & Lin, 2007). Individuals with Asperger’s Disorder have been found to perform better on visual, hands on mathematics tasks compared to basic mathematical calculation (Griswold et al., 2002). There are very few studies tracking the development of academic skills over time, and even fewer examining what factors might account for both the strengths and weaknesses in these domains in children with ASD. Research on organizational difficulties in ASD has primarily examined a group of attentional components known collectively as Executive Functions (Pennington & Ozonoff, 1996). Executive Functioning (EF) is a broad term and includes the subfunctions of working memory, the ability to switch attention according to changes in the environment, the ability to sustain attention on the task at hand, and the ability to inhibit responses to irrelevant stimuli. These components of attention are important for planning and organizing goal-directed behaviour. The Executive Dysfunction theory of ASD proposes that deficits in these components of attention account for the behavioural symptoms of ASD (Pennington & Ozonoff, 1996). While not all studies show clear evidence that the ability to switch attention is universally impaired in ASD, most studies find deficits (see Ames & Fletcher-Watson, 2010; Kaland, Smith, & Mortensen, 2008; Reed & McCarthy, 2012) but see also (Kaland et al., 2008; Lopez, Lincoln, Ozonoff, & Lai, 2005; Sinzig, Morsch, Bruning, Schmidt, & Lehmkuhl, 2008). In contrast sustained attention appears generally intact in ASD (Goldstein, Johnson, & Minshew, 2001; Johnson et al., 2007). The rate of development of EF skills also appears atypical in ASD. A second wave of impairment in EF skills, such as switching attention, appears to occur in the second decade of life in ASD (Luna, Doll, Hegedus, Minshew, & Sweeney, 2007; Minshew & Williams, 2007; Rosenthal et al., 2013). Very few follow-up studies examining the rate of development in EF in ASD exist. Ozonoff and McEvoy (1994) examined a planning efficiency (Tower of Hanoi) and switching task in adolescents with ASD and found no improvement at three year follow-up in contrast to controls. Griffith, Pennington, Wehner, and Rogers (1999) found no improvement after one year on a cognitive flexibility task in preschoolers with autism but this was also the case for controls. In contrast, Pellicano (2010a) showed that although children with autism, aged 4–7 years, performed more poorly than controls on a set-shifting and planning task (Tower of London), there was greater gain in planning ability after 3 years than in controls. This suggested a lag in development plus a different rate of development compared with typically developing children. Atypical development of these attention components may have implications for downstream dependent functions, in particular, learning (Cartwright, 2012; Clark, Pritchard, & Woodward, 2010; Steele, Karmiloff-Smith, Cornish, & Scerif, 2012). EF components are vital for the development of academic skills in typically developing children (Bull & Scerif, 2001; Cartwright, 2012; Christopher et al., 2012; Espy et al., 2004; Foy & Mann, 2013). They serve as predictors of literacy and numeracy scores in preschool through high school in typical development (Clark et al., 2010; Steele et al., 2012). Early reading skills have been associated with cognitive flexibility and inhibitory control (Cartwright, 2012). Mathematics requires a range of cognitive skills such as the ability to shift attention, inhibit responses and working memory (Bull & Scerif, 2001). Despite well established impairments in EF in children with ASD, few studies have examined their impact on academic attainment. Our cross-sectional study found switching attention but not sustained attention concurrently predicted mathematics but not basic word reading in children aged 7–12 with ASD (May, Rinehart, Wilding, & Cornish, 2013). The purpose of the present study was to extend this cross-sectional study (May et al., 2013) to explore how literacy, numeracy and attentional skills develop over one year in primary school aged children with ASD. It is particularly important to track the development of EF and academic skills using relatively short timeframes given the speedy development of both reading and mathematics in the school years. For example, there is rapid improvement in basic reading skills during the early primary school years which reduces as children age (Skibbe et al., 2008). Similarly, numeracy skills develop rapidly in the early primary school years with measurable growth in time periods under 12 months (Jordan, Hanich, & Kaplan, 2003). We therefore aimed to follow-up the 7–12 year old group of children one year later to capture the rapid development of academic skills (Jordan et al., 2003) and to ensure the reliability of the Time 1 findings. The following research questions were posed: (1) Are there any differences in the development of word reading and mathematics skills over one year in children with and without ASD? (2) Are there differences in the development of attention switching and sustained attention in children with and without ASD over one year? (2) Do these attentional components predict word reading and mathematics one year later and is this relationship similar in children with ASD compared to typically developing children? The following hypotheses were made: (1) given atypical development of EF skills in children with ASD which underpins academic performance in typically developing children, children with ASD would differ in their word reading and mathematics attainment over two time points relative to typically developing children; (2) Consistent with prior studies (Ames & Fletcher-Watson, 2010; Hughes & Russell, 1993), children with ASD would show more difficulties on with attention switching, but comparable performance to typical children with sustained attention, and (3) attention switching and sustained attention would both contribute to the prediction of word reading and mathematics performances one year later, based on past studies with typically developing children which closely link the development of these areas (Cartwright, 2012; Clark et al., 2010; Steele et al., 2012).


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1. Method 1.1. Participants At Time 1, 124 children aged 7–12 years were recruited (May et al., 2013). This included 64 children, 32 male and 32 female, with Autistic Disorder or Asperger’s Disorder. The original study investigated gender differences in ASD, hence, females were oversampled compared to the typical gender ratio of four males to every one female found in ASD (Fombonne, 2003). Gender was therefore considered in the analyses. Only children who had a current diagnosis of ASD from their paediatrician or psychologist were recruited. The DSM-IV-TR criteria for Autistic Disorder (16 male, seven female) or Asperger’s Disorder (16 male, 25 female) was confirmed for all clinical participants using our standard process involving reviewing diagnostic reports from registered psychologists and paediatricians with a symptom checklist to ensure the DSM-IV-TR criteria were fulfilled. In addition, all clinical participants scored within the clinical range on the Social Responsiveness Scale parent report (Constantino, 2002). Participants were recruited through the Monash University Centre for Developmental Psychology and Psychiatry, the Autism Victoria ‘Get Involved’ volunteer register, and from private clinics in the Melbourne metropolitan area. Only children with a full-scale IQ of 70 and above were included. Seven of the 64 children with ASD (two female, five male) were taking psychostimulant medication (5 methylphenidate, 2 atomoxetine) at Time 1. At Time 2, four females and six males with ASD were taking psychostimulant medication (7 methylphenidate, 2 atomoxetine, with one participant taking both). Sixty typically developing children, 30 male and 30 female, were recruited from a Melbourne metropolitan Primary School. These children were screened to ensure they had no history of developmental disability or psychopathology according to both parent and teacher report. Children in both groups were excluded if they had a history of brain injury or any genetic disorders (such as Fragile X syndrome). Of the 64 children in the ASD group, 56 were reassessed at follow-up for this study (28 males and 28 females), which was a drop-out rate of 12.5%. Of the 60 typically developing children, 52 were reassessed at Time 2 (26 males and 26 females), with a drop out rate of 8.6%. Drop out was due to not being able to contact families, or due to parents reporting being too busy to participate in Time 2. Given that one year was the time gap in this study it was important to closely match the children on age in addition to matching for perceptual IQ. This resulted in 80 children in the age matched sample, 40 with ASD (20 males, 20 females) and 40 TYP (20 male, 20 female). This sample of 80 children was included in the remaining analyses. 1.2. Measures Intellectual functioning. Intellectual functioning was assessed at Time 1 using the Wechsler Intelligence Scale for Children IV (WISC-IV) Australian version (Wechsler, 2005) for children with ASD, and the Wechsler Abbreviated Scales of Intelligence (WASI; Wechsler, 1999) for TYP children. The WASI Verbal IQ is comparable to the WISC-IV Verbal Comprehension Index, and the WASI Performance IQ is comparable to the WISC-IV Perceptual Reasoning Index (Wechsler, 1999). Short-term memory. At Time 1 and 2 the digit length forward task and the sentence length task from the Auditory Processing Test were administered (APT; Rowe, Pollard, & Rowe, 2006). Attention switching task. The Visearch dual-target task from the Wilding Attention Tasks (WATT; Wilding, Munir, & Cornish, 2001) is a computerized visual search task described previously (May et al., 2013). In summary, participants search alternately for a black vertical ellipse then a brown horizontal ellipse to reveal a hidden monster by clicking on the appropriate target. There is one trial, with a maximum of 20 targets to be found (10 for the first shape and 10 for the second). The number of false alarms is recorded. This task is regarded as a test of the executive control function involved in switching attention between different stimuli (Wilding et al., 2001). Past research has found that the number of false alarms to nontargets is characteristic of poor attention, reflecting not only impulsivity but the additional demands of switching (Wilding, 2003; Wilding et al., 2001). This task was administered at Times 1 and 2 using the same scene. Sustained attention task. The Vigilan task (WATT) is a computerized vigilance task using the same screen display as for the Visearch task (see above), described previously (May et al., 2013). Children watch for a yellow border that appears randomly surrounding a target shape on the screen and click on it within seven seconds, after which the yellow border vanishes and a miss is recorded. Sixteen targets appear one by one at irregular intervals. The number of false alarms was recorded. The Visearch and Vigilan accuracy factors (number of false alarms) relate to the behavioural ratings of attentional ability (Cornish, Wilding, & Hollis, 2008; Wilding & Cornish, 2007). Hence, the accuracy factor (false alarms) in Visearch was used as the measure of attention switching, and the accuracy factor (false alarms) in Vigilan as the measure of sustained attention. This task was administered at Times 1 and 2 using the same scene. Academic achievement. Literacy and numeracy were assessed at Times 1 and 2 using two subtests from the Wechsler Individual Achievement Test II Australian version (Wechsler, 2007). Reading achievement was determined using the Word Reading subtest, where children were required to read words from a word card and the total number of words read aloud correctly is recorded. Mathematics achievement was assessed via the Numerical Operations subtest where children were required to solve paper and pencil computations with the total number of correct responses recorded. The WIAT-II shows adequate test–retest reliability with stability coefficients for the Reading and Mathematics composites of .96 and split-half reliability of .97. Concurrent validity has also been demonstrated with other achievement tests (Wide Range Reading Achievement Test).

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1.3. Procedure The study was approved by the Human Research Ethics Committees of Monash University and the Victorian Government Department of Education and Early Childhood. Parents received an explanatory statement and provided written informed consent and children provided assent. Participation was voluntary and participants did not receive any monetary reward for participation other than reimbursement for travel costs. At Time 1 and 2 parents of participants were invited to participate via email or letter and follow-up telephone call. Participants were tested at a home visit, Monash University, or their primary school. Participants completed the Visearch and Vigilan tasks using a laptop and mouse. All participants were tested individually in a quiet room, with task presentation counterbalanced across children. Data were entered into Statistical Package for the Social Sciences (SPSS) version 22.0 for statistical analyses. 1.4. Analyses Distributions and outliers were assessed for each variable by group. There were no outliers detected. Time 1 and 2 Word Reading raw scores were normally distributed. Time 1 and 2 Numerical Operations raw score were normalized with a log 10 transformation. Normality was improved with log 10 transformations for the Time 1 and 2 Visearch and Vigilan false alarms. Independent t-tests were used to compare groups on demographic variables. To compare group differences over time on the academic and attention tasks Repeated Measures ANOVA was employed. Raw scores were used in all analyses unless otherwise stated. Stepwise linear multiple regression analyses were used to determine which Time 1 variables predicted Time 2 reading and mathematics separately in each group. Bonferonni corrections were employed for post hoc tests. 2. Results 2.1. Preliminary analyses Table 1 shows the means and standard deviations for the groups across the demographic and outcome variables. One child in the ASD group did not complete the digit span task. An independent t-test showed there was no difference in the time interval between Time 1 and Time 2 testing for the ASD (M = 13.0 months, SD = 1.0 months) and TYP group (M = 12.9 months, SD = 1.0 months), t(78) = 0.544, p = .588. An independent t-test showed the children with ASD had significantly higher parent reported ASD symptoms on the Social responsiveness Scale, t(78) = 15.096, p < .001. There were an equal number of males and females in the TYP and ASD groups. By selection, the groups were matched on age (Time 1: t(78) = 0.000, p = 1.00; Time 2 t(78) = 0.032, p = .975) and perceptual IQ, t(78) = 1.321, p = .190. Children with ASD had lower verbal IQ’s than TYP children, t(78) = 2.433, p = .017. This was further investigated to understand the association between Verbal IQ and the variables of interest. Verbal IQ was correlated with Word Reading and Numerical Operations performance in the ASD group and with Word Reading in the TYP group (Table 2). Verbal IQ was correlated with Time 1 sustained attention in the ASD group (r = .423, p = .007) but not with switching attention in either group or with sustained attention in the TYP group. The influence of Verbal IQ on the academic variables and sustained attention was therefore examined in later analyses by comparing the trajectories between groups as described below. There were no correlations between gender and the variables of interest, hence, gender was excluded from further analyses.

Table 1 Time 1 and 2 demographic, academic and attention variables for the TYP and ASD groups. Variable

Time 1

Time 1

ASD N = 40 M (SD)

TYP N = 40 M (SD)

ASD M (SD)

TYP M (SD)

Age (months) Verbal IQ Perceptual IQ Social Responsiveness Scale Raw Score Numerical Operations Raw Score Word Reading Raw Score Digit Span raw score Sentence Length raw score Vigilan false alarms raw score Visearch false alarms raw score

115.4 100.7 100.5 94.3 18.0 99.8 4.5 12.9 5.2 5.0

115.4 107.2 104.8 25.2 20.0 100.4 4.6 12.8 1.9 3.3

128.4 (18.7)

128.3 (16.2)

21.3 106.7 4.6 13.7 3.7 3.5

24.2 107.1 4.8 13.8 2.6 1.4

ASD, Autism Spectrum Disorder; TYP, typically developing.

(18.8) (13.4) (15.1) (24.3) (7.9) (18.3) (1.0) (1.8) (6.8) (5.2)

Time 2

(16.1) (10.5) (14.4) (15.7) (6.6) (15.2) (1.0) (1.8) (2.8) (3.3)

(8.6) (14.7) (1.2) (N = 39) (2.2) (4.6) (4.2)

(7.9) (12.3) (0.9) (2.1) (3.0) (1.7)


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Table 2 Pearson’s correlations between Word Reading and Numerical Operations Raw Scores and Time 1 predictors for the ASD and TYP groups. Time 1 predictors

Time 1 NO Raw (log 10) Age Gender Verbal IQ Perceptual IQ Digit Length Sentence Length Vigilan False Alarms (log 10) Visearch False Alarms (log 10)

Time 2 Numerical Operations Raw (log 10) ASD

TYP

.908*** .749*** .238 .406** .514** .690** .477** .413* .507**

.794*** .727*** .024 .306 .454** .510** .474** .060 .221

Time 1 predictors

Time 1 Word Reading Raw Age Gender Verbal IQ Perceptual IQ Digit Length Sentence Length Vigilan False Alarms (log 10) Visearch False Alarms (log 10)

Time 2 Word Reading Raw ASD

TYP

.964*** .585*** .170 .565*** .450** .742*** .528** .340 .420**

.947*** .632*** .023 .375* .269 .498** .422** .131 .125

ASD, Autism Spectrum Disorder; TYP, typically developing. * p < .05. ** p < .01. *** p < .001.

2.2. Word reading Pearson correlations revealed significant relationships between Time 1 and Time 2 measures of Word Reading raw scores [ASD group r = .965, p < .001; TYP group r = .947, p < .001]. To examine if there were any differences in Word Reading over time and whether performance differed by group, a repeated measures ANOVA was conducted with time (Time 1 and Time 2) as the repeated measure, group as the between groups factors and word reading as the dependent variables. This revealed significant improvement in Word Reading over time, F(1,78) = 123.31, p < .001, h2P ¼ :613, with no group difference, F(1,78) = 0.022, p = .882, h2P ¼ :001, and no significant interactions. Given Verbal IQ was significantly different in both groups and correlated with Word Reading performance, we sought to understand whether the level of reading performance was as expected given the level of verbal IQ. We used the method specified by Thomas et al. (2009) to compare the linear regressions for each trajectory in TYP and ASD children. We used ANCOVA to examine the interaction between Verbal IQ and Group in the prediction of Word Reading. Results from this analysis indicated that Verbal IQ was a strong predictor of performance over all participants, F(1,77) = 20.654, p < .001, h2P ¼ :214. The group difference in Word Reading performance was not significant, F(1,77) = 0.452, p = .504, h2P ¼ :006. Importantly, the interaction between Verbal IQ and Group was not significant in the prediction of Word Reading, F(1,77) = 0.649, p = .423, h2P ¼ :008. Thus, the trajectories of Word Reading performance of TYP and ASD children were similar based on their level of verbal IQ. Overall, these analyses indicated similar development and no delay in Word Reading performance in the children with ASD relative to TYP children. 2.3. Numerical operations Pearson correlations revealed significant relationships between Time 1 and 2 measures of Numerical Operations raw scores [ASD group r = .908, p < .001; TYP group r = .794, p < .001]. Repeated measures ANOVA was conducted as for Word Reading with Numerical Operations raw score (log 10) as the dependent variable. This revealed a significant improvement in Numerical Operations over time (F(1,78) = 76.810, p < .001, h2P ¼ :496) with the group difference just failing to reach significance, F(1,78) = 3.934, p = .051, h2P ¼ :048, and no significant interactions. Given that Verbal IQ was significantly different in both groups and also correlated with Numerical Operations performance, we repeated the analysis specified above (Thomas et al., 2009) to understand whether mathematics performance was as expected given the level of verbal IQ. Verbal IQ predicted mathematics performance over all participants, F(1,77) = 10.715, p = .002, h2P ¼ :1324. The group difference in Numerical Operations performance was not significant, F(1,77) = .380, p = .539, h2P ¼ :005. The interaction between Verbal IQ and Group was also not significant in the prediction of Numerical Operations performance, F(1,77) = 0.245, p = .622, h2P ¼ :003. Thus, the trajectories of Numerical Operations performance of TYP and ASD children were similar based on their level of verbal IQ. This indicated similar development and no delay in Numerical Operations performance in these children with ASD relative to TYP children. 2.4. Sustained attention Pearson correlations revealed significant relationships between Time 1 and 2 measures of Vigilan false alarms in the ASD group r = .418, p = .008; but not the TYP group r = .241, p > .05. A repeated measures ANOVA was again conducted with and Vigilan False Alarms as the dependent variable. There was no significant change over time, F(1,78) = 0.331, p = .567, h2P ¼ :004). The group difference in Vigilan False Alarms was significant, F(1,86) = 4.897, p = .030, h2P ¼ :059, with means (Table 1) showing that the ASD group made more errors than the TYP group. There were no significant interactions.


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Given that Verbal IQ was significantly different in both groups and also correlated with Sustained attention performance, we repeated the analysis specified above (Thomas et al., 2009) to understand whether sustained attention performance was as expected given the level of verbal IQ. Verbal IQ predicted of sustained attention errors over all participants, F(1,77) = 7.305, p = .008, h2P ¼ :088. The group difference in Sustained attention was no longer significant, F(1,77) = .018, p = .894, h2P ¼ :001. The interaction between Verbal IQ and Group was not significant in the prediction of Numerical Operations performance, F(1,77) = 0.001, p = .974, h2P ¼ :001. Thus, the trajectories of attention switching performance of TYP and ASD children were similar based on their level of verbal IQ. These analyses indicated similar development and no delay in sustained attention in ASD relative to TYP children when Verbal IQ was considered. 2.5. Switching attention Unexpectedly, Pearson’s correlations revealed there was not a significant relationship between Time 1 and Time 2 measures of Visearch false alarms in the ASD group r = .247, p > .05, and TYP group r = .062, p > .05. A repeated measures ANOVA was conducted as above. There was a significant change over time with a reduction in errors made, F(1,78) = 9.182, p = .003, h2P ¼ :105). The ASD group made more Visearch False Alarm errors, F(1,78) = 8.291, p = .005, h2P ¼ :096. There were no significant interactions. 2.6. Prediction of academic development Finally, to investigate whether Time 1 EF predicted the Time 2 reading and mathematics performances, hierarchical linear regression analyses were conducted for each group (TYP N = 40, ASD N = 39). The Time 1 predictor variables entered into the analyses in Model 1 were word reading or numerical operations, age, gender, verbal IQ, perceptual IQ, digit length, sentence length. In Model 2 the following predictors were added: Vigilan False Alarms, Visearch False Alarms. Correlations are found in Table 2. Time 1 Vigilan and Visearch false alarms showed a negative significant correlation with Time 2 Numerical Operations in the ASD group only. Time 1 Visearch false alarms also correlated negatively with Time 2 Word Reading in the ASD group only. Results for the regression analyses are found in Table 3. For Time 2 Word Reading, in both groups Time 1 Word Reading was the only significant predictor with the EF predictors not adding significantly to the model. For Time 2 Numerical Operations, Time 1 Numerical Operations was a significant predictor in both groups, with age also significant in the ASD group. The Time 1 EF predictors did not add significantly to the model.

Table 3 Results of regression analysis on Time 2 Word Reading and Numerical Operations in the ASD and TYP groups using Time 1 measures as predictors. Model 1

Model 2

Model 1

B Time 2 NO raw log 10 Time 1 NO raw log 10 Age Sex Verbal IQ Perceptual IQ Sentence Length Digit Length Visearch False Alarms Vigilan False Alarms R2 F for change in R2 Time 2 WR raw Time 1 WR raw Age Sex Verbal IQ Perceptual IQ Sentence Length Digit Length Visearch False Alarms Vigilan False Alarms R2 F for change in R2

.529 .003 .003 .002 .002 .014 .024 .529

SE B .112 .001 .024 .001 .001 .009 .020 .112

b .582 .269*** .009* .126 .143 .138 .139 .582

.883 33.307

.684 .016 .237 .099 .067 .046 1.260

Model 2

TYP

ASD

.067 .050 1.350 .065 .060 .515 1.119

.940 69.498

NO, numerical operations; WR, word reading. * p < .05. ** p < .01. *** p < .001.

.862*** .021 .008 .091 .060 .006 .092

B

SE B .500 .003 .004 .002 .002 .014 .026 .018 .005 .500 .884 0.155

.127 .001 .025 .001 .001 .009 .022 .039 .035 .127

.681 .026 .189 .145 .068 .088 .741 2.617 2.426 .945 1.160

.068 .052 1.344 .074 .060 .513 1.176 2.032 1.889

B .550 .272*** .011* .121 .143 .138 .149 .037 .011 .550

B

SE B .226 .004 .042 .001 .002 .004 .017 .226

.174 .001 .025 .001 .001 .008 .013 .174

b

B .231 .503** .165 .057 .214 .048 .134 .231

.759 14.365 .857*** .034 .007 .134 .062 .011 .054 .069 .078

.734 .039 .626 .102 .026 .361

.902 41.938

.075 .066 1.519 .090 .055 .502

.902*** .051 .026 .086 .031 .052

SE B .237 .004 .041 .001 .002 .002 .016 .009 .016 .237 .760 0.086

.181 .001 .026 .001 .001 .009 .014 .036 .040 .181

.727 .048 .824 .109 .019 .576 .225 2.136 2.327 .906 .707

.078 .068 1.542 .093 .058 .539 .794 2.153 2.448

B .242 .500** .161 .059 .220 .032 .126 .026 .041 .242

.894*** .062 .034 .092 .022 .083 .019 .065 .062


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3. Discussion This study compared basic attentional and academic skills over one year of development in cognitively able children with and without ASD, to determine if early attentional deficits impacted on later word reading and mathematics development. Cognitively able children with ASD exhibited intact word reading and mathematics performances but impairment on an attention switching task. The attentional components correlated significantly with word reading and mathematics performance in ASD children only. However, when all factors were entered into a regression analysis there were similar predictors of word reading and numerical operations in TYP and ASD children. These findings highlight both preserved areas and areas of deficit in children with ASD and improve our understanding of the progression of academic and attentional skills over primary school. 3.1. Word reading There was no difference in word reading attainment in children with ASD and TYP children over the two time points. This is consistent with past findings (Minshew et al., 1994; Nation et al., 2006) and indicates that word reading is generally an intact area in cognitively able children with ASD. Although verbal IQ was significantly lower in children with ASD and a significant predictor of word reading across the groups the Verbal IQ-word reading relationship was not different in TYP and ASD children. There was similar improvement in word reading over the year across the groups indicating a similar developmental trajectory. Together these findings suggest word reading is a preserved skill in cognitively able children with ASD. 3.2. Numerical operations Children with ASD also performed similarly to TYP children in regard to their performance on the numerical operations task and they showed improvement in the mathematics task similar to TYP children over the year. This finding is largely consistent with the limited past research into mathematics in ASD (Chiang & Lin, 2007). Again, even though verbal IQ was lower in children with ASD and was strongly related to mathematics performance, the relationship between verbal IQ and numerical operations was similar in children with and without ASD. Examining more complex reading and mathematics tasks over time will be important to extend the present research, given past studies which have shown impairment particularly in reading comprehension in children with ASD (Griswold et al., 2002; Huemer & Mann, 2010; Jones et al., 2009; Minshew et al., 1994). 3.3. Attention switching As predicted, children with ASD performed more poorly than TYP children on the attention switching Visearch task, making more errors. However, the poor correlation between Time 1 and Time 2 Visearch False Alarms in both groups raises some concerns regarding the reliability of this measure, hence this finding should be interpreted with caution. There have been some past mixed findings in regards to attention switching (Kaland et al., 2008; Russell, Jarrold, & Hood, 1999), however, our finding of poorer attention switching over two time points is consistent with the theory of underlying executive dysfunction in ASD with a specific switching deficit in this group (Pennington & Ozonoff, 1996; Rinehart et al., 2001). There was a reduction in errors made over the year. Notably, however, the delay in attention switching remained stable over the year indicating this deficit may persist over the primary school years. 3.4. Sustained attention There was no difference on the sustained attention task after verbal IQ was taken into account. Most studies have generally shown intact sustained attention in ASD (Goldstein et al., 2001; Johnson et al., 2007), but some have also shown impairment (Corbett & Constantine, 2006). It is also noteworthy that the TYP children exhibited more errors at Time 2 than at Time 1 on the sustained attention task, although this was not statistically different. Given the poor correlations between Time 1 and Time 2 sustained attention performance in the TYP group (but not the ASD group) these interpretations should again be taken with caution. These findings appear consistent with past studies showing delays in only specific EF skills in children with ASD (Ozonoff & McEvoy, 1994; Pellicano, 2010a,b). Given the high rate of Attention Deficit Hyperactivity Disorder (ADHD) in children with ASD (Simonoff et al., 2008), it may be possible that some of the attentional issues found may relate to comorbid ADHD symptoms (Sinzig et al., 2008). 3.5. Predictions of word reading and mathematics Although there were significant negative correlations between Time 1 switching attention and sustained attention with numerical operations, when all factors were considered in a regression analysis the attention predictors did not contribute additional variance to the prediction of Time 2 mathematics. This may have been due to the large correlations between Time 1 and Time 2 Numerical Operations which left little variance for the other predictors to explain. In the ASD group only, age also contributed to the prediction of Time 2 Numerical Operations.

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For the prediction of word reading, there was a similar finding. Although Time 1 switching attention correlated significantly with Time 2 Word Reading, the attention predictors did not contribute additional variance in the full regression model. Again, there were large correlations between Time 1 and Time 2 word reading leaving little variance for any other variables to explain. Potentially, one year may not have been a long enough period to show these associations, or the attention tasks used may not have been sensitive enough. Future studies will be important to extend this work to examine more complex academic tasks, such as reading comprehension where inferences and abstract thinking are required whice places more demands on the executive functions. Additionally, examining how other theoretical explanations of autism, such as Theory of Mind, and Weak Central Coherence, might relate to reading and mathematics difficulties will be important (El Zein, Solis, Vaughn, & McCulley, 2014). 3.6. Limitations A limitation of the present study was the wide age range of participants from 7 to 12 years at Time 1. Although this is a relatively narrow age range in the ASD literature, given the developmental sequences of attention functions and academic attainment over time (Klenberg, Korkman, & Lahti-Nuuttila, 2001; Zhan et al., 2011), examining children with ASD within more highly delimited age ranges will be important to detect change. Future research in this area which focuses on adolescence may also be informative given the findings of a second wave of executive functioning deficit that occurs in the second decade of life in children with ASD (Luna et al., 2007; Minshew & Williams, 2007). Examining whether this may interrelate with poorer academic attainment during this period will be beneficial for understanding what additional educational supports may be required for children with ASD transitioning to secondary school. 3.7. Clinical implications The present study found that children with ASD may show similar performances to typically developing children in word reading and mathematics, which could potentially mask their well described deficits in more complex academic tasks (Nation et al., 2006). Cognitively able children with ASD who do experience word reading difficulties may benefit from being assessed for the possibly of a comorbid learning difficulty. It is also noteworthy that gender was not associated with any of the dependent variables which is consistent with past findings of minimal gender differences between boys and girls with ASD (May et al., 2013; Solomon, Miller, Taylor, Hinshaw, & Carter, 2011).

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