Executive Function and Academic Achievement in Autism Spectrum Disorder

Background: Executive function (EF) is predictive of academic achievement in the general student population (Ahmed et al., 2018). Children who come to pre-Kindergarten with fewer EF skills have trouble engaging in learning opportunities in the classroom and thus make fewer gains in math and literacy during the school year (Nesbitt et al., 2015). The limited evidence to date suggests associations between EF and academics in Autism Spectrum Disorder (ASD); however, the two existing studies have focused on children in the preschool to early primary school age range (Pellicano et al., 2017, St. John et al., 2018). EF might exert an even greater influence over academic outcomes in older children and adolescents with ASD.

Objectives: To determine whether performance-based EF tasks of working memory, inhibition, and flexibility predict academic performance on mathematical and literacy tasks in children and adolescents with ASD.

Methods: 60 individuals (28 with ASD, 32 neurotypical controls [NT]), ages 8-18 years (M=12.94, SD=2.5) matched on age and gender ratio (see Table 1) completed testing procedures. IQ was estimated with WASI Vocabulary (Voc, M=57.78, SD=10.11) and Block Design (BD, M=54.67, SD=10.81) T-scores. For all regressions examining EF-academics relations, Model 1 (M1) included diagnostic group, age, gender, Voc and BD as nuisance variables, while Model 2 (M2) added performance-based measures of EF: WISC-V Digit Span (DS) measuring working memory, TEA-Ch Walk Don’t Walk measuring inhibition, and DKEFS Switching Accuracy measuring flexibility. Academic performance-dependent variables included WIAT-III Math Fluency, WJ-IV Math Calculation, GORT-V Reading Fluency, and GORT-V Reading Comprehension.

Results: WIAT Math Fluency (N=45): M2 explained a significant proportion of the variance in math fluency, R²=.45, F(8, 36)=3.62, p<.05. In M2, DS significantly predicted math fluency, b=2.31, t(36)=3.90, p<.001.

WJ Calculation (N=38): M2 explained a significant proportion of the variance in math calculation, R²=.55, F(8, 29)=4.45, p<.05 (see Figure 1). In M2, DS significantly predicted math calculation, b=2.06, t(29)=2.92, p< .05.

GORT Reading Fluency (N=44): M2 explained a significant proportion of the variance in reading fluency, R²=.63, F(8, 35)=7.33, p<.001. In M2, there was a significant association between gender, b=1.62, t(35)=2.01, p=.05; Voc, b=0.15, t(35)=3.06, p<.05; DS, b=0.32, t(35)=2.85, p<.05, and reading fluency.

GORT Reading Comprehension (N=43): M2 explained a significant proportion of the variance in reading comprehension, R²=.72, F(8, 34)=10.85, p<.05. In M2, there was a significant association between gender, b=1.75, t(34)=2.35, p<.05; Voc, b=0.15, t(34)=3.52, p=.001; DS, b=0.34, t(34)=3.31, p<.05; and reading comprehension. For both reading fluency and comprehension, girls have higher mean scores than boys.

Conclusions: Of the EF measures considered, only DS, a measure of working memory, explained a significant proportion of variance in all four measures of math and reading skills. This study suggests that during the school years, EF, specifically working memory, is associated with academic performance in ASD, consistent with studies of younger children with ASD (St. John et al., 2018) and the broader population of school age youth (Ahmed et al., 2018). Further work should consider whether scores from more ecologically-valid measures of EF play a predictive role in academic performance in ASD.