Predicting Math Achievement from Attentional Ability and Perceptual Reasoning in Students with Autism Spectrum Disorder

Background: Children and adolescents with Autism Spectrum Disorder (ASD) often present a profile that includes academic difficulties compared to their typically developing peers (Ashburner, Ziviani, & Rodger, 2010). Early math skills are an integral component to academic success and can be used to predict future achievement (Claessens & Engel, 2013). Although domain-specific numerical skills and knowledge are critical for success in mathematics, cognitive factors also play important roles in math proficiency. In particular, domain-general attentional skills, such as concentration and working memory, have been found to be factors contributing to differences in mathematics achievement (Cragg & Gilmore, 2014). Research involving these skills can clarify the degree to which attention contributes to the math abilities of individuals with ASD. A better understanding of this relationship can lead to the development and implementation of academic interventions in the form of attention training programs aimed at far-transfer to improvement of mathematics ability.

Objectives:  The purpose of this study is to assess the contribution of attention to math achievement within the context of a school-based study, and to determine the role of perceptual reasoning in this relationship. The objectives are two-fold: (i) determine whether the math proficiency of children with ASD can be predicted by performance on a clinical test of attention, and (ii) measure whether perceptual reasoning intelligence acts as a covariate in the relationship between attention and math.

Methods: All participants (N = 99) completed measures of attention, intelligence and math. The participants were grouped by diagnosis; ASD and non-ASD neurodevelopmental disorder. Attention was assessed using the Conner’s Continuous Performance Task, Third Edition (CPT-3). The Wechsler Abbreviated Scale of Intelligence (WASI)-II was used to assess intelligence, Non-Verbal Perceptual Reasoning Index (PRI) and Verbal Comprehension Index (VCI). Lastly, the Pearson KeyMath was used to assess math ability.

Results:  The results of a one-way ANOVA indicate that students diagnosed with ASD performed significantly higher on KeyMath than students with a non-ASD neurodevelopmental disorder, p = .016. The results of a separate ANOVA indicate that individuals with ASD also performed significantly higher on tests of attention, p = .014, as well as perceptual reasoning, p < .001. All variables were significantly correlated with each other (PRI, CPT-3 and KeyMath), p < .001 for all correlations. The PRI score of the WASI-II (a measure of non-verbal fluid abilities) served as a significant mediator in the relationship between attention and math ability in students with ASD, but not for students with a non-ASD diagnosis.

Conclusions: The results of this study indicate that math proficiency can be predicted by attention for students diagnosed with ASD. Furthermore, PRI serves as a significant mediator in the aforementioned relationship for students with ASD only. The results help to gain a better understanding of the cognitive and academics profile of individuals with ASD, specifically in terms of the selective influence of attention capability and non-verbal intelligence. These results provide the theoretic foundation for choosing non-verbal assessment and remediation approaches for students with ASD.