Age-Related Changes in Effortful Processing in Adults with ASD

Background: Little is known about age-related changes in cognitive functioning in adults with ASD. However, three previous studies of cognitive aging in ASD show similar or reduced age-related changes in cognitive functioning compared to adults with typical development (Geurts & Vissers, 2012; Lever & Geurts 2015; Lever et al., 2015).

Objectives: The primary objective of this study was to further examine age-related effects on cognition using measures sensitive to age-related decline while controlling for moderating variables (e.g., IQ, baseline motor abilities) that might obscure the relationship between age and cognitive functioning.

Methods: Twenty-nine adults with ASD and 30 adults with typical development were recruited for this study. Participants were group-matched on age (range: 30 – 67), gender, and IQ (range: 92-130), all p-values > .42. ASD diagnoses were confirmed via the self-report adult version of the SRS-2 and the ADOS-2. Participants completed several measures of cognitive functioning including measures of processing speed (Trail Making Test A), explicit memory (RAVLT Free Recall), executive functioning (Trail Making Test B, MoCA), and category learning (Woodcock-Johnson Concept Formation subtest).

Results: To assess diagnostic and age-related effects on measures of cognitive functioning a multivariate regression was conducted including Z-scores taken from standardized assessments of explicit memory, concept formation, executive functioning, and processing speed as dependent variables. Results revealed significant main effects of IQ [Wilks’ λ = .467, F(10, 43) = 4.91, p < .001, η_p² = .53], age [Wilks’ λ = .567, F(7, 46) = 5.02, p < .01], and diagnosis [Wilks’ λ = .532, F(7, 46) = 5.78, p = .01, η_p² = .47], were qualified by a significant age by diagnosis interaction [Wilks’ λ = .710, F(7, 46) = 2.70, p = .02, η_p² = .29]. Results suggest age may have a larger impact on cognitive functioning in older adults with ASD than older adults with typical development. The significant age by diagnosis interaction was primarily driven by performance on three measures thought to reflect components of working memory/executive function (e.g., Trail Making Test B, MoCA, Free Recall), Wilks’ λ = .800, F(3, 53) = 4.34, p < .01, η_p² = .20.

Conclusions:  Closer inspection of measures of cognitive functioning revealed greater age-related decline in ASD was not present across all cognitive domains, instead evidence points to accelerated cognitive aging in adults with ASD on tasks related to working memory. To the best of our knowledge, these findings are the first to document specific cognitive domains that are more disrupted by the aging process in adults with ASD compared to adults with typical development. Although findings are somewhat in contrast to previous studies of aging in ASD, which may be due to differences in sample characteristics (i.e., inclusion of individuals diagnosed in childhood) or analytic methods (i.e., controlling for IQ), they provide insight for the development of cognitive training interventions targeting specific cognitive difficulties faced by older adults with ASD. Yet, given that this study used a cross-sectional design, it is important that future studies replicated findings with a longitudinal sample.