Multiple skills underlie arithmetic performance: A large-scale structural equation modeling analysis

Abstract

Current theoretical approaches point to the importance of several cognitive skills not specific to mathematics for the etiology of mathematics disorders (MD). In the current study, we examined the role of many of these skills, specifically: rapid automatized naming, attention, reading, and visual perception, on mathematics performance among a large group of college students (N = 1,322) with a wide range of arithmetic proficiency. Using factor analysis, we discovered that our data clustered to four latent variables 1) mathematics, 2) perception speed, 3) attention and 4) reading. In subsequent structural equation modeling, we found that the latent variable perception speed had a strong and meaningful effect on mathematics performance. Moreover, sustained attention, independent from the effect of the latent variable perception speed, had a meaningful, direct effect on arithmetic fact retrieval and procedural knowledge. The latent variable reading had a modest effect on mathematics performance. Specifically, reading comprehension, independent from the effect of the latent variable reading, had a meaningful direct effect on mathematics, and particularly on number line knowledge. Attention, tested by the attention network test, had no effect on mathematics, reading or perception speed. These results indicate that multiple factors can affect mathematics performance supporting a heterogeneous approach to mathematics. These results have meaningful implications for the diagnosis and intervention of pure and comorbid learning disorders.Keywords: perception speed, mathematics performance, pure and comorbid mathematics disability Nobes, and Gabriel (2013) proposed that abnormalities in executive functions, specifically inhibition and visuospatial working memory, are the source of MD, rather than mathematics-specific quantitative understanding. Moreover, there is evidence that these cognitive skills affect individual differences in arithmetic abilities across the spectrum of mathematics proficiency. Szűcs, Devine, Soltesz, Nobes, and Gabriel (2014) demonstrated that individual differences in arithmetic abilities was predicted by phonological processing, verbal knowledge, visuospatial short-term and working memory, spatial ability and executive functioning. Journal of Numerical Cognitionjnc.psychopen.eu | 2363-8761The prevalence rate of MD is between 3-6% among children (Reigosa-Crespo et al., 2012;R. Shalev, Manor, Amir, & Gross-Tsur, 1993;von Aster, Schweiter, & Weinhold Zulauf, 2007;von Aster & Shalev, 2007) while there is less evidence in regard to prevalence rates among adults. However, the prevalence of the comorbidity of MD with other disorders that affect learning (such as reading and attention deficits) vary dramatically between studies, and range between 17-66% (Barbaresi, Katusic, Colligan, Weaver, & Jacobsen, 2005;Dirks, Spyer, van Lieshout, & de Sonneville, 2008;Gross-Tsur, Manor, & Shalev, 1996;Landerl & Moll, 2010;Lewis, Hitch, & Walker, 1994;R. Shalev et al., 1993;. Even the lowest reported comorbidit...

The contributions of executive functions to mathematical learning difficulties and mathematical talent during adolescence

“…Consistent with previous studies [31,60], our results suggest that there is an interplay between different cognitive skills and executive functions and the level achieved in solving specific types of mathematical problems. Research has shown that working memory and inhibition, but not switching, are associated with individual differences in solving problems with whole numbers [26].…”

Impairment of non-symbolic number processing in children with mathematical learning disability

“…First, this is not surprising because a high comorbidity of MLD and other developmental deficits such as dyslexia (Barbaresi et al, 2005;Gross-Tsur et al, 1996) or other cognitive deficit such as executive control impairment (Geary, Bailey, Littlefield, et al, 2009;Rotzer et al, 2009) has been frequently reported. Furthermore, Ashkenazi and Silverman (2017) recently indicated that multiple factors can affect mathematics performance (processing speed, perception, sustained attention, reading). In the present study, we adopted a practical selection procedure, which reflects the reality existing in clinical and school settings.…”

The relationship between sustained attention and automatic versus procedural arithmetic: The case of populations with typical development and subgroups of developmental dyscalculia.

“…These consisted of multiple different measures (standardized to z scores), such as RT and accuracy rate (ACC) in the arithmetic tasks, or RT, RT variance, and error types (omission and commission) in the CPT task. Prior to the analysis, efficiency scores for each task were calculated (Ashkenazi & Silverman, 2017). For both arithmetic automaticity and procedural knowledge, efficiency scores were calculated by averaging the ACC and RT z score.…”

“…The test battery was developed by the Israeli National Institute for Testing and Evaluation in cooperation with the Israeli Council for Higher Education. The MATAL test battery includes 20 tests and 54 performance measures used to assess proficiency in mathematics, reading, writing, English (as a second language), and attention (for further information on the MATAL, see Ashkenazi & Silverman, 2017; Ben-simon et al, 2013; Goldfarb & Shaul, 2013; Mazor-Karsenty et al, 2015). All students were Hebrew speakers, diagnosed at the University of Haifa diagnostic center between the years 2010 and 2019.…”