![]() ![]() The relation between symbolic numerical magnitude processing and mathematics performance Finally, we will also use a relatively comprehensive set of measures for mathematics performance and take into account the influence of various domain-general skills, in order to tease out better the unique effects of SNMP and WP on mathematics performance. ![]() As to classifying children according to their performance profiles, we will extract empirical profiles of SNMP and WM by means of latent class clustering. We will focus particularly on SNMP (1- and 2-digit comparison), due to the more robust and stronger predictions obtained with it, and include all three different components of WM for a more inclusive representation of WM. With this study, we aim to expand on previous research on the joint role NMP and WM play in early mathematics performance. It would therefore seem informative to examine the extent to which an alternative, data-driven classification matched the previous findings. Of particular interest is also the fact that previous studies have typically used a priori defined cut-off scores for defining different performance levels, and, consequently, grouping children accordingly to those levels (Kroesbergen & van Dijk, 2015 Toll et al., 2016). Finally, as to the criterion variables, focus in terms of mathematics performance has mainly been on arithmetic facts and word problem–solving, while also some other core skills, such as counting, have shown to be important predictors of later mathematics performance (Aunola et al., 2004). Along with NMP and WM, several domain-general factors, such as fluid intelligence, rapid automatized naming and language skills, have also been linked with mathematics performance (Koponen et al., 2018 Purpura & Ganley, 2014), due to which further attention to these individual differences might be informative. Prior studies have often used visual WM as a measure of WM, but it would seem beneficial to include verbal WM and central executive in these investigations as well, as both are known to be associated with mathematics performance (Friso-van den Bos et al., 2013). Most of these studies looking at how the patterns of NMP and WM among children predict mathematics performance have used measures of non-symbolic NMP or employed varying operationalisations of NMP, while recent research suggests that symbolic NMP (SNMP) might provide more consistent findings and stronger predictions on mathematics performance among school-aged children (De Smedt et al., 2013). The intriguing findings from these few studies, varying in designs and methods, on one hand suggest WM to be a stronger predictor than NMP of early mathematics performance (Passolunghi et al., 2014), but on the other hand, children having weakness in both NMP and WM to struggle most in mathematics compared to those having no weakness or weakness in either NMP or WM (Kroesbergen & van Dijk, 2015 Toll et al., 2016). ![]() Brankaer et al., 2017 Cañizares et al., 2012), surprisingly few studies have investigated them simultaneously (Chan & Wong, 2019 Kroesbergen & van Dijk, 2015 Passolunghi et al., 2014 Toll et al., 2016). Both NMP and WM have been linked with individual differences in mathematics performance, and although weakness in either of them seem to result in inferior performance (e.g. Alloway & Alloway, 2010 De Smedt et al., 2013 Friso-van den Bos et al., 2013 Kroesbergen & van Dijk, 2015 Li et al., 2018 Toll et al., 2016). Several cross-sectional and longitudinal studies have looked at numerical magnitude processing (NMP) and working memory (WM) as possible domain-specific and domain-general cognitive precursors of children’s mathematics performance and development (e.g.
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