How number line estimation skills relate to neural activations in single digit subtraction problems

Abstract

The Number Line (NL) task requires judging the relative numerical magnitude of a number and estimating its value spatially on a continuous line. Children's skill on this task has been shown to correlate with and predict future mathematical competence. Neurofunctionally, this task has been shown to rely on brain regions involved in numerical processing. However, there is no direct evidence that performance on the NL task is related to brain areas recruited during arithmetical processing and that these areas are domain-specific to numerical processing. In this study, we test whether 8- to 14-year-old's behavioral performance on the NL task is related to fMRI activation during small and large single-digit subtraction problems. Domain-specific areas for numerical processing were independently localized through a numerosity judgment task. Results show a direct relation between NL estimation performance and the amount of the activation in key areas for arithmetical processing. Better NL estimators showed a larger problem size effect than poorer NL estimators in numerical magnitude (i.e., intraparietal sulcus) and visuospatial areas (i.e., posterior superior parietal lobules), marked by less activation for small problems. In addition, the direction of the activation with problem size within the IPS was associated to differences in accuracies for small subtraction problems. This study is the first to show that performance in the NL task, i.e. estimating the spatial position of a number on an interval, correlates with brain activity observed during single-digit subtraction problem in regions thought to be involved numerical magnitude and spatial processes.

A Mechanistic Study of the Association Between Symbolic Approximate Arithmetic Performance and Basic Number Magnitude Processing Based on Task Difficulty

“…A developmental study ( Sasanguie et al, 2012 ) found that when mathematical ability was tested with complex problems, the number line estimation ability predicted performance more strongly than the number comparison ability. Previous neuroimaging studies for children have demonstrated that complex arithmetic problem activates the parietal lobe more than simple arithmetic problem ( Menon et al, 2000 ; De Smedt et al, 2011 ; Ashkenazi et al, 2012 ; Berteletti et al, 2015 ). Moreover, the number line estimation ability has significant correlations with complex arithmetic performance in the brain.…”

“…Moreover, the number line estimation ability has significant correlations with complex arithmetic performance in the brain. One neuroimaging study showed that number line estimation ability was related to arithmetic performance by comparing the activation of the parietal lobe for simple and complex arithmetic problems ( Berteletti et al, 2015 ). A training study showed that less activation occurred in the parietal lobe in response to a number task following number line estimation training ( Kucian et al, 2011 ).…”

“…Spatial information always depends on the parietal lobe (see Zacks, 2008 , for a meta-analysis and review). Spatial attention and visuospatial working memory abilities were needed to solve the complex arithmetic problems when compared with the simple ones ( Zago et al, 2001 ; Zago and Tzourio-Mazoyer, 2002 ; Berteletti et al, 2015 ). In this study, with the increase in difficulty, the sum of the problem was closer to the comparison number.…”

“…Complex mathematical problems are more dependent on spatial processing when compared with simple problems. For the simple arithmetic problems, participants retrieved the answers from long-term semantic working memory ( Geary et al, 1996 ; LeFevre et al, 1996 ; Delazer and Benke, 1997 ; McLean and Hitch, 1999 ), whereas much more visuospatial processing was involved in the processing of complex arithmetic problems ( Zago et al, 2001 ; Berteletti et al, 2015 ).…”

Training Studies: An Experimental Design to Advance Educational Neuroscience

“…Instead, a standard approach is to explore the relationship between outside‐the‐scanner behavioral measures and patterns of brain activity (so‐called “brain–behavior relations”). This approach includes cross‐sectional comparisons, such as contrasting activity patterns between children with and without mathematical learning disabilities (Price, Holloway, Rasanen, Vesterinen, & Ansari, ; Rosenberg‐Lee et al, ) or between children versus adults (Ansari & Dhital, ; Kawashima et al, ), and individual difference designs that involve correlating brain responses with participant age (Rivera, Reiss, Eckert, & Menon, ) or a math‐relevant skill measured outside the scanner (Berteletti, Man, & Booth, ; Metcalfe, Ashkenazi, Rosenberg‐Lee, & Menon, ). Notably, these sorts of “neural correlates” analyses are fairly straightforward in most neuroimaging software platforms, likely contributing to their ubiquity.…”