Individual differences in solving arithmetic word problems

Abstract

BackgroundWith the present functional magnetic resonance imaging (fMRI) study at 3 T, we investigated the neural correlates of visualization and verbalization during arithmetic word problem solving. In the domain of arithmetic, visualization might mean to visualize numbers and (intermediate) results while calculating, and verbalization might mean that numbers and (intermediate) results are verbally repeated during calculation. If the brain areas involved in number processing are domain-specific as assumed, that is, that the left angular gyrus (AG) shows an affinity to the verbal domain, and that the left and right intraparietal sulcus (IPS) shows an affinity to the visual domain, the activation of these areas should show a dependency on an individual’s cognitive style.Methods36 healthy young adults participated in the fMRI study. The participants habitual use of visualization and verbalization during solving arithmetic word problems was assessed with a short self-report assessment. During the fMRI measurement, arithmetic word problems that had to be solved by the participants were presented in an event-related design.ResultsWe found that visualizers showed greater brain activation in brain areas involved in visual processing, and that verbalizers showed greater brain activation within the left angular gyrus.ConclusionsOur results indicate that cognitive styles or preferences play an important role in understanding brain activation. Our results confirm, that strong visualizers use mental imagery more strongly than weak visualizers during calculation. Moreover, our results suggest that the left AG shows a specific affinity to the verbal domain and subserves number processing in a modality-specific way.

Cognitive loading via mental arithmetic modulates effects of blink‐related oscillations on precuneus and ventral attention network regions

“…These findings help to validate the involvement of the precuneus regions in BRO effects. This has been further substantiated by studies reporting differential activation patterns when participants solved mental arithmetic problems using different calculation strategies (i.e., visualization vs verbalization of numbers) (Ischebeck et al, 2006;Zarnhofer et al, 2012Zarnhofer et al, , 2013. gyri during PF is consistent with previous reports (Berman, Horovitz, Morel, & Hallett, 2012;Bonfiglio et al, 2014;Liu et al, 2017), and may be related to sensory processing of blink-related information as these regions have been previously linked with visual processes including complex image encoding, face perception, and object recognition (Bar et al, 2001;Machielsen, Rombouts, Barkhof, Scheltens, & Witter, 2000;Weiner & Zilles, 2016).…”

“…In particular, the triple-code model postulates that visual recognition of number forms is subserved by occipital regions along with the lingual and fusiform gyri (Dehaene, Molko, Cohen, & Wilson, 2004), while verbal representation of numbers is associated with left-lateralized perisylvian areas (Grabner et al, 2009). This has been further substantiated by studies reporting differential activation patterns when participants solved mental arithmetic problems using different calculation strategies (i.e., visualization vs verbalization of numbers) (Ischebeck et al, 2006;Zarnhofer et al, 2012Zarnhofer et al, , 2013. Since participants in this study were instructed to perform mental calculations internally without moving their mouths, they may have been more likely to employ a visualization strategy that recruits occipital, lingual, and fusiform ing task demands due to mental calculation.…”

Fronto‐insular‐parietal network engagement underlying arithmetic word problem solving

“…Prabhakaran et al (2001) investigated brain responses of reading word problems involving the four basic arithmetic operations in adults. In another more recent study, Zarnhofer et al (2013) compared brain responses during reading of an arithmetic word problem (e.g., Anna goes for a walk. In another more recent study, Zarnhofer et al (2013) compared brain responses during reading of an arithmetic word problem (e.g., Anna goes for a walk.…”

“…They found that increasing problem complexity with additional arithmetic operands resulted in increased engagement of DLPFC. In another more recent study, Zarnhofer et al (2013) compared brain responses during reading of an arithmetic word problem (e.g., Anna goes for a walk. She walks 4 km/hr.…”

Decoding individual differences in STEM learning from functional MRI data

“…The few studies that have explored the relationship between brain activity and individual differences in concept knowledge employed univariate measures and hypothesis-driven approaches. Specifically, these studies tested for correlations between concept-related test performance and brain activity in particular regions of interest, which were often selected a priori [18][19][20][21] . Furthermore, these studies relied on metrics derived at the group level or from literature-driven hypotheses about unique brain regions.…”