Artemenko Christina, Soltanlou Mojtaba, Bieck Silke M, Ehlis Ann-Christine, Dresler Thomas, Nuerk Hans-Christoph
LEAD Graduate School & Research Network, University of Tuebingen, Tuebingen, Germany.
Department of Psychology, University of Tuebingen, Tuebingen, Germany.
Front Hum Neurosci. 2019 Jul 3;13:227. doi: 10.3389/fnhum.2019.00227. eCollection 2019.
Some individuals experience more difficulties with math than others, in particular when arithmetic problems get more complex. Math ability, on one hand, and arithmetic complexity, on the other hand, seem to partly share neural underpinnings. This study addresses the question of whether this leads to an interaction of math ability and arithmetic complexity for multiplication and division on behavioral and neural levels. Previously screened individuals with high and low math ability solved multiplication and division problems in a written production paradigm while brain activation was assessed by combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). Arithmetic complexity was manipulated by using single-digit operands for simple multiplication problems and operands between 2 and 19 for complex multiplication problems and the corresponding division problems. On the behavioral level, individuals with low math ability needed more time for calculation, especially for complex arithmetic. On the neural level, fNIRS results revealed that these individuals showed less activation in the left supramarginal gyrus (SMG), superior temporal gyrus (STG) and inferior frontal gyrus (IFG) than individuals with high math ability when solving complex compared to simple arithmetic. This reflects the greater use of arithmetic fact retrieval and also the more efficient processing of arithmetic complexity by individuals with high math ability. Oscillatory EEG analysis generally revealed theta and alpha desynchronization with increasing arithmetic complexity but showed no interaction with math ability. Because of the discovered interaction for behavior and brain activation, we conclude that the consideration of individual differences is essential when investigating the neurocognitive processing of arithmetic.
一些人在数学方面比其他人遇到更多困难，尤其是当算术问题变得更加复杂时。一方面，数学能力，另一方面，算术复杂性，似乎部分共享神经基础。本研究探讨了这是否会导致数学能力与乘法和除法的算术复杂性在行为和神经层面上产生相互作用。之前筛选出的数学能力高和低的个体在书面生成范式中解决乘法和除法问题，同时通过功能近红外光谱(fNIRS)和脑电图(EEG)联合评估大脑激活情况。通过使用个位数操作数来解决简单乘法问题，以及使用2到19之间的操作数来解决复杂乘法问题和相应的除法问题，来操纵算术复杂性。在行为层面上，数学能力低的个体计算需要更多时间，尤其是对于复杂算术。在神经层面上，fNIRS结果显示，与数学能力高的个体相比，这些个体在解决复杂算术与简单算术时，左缘上回(SMG)、颞上回(STG)和额下回(IFG)的激活较少。这反映了算术事实检索的更多使用，以及数学能力高的个体对算术复杂性的更有效处理。振荡脑电图分析一般显示，随着算术复杂性的增加，θ波和α波去同步，但未显示与数学能力的相互作用。由于发现了行为和大脑激活之间的相互作用，我们得出结论，在研究算术的神经认知处理时，考虑个体差异至关重要。
