Many students suffer from anxiety when performing numerical calculations. Mathematics anxiety is a condition that has a negative effect on educational outcomes and future employment prospects. While there are a multitude of behavioral studies on mathematics anxiety, its underlying cognitive and neural mechanism remain unclear. This article provides a systematic review of cognitive studies that investigated mathematics anxiety. As there are no prior neural network models of mathematics anxiety, this article discusses how previous neural network models of mathematical cognition could be adapted to simulate the neural and behavioral studies of mathematics anxiety. In other words, here we provide a novel integrative network theory on the links between mathematics anxiety, cognition, and brain substrates. This theoretical framework may explain the impact of mathematics anxiety on a range of cognitive and neuropsychological tests. Therefore, it could improve our understanding of the cognitive and neurological mechanisms underlying mathematics anxiety and also has important applications. Indeed, a better understanding of mathematics anxiety could inform more effective therapeutic techniques that in turn could lead to significant improvements in educational outcomes.

中文翻译：

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数学焦虑和认知：一个集成的神经网络模型

许多学生在进行数值计算时会感到焦虑。数学焦虑是一种对教育成果和未来就业前景产生负面影响的状况。虽然有大量关于数学焦虑的行为研究，但其潜在的认知和神经机制仍不清楚。本文对调查数学焦虑的认知研究进行了系统评价。由于没有先前的数学焦虑神经网络模型，本文讨论了如何调整先前的数学认知神经网络模型来模拟数学焦虑的神经和行为研究。换句话说，我们在这里提供了一种关于数学焦虑、认知和大脑基质之间联系的新型综合网络理论。这个理论框架可以解释数学焦虑对一系列认知和神经心理学测试的影响。因此，它可以提高我们对数学焦虑背后的认知和神经机制的理解，并具有重要的应用价值。事实上，更好地理解数学焦虑可以为更有效的治疗技术提供信息，进而可以显着改善教育成果。