TY - JOUR

T1 - Mathematics anxiety and cognition: An integrated neural network model

AU - Moustafa, Ahmed A.

AU - Porter, Angela

AU - Megreya, Ahmed M.

N1 - De Gruyter allows authors the use of the final published version of an article (publisher pdf) for self-archiving (author's personal website) and/or archiving in an institutional repository (on a non-profit server) after an embargo period of 12 months after publication.

PY - 2020/4/1

Y1 - 2020/4/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85075260724&partnerID=8YFLogxK

U2 - 10.1515/revneuro-2019-0068

DO - 10.1515/revneuro-2019-0068

M3 - Article

C2 - 31730536

AN - SCOPUS:85075260724

SN - 0334-1763

VL - 31

SP - 287

EP - 296

JO - Reviews in the Neurosciences

JF - Reviews in the Neurosciences

IS - 3

ER -