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Mathematics anxiety and cognition: an integrated neural network model
Ahmed A Moustafa1,2, Angela Porter1, Ahmed M Megreya3
1School of Social Sciences and Psychology, Western Sydney University, 2 Bullecourt Ave, Milperra, 2214 Sydney, New South Wales, Australia.
Reviews in the Neurosciences
|November 16, 2019
Summary
Mathematics anxiety negatively impacts students
Area of Science:
- Cognitive Neuroscience
- Educational Psychology
- Mathematics Education
Background:
- Mathematics anxiety affects educational outcomes and career prospects.
- Cognitive and neural mechanisms of mathematics anxiety are not well understood.
- Existing research primarily focuses on behavioral studies.
Purpose of the Study:
- To systematically review cognitive studies on mathematics anxiety.
- To propose an integrative network theory linking mathematics anxiety, cognition, and brain substrates.
- To adapt existing neural network models of mathematical cognition for mathematics anxiety research.
Main Methods:
- Systematic review of cognitive studies investigating mathematics anxiety.
- Discussion of adapting neural network models from mathematical cognition.
- Development of a novel integrative network theory.
Main Results:
- Identified gaps in understanding the cognitive and neural underpinnings of mathematics anxiety.
- Proposed a theoretical framework to explain the impact of mathematics anxiety on cognitive tests.
- Highlighted the potential for network models to simulate behavioral and neural data.
Conclusions:
- The proposed integrative network theory offers a novel framework for understanding mathematics anxiety.
- This framework can explain the effects of mathematics anxiety on cognitive and neuropsychological assessments.
- Improved understanding can lead to more effective therapeutic interventions and better educational outcomes.
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