Neural Representation Precision of Distance Predicts Arithmetic Performance

Focusing on the distance between magnitudes as the start point to investigate the mechanism of relationship detecting and its contribution to mathematical thinking, this study explores the precision of neural representations of numerical distance and their impact on arithmetic performance. By employing neural decoding techniques and representational similarity analysis, the present study investigates how accurately the brain represents numerical distances and how this precision relates to arithmetic skills. Thirty-two children participated, completing a dot number comparison task during fMRI scanning and an arithmetic fluency test. Results indicated that neural activation patterns in the intra-parietal sulcus decoded the distance between the presented pair of dots, and higher precision in neural distance representation correlates with better arithmetic performance. These findings suggest that the accuracy of neural decoding can serve as an index of the neural representation precision and that the ability to precisely encode numerical distances in the brain is a key factor in mathematical abilities. This provides new insights into the neural basis of mathematical cognition and learning. Highlights Utilizing representational similarity analysis and neural decoding techniques, the research proposes that the accuracy of neural decoding serve as an index of neural representation precision. The precision of neural representation of numerical distances in the brain predicts task performance and math arithmetic proficiency in children. These findings imply the potential significance of relational information in general cognition and learning beyond mathematical learning.