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Updated: Jan 15, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Neural networks recruited for numerical rule learning and application
Peijuan Li1, Weiye Xie1, Xin Tan1
1Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, PR China.
Abstract:
How do brain regions specialized for number processing interact within broader neural systems to support learning to identify and apply numeric rules? Participants performed a number rule learning task in which they viewed sequences of three numbers and across trials identified via trial and error which of 4 possible rules the number sequences followed. Rules were chosen to differ in numeric and executive control task demands. Constrained Principal Components Analysis identified three networks supporting performance in this task. One network overlapped with the dorsal and ventral attentional networks and included both known number sensitive regions in the Intraparietal Sulcus (IPS) along with motor planning associated regions (premotor and SMA) and visual attention associated regions (insula and anterior cingulate). The time course of activity in this network was consistent with it playing a role in processing the numerosity of the stimuli, forming a decision, and performing an associated motor response. A second network followed the time course of visual presentation of the number stimuli and involved visual cortex broadly, including regions associated with the number form area (NFA). A third network was sensitive to rule complexity, with greater activity at the time of decision for rules requiring more complex evaluation. This network included regions of the medial prefrontal and parietal cortex and inferior parietal cortex often associated with the default mode network. Overall, these results demonstrate how multiple neural networks underlying both numeric and nonnumeric processing can interact to allow people to make mathematical decisions.
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