Claims

Gray matter volume significantly predicts children's multiplication skill in a cross-validated model

Claim · All claims
Evidence · 1 study · associational r2 · q2 · i2 medium
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i2 A GMV-based LOOCV predictive model significantly predicted individual differences in multiplication performance (r = 0.2824). → Zhang 2024

Evidence

Zhang 2024

Zhang, D., Xie, Y., Wang, L. & Zhou, K. (2024). Structural and transcriptional signatures of arithmetic abilities in children. npj Science of Learning 9, 58. https://doi.org/10.1038/s41539-024-00270-6

q2 · i2 · associational · r2

Same LOOCV predictive-modeling analysis on 130 children; the multiplication model reached "multiplication: r = 0.2824, P = 0.0011" and involved motor, occipital, frontal, temporal and parietal cortex regions with non-zero weights.

"For both the subtraction and multip lication predictive model, the correlation between observed and predicted scores was signi ficant (sub- traction: r = 0.3506, P =4 . 3 2×1 0−6; multiplication: r = 0.2824, P = 0.0011"

Discussion

Page metadata
Field Value
type claim
id gmv-predicts-multiplication-children
title Gray matter volume significantly predicts children's multiplication skill in a cross-validated model
description Gray matter volume significantly predicts children's multiplication skill in a cross-validated model
status review
evidence_strength moderate
newest_evidence 2024
generated by process:wiki-ingest on 2026-10-26
trust unverified — no one has confirmed this page's content
sources 1 — zhang-2024

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