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Updated: May 28, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Cognitive Load Across Interaction Formats in Digital Attention Assessment for Children: Within-Subject Neuroimaging
Harim Jeong1, Yong Jeon Cheong1, Jihyeong Ro1
1Korea Brain Research Institute, Dong-gu, Daegu, Republic of Korea.
Background:
Digital health technologies increasingly use tablet-based cognitive assessments for children, yet interaction design choices can substantially influence cognitive load and measurement validity. Although cognitive load has been extensively studied in educational settings, its impact on patient-facing digital assessment tools for pediatric populations remains underexplored.
Objective:
This study examined how the interaction format influences cognitive load and measurement validity in a tablet-based Stroop task for children, comparing text-based with color-based response selection to determine which format better supports valid attention assessment.
Methods:
Using a within-subject design, 127 typically developing children (n=55, 43.3%, girls; n=72, 56.7%, boys) aged 6-12 (mean 9.15, SD 1.56) years were recruited via convenience sampling from local communities in the Republic of Korea. Participants completed both a text-based and a color-based Stroop task on a tablet. Cognitive load was indexed using prefrontal functional near-infrared spectroscopy (fNIRS), measuring functional connectivity (FC) and global network efficiency. Behavioral outcome measures included accuracy, reaction time, and the composite efficiency score. Clinical validity was assessed by correlating task performance with parent-reported attention problems using a standardized behavioral rating scale. General cognitive ability was controlled using a standardized intelligence measure. Condition differences were analyzed using paired-sample t tests (α=.05), clinical associations using Pearson correlations with Fisher r-to-z transformation, and predictor contributions using random forest regression.
Results:
In the paired-sample t tests, the color-based format yielded significantly higher accuracy (0.91 vs 0.86; mean difference 0.05, 95% CI 0.03-0.08; t126=3.81, Cohen d=0.34, P<.001), faster reaction times (1183 vs 1269 ms; mean difference -85.4 ms, 95% CI -107.2 to -63.5; t126=-7.72, Cohen d=-0.69, P<.001), and superior composite performance (t126=6.81, Cohen d=0.62, P<.001). Pearson correlations revealed that color-based performance was significantly associated with parent-reported attention problems (r=-0.20, 95% CI -0.37 to -0.03; P=.03), whereas text-based performance was not (r=-0.05, 95% CI -0.23 to 0.12;' P=.56); the Fisher r-to-z test indicated this difference was not statistically significant (z=1.54, P=.12). fNIRS-derived neural indices (ΔFC, global efficiency) did not differ significantly between conditions (P>.05). Random forest analyses indicated that after controlling for age and general cognitive ability, individual variations in prefrontal efficiency accounted for 40% to 44% of residual performance variance.
Conclusions:
This study provides some of the first empirical evidence that the interaction format substantially influences task demands and measurement validity in pediatric digital assessments, extending cognitive load theory into digital health assessment design. Color-based response formats that minimize extraneous semantic processing yield superior performance and stronger clinical validity compared to text-based formats. These findings suggest that prioritizing response modalities aligned with children's developmental capabilities may improve the clinical utility of digital attention assessments.
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