Visual selective attention and visual search performance in children with CVI, ADHD, and Dyslexia: a scoping review

Marinke J Hokken1,2, Elise Krabbendam3, Ymie J van der Zee2

  • 1Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands.

Insights

Children with Cerebral Visual Impairment (CVI), Attention Deficit Hyperactivity Disorder (ADHD), and Dyslexia show impaired visual search performance (VSP). Differentiating these conditions using VSP is complex, suggesting a need for advanced measures like eye tracking.

Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Pediatric Neuropsychology

Background:

  • Visual selective attention is crucial for processing visual information.
  • Dysfunctions in visual selective attention are common in Cerebral Visual Impairment (CVI), Attention Deficit Hyperactivity Disorder (ADHD), and Dyslexia.
  • Discriminating between these conditions in pediatric neuropsychology is challenging.

Purpose of the Study:

  • To conduct a scoping review of visual search performance (VSP) in children aged 6-12 with CVI, ADHD, and Dyslexia.
  • To analyze and compare VSP across these pediatric populations.
  • To identify potential biomarkers for differential diagnosis.

Main Methods:

  • Systematic literature search for studies on VSP in children with CVI, ADHD, and Dyslexia.
  • Inclusion of 35 studies in the review.
  • Analysis of reaction time and accuracy data.

Main Results:

  • All patient groups exhibited impaired VSP compared to typically developing children.
  • Children with CVI showed slower and less accurate VSP.
  • ADHD was associated with accuracy deficits, while Dyslexia showed mixed results depending on stimuli.
  • Other factors like speed-accuracy trade-off and executive/phonological deficits may influence VSP.

Conclusions:

  • VSP is impaired across CVI, ADHD, and Dyslexia, but patterns differ.
  • Clinical differentiation based solely on current VSP measures is complex due to sparse and inconclusive data.
  • Advanced, quantitative VSP measures, such as eye-tracking, may improve diagnostic classification.