The Use of Cognitive Tests in the Assessment of Dyslexia

Abstract

In this literature review, we address the use of cognitive tests, including intelligence tests, in the assessment and diagnosis of dyslexia, from both historic and present-day perspectives. We discuss the role of cognitive tests in the operationalization of the concepts of specificity and unexpectedness, two constructs considered essential to the characterization of dyslexia since the publication of early case reports in the late nineteenth century. We review the advantages and disadvantages of several approaches to specific learning disabilities’ identification that are used in schools. We also discuss contemporary debates around the use of standardized cognitive testing in dyslexia evaluations, in particular, the arguments of those who favor an approach to diagnosis based on prior history and the results of a comprehensive evaluation and those who favor an approach based on an individual’s response to intervention. We attempt to explain both perspectives by examining clinical observations and research findings. We then provide an argument for how cognitive tests can contribute to an accurate and informed diagnosis of dyslexia.

Investigating Dyslexia through Diffusion Tensor Imaging across Ages: A Systematic Review

“…The intelligence measure showed an expected outcome in terms of experimental and control group matching, with more studies reporting lower performance of children with dyslexia on verbal but not on non-verbal tests of intelligence. The intelligence assessment has generated a long-time debate in the field of learning, and while in typically developing children intelligence performance generally correlates with the achievement level, in reading impairment the intellectual disability is inconsistent with a diagnosis of dyslexia [65].…”

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

“…They also used ML methods for identifying individuals using eye movement recordings, random forests for selection of eye movement features, and SVM classifiers, and obtained an accuracy of 89.7%. They have also explained it by reviewing the clinical observations and research data perspectives in Mather and Schneider (2023) and Wagner et al (2023), evaluated with the help of Bayesian identification model and included set of predictors. In Alqahtani et al (2023), Ahire et al (2023), Jan and Khan (2023), and Parmar and Paunwala (2023), the authors have worked on detection and categorization of dyslexia with a strong focus on implementing ML methods, like deep learning and electroencephalogram data analysis, in the detection and categorization of dyslexia.…”

Synergistic Interventions for Silencing Oppositional Defiant Disorder and Dyslexia in a Child With Attention-Deficit/Hyperactivity Disorder: A Case Report From Albania

“…The WRAT-5, together with the Wechsler Intelligence Scale for Children, Fifth Edition (WISC-V) [ 18 ], were used to objectively assess the presence of a specific learning disorder, in that case, dyslexia, as well as evaluate the treatment efficacy. After a series of tests, the clearest evidence was in the Ability-Achievement Discrepancy Analysis [ 20 ] (Table 1 ), in which the spelling, word reading, and reading composite were disrupted, with the spelling on the lower spectrum. The Pattern of Strengths and Weaknesses Analysis (Table 2 ) supported the theory of a specific learning disability (SLD), namely dyslexia, as clinically evident during the CBT sessions.…”