A1 Journal article (refereed)
Project DyAdd : Nonlinguistic theories of dyslexia predict intelligence (2020)
Laasonen, Marja; Lahti-Nuuttila, Pekka; Leppämäki, Sami; Tani, Pekka; Wikgren, Jan; Harno, Hanna; Oksanen-Hennah, Henna; Pothos, Emmanuel; Cleeremans, Axel; Dye, Matthew W.; Cousineau, Denis et al. (2020). Project DyAdd : Nonlinguistic theories of dyslexia predict intelligence . Frontiers in Human Neuroscience, 14, 316. DOI: 10.3389/fnhum.2020.00316 https://www.frontiersin.org/articles/10.3389/fnhum.2020.00316/abstract
JYU authors or editors
Publication details
All authors or editors: Laasonen, Marja; Lahti-Nuuttila, Pekka; Leppämäki, Sami; Tani, Pekka; Wikgren, Jan; Harno, Hanna; Oksanen-Hennah, Henna; Pothos, Emmanuel; Cleeremans, Axel; Dye, Matthew W.; et al.
Journal or series: Frontiers in Human Neuroscience
eISSN: 1662-5161
Publication year: 2020
Volume: 14
Article number: 316
Publisher: Frontiers Research Foundation
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.3389/fnhum.2020.00316
Persistent website address: https://www.frontiersin.org/articles/10.3389/fnhum.2020.00316/abstract
Open Access: Publication published in an open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71437
Abstract
Two themes have puzzled the research on developmental and learning disorders for decades. First, some of the risk and protective factors behind developmental challenges are suggested to be shared and some to be specific for a given condition. Second, language-based learning difficulties like dyslexia are suggested to result from or correlate with also nonlinguistic aspects of information processing. In the current study, we investigated how adults with developmental dyslexia and ADHD as well as healthy controls cluster across various dimensions designed to tap the prominent nonlinguistic theories of dyslexia. Participants were 18–55-year-old adults with dyslexia (n = 36), ADHD (n = 22), and controls (n = 35). Nonlinguistic theories investigated with experimental designs included temporal processing impairment, abnormal cerebellar functioning, procedural learning difficulties as well as visual processing and attention deficits. Latent profile analysis (LPA) was used to investigate the emerging groups and patterns of results across these experimental designs. LPA suggested three groups: 1) a large group with average performance in the experimental designs, 2) participants predominantly from the clinical groups but with enhanced conditioning learning, and 3) participants predominantly from the dyslexia group with temporal processing as well as visual processing and attention deficits. Despite the presence of these distinct patterns, participants did not cluster very well based on their original status, nor did the LPA groups differ in their dyslexia or ADHD-related neuropsychological profiles. Remarkably, the LPA groups did differ in their intelligence. These results highlight the continuous and overlapping nature of the observed difficulties and support the multiple deficit model of developmental disorders, which suggests shared risk factors for developmental challenges. It also appears that some of the risk factors suggested by the prominent nonlinguistic theories of dyslexia relate to the general level of functioning in tests of intelligence.
Keywords: dyslexia; ADHD; developmental disabilities (mental and physical); learning difficulties; neuropsychology
Free keywords: dyslexia; ADHD; temporal processing; procedural learning; eyeblink conditioning; visual attention; comorbidity; visual processing
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