Testing the Specificity of Predictors of Reading, Spelling and Maths: A New Model of the Association Among Learning Skills Based on Competence, Performance and Acquisition

• Published in: Frontiers in human neuroscience 2020-12, Vol.14, p.573998-573998
• Main Authors: Zoccolotti, Pierluigi, De Luca, Maria, Marinelli, Chiara Valeria, Spinelli, Donatella
• Format: Article
• Language: English
• Subjects: Automation; Behavior disorders; Brain research; Cognition & reasoning; Cognitive ability; Cognitive models; Comorbidity; Dyslexia; Elementary school students; Fluency; Italian language; Language; Learning; Learning disabilities; Mathematics; maths; Memory; Neuroscience; Novels; Phonology; proximal predictors; Reading; Reading tests; Sensory integration; Spelling
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2020.573998

In a previous study (Zoccolotti et al., 2020) we examined reading, spelling, and maths skills in an unselected group of 129 Italian children attending fifth grade by testing various cognitive predictors; results showed a high degree of predictors' selectivity for each of these three behaviors. In the present study, we focused on the specificity of the predictors by performing cross-analyses on the same dataset; i.e., we predicted spelling and maths skills based on reading predictors, reading based on maths predictors and so on. Results indicated that some predictors, such as the Orthographic Decision and the Arithmetic Facts tests, predicted reading, spelling and maths skills in similar ways, while others predicted different behaviors but only for a specific parameter, such as fluency but not accuracy (as in the case of RAN), and still others were specific for a single behavior (e.g., Visual-auditory Pseudo-word Matching test predicted only spelling skills). To interpret these results, we propose a novel model of learning skills separately considering factors in terms of competence, performance and acquisition (automatization). Reading, spelling and calculation skills would depend on the development of discrete and different abstract competences (accounting for the partial dissociations among learning disorders reported in the literature). By contrast, overlap among behaviors would be accounted for by defective acquisition in automatized responses to individual "instances"; this latter skill is item specific but domain independent. Finally, performance factors implied in task's characteristics (such as time pressure) may contribute to the partial association among learning skills. It is proposed that this new model may provide a useful base for interpreting the diffuse presence of comorbidities among learning disorders.