Toward a Model of Statistical Learning and Reading: Evidence From a Meta-Analysis

Abstract

A compelling demonstration of implicit learning is the human ability to unconsciously detect and internalize statistical patterns of complex environmental input. This ability, called statistical learning, has been investigated in people with dyslexia using various tasks in different orthographies. However, conclusions regarding impaired or intact statistical learning in dyslexia remain mixed. This study conducted a systematic literature search of published and unpublished studies that compared statistical learning between people with and without dyslexia using different learning paradigms in different orthographies. We identified 49 papers consisting of 59 empirical studies, representing the data from 1,259 participants with dyslexia and 1,459 typically developing controls. The results showed that, on average, individuals with dyslexia performed worse in statistical learning than age-matched controls, regardless of the learning paradigm or orthography (average weighted effect size d = 0.47, 95% confidence interval [0.36, 0.59], p < .001). Meta-regression analyses further revealed that the heterogeneity of effect sizes between studies was significantly explained by one reader characteristic (i.e., verbal IQ) but no task characteristics (i.e., task paradigm, task modality, and stimulus type). These findings suggest domain-general statistical learning weakness in dyslexia across languages, and support the need for a new theoretical model of statistical learning and reading, that is, the SLR model, which elucidates how reader and task characteristics are regulated by a multicomponent memory system when establishing statistically optimal representations for deep learning and reading.