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 This study conducted a systematic literature search of published and unpublished studies that compared statistical learning between people with and without dyslexia using different learning paradigms in different orthographies.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.

Multiple mechanisms regulate statistical learning of orthographic regularities in school-age children: Neurophysiological evidence

“…Previous behavioral studies have demonstrated that statistical learning—the ability to involuntarily extract and encode relational patterns of environmental input—plays a critical role in reading acquisition (for a review, see Lee et al, 2022 ). This is partly because many orthographies contain myriad statistical patterns that can be detected and learned by young children through mere repeated exposure (e.g., Frost et al, 2013 ; Treiman and Kessler, 2022 ).…”

Neural Correlates of Statistical Learning in Developmental Dyslexia: An Electroencephalography Study

“…Some new theoretical frameworks have been proposed to explain statistical learning impairment in dyslexia. For example, a multicomponent memory network, referred to as the Statistical Learning and Reading (SLR) model (Lee et al, 2022), represents a domain-general system that consists of a short-term memory subsystem, an explicit declarative long-term memory subsystem, and an implicit procedural long-term memory subsystem. The two long-term memory subsystems are distinguishable in terms of the attentional demands required for encoding and storage of information, with more controlled attention (i.e., a top-down selective attention to learning stimuli) than automatic attention (i.e., a bottom-up involuntary attention to salient stimuli) needed in the explicit declarative subsystem, and, conversely, more automatic attention than controlled attention needed in the implicit procedural subsystem.…”

The role of visual factors in dyslexia

“…Dyslexia might therefore reflect deficits in the learning of statistical regularities in the environment more generally. A recent meta-analysis of 49 empirical studies (Lee et al, 2022) concluded that dyslexic readers have domaingeneral and language-independent statistical learning deficits. Banai & Ahissar (2018) reported that problems with picking up sound statistics occur in about 50% of those with dyslexia.…”

“…They could also be a manifestation of a more general deficit in procedural learning (Gabay et al, 2015;Nicolson et al, 2010) or reflect lessened effects of recent perceptual information or top-down expectations on current perceptual representations due to dysfunction in rapid neural plasticity (Ahissar, 2007;Beach et al, 2022;Perrachione et al, 2016). Also, some studies have not found any problems at all with visual statistical learning in dyslexic readers (e.g., (van Witteloostuijn et al, 2019(van Witteloostuijn et al, , 2021; see also discussion in Schmalz et al, 2017) but a recent meta-analysis by Lee et al (2022) does indicate that the problem is real. It is also possible that people with dyslexia show atypical predictive processing in visual statistical learning paradigms as measured neurally that does not always manifest in overt behavior (Singh et al, 2018).…”