Statistical learning is related to early literacy-related skills

Abstract

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one’s environment, plays a role in young children’s acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy—oral language, vocabulary knowledge, and phonological processing—within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed.

Beyond Cumulative Risk

“…Pattern learning refers to the detection of regularities in the environment when these regularities are not linked with rewards or punishments (e.g., that cue-A always follows cue-B). Pattern learning plays a central role in language acquisition and expertise (Romberg & Saffran, 2010; Spencer, Kaschak, Jones, & Lonigan, 2015). …”

Measuring individual differences in statistical learning: Current pitfalls and possible solutions

“…However, if the same two individuals are tested with 32 2AFC trials, the chances of correctly identifying individual B increase to 0.87, whereas the chances of falsely identifying individual A decrease to 0.13. Perhaps unaware of this problem, some recent studies have used test trials as few as four (e.g., Spencer et al, 2014), to predict individual abilities. Our simple simulation demonstrates that correlations obtained in such studies are most probably spurious, reflecting noise.…”

“…In this vein, performance in implicit artificial grammar learning (AGL) tasks was shown to predict sentence comprehension (Misyak & Christiansen, 2012), the processing of relative-clause sentences with long-distance dependencies (Misyak, Christiansen, & Tomblin, 2010), and speech perception abilities (Conway, Bauernschmidt, Huang, & Pisoni, 2010; Conway, Karpicke, & Pisoni, 2007). Similarly, visual SL has been demonstrated to predict reading abilities in one’s first language (L1; Arciuli & Simpson, 2012), and also literacy acquisition in a second language (L2; Frost, Siegelman, Narkiss, & Afek, 2013), and auditory SL was found to predict lexical and oral language skills in L1 (Mainela-Arnold & Evans, 2014; Singh, Steven Reznick, & Xuehua, 2012; Spencer, Kaschak, Jones, & Lonigan, 2014). A second approach to the study of individual differences stems from the assumption that understanding the source of individual differences in SL holds the promise of revealing critical insight regarding the cognitive operations underlying its mechanisms, leading to deeper comprehension of what SL capacity could predict and why (see Frost, Armstrong, Siegelman, & Christiansen, 2015, for a theoretical discussion).…”

The multi-component nature of statistical learning

“…[19][20][21][22][23][24][25][26][27][28]). Better performance on independent tasks of SL tends to be associated with greater language proficiency.…”