Do current statistical learning tasks capture stable individual differences in children? An investigation of task reliability across modality

Abstract

Do commonly used statistical-learning tasks capture stable individual differences in children? Infants, children, and adults are capable of using statistical learning (SL) to extract information about their environment. Although most studies have looked at group-level performance, a growing literature examines individual differences in SL and their relation to language-learning outcomes: Individuals who are better at SL are expected to show better linguistic abilities. Accordingly, studies have shown positive correlations between SL performance and language outcomes in both children and adults. However, these studies have often used tasks designed to explore group-level performance without modifying them, resulting in psychometric shortcomings that impact reliability in adults (Siegelman, Bogaerts, Christiansen, & Frost in Transactions of the Royal Society B, 372, 20160059, 2017a; Siegelman, Bogaerts, & Frost in Behavior Research Methods, 49, 418-432, 2017b). Even though similar measures are used to assess individual differences in children, no study to date has examined the reliability of these measures in development. This study examined the reliability of common SL measures in both children and adults. It assessed the reliability of three SL tasks (two auditory and one visual) twice (two months apart) in adults and children (mean age 8 years). Although the tasks showed moderate reliability in adults, they did not capture stable individual variation in children. None of the tasks were reliable across sessions, and all showed internal consistency measures well below psychometric standards. These findings raise significant concerns about the use of current SL measures to predict and explain individual differences in development. The article ends with a discussion of possible explanations for the difference in reliability between children and adults.

Keywords: Children; Domain generality; Individual differences; Reliability; Statistical learning.