Imagine trying to learn a new language. It’s like a puzzle – a complex web of sounds, words, and rules. One crucial piece of this puzzle is statistical learning, which helps us acquire and understand language. In a recent meta-analysis spanning 25 years of research, scientists explored how statistical learning varies across different aspects of language in infants, children, and adults. They discovered that infants show robust learning across various stimuli, like syllables and words. Meanwhile, adults exhibit consistent learning across stimuli and structures, including adjacent and non-adjacent dependencies. Interestingly, larger effect sizes were observed when multiple cues were present. However, the analysis also revealed potential biases in published studies and a tendency to focus on simplified language properties during training. The researchers found that factors like exposure and transitional probability strength had limited impact on learning, challenging some established theories. Additionally, the choice of task significantly influenced learning outcomes in both adults and children. Overall, this study suggests that statistical learning has a strong role in language acquisition but calls for further investigation to uncover its full potential.

Abstract
Statistical learning is a key concept in our understanding of language acquisition. Ample work has highlighted its role in numerous linguistic functions—yet statistical learning is not a unitary construct, and its consistency across different language properties remains unclear. In a meta-analysis of auditory-linguistic statistical learning research spanning the last 25 years, we evaluated how learning varies across different language properties in infants, children, and adults and surveyed the methodological trends in the literature. We found robust learning across stimuli (syllables, words, etc.) in infants, and across stimuli and structures (adjacent dependencies, non-adjacent dependencies, etc.) in adults, with larger effect sizes when multiple cues were present. However, the analysis also showed significant publication bias and revealed a tendency toward using a narrow range of simplified language properties, including in the strength of the transitional probabilities used during training. Bayes factor analyses revealed prevalent data insensitivity of moderators commonly hypothesized to impact learning, such as the amount of exposure and transitional probability strength, which contradict core theoretical assumptions in the field. Methodological factors, such as the tasks used at test, also significantly impacted effect sizes in adults and children, suggesting that choice of task may critically constrain current theories of how statistical learning operates. Collectively, our results suggest that auditory-linguistic statistical learning has the kind of robustness needed to play a foundational role in language acquisition, but that more research is warranted to reveal its full potential.

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