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Statistical Learning in a Natural Language by 8-Month-Old Infants


  • This research was funded by National Institute of Child Health and Human Development (NICHD) grants to JRS (R01HD37466) and JFH (F32-HD557032), and by a core grant to the Waisman Center from NICHD (P30HD03352). We would like to thank Katharine Graf Estes and three anonymous referees for helpful suggestions on a previous version of this manuscript. We would also like to thank Diana Dovorany, Jessica Hersh, Natalie Gordon, Jenna Louwagie, and Jessica Rich for their assistance in conducting this research. Last but not least, we express gratitude to the families who generously contributed their time.

concerning this article should be addressed to Bruna Pelucchi, Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705. Electronic mail may be sent to


Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants’ ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition.