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Updated: Oct 11, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Visual statistical learning in infancy: Discrimination of fine-grained regularities depends on early test trials
Julie Bertels1,2, Estibaliz San Anton1, Emeline Boursain1
1ULBabyLab, Consciousness, Cognition, and Computation Group (CO3), Center for Research in Cognition and Neurosciences (CRCN), Université libre de Bruxelles, Brussels, Belgium.
Abstract:
Infants' ability to detect statistical regularities between visual objects has been demonstrated in previous studies (e.g., Kirkham et al., Cognition, 83, 2002, B35). The extent to which infants extract and learn the actual values of the transitional probabilities (TPs) between these objects nevertheless remains an open question. In three experiments providing identical learning conditions but contrasting different types of sequences at test, we examined 8-month-old infants' ability to discriminate between familiar sequences involving high or low values of TPs, and new sequences that involved null TPs. Results showed that infants discriminate between these three types of sequences, supporting the existence of a statistical learning mechanism by which infants extract fine-grained statistical information from a stream of visual stimuli. Interestingly, the expression of this statistical knowledge varied between experiments and specifically depended on the nature of the first two test trials. We argue that the predictability of this early test arrangement-namely whether the first two test items were either predictable or unexpected based on the habituation phase-determined infants' looking behaviors.
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