[PDF][PDF] Linking the neural basis of hierarchical prediction with statistical learning: The paradox of attention

JM Schneider, Y Weng, A Hu, Z Qi, Z Qi - preprint - researchgate.net
preprintresearchgate.net
Statistical learning, the process of tracking distributional information and discovering
embedded patterns, is traditionally regarded as a form of implicit learning. However, recent
studies proposed that both implicit (attention-independent) and explicit (attention-
dependent) learning systems are involved in statistical learning. To understand the role of
attention in statistical learning, the current study investigates the cortical processing of
prediction errors in speech based on either local or global distributional information. We …
Abstract
Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of prediction errors in speech based on either local or global distributional information. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found ERP evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results provide important neural evidence elucidating the facilitatory role of attention in statistical learning.
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