November 28, 2020

Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge

Humans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected at central sensors, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in right temporo-parietal and frontal areas as well as left pre-central and frontal areas significantly predicted behavioral performance, especially in the last third of stimulus exposure. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two components are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on both sufficient exposure time and attention.

 bioRxiv Subject Collection: Neuroscience

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