Electrophysiology of statistical learning: Exploring the online learning process and offline learning product


Electrophysiology of statistical learning: Exploring the online learning process and offline learning product

Low TPs between elements (e.g. syllables) can be used to detect boundaries between constituents (e.g. words) in a continuous sensory input (e.g. speech). High TPs between elements can be used to detect frequently co‐occurring elements and to cluster them as parts of the same constituent. We found evidence for boundary‐finding mechanisms to be employed for segmentation of a continuous input into discrete constituents, and for the existence of memory representations of the whole constituents used to recognize the extracted units following the exposure.


A continuous stream of syllables is segmented into discrete constituents based on the transitional probabilities (TPs) between adjacent syllables by means of statistical learning. However, we still do not know whether people attend to high TPs between frequently co‐occurring syllables and cluster them together as parts of the discrete constituents or attend to low TPs aligned with the edges between the constituents and extract them as whole units. Earlier studies on TP‐based segmentation also have not distinguished between the segmentation process (how people segment continuous speech) and the learning product (what is learnt by means of statistical learning mechanisms). In the current study, we explored the learning outcome separately from the learning process, focusing on three possible learning products: holistic constituents that are retrieved from memory during the recognition test, clusters of frequently co‐occurring syllables, or a set of statistical regularities which can be used to reconstruct legitimate candidates for discrete constituents during the recognition test. Our data suggest that people employ boundary‐finding mechanisms during online segmentation by attending to low inter‐syllabic TPs during familiarization and also identify potential candidates for discrete constituents based on their statistical congruency with rules extracted during the learning process. Memory representations of recurrent constituents embedded in the continuous speech stream during familiarization facilitate subsequent recognition of these discrete constituents.


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