Electrophysiology of statistical learning: Exploring the online learning process and offline learning product
Ordin, M, Polyanskaya, L, Soto, D, Molinaro, N. Electrophysiology of statistical learning: Exploring the online learning process and offline learning product. Eur J Neurosci. 2020; 51: 2008– 2022. https://doi.org/10.1111/ejn.14657
Laburpena
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.