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

First published: 24 December 2019A 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.Secretaría de Estado de Investigación, Desarrollo e Innovación, Grant/Award Number: RTI2018-098317-B-I00 ; H2020 Marie Skłodowska-Curie Actions, Grant/Award Number: DLV-792331; Ekonomiaren Garapen eta Lehiakortasun Saila, Eusko Jaurlaritza, Grant/Award Number: PI-2017-25; Spanish Ministry of Economy and Competitiveness (MINECO) through the “Severo Ochoa” Programme for Centres/Units of Excellence in R&D, Grant/Award Number: SEV-2015-490; Basque Government, Grant/Award Number: PI-2017-25; European Commission through the Marie Skłodowska-Curie Research Fellowshi