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      Unlocking adults’ implicit statistical learning by cognitive depletion

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          Statistical learning mechanisms enable extraction of patterns in the environment from infancy to adulthood. For example, they enable segmentation of continuous speech streams into novel words. Adults typically become aware of the hidden words even when passively listening to speech streams. It remains poorly understood how cognitive development and brain maturation affect implicit statistical learning (i.e., infant-like learning without awareness). Here, we show that the depletion of the cognitive control system by noninvasive brain stimulation or by demanding cognitive tasks boosts adults’ implicit but not explicit word-segmentation abilities. These findings suggest that the adult cognitive architecture constrains statistical learning mechanisms that are likely to contribute to early language acquisition and opens avenues to enhance language-learning abilities in adults.

          Abstract

          Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 ( n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 ( n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.

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          Most cited references67

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            Statistical learning by 8-month-old infants.

            Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.
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              Learning and development in neural networks: the importance of starting small.

              J L Elman (1993)
              It is a striking fact that in humans the greatest learning occurs precisely at that point in time--childhood--when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language), as investigated in connectionist networks. The networks are trained to process complex sentences involving relative clauses, number agreement, and several types of verb argument structure. Training fails in the case of networks which are fully formed and 'adultlike' in their capacity. Training succeeds only when networks begin with limited working memory and gradually 'mature' to the adult state. This result suggests that rather than being a limitation, developmental restrictions on resources may constitute a necessary prerequisite for mastering certain complex domains. Specifically, successful learning may depend on starting small.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                4 January 2022
                11 January 2022
                4 January 2022
                : 119
                : 2
                : e2026011119
                Affiliations
                [1] aDepartment of Experimental Psychology, Ghent University , 9000 Ghent, Belgium;
                [2] bInternational Research Center for Neurointelligence, The University of Tokyo , Tokyo 158-8557, Japan;
                [3] cPsychological Research Institute, Université catholique de Louvain , 1348 Ottignies-Louvain-la-Neuve, Belgium;
                [4] dInstitute of Neuroscience, Université catholique de Louvain , 1348 Ottignies-Louvain-la-Neuve, Belgium;
                [5] eSchool of Psychology, University of Nottingham , Nottingham NG7 2QL, United Kingdom;
                [6] fCognitive Science, Department of Digital Humanities, University of Helsinki , 00014 Helsinki, Finland
                Author notes
                1To whom correspondence may be addressed. Email: eleonore.smalle@ 123456ugent.be .

                Edited by Richard Aslin, Haskins Laboratories, New Haven, CT; received December 17, 2020; accepted November 4, 2021

                Author contributions: E.H.M.S. and R.M. designed research; E.H.M.S. performed research; E.H.M.S., T.D., and R.M. analyzed data; W.D. helped in funding of the first author; R.M. helped in funding of the research (tools and participants); E.H.M.S. and R.M. wrote the paper; and A.S. and W.D. reviewed and edited the paper.

                Author information
                https://orcid.org/0000-0003-2363-5693
                https://orcid.org/0000-0002-6464-2964
                https://orcid.org/0000-0003-3903-3953
                https://orcid.org/0000-0003-2114-6212
                https://orcid.org/0000-0003-4533-4277
                Article
                202026011
                10.1073/pnas.2026011119
                8764693
                34983868
                d5780405-29e7-43ee-8401-70a23477c939
                Copyright @ 2021

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 04 November 2021
                Page count
                Pages: 9
                Funding
                Funded by: Fonds Wetenschappelijk Onderzoek (FWO) 501100003130
                Award ID: 1211421N
                Award Recipient : Eleonore Smalle
                Funded by: RCUK | Medical Research Council (MRC) 501100000265
                Award ID: G1000566
                Award Recipient : Riikka Möttönen
                Categories
                431
                Biological Sciences
                Psychological and Cognitive Sciences
                Social Sciences
                Psychological and Cognitive Sciences
                From the Cover

                auditory statistical learning,implicit learning,electroencephalography,cognitive load,transcranial magnetic stimulation

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