Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood...

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Main Authors: Ethan Jost, Katherine Brill-Schuetz, Kara Morgan-Short, Morten H. Christiansen
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-10-01
Series:Frontiers in Human Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fnhum.2019.00358/full
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spelling doaj-3befde3dd9b9435a88b779709baab4ae2020-11-25T03:52:54ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612019-10-011310.3389/fnhum.2019.00358478698Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning TaskEthan Jost0Katherine Brill-Schuetz1Kara Morgan-Short2Morten H. Christiansen3Department of Psychology, Cornell University, Ithaca, NY, United StatesDepartment of Psychology, University of Illinois at Chicago, Chicago, IL, United StatesDepartment of Psychology, University of Illinois at Chicago, Chicago, IL, United StatesDepartment of Psychology, Cornell University, Ithaca, NY, United StatesStatistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants’ endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item’s chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities.https://www.frontiersin.org/article/10.3389/fnhum.2019.00358/fullstatistical learningartificial language learningsecond language learningretentionmemory
collection DOAJ
language English
format Article
sources DOAJ
author Ethan Jost
Katherine Brill-Schuetz
Kara Morgan-Short
Morten H. Christiansen
spellingShingle Ethan Jost
Katherine Brill-Schuetz
Kara Morgan-Short
Morten H. Christiansen
Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
Frontiers in Human Neuroscience
statistical learning
artificial language learning
second language learning
retention
memory
author_facet Ethan Jost
Katherine Brill-Schuetz
Kara Morgan-Short
Morten H. Christiansen
author_sort Ethan Jost
title Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
title_short Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
title_full Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
title_fullStr Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
title_full_unstemmed Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
title_sort input complexity affects long-term retention of statistically learned regularities in an artificial language learning task
publisher Frontiers Media S.A.
series Frontiers in Human Neuroscience
issn 1662-5161
publishDate 2019-10-01
description Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants’ endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item’s chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities.
topic statistical learning
artificial language learning
second language learning
retention
memory
url https://www.frontiersin.org/article/10.3389/fnhum.2019.00358/full
work_keys_str_mv AT ethanjost inputcomplexityaffectslongtermretentionofstatisticallylearnedregularitiesinanartificiallanguagelearningtask
AT katherinebrillschuetz inputcomplexityaffectslongtermretentionofstatisticallylearnedregularitiesinanartificiallanguagelearningtask
AT karamorganshort inputcomplexityaffectslongtermretentionofstatisticallylearnedregularitiesinanartificiallanguagelearningtask
AT mortenhchristiansen inputcomplexityaffectslongtermretentionofstatisticallylearnedregularitiesinanartificiallanguagelearningtask
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