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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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 |
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