• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Frontiers in Human Neuroscience >Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
【2h】

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

机译：输入复杂性影响人工语言学习任务中统计学习规律的长期保留

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：已经提出，涉及对环境中分布规律的敏感性的统计学习（SL）是认知的许多方面的重要因素，包括语言。但是，人们对统计学习信息随时间的保留程度还不甚了解。为了确定学习者是否能够随着时间的推移保持这种规律性，我们在训练后立即以及在2周后的后续会议中检查了人工第二语言学习任务的表现。参与者使用人工语言（Brocanto2），其中一半接受简化的训练项目，其中只有20％的序列包含复杂的结构，而另一半则接受训练集，其中80％的项目由复杂的结构组成序列。总体而言，参与者在第一届会议上表现出学习的迹象，而在第二届会议上则表现出保留的态度，但是学习程度受所接受培训的性质的影响。接受简化输入的参与者在更复杂的训练条件下的表现要好于后者。 GLMM用于模拟两个会话中刺激属性和参与者认可策略之间的关系。结果表明，复杂训练条件下的参与者比简单训练条件下的参与者更依赖于项目的块强度。两者合计，这组发现表明在2周的过程中保留了统计学习的规律性。结果还表明，对具有简单项目的输入进行训练可以改善学习和保持语法规律性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号