Training a non-native vowel contrast with a distributional learning paradigm results in improved perception and production
Training a non-native vowel contrast with a distributional learning paradigm results in improved perception and production
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DOI：
10.1016/j.wocn.2019.100940
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发表时间：
2020-01-01
影响因子：
1.9
通讯作者：
Levi, Susannah V.
中科院分区：
文献类型：
--
作者：
Kabakoff, Heather;Go, Gretchen;Levi, Susannah V.
先前的分布学习研究表明，当成年人暴露于双峰分布比单峰分布时，可以更有效地提高对非本地对比的感知。研究还表明，感知学习可以转移到生产中。本研究测试了添加视觉图像来加强对比和主动反馈学习是否会导致两种情况下的学习并转化为生产收益。以英语为母语的成年人从双峰或单峰/o-/oe/连续体中听到刺激。在歧视任务中没有发现群体差异，这可能表明支持消除了之前记录的群体差异。在一项识别任务中，双峰组的听者在终点刺激上的表现优于单峰组。生产结果表明，两组在训练后目标元音之间的欧几里得距离都有所增加，这表明知觉训练提高了两组的生产技能。与预期相反，感知程度与生产学习不相关。综上所述，这些结果表明，双峰分布可能有助于学习，但在训练范式中添加图像以加强对比和主动学习可以减轻先前暴露于单峰分布的参与者所发现的缺点。(C) 2019 Elsevier Ltd.版权所有。
Previous distributional learning research suggests that adults can improve perception of a non-native contrast more efficiently when exposed to a bimodal than a unimodal distribution. Studies have also suggested that perceptual learning can transfer to production. The current study tested whether the addition of visual images to reinforce the contrast and active learning with feedback would result in learning in both conditions and would transfer to gains in production. Native English-speaking adults heard stimuli from a bimodal or unimodal /o-/oe/ continuum. No group differences were found on a discrimination task, possibly suggesting that the supports eliminated previously documented group differences. On an identification task, listeners in the bimodal group showed better performance than the unimodal group on the endpoint stimuli. Production results indicated that both groups showed increased Euclidean distance between the target vowels after training, suggesting that perceptual training improved production skills in both conditions. Contrary to expectations, degree of perception and production learning were not correlated. Together, these results suggest that a bimodal distribution may aid learning, but that adding images to reinforce the contrast and active learning to the training paradigm could mitigate disadvantages found previously for participants exposed to a unimodal distribution. (C) 2019 Elsevier Ltd. All rights reserved.