Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15232, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15232, USA.
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15232, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15232, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15232, USA.
Cognition. 2020 Sep;202:104328. doi: 10.1016/j.cognition.2020.104328. Epub 2020 Jun 5.
Speech is notoriously variable, with no simple mapping from acoustics to linguistically-meaningful units like words and phonemes. Empirical research on this theoretically central issue establishes at least two classes of perceptual phenomena that accommodate acoustic variability: normalization and perceptual learning. Intriguingly, perceptual learning is supported by learning across acoustic variability, but normalization is thought to counteract acoustic variability leaving open questions about how these two phenomena might interact. Here, we examine the joint impact of normalization and perceptual learning on how acoustic dimensions map to vowel categories. As listeners categorized nonwords as setch or satch, they experienced a shift in short-term distributional regularities across the vowels' acoustic dimensions. Introduction of this 'artificial accent' resulted in a shift in the contribution of vowel duration in categorization. Although this dimension-based statistical learning impacted the influence of vowel duration on vowel categorization, the duration of these very same vowels nonetheless maintained a consistent influence on categorization of a subsequent consonant via duration contrast, a form of normalization. Thus, vowel duration had a duplex role consistent with normalization and perceptual learning operating on distinct levels in the processing hierarchy. We posit that whereas normalization operates across auditory dimensions, dimension-based statistical learning impacts the connection weights among auditory dimensions and phonetic categories.
语音具有显著的可变性，无法简单地将声学特征映射到单词和音素等具有语言学意义的单位上。针对这一理论核心问题的实证研究至少确定了两类感知现象，可以适应声学可变性：归一化和感知学习。有趣的是，感知学习支持跨声学可变性的学习，但归一化被认为可以抵消声学可变性，这使得关于这两种现象如何相互作用的问题仍然存在。在这里，我们研究了归一化和感知学习对语音维度如何映射到元音类别这两个现象的联合影响。当听众将非单词归类为 setch 或 satch 时，他们经历了元音声学维度上短期分布规律的变化。引入这种“人为口音”导致元音时长在分类中的贡献发生变化。尽管基于维度的统计学习影响了元音时长对元音分类的影响，但这些相同元音的时长仍然通过时长对比（一种归一化形式）对后续辅音的分类保持一致的影响。因此，元音时长的作用具有双重性，符合在处理层次结构的不同水平上进行归一化和感知学习的作用。我们假设，归一化在听觉维度之间起作用，而基于维度的统计学习则影响听觉维度和语音类别之间的连接权重。
