Doctoral Dissertation Research: Bias in Novel Category Learning

理解先前的语言经验在新类别习得中所起的作用，对于我们发展关于个体如何学习第一或第二语言的理论至关重要。本文研究了人工语言学习实验和二语习得中新语音类别学习中的学习偏差。在类别学习过程中，学习者发现类别很大程度上是基于他们的输入沿语音线索维度的分布。研究表明，学习者在使用单一线索发现类别时，无论他们的母语是否使用给定线索，都能近似最佳地利用分布信息。相反，当学习类别需要多个线索时，学习者往往只注意一些可用的线索，导致较差的类别学习。例如，说西班牙语的人在学习英语时经常难以区分“sheep”和“ship”的元音。许多学习者最初主要关注统计上不可靠的元音持续时间差异，尽管谱差异形式有更强的线索。在Lisa Davidson博士的指导下，Sean Christopher Martin将进行一系列实验来测试母语经验在多大程度上导致了类别学习中的这种学习偏差。以英语为母语的人将接受训练，以识别新的元音类别，这些类别是通过对熟悉和不熟悉的线索来区分的。然后系统地操纵刺激，以控制哪一个线索更可靠。通过这种方式，可以区分对统计上可靠的线索的偏好和对学习者先前经验表明可靠的线索的偏好。学习者行为的计算模型根据类别学习的层次描述来解释预期结果。为了从像语音信号这样的高维输入中学习，该模型在学习类别结构的同时发展了关于线索可靠性的泛化，从而提高了母语学习的效率，但随后产生了学习偏差，因为它最初预测不熟悉的线索是低可靠性的。这项研究的结果将有助于理解第二语言学习，突出可能导致第二语言学习效果差的问题。此外，所提出的计算模型展示了一种允许更灵活和适应性更强的类别表示的方法，这可能允许未来的语音识别系统更容易地处理语音信号的自然变异性。

Understanding the role that prior linguistic experience plays in the acquisition of novel categories is critical to our development of theories of how individuals learn a first or second language.This dissertation examines learning bias in novel phonetic category learning, as in artificial language learning experiments or second language acquisition. During category learning, learners discover categories largely based on the distribution of their input along phonetic cue dimensions. Learners have been shown to make approximately optimal use of distributional information when discovering categories with single cues, whether or not their native language uses a given cue. In contrast, when learning categories requiring multiple cues, learners frequently attend to only some of the available cues, resulting in poorer category learning. For example, Spanish speakers learning English often show difficulty with the vowel distinction in 'sheep' vs 'ship'. Many learners initially attend primarily to statistically unreliable vowel duration differences although stronger cues are available in the form of spectral differences.Under the direction of Dr. Lisa Davidson, Sean Christopher Martin will carry out a series of experiments to test the degree to which native language experience gives rise to this type of learning bias in category learning. Native speakers of English will be trained to recognize new vowel categories which are distinguished by pairs of familiar and unfamiliar cues. The stimuli are then systematically manipulated to control which of the cues is more reliable. In this way, the respective contributions of preference for statistically robust cues and bias to attend to cues which the learner's prior experience suggests are reliable can be distinguished.A computational model of learner behavior accounts for expected results in terms of a hierarchical account of category learning. In order to learn from high-dimensional input like the speech signal, the model develops generalizaitons about cue reliability while learning category structure, giving rise to more efficient first-language learning but later generating learning bias because it initially predicts unfamiliar cues to be low-reliability.The results of this research will aid understanding of second language learning, highlighting issues which might contribute to poor second language learning outcomes. In addition, the proposed computational model demonstrates an approach which allows for more flexible and adaptable category representations which might allow future speech recognition systems to more easily cope with the natural variability of the speech signal.