Doctoral Dissertation Research: Bias in Novel Category Learning

博士论文研究：小说类别学习中的偏见

• 批准号: 1349080
• 负责人: Lisa Davidson
• 金额: $ 0.49万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1349080&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Bias; Novel

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.

了解以前的语言经验在获得新类别中所扮演的角色对于我们对个人学习第一语言或第二语言的理论的发展至关重要。本文论文研究了新型语音类别学习中的学习偏见，就像在人工语言学习实验或第二语言习惯中一样。在类别学习期间，学习者在很大程度上基于其输入沿语音提示维度的分布来发现类别。在发现具有单个线索的类别时，学习者已被证明可以大致最佳地使用分布信息，无论其母语是否使用给定的提示。相反，当学习类别需要多个提示时，学习者经常仅参加一些可用的提示，从而导致类别学习较差。例如，西班牙语的人学习英语通常会在“绵羊”与“船”中的元音区分时表现出难以表现出难以理解。尽管最初以频谱差异的形式获得了更强的提示，但许多学习者最初主要参加统计上不可靠的元音持续时间差异。在丽莎·戴维森（Lisa Davidson）博士的方向上，肖恩·克里斯托弗·马丁（Sean Christopher Martin）将进行一系列的实验，以测试该程度的本地语言体验在类别学习中产生这种类型的学习偏见。以英语为母语的人将接受培训，以识别新的元音类别，这些类别以熟悉和陌生的提示为特色。然后系统地操纵刺激以控制哪些提示更可靠。这样，偏爱对统计上强大的提示和偏见的各自贡献可以分辨出学习者先前的经验表明可靠的线索。学习者行为的计算模型在类别学习的层次结构账户方面对预期结果产生了预期。为了从语音信号等高维输入中学习，该模型在学习类别结构的同时开发了有关提示可靠性的概括，从而提高了更有效的第一语言学习，但后来产生了学习偏见，因为它最初预测了陌生的提示为低核能。这项研究的结果将有助于了解第二语言学习的结果，以了解第二种语言的知识，这可能会贡献贫穷的学习，这可能会造成多个学习的贡献。此外，提出的计算模型展示了一种方法，该方法允许更灵活，适应性的类别表示，这可能使未来的语音识别系统更容易地应对语音信号的自然变异性。