Measurement of the time course of statistical learning in word segmentation

分词统计学习时间过程的测量

• 批准号: 8326039
• 负责人: ERIK THIESSEN
• 金额: $ 7.84万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8326039
• 关键词: Accounting; Adult; Agreement; Architecture; Auditory; Behavioral; Categories; Characteristics; Child; Cues; Data; Detection; Elements; Entropy; Exposure to; Frequencies; Grouping; Hearing; Human; Individual Differences; Knowledge; Language; Language Development; Learning; Length; Linguistics; Literature; Location; Machine Learning; Measurement; Measures; Methodology; Methods; Modeling; Music; Neurologic; Outcome; Participant; Performance; Positioning Attribute; Probability; Process; Reaction Time; Recurrence; Research; Research Proposals; Shapes; Short-Term Memory; Signal Transduction; Simulate; Speech; Statistical sensitivity; Stream; Structure; Testing; Time; Visual; Work; Yang; auditory stimulus; base; diene; improved; insight; lexical; novel; phrases; research study; statistics; theories; therapy design

DESCRIPTION (provided by applicant): Statistical learning refers to a wide variety of phenomena, many of which have been argued to be related to language acquisition. However, there is little agreement on the process that underlies statistical learning. Several different accounts have been proposed, but it has been difficult to differentiate between these accounts as many converge on the same end result of learning. To identify the process responsible for statistical learning, it is necessary to more closely examine behavioral data that can characterize the dynamic characteristics of learning over the course of exposure. The objective of the current project is to develop and apply a novel method for examining statistical learning of linguistic materials. This method will provide more comprehensive and sensitive results than prior methods, which will enable these experiments to distinguish between theories of statistical learning in a way that has not been previously possible. In these experiments, participants will be exposed to a stream of syllables made up for nonsense words. Within this stream, words consistently co-occur, while syllable conjunctions formed across word boundaries are less predictable. Participants will be asked to listen for a particular syllable within the speech stream, and respond with a button press when they hear it. For some of these participants, the syllable will occur in an unpredictable location (for example, go in golabu is relatively unpredictable, because it can occur after the end of any word in the speech stream). For other participants, the syllable will occur in a predictable location (for example, bu in golabu is consistently signaled by the presence of both go and la). The experiments outlined in this proposal are a first step towards a process-based, mechanistic account of statistical learning. The first experiment demonstrates that this novel methodology is feasible, and will assess the extent to which the serial reaction time measure correlates with more standard post-test measures. The second and third experiments seek to test process-level predictions of a theory of statistical learning. Experiment 2 assesses the extent to which working memory is related to performance in the task, especially on different word lengths. Experiment 3 assesses a proposal about how chunking might be supplemented by processes of comparison to make learning of non-adjacent regularities possible. Finally, Experiment 4 asks how multiple cues to segmentation are integrated while learning is occurring in real time. By identifying the dynamic characteristics of learning over the course of exposure to the input, this research will test and refine theories of statistical learning in ways that have not previously been possible.

描述（由申请人提供）：统计学习是指各种各样的现象，其中许多已被认为是与语言习得。然而，在统计学习的基础过程上几乎没有达成一致。已经提出了几种不同的解释，但很难区分这些解释，因为许多解释都集中在相同的学习结果上。为了确定负责统计学习的过程，有必要更仔细地检查行为数据，这些数据可以表征暴露过程中学习的动态特征。本项目的目标是开发和应用一种新的方法来检查语言材料的统计学习。这种方法将提供比以前的方法更全面和敏感的结果，这将使这些实验能够以以前不可能的方式区分统计学习理论。在这些实验中，参与者将接触到一系列由无意义单词组成的音节。在这个流中，单词一致地共同出现，而跨单词边界形成的音节连接词则较难预测。参与者将被要求在语音流中收听特定的音节，并在听到时按下按钮。对于其中一些参与者来说，音节将出现在不可预测的位置（例如，go in golabu相对不可预测，因为它可以出现在语音流中任何单词的结尾）。对于其他参与者来说，音节将出现在一个可预测的位置（例如，golabu中的bu总是通过go和la的出现来表示）。本提案中概述的实验是迈向基于过程的统计学习机制的第一步。第一个实验表明，这种新的方法是可行的，并将评估在何种程度上的串行反应时间测量与更标准的后测措施。第二个和第三个实验试图测试统计学习理论的过程级预测。实验2评估了工作记忆在多大程度上与任务中的表现有关，特别是在不同的单词长度上。实验3评估了一个关于组块如何通过比较过程来补充的建议，以使非相邻组块的学习成为可能。最后，实验4询问如何在真实的时间内整合多个分割线索。通过识别学习过程中的动态特征，本研究将以以前不可能的方式测试和完善统计学习理论。