Measurement of the time course of statistical learning in word segmentation

描述(申请人提供)：统计学习指的是各种各样的现象，其中许多被认为与语言习得有关。然而，对于统计学习的基础过程，人们几乎没有达成一致意见。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学习的最终结果上。为了确定统计学习的过程，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检查语言材料统计学习的新方法。这种方法将提供比以前的方法更全面和更敏感的结果，这将使这些实验能够以一种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废话组成的音节。在这一流中，单词一致地并存，而跨越单词边界形成的音节连词更难预测。参与者将被要求在语音流中听一个特定的音节，并在听到时按下按钮进行回应。对于这些参与者中的一些人来说，音节会出现在一个不可预测的位置(例如，Golabu中的Go相对不可预测，因为它可能出现在语音流中任何单词的结尾之后)。对于其他参与者，音节将出现在一个可预测的位置(例如，Golabu中的bu始终由go和la的存在来表示)。这项提案中概述的实验是朝着统计学习的基于过程的机械性解释迈出的第一步。第一个实验表明，这种新的方法是可行的，并将评估系列反应时测量与更标准的测试后测量的相关性程度。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评估了工作记忆与任务成绩的关系，特别是在不同字长的情况下。实验3评估了一项关于如何通过比较过程来补充组块以使学习非相邻规则成为可能的建议。最后，实验4询问当学习实时发生时，如何整合多个分割线索。通过确定在接触输入的过程中学习的动态特征，这项研究将以以前不可能实现的方式测试和完善统计学习理论。

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.