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When I move, you move: Coordination in conversation.

机译:当我移动时,您也会移动:对话中的协调。

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摘要

Researchers have been interested in the dynamics of human conversation for several decades. One major focus of this line of research has been the mechanisms by which humans coordinate in conversation. To study the coordination of conversation within dyads often requires an understanding of statistical methods, which handle time series data. Traditionally, there are no definitive standards for defining one of the important parameters in time series analysis, window size. Therefore, the purpose of the current project was to elucidate the mechanisms through which conversational coordination emerges by utilizing conversational turn-taking as the basis for choosing the appropriate window size in the time series analysis.;Data from previously collected videotaped conversational interactions were analyzed for the presence of movement coordination using an image-differencing algorithm in MATLAB. Additionally, speech signals time-locked to the video segments were examined for vocal synchrony in pitch using Praat. The participants from the original study were asked to engage in three tasks designed to elicit sarcasm. Only data from one of these tasks, discussing an ironic scenario, were analyzed for the current project. There were eleven dyads each contributing one conversation and each lasting between 2 and 8 minutes. It was thought that both movement and pitch were possible coordination mechanisms, but that coordination patterns would only be uncovered by using time series windows adjusted for turn-taking rates in each dyad.;Results from windowed cross-correlations revealed that participants significantly coordinated movement. Post-hoc tests revealed an effect of window size on mean correlations. Although not significant, results from the analysis of vocal coordination revealed a pattern of results similar to those from the movement coordination study. This could be due to a lack of statistical power. Interestingly, patterns of movement coordination were found strictly as a function of a vocal parameter: turn-taking. These results suggest a novel approach to the study of conversational coordination and a more crucial role for turn-taking in the emergence of coordinative structures.
机译:几十年来,研究人员一直对人类对话的动态感兴趣。该研究领域的主要重点是人类在对话中进行协调的机制。要研究二元组中对话的协调性,通常需要了解处理时间序列数据的统计方法。传统上,没有明确的标准来定义时间序列分析中的重要参数之一,窗口大小。因此,本项目的目的是通过在时间序列分析中以对话轮换为基础选择合适的窗口大小,阐明对话协调的机制。在MATLAB中使用图像差分算法进行运动协调的情况。此外,使用Praat检查了锁定在视频片段上的语音信号的音调是否与语音同步。原始研究的参与者被要求从事旨在引起讽刺的三项任务。对于当前项目,仅分析了其中一项讨论了具有讽刺意味的情况的任务的数据。有11个二元组,每个二元组都进行一次对话,并且每次持续2至8分钟。有人认为运动和俯仰都是可能的协调机制,但只有通过使用针对每个二分位数的转弯速率调整的时间序列窗口才能发现协调模式。窗口互相关的结果表明,参与者显着地协调了运动。事后检验揭示了窗口大小对均值相关性的影响。尽管不显着,但人声协调分析的结果显示出与运动协调研究相似的结果模式。这可能是由于缺乏统计能力所致。有趣的是,严格根据声音参数:转弯来发现运动协调的模式。这些结果表明了一种新的对话协调研究方法,并且在协调结构的出现中对于转弯具有更重要的作用。

著录项

  • 作者

    Caucci, Gina M.;

  • 作者单位

    The University of Memphis.;

  • 授予单位 The University of Memphis.;
  • 学科 Psychology Cognitive.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 104 p.
  • 总页数 104
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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