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Statistical and computational models of the visual world paradigm: Growth curves and individual differences
Journal of Memory and Language ( IF 4.3 ) Pub Date : 2008-11-01 , DOI: 10.1016/j.jml.2007.11.006
Daniel Mirman 1 , James A Dixon , James S Magnuson
Affiliation  

Time course estimates from eye tracking during spoken language processing (the "visual world paradigm", or VWP) have enabled progress on debates regarding fine-grained details of activation and competition over time. There are, however, three gaps in current analyses of VWP data: consideration of time in a statistically rigorous manner, quantification of individual differences, and distinguishing linguistic effects from non-linguistic effects. To address these gaps, we have developed an approach combining statistical and computational modeling. The statistical approach (growth curve analysis, a technique explicitly designed to assess change over time at group and individual levels) provides a rigorous means of analyzing time course data. We introduce the method and its application to VWP data. We also demonstrate the potential for assessing whether differences in group or individual data are best explained by linguistic processing or decisional aspects of VWP tasks through comparison of growth curve analyses and computational modeling, and discuss the potential benefits for studying typical and atypical language processing.

中文翻译:

视觉世界范式的统计和计算模型:增长曲线和个体差异

在口语处理(“视觉世界范式”,或 VWP)期间眼动追踪的时间进程估计使得关于随着时间的推移激活和竞争的细粒度细节的辩论取得进展。然而,当前对 VWP 数据的分析存在三个差距:以统计上严格的方式考虑时间、个体差异的量化以及区分语言效应与非语言效应。为了解决这些差距,我们开发了一种结合统计和计算建模的方法。统计方法(生长曲线分析,一种明确设计用于评估群体和个人水平随时间变化的技术)提供了一种分析时间进程数据的严格方法。我们介绍该方法及其在 VWP 数据中的应用。
更新日期:2008-11-01
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