Statistical and computational models of the visual world paradigm: Growth curves and individual differences.
Statistical and computational models of the visual world paradigm: Growth curves and individual differences.
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DOI：
10.1016/j.jml.2007.11.006
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发表时间：
2008-11
影响因子：
4.3
通讯作者：
Magnuson, James S.
中科院分区：
文献类型：
--
作者：
Mirman, Daniel;Dixon, James A.;Magnuson, James S.
口语处理过程中的眼动跟踪（“视觉世界范式”，或VWP）的时间过程估计已经使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了进展。然而，目前的VWP数据分析存在三个差距：以严格的统计方式考虑时间，量化个体差异，区分语言影响和非语言影响。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组和个体水平随时间变化的技术）提供了分析时程数据的严格方法。本文介绍了该方法及其在VWP资料中的应用。我们还展示了评估组或个人数据的差异是否最好的解释VWP任务的语言处理或决策方面通过比较增长曲线分析和计算建模的潜力，并讨论了研究典型和非典型语言处理的潜在好处。
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.
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影响因子：
2.6
作者：
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通讯作者：
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影响因子：
3.4
作者：
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通讯作者：
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作者：
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影响因子：
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作者：
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DOI：
10.1037/0278-7393.32.1.1
发表时间：
2006-01-01
影响因子：
2.6
作者：
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通讯作者：
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