Bayesian Models for Assessing Shape and Covariance in Behavioral Data

用于评估行为数据的形状和协方差的贝叶斯模型

• 批准号: 0720229
• 负责人: Dongchu Sun
• 金额: $ 29万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720229&HistoricalAwards=false
• 关键词: Bayesian; Models; Assessing; Shape; Covariance

The project will investigate two distinct but related questions in psychological research. The first concerns response time (RT), the time taken to complete an experimental task. The shape of the RT distribution serves as a key marker of cognitive processing. Research will develop statistical methodology for testing whether shape is invariant or depends on covariates such as participant characteristics and experimental manipulations. The approach will be to choose appropriate distributions from a large class of three parameter distributions derived from the two-parameter exponential families introduced by Bar-Lev and Reiser augmented with a shift parameter. A unified approach will develop objective Bayesian methodology for this class of nonregular distributions. The second key research question is about the association of latent mental processes across people, items, and conditions. Understanding how these processes are related will provide insight into understanding cognition. The specific problem is to model the association of covariance matrices of two or more related bivariate distributions in a hierarchical setting. The project will develop objective priors for Bayesian analysis of these covariance matrices, generalizing recent developments in univariate signal-to-noise ratio priors.Both phases of the project address fundamental and significant questions in cognitive research in psychology, with potential impact in related areas as well. The study of response times also is fundamental to research in developmental and social psychology as well as psychopathology. A number of theories have been developed for cognitive processing in these fields and how it is affected by experimental conditions and characteristics of the participant. Most of these theories predict shape changes in the distribution of response times. However, the fundamental question of whether or not the shape of these distributions actually changes as a response to experimental condition has not been studied. Understanding if and how response time distribution shape changes will spur new theoretical directions. In addition, useful new statistical methods will be developed applicable beyond psychology. The second problem, estimating covariance matrices of latent variables, is motivated by the study of different modes of recall in memory tasks. Participants given lists of words to study and subsequently queried on these words may respond because of an "automatic" response, based perhaps on previous familiarity, or they may respond based on actual recollection. Assessment of these two processes is complicated by the fact that some words may be simultaneously easier to recall automatically or easier to remember; similarly, people may tend to be better simultaneously at automatic response or recollection. Assessment of these relationships is delicate and challenging. The research will have impact on the psychological study of working memory and cognitive aging. In addition, the statistical models are closely related to those used in areas such as epidemiology, economics, and ecology. Thus the results of the project will have impact well beyond the psychological sciences.

该项目将探讨心理学研究中两个不同但相关的问题。 第一个是反应时间（RT），即完成一项实验任务所需的时间。 RT分布的形状是认知处理的关键标志。 研究将开发统计方法来测试形状是否是不变的或取决于协变量，如参与者的特征和实验操作。 该方法将是选择适当的分布从一个大类的三个参数的分布来自两个参数的指数由巴列夫和雷泽增加了移位参数的家庭介绍。 一个统一的方法将开发这类非正则分布的客观贝叶斯方法。 第二个关键的研究问题是关于人、物品和条件之间潜在心理过程的关联。 了解这些过程是如何相关的将提供深入了解认知。 具体的问题是在分层设置中对两个或多个相关双变量分布的协方差矩阵的关联进行建模。 该项目将为这些协方差矩阵的贝叶斯分析开发客观先验，概括单变量信噪比先验的最新发展。该项目的两个阶段都涉及心理学认知研究中的基本和重要问题，并在相关领域产生潜在影响。 对反应时间的研究也是发展心理学、社会心理学和精神病理学研究的基础。 在这些领域的认知加工以及它如何受到实验条件和参与者特征的影响，已经发展了许多理论。 这些理论中的大多数预测响应时间分布的形状变化。 然而，这些分布的形状是否真的随着实验条件的响应而变化的根本问题尚未得到研究。 了解响应时间分布形状是否以及如何变化将激发新的理论方向。 此外，将开发出适用于心理学以外的有用的新统计方法。 第二个问题，估计潜变量的协方差矩阵，是由记忆任务中不同回忆模式的研究所激发的。 参与者给出了一系列要学习的单词，然后询问这些单词，他们可能会因为“自动”反应而做出反应，可能是基于以前的熟悉程度，或者他们可能会基于实际的回忆做出反应。 对这两个过程的评估是复杂的，因为有些单词可能同时更容易自动回忆或更容易记住;同样，人们可能倾向于同时更好地自动反应或回忆。 对这些关系的评估是微妙和具有挑战性的。 该研究对工作记忆和认知老化的心理学研究具有一定的借鉴意义。 此外，统计模型与流行病学、经济学和生态学等领域的统计模型密切相关。 因此，该项目的结果将产生远远超出心理科学的影响。