Bayesian hierarchical models for inference with behavorial data

本项目将开发贝叶斯层次分析的新方法，以便为一些非线性心理处理模型提供有效的推理。具体的方法目标包括：(1)开发新的半参数方法；(2)开发和实施适合所提出的贝叶斯层次模型的模型选择策略和贝叶斯因子。新的半参数方法将使心理学家能够批判性地检查提出的模型以及分析数据，而不会做出不当的假设。模型选择方法，特别是贝叶斯因子是贝叶斯对假设检验的类似物，是验证或反驳心理模型的关键工具。新方法将应用于实验心理学的两个实质性领域：(1)学习或技能习得曲线的测量，以及(2)对记忆的有意识和无意识影响的评估。这种基本的方法论研究，以及提出的应用，将为更好地理解学习和记忆提供工具。实验心理学对感知、学习和记忆的本质提供了深刻的见解。目前的研究解决了实验实践中的一个缺陷。随着心理学理论的发展，它们趋于非线性。不幸的是，非线性设置中未建模的方差通常会扭曲推理，使从数据到理论的联系变得脆弱。心理学研究的特点是有几个差异来源，包括参与者的选择、测试项目和表现的瞬间波动。这些不同来源的存在对非线性理论测试提出了重大挑战。该项目将开发新的统计工具，用于在心理过程的具体、相关、非线性模型的几个层次上对变异性进行建模。这些新方法将用于解决几个长期存在争议的问题，即学习是如何发生的以及记忆是如何运作的。

This project will develop new methodology in Bayesian hierarchical analysis in order to provide efficient inference for a number of nonlinear models of mental processing. Specific methodological goals include: (1) developing new semiparametric methods and (2) developing and implementing model selection strategies and Bayes factors appropriate for the proposed Bayesian hierarchical models. New semiparametric methods will enable psychologists to critically examine proposed models as well as analyze data without making undue assumptions. Model selection methods and Bayes factors in particular are Bayesian analogues to hypothesis testing and are crucial tools for verifying or disproving psychological models. The new methodology will be applied in two substantive domains of experimental psychology: (1) the measurement of learning or skill acquisition curves, and (2) the assessment of conscious and unconscious influences in memory. This basic methodological research, along with the proposed applications, will provide the tools for better understanding of learning and memory.Experimental psychology has provided profound insights into the nature of perception, learning, and memory. The current research addresses a flaw in experimental practice. As psychological theories progress, they tend to become nonlinear. Unfortunately, unmodeled variance in nonlinear settings generally distort inference, making the link from data to theory tenuous. Psychological research is characterized by several sources of variance, including those from the selection of participants, test items, and moment-to-moment fluctuations in performance. The presence of these distinct sources presents a significant challenge to nonlinear theory testing. The project will develop new statistical tools for modeling variability at several levels in specific, pertinent, nonlinear models of psychological process. The new methods will then be used to address several long-standing controversial issues in how learning occurs and how memory operates.