Bayesian hierarchical models for inference with behavorial data

University Of Missouri-Columbia, Columbia MO

Investigators

Abstract

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.