Hierarchical Bayesian Approaches To The Exploration Of Mechanisms Underlying Group And Individual Differences

Download or read book Hierarchical Bayesian Approaches to the Exploration of Mechanisms Underlying Group and Individual Differences written by Yiyang Chen (Ph. D. in psychology) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Populations and individuals diverge from each other in their cognitive abilities, and re- searchers have a great interest in characterizing and explaining these group and individual differences. Among research tools, behavioral tasks are widely adopted to assess cognitive abilities due to their simplicity and applicability. In behavioral tasks, descriptive statistics are commonly used as measurement indices for the cognitive abilities of interest. However, because these statistics have a limited ability to characterize the mechanisms underlying each task based on cognitive theories, they cannot fully explain the reasons that may cause group and individual differences. In this dissertation, I adopt hierarchical Bayesian approaches to model several behav- ioral tasks for cognition, with the aim to explore the mechanisms underlying the group and individual differences in populations tested by these tasks. I incorporate existing cognitive theories into the hierarchical Bayesian models, and use estimated parameters to characterize the cognitive abilities of interest. At the group difference level, I show that the hierarchi- cal Bayesian models can be used to identify the potential deficits in populations that have poorer task performance. At the individual level, I show that these models can reveal the behavioral patterns of each individual, and identify potential causes of individual differences. I built theory-based hierarchical Bayesian models to three behavioral tasks respectively: the progressive ratio task that measures motivation; the continuous performance task that measures sustained attention; and the memory updating task that measures working memory abilities. I show that these models have reasonable parameter recovery abilities and good fits to data. I apply these models to several empirical data sets. The progressive ratio task model is applied to a data set measuring motivation of people with and without schizophrenia (Wolf et al., 2014) and first-degree relatives of people with schizophrenia (Wolf, 2015). The continuous performance task model is applied to a data set measuring sustained attention of people with and without first-episode psychosis. The memory updating task model is applied to data sets from two studies. The first study, performed by Oberauer and Kliegl (2001), investigated aging effects in working memory. The second study, performed by De Simoni and von Bastian (2018), investigated transfer effects in working memory training. I explore the mechanisms underlying group and individual differences shown in these tasks based on the estimated parameters from the hierarchical Bayesian models.