I am a post doctoral reseacher at the Caliber Lab in the Department of Psychology, New Mexico State University. I recieved my Ph.D. in Applied Cognitive Psychology at Claremont Graduate University under the supervision of Andrew R.A. Conway, PhD. My primary research interests include the impact of working memory on selective attention, individual differences in cognitive ability, and statistical methods (e.g., structural equation modeling, item response theory, and psychometric network analysis) for psychometric and cognitive modeling of human complex cognition. I am also interested in programming and data visualization with R and Python.
Ph.D. in Applied Cognitive Psychology, 2022
Claremont Graduate University, USA
M.A. in Positive Organizational Psychology & Evaluation, 2017
Claremont Graduate University, USA
B.Sc. in Psychology, 2013
Zhejiang University, PRC
Traditional theories of intelligence either prioritize a psychometric or a cognitive perspective, but their limitations and incompatibilities hinder a comprehensive understanding. Contemporary theories, like the process overlap theory (POT; Kovacs & Conway, 2016; 2019), aim to bridge the gap between the two perspectives, by explaining inter-individual differences in intelligence through intra-individual psychological processes. The current study investigates POT as a unified framework for understanding human intelligence, incorporating psychometric and cognitive theories. POT proposes a novel psychometric structure and cognitive architecture that explains individual differences in cognitive abilities. We developed dynamics to simulate potential correlational/causal structures of cognitive processes involved in human cognitive activities based on POT, examining how these structures align with psychometric models. Test scores were generated from a sampling of simulated cognitive processes and fitted by typical latent factor models. Despite the absence of a general cognitive ability in generating the data, results showed that a standard higher-order “general intelligence” model fit the data well. As POT rejects the notion of a general factor of intelligence (g), psychometric network models (Borsboom et al., 2021; Epskamp et al., 2018) were also implemented to simulated test scores, as they align better with the theory. Finally, we implemented a network structure at the latent factor level to retain latent variable models' benefits in accounting for measurement error. Estimated factor scores for simulated broad abilities from the three different models are compared and discussed. This study demonstrates POT’s compatibility with standard psychometric models, including the general intelligence factor, without assuming a common cognitive cause. The results support POT and provide an alternative theoretical and statistical framework for contemporary research on human cognition, combining psychometric and cognitive theories of intelligence.
Under Construction!
Overview The goal of this document is to introduce applications of R for item response theory (IRT) modeling. Specifically, this document is focused on introducing basic IRT analyses for beginners using the “mirt” package (Chalmers, 2012).
An example of formative factor model with lavaan package.
An example of generalized mixed effects model (GLMM) in with lmer package.