Date of Award

Fall 2022

Degree Type

Open Access Dissertation

Degree Name

Psychology, PhD


School of Social Science, Politics, and Evaluation

Advisor/Supervisor/Committee Chair

Andrew Conway

Dissertation or Thesis Committee Member

Kathy Pezdek

Dissertation or Thesis Committee Member

Gabriel Cook

Dissertation or Thesis Committee Member

Michael J. Kane

Terms of Use & License Information

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Rights Information

© 2022 Han Hao


Cognitive Theory, Intelligence, Network Analysis, Process Overlap Theory, Psychometrics, Working Memory

Subject Categories

Cognitive Psychology | Quantitative Psychology


Human intelligence has been scientifically investigated as a psychological construct for over a century but there has not been a universally accepted definition or theory. One cause of this problem is that traditional theories attempt to explain the robust findings in cognitive ability testing, such as the positive manifold, from two different perspectives: psychometric or cognitive. Both approaches have their own limitations and are sometimes incompatible with each other. Therefore, contemporary theories of intelligence have been developed to provide a more unified perspective by combining both types of approaches, allowing the psychometric structure of cognitive abilities to be represented and explained by cognitive mechanisms. In other words, inter-individual differences in intelligence are explained in terms of intra-individual psychological processes. This dissertation investigated a contemporary theoretical framework of intelligence, the process overlap theory (POT; Kovacs & Conway, 2016; 2019), that attempts to bridge the gap between psychometric and cognitive theories of human intelligence. POT proposed a novel psychometric structure and cognitive architecture to explain individual differences in higher-order cognition. This dissertation consisted of three studies that illustrated and investigated the POT framework using a combination of latent factor models, item response models, and psychometric network models of both simulated data and real-world cognitive testing data. These exploratory studies provide an account of the psychometric structure and the cognitive mechanisms of higher-order cognition from a POT perspective. Results of the studies demonstrated that, based on the POT algorithm, the positive manifold of intelligence can emerge at the psychometric level in the absence of a general mental ability at the cognitive level. This dissertation therefore provides critical supportive evidence for POT and illustrates an alternative theoretical and statistical framework for contemporary research of human cognition that combines psychometric and cognitive theories of intelligence.