Simulating the process overlap theory of intelligence: A unified framework bridging psychometric and cognitive perspectives

Abstract

This study investigates process overlap theory (POT; Kovacs & Conway, 2016, 2019) as a contemporary unified framework for understanding individual differences in cognitive abilities, incorporating psychometric and cognitive theories. 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. Estimated factor/cluster scores for simulated broad abilities from the latent factor and network 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.