Intelligence test items varying in capacity demands cannot be used to test the causality of working memory capacity for fluid intelligence

Abstract

There is a strong relationship between fluid intelligence and working memory capacity (WMC). Yet, the cognitive mechanisms underlying this relationship remain elusive. The capacity hypothesis states that this relationship is due to limitations in the amount of information that can be stored and held active in working memory. Previous research aimed at testing the capacity hypothesis assumed that it implies stronger relationships of intelligence test performance with WMC for test items with higher capacity demands. The present article addresses this assumption through simulations of three theoretical models implementing the capacity hypothesis while systematically varying different psychometric variables. The results show that almost any relation between the capacity demands of items and their correlation with WMC can be obtained. Therefore, the assumption made by previous studies does not hold: The capacity hypothesis does not imply stronger correlations of WMC and intelligence test items with higher capacity demands. Items varying in capacity demands cannot be used to test the causality of WMC (or any other latent variable) for fluid intelligence.

Fluid intelligence correlates with working memory capacity for both real-world objects and simple-feature stimuli

“…However, some other research has challenged the view that these capacity limits per se underlie the relation (e.g., Hagemann et al, 2023). Specifically, some studies showed that questions on fluid intelligence tests that are more cognitively demanding, and thus should require more working memory storage to be solved, do not correlate more strongly with working memory capacity (Salthouse & Pink, 2008;Burgoyne et al, 2019;Wiley et al, 2011;Unsworth & Engle, 2005; but also see Frischkorn & Oberauer, 2021;Smolen & Chuderski, 2015). Instead, it has been suggested that other aspects of working memory tasks such as attentional control processes (Draheim et al, 2022), the usage of encoding strategies (Babic et al, 2019), or the ability to adapt and apply rules (Salthouse & Pink, 2008;Wiley et al, 2011;Duncan et al, 2012) might be the crucial factors that drive the correlation between working memory performance and fluid intelligence.…”

Relationship Between Working Memory, Fluid Intelligence and Age Based on the Results of Mediation and Moderation Analyses

“…Kovacs and Conway (2016), in the context of their process overlap theory, conclude that the link between WM and Gf is driven by the operation of multiple domain-general cognitive processes which are involved in both groups of task, measuring Gf and WM. Despite years of research and numerous results confirming the relationship between WM and Gf, researchers have been unable to provide a conclusive answer as to why the two constructs are related, and the question of what explains this relationship remains open (Burgoyne et al, 2019;Frischkorn & Oberauer, 2021;Harrison et al, 2015).…”