Fluid intelligence is related to capacity in memory as well as attention: Evidence from middle childhood and adulthood

Abstract

Human fluid intelligence emerges from the interactions of various cognitive processes. Although some classic models characterize intelligence as a unitary “general ability,” many distinct lines of research have suggested that it is possible to at least partially decompose intelligence into a set of subsidiary cognitive functions. Much of this work has focused on the relationship between intelligence and working memory, and more specifically between intelligence and the capacity-loading aspects of working memory. These theories focus on domain-general processing capacity limitations, rather than limitations specifically linked to working memory tasks. Performance on other capacity-constrained tasks, even those that have typically been given the label of “attention tasks,” may thus also be related to fluid intelligence. We tested a wide range of attention and working memory tasks in 7- to 9-year-old children and adults, and we used the results of these cognitive measures to predict intelligence scores. In a set of 13 measures we did not observe a single “positive manifold” that would indicate a general-ability understanding of intelligence. Instead, we found that a small number of measures were related to intelligence scores. More specifically, we found two tasks that are typically labeled as “attentional measures”, Multiple Object Tracking and Enumeration, and two tasks that are typically labeled as “working memory” measures, N-back and Spatial Span, were reliably related to intelligence. However, the links between attention and intelligence scores were fully mediated by working memory measures. In contrast, attention scores did not mediate the relations between working memory and intelligence. Furthermore, these patterns were indistinguishable across age groups, indicating a hierarchical cognitive basis of intelligence that is stable from childhood into adulthood.

Assessing Psychological Fitness to Drive for Intoxicated Drivers: Relationships of Cognitive Abilities, Fluid Intelligence, and Personality Traits

“…Our results show that participants with higher performance levels in fluid intelligence also had higher test results in hand-eye coordination speed and quality, selective attention, resilience of attention, reaction speed, physical motor speed, perceptual speed and short-term memory, thus indicating a higher level of fitness to drive. Our findings are supported by a great body of literature stating equal associations between fluid intelligence and all cognitive domains (e.g., Sommer et al, 2008b;Hartshorne and Germine, 2015;Hird et al, 2016;Cochrane et al, 2019).…”

Frontoparietal and default mode network connectivity varies with age and intelligence

“…IQ, as a proxy for global intellectual functioning, is an important predictor of youth’s future educational and occupational attainment, as well as risk for early mortality and justice involvement ( Gur et al, 2012 ; Roalf et al, 2014 ; Loeber et al, 2012 ; Whalley and Deary, 2001 ; DiRago and Vaillant, 2006 ). FP-DMN anticorrelations likely support intellectual functioning through the mechanism of cognitive control capacity ( Cochrane et al, 2019 ; Chen et al, 2019b ). Specifically, greater anticorrelation between the frontal pole and the precentral gyrus may support enhanced efficiency between these networks and underlie improvements in attentional control, facilitating better integration of learned information over time and better performance on tests of intelligence.…”

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

“…Similarly, a reanalysis focused on the outcomes of latent-variable studies found stronger correlation between WM capacity and Gf (median r = 0.72), indicating that the WM capacity and Gf constructs share approximately 50% of their variance (Kane et al, 2005). A strong link between WM and Gf have also been confirmed by subsequent studies (e.g., Cochrane et al, 2019;Colom et al, 2015;Jastrzębski et al, 2018;Rey-Mermet et al, 2019;Rose, 2013;Smoleń & Chuderski, 2015;Thomas et al, 2015). This link, observed at the behavioural level, is also supported by data on neuronal mechanisms shared by Gf and WM, primarily involving the dorsolateral prefrontal cortex, parietal cortex and fronto-parietal network (Barbey et al, 2014;Burgess et al, 2011;Dehn, 2017;Eriksson et al, 2015;Gray et al, 2003;Kane & Engle, 2002).…”

“…Theoretical explanations of the nature of the relationship between WM and Gf have focused on the search for the lower-level mechanisms underlying the variance shared by these two constructs. Current theoretical understandings of this relationship are mostly based on two explanatory factors: (1) executive control also referred to as attention control or executive attention (Burgess et al, 2011;Cowan et al, 2006;Engle & Kane, 2004;Engle, Tuholski et al, 1999;Nęcka & Lulewicz, 2016;Shipstead et al, 2016) and (2) WM storage capacity Cochrane et al, 2019;Colom et al, 2008;Conway et al, 2002;Rey-Mermet et al, 2019;Smolen & Chuderski, 2015). In addition, as mechanisms underlying the WM-Gf relationship the updating of WM content Cochrane et al, 2019;Kane et al, 2007) and learning efficiency (Harrison et al, 2015;Wiley et al, 2011) are also mentioned.…”