Strategy use moderates the relation between working memory capacity and fluid intelligence: A combined approach

Intelligence ◽  
2022 ◽  
Vol 91 ◽  
pp. 101627
Author(s):  
Chenyu Li ◽  
Xuezhu Ren ◽  
Karl Schweizer ◽  
Tengfei Wang
Keyword(s):  
Working Memory ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Strategy Use ◽  
Combined Approach

Strategy use moderates the relation between working memory capacity and fluid intelligence: A combined approach

2021 ◽  
Author(s):  
Chenyu Li ◽  
Xuezhu Ren ◽  
Karl Schweizer ◽  
TENGFEI WANG
Keyword(s):  
Working Memory ◽  
Latent Profile Analysis ◽  
Fluid Intelligence ◽  
Profile Analysis ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Strategy Use ◽  
Response Elimination ◽  
Complex Span ◽  
Latent Profile

This study investigated whether the strength of the link between working memory capacity (WMC) and fluid intelligence (Gf) differs as people use different strategies to solve Gf problems. A sample of 214 university students completed three complex span tasks and Raven's Advanced Progressive Matrices (APM). Strategic behavior was measured by both a strategy questionnaire and eye tracking. Using latent profile analysis, three strategies described as constructive matching, response elimination, and isolate-and-eliminate were identified. Participants adopting constructive matching and response elimination exhibited substantial differences in the eye-movement measures across the APM items, confirming the validity of the strategy questionnaire in combination of latent profile analysis for determining strategy use. Furthermore, strategy use moderated the relationship between WMC and APM performance. The link between WMC and APM scores was significantly higher for participants who used isolate-and-eliminate (r = .54) and response elimination (r = .63) than that for participants who used constructive matching (r = .27). Our findings suggest that the simple dichotomy of strategy use in the APM is not tenable, and the extent to which WMC relates to performance on the APM varies as a function of strategy use.

scholarly journals Differential involvement of working memory capacity and fluid intelligence in verbal associative learning as a possible function of strategy-use

2020 ◽  
Author(s):  
David Martinez
Keyword(s):  
Working Memory ◽  
Cognitive Processes ◽  
Latent Class ◽  
Fluid Intelligence ◽  
Paired Associate ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Strategy Use ◽  
Human Memory And Learning ◽  
Associate Learning

Paired-associate tasks are popular tasks used in basic and applied research on human memory and learning. A number of studies have shown that individuals differ in the strategies they use to encode information in paired-associate tasks and, importantly, that strategies differ in their effectiveness. What is not so well documented is how different strategies may affect the cognitive processes assessed by paired-associate tasks. In this study, we submit archival data to distributional and latent class analyses to infer strategy-use and classify individuals as elaborators or non-elaborators. We then used regression analyses within subgroups to identify differences in dependence on fluid intelligence and working memory capacity. To the extent that our classification of individuals was accurate, the results suggest that paired-associate learning is more reliant on fluid intelligence when elaborative rehearsal is utilized and more reliant on working memory capacity when using non-elaborative strategies. To offer further evidence of the validity of our approach, we also investigated correlations between strategy-use and fluid intelligence and working memory capacity. In accord with prior research, we found that cognitive abilities were positively correlated with what we infer to be differences in strategy-use. That the cognitive processes assessed by verbal paired-associate tasks may vary as a function of strategy-use should be a concern for all researchers and practitioners who use such tasks.

Understanding the Relationship Between Rationality and Intelligence: A Latent-Variable Approach

2021 ◽  
Author(s):  
Alexander P. Burgoyne ◽  
Cody Mashburn ◽  
Jason S. Tsukahara ◽  
Zach Hambrick ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Latent Variable ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Equation Modeling ◽  
General Intelligence ◽  
Variable Approach ◽  
The Relationship

A hallmark of intelligent behavior is rationality—the disposition and ability to think analytically to make decisions that maximize expected utility or follow the laws of probability, and therefore align with normative principles of decision making. However, the question remains as to whether rationality and intelligence are empirically distinct, as does the question of what cognitive mechanisms underlie individual differences in rationality. In a large sample of participants (N = 331), we used latent variable analyses to assess the relationship between rationality and intelligence. The results indicated that there was a common ability underpinning performance on some, but not all, rationality tests. Latent factors representing rationality and general intelligence were strongly correlated (r = .54), but their correlation fell well short of unity. Indeed, after accounting for variance in performance attributable to general intelligence, rationality measures still cohered on a latent factor. Confirmatory factor analysis indicated that rationality correlated significantly with fluid intelligence (r = .56), working memory capacity (r = .44), and attention control (r = .49). Structural equation modeling revealed that attention control fully accounted for the relationship between working memory capacity and rationality, and partially accounted for the relationship between fluid intelligence and rationality. Results are interpreted in light of the executive attention framework, which holds that attention control supports information maintenance and disengagement in service of complex cognition. We conclude by speculating about factors rationality tests may tap that other cognitive ability tests miss, and outline directions for further research.

scholarly journals Is working memory capacity a causal factor in fluid intelligence?

2019 ◽  
Vol 26 (4) ◽  
pp. 1333-1339 ◽  
Author(s):  
Alexander P. Burgoyne ◽  
David Z. Hambrick ◽  
Erik M. Altmann
Keyword(s):  
Working Memory ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Causal Factor

scholarly journals Working memory capacity mediates the relationship between removal and fluid intelligence

2018 ◽  
Vol 101 ◽  
pp. 18-36 ◽  
Author(s):  
Krishneil A. Singh ◽  
Gilles E. Gignac ◽  
Christopher R. Brydges ◽  
Ullrich K.H. Ecker
Keyword(s):  
Working Memory ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
The Relationship

scholarly journals Working memory capacity and strategy use

2001 ◽  
Vol 29 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Danielle S. McNamara ◽  
Jennifer L. Scott
Keyword(s):  
Working Memory ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Strategy Use

scholarly journals The Development of Latin Square Tasks. More Science Than Art?

2021 ◽  
Author(s):  
Johanna Hartung ◽  
Benjamin Goecke ◽  
Ulrich Schroeders ◽  
Florian Schmitz ◽  
Oliver Wilhelm
Keyword(s):  
Working Memory ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Theoretical Perspective ◽  
Latin Square ◽  
Memory Capacity ◽  
The Novel ◽  
Improve Measurement ◽  
Item Characteristics ◽  
Underlying Processes

In contrast to measures of working memory capacity, tests for fluid intelligence are elusive in their psychometric properties. Somewhat surprisingly, fluid intelligence is not as tractable as often conceived. We studied Latin Square Tasks (LSTs) as a group of indicators that supposedly can improve measurement of fluid intelligence. In four studies (N > 3,300), we compared competing theoretical accounts that differ in the cognitive processes proposed for successfully completing items. To this end, the cognitive demand was operationalized by two key requirements that decisively influence the task difficulty: a) processing of information with differing complexity and b) memorizing steps to the final solution. Confirming predictions, the underlying processes of LSTs are independent of stimulus type and rotation of the matrices. Relations with reasoning confirmed the validity of the novel Latin Square Tasks. Working memory capacity was a limiting resource that determined performance, however more precise predictions of item difficulties might be possible when further item characteristics will be considered. From a theoretical perspective, we discuss the superiority of a perspective on LSTs inspired by the binding hypothesis compared to relational complexity theory.

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