Elucidating the Functional Relationship Between Working Memory Capacity and Psychometric Intelligence: A Fixed-Links Modeling Approach for Experimental Repeated-Measures Designs

Abstract

Numerous studies reported a strong link between working memory capacity (WMC) and fluid intelligence (Gf), although views differ in respect to how close these two constructs are related to each other. In the present study, we used a WMC task with five levels of task demands to assess the relationship between WMC and Gf by means of a new methodological approach referred to as fixed-links modeling. Fixed-links models belong to the family of confirmatory factor analysis (CFA) and are of particular interest for experimental, repeated-measures designs. With this technique, processes systematically varying across task conditions can be disentangled from processes unaffected by the experimental manipulation. Proceeding from the assumption that experimental manipulation in a WMC task leads to increasing demands on WMC, the processes systematically varying across task conditions can be assumed to be WMC-specific. Processes not varying across task conditions, on the other hand, are probably independent of WMC. Fixed-links models allow for representing these two kinds of processes by two independent latent variables. In contrast to traditional CFA where a common latent variable is derived from the different task conditions, fixed-links models facilitate a more precise or purified representation of the WMC-related processes of interest. By using fixed-links modeling to analyze data of 200 participants, we identified a non-experimental latent variable, representing processes that remained constant irrespective of the WMC task conditions, and an experimental latent variable which reflected processes that varied as a function of experimental manipulation. This latter variable represents the increasing demands on WMC and, hence, was considered a purified measure of WMC controlled for the constant processes. Fixed-links modeling showed that both the purified measure of WMC (β = .48) as well as the constant processes involved in the task (β = .45) were related to Gf. Taken together, these two latent variables explained the same portion of variance of Gf as a single latent variable obtained by traditional CFA (β = .65) indicating that traditional CFA causes an overestimation of the effective relationship between WMC and Gf. Thus, fixed-links modeling provides a feasible method for a more valid investigation of the functional relationship between specific constructs.

Elucidating the Functional Relationship Between Speed of Information Processing and Speed-, Capacity-, and Memory-Related Aspects of Psychometric Intelligence

“…These sources remained constantly effective across the three Hick conditions and, thus, caused individual differences in reaction time independently of the experimental manipulation. These sources of constant influence might comprise basic processing speed (e.g., Heitz, Unsworth, & Engle, 2005 ), basal speed of sensorimotor processing (e.g., Jensen, 2006 ; Schweizer, 2007 ), or also subjective state variables such as the motivation to perform or current levels of individual fatigue (e.g., Thomas et al, 2015 ). The obtained fixed-links model fit the data well.…”

“… Schweizer, 2007 , 2008 ). This latent variable includes, for example, general (i.e., task-independent) SIP such as perceptual and motor speed ( Schweizer, 2007 ; Stauffer, Troche, Schweizer, & Rammsayer, 2014 ) or an individual’s general state of alertness, fatigue, or motivation ( Thomas, Rammsayer, Schweizer, & Troche, 2015 ). In contrast to the slope-intercept approach, the fixed-links approach is not affected by suppression effects, since the correlation between the experimental and the nonexperimental latent variable is explicitly set to zero.…”

“…Given that all factor loadings are fixed, variance of the experimental as well as the nonexperimental latent variable is set free for estimation and needs to reach statistical significance in order to be interpreted as psychologically meaningful. Fixed-links modeling has been successfully applied to decompose experimental from nonexperimental variance for various cognitive processes such as working memory ( Schweizer, 2007 ; Thomas et al, 2015 ), visual change detection ( Stauffer et al, 2014 ), and attention ( Ren, Schweizer, & Xu, 2013 ; Troche, Schweizer, & Rammsayer, 2009 ; Wagner, Rammsayer, Schweizer, & Troche, 2014 , 2015 ). In a previous study, Rammsayer, Pahud, and Troche ( 2017 ) successfully dissociated Hick reaction time variance into an experimental as well as a nonexperimental latent variable and related them to g. This study showed that the relationship between SIP and psychometric g is primarily driven by individual differences in reaction time influenced by the experimental manipulation of response alternatives, whereas individual differences in reaction time represented by the nonexperimental component only explained a marginal portion of variance in g .…”

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).…”

Calibración de un banco de Ítems mediante el modelo de Rasch para medir razonamiento numérico, verbal y espacial

“…Al estudiar la inteligencia, principalmente relacionada con el desempeño académico, numerosas investigaciones abordan los dominios específicos de razonamiento numérico (rn), verbal (rv) y espacial (re) (Bresgi, Alexander, & Seabi, 2017;Schillinger, Vogel, Diedrich, & Grabner, 2018;Thomas, Rammsayer, Schweizer, & Troche, 2015;van de Weijer-Bergsma, Kroesbergen, & van Luit, 2015). El rn permite, fundamentalmente, comprender relaciones y conceptos numéricos; el rv remite a las capacidades de las personas en la comprensión del lenguaje (Gambari, Kutigi, & Fagbemi, 2014), y el re a encontrar relaciones entre formas y figuras (Wai, Lubinski, & Benbow, 2009).…”