More support for the Gs—>Gwm—>—Gf/ Gc developmental cascade model as per CHC taxonomy

 More support for the developmental cascade model

https://www.sciencedirect.com/science/article/abs/pii/S0160289620300817?via%3Dihub

Speed of processing, control of processing, working memory and crystallized and fluid intelligence: Evidence for a developmental cascade 

Anna Tourva, George Spanoudis

 

Keywords: Fluid intelligence Crystallized intelligence Working memory Speed of processing Executive attention Developmental-cascade model 

A B S T R A C T  

The present study investigated the causal relations among age, speed of processing, control of processing, working memory and intelligence, fluid and crystallized. 158 participants aged from 7 to 18 years old completed a large battery of tests measuring latent factors of speed, control of processing and working memory. Intelligence was assessed using the Wechsler Abbreviated Scale of Intelligence. Structural equation modeling was performed to determine whether there is a cognitive-developmental cascade in which age-related increases in processing speed lead to improvements in control of processing that leads to increases in working memory, and whether improved working memory, in turn, leads to increases in both fluid and crystallized intelligence. Several alternative models of a different cascade order of the above factors were also tested. The results of the present study provide evidence of a cognitive-developmental cascade, confirming that this model describes cognitive development during childhood and adolescence.  

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More support for the developmental cascade model (Gs, Gwm, Glr and Gf)

As readers of this blog know, I am partial to research that continues to support Fry and Hale's developmental cascade model, which indicates that processing speed (Gs) has a direct effect on working memory (Gwm/Gsm), which in turn has direct effect on fluid reasoning (Gf) or general intelligence (g). Gs influence is indirect--mediated by Gwm. Another study continues to support this model but adds the wrinkle of secondary memory (some Glr abilities), which also only has an indirect effect on Gf as mediated by Gwm.

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Gs->working memory->Gf developmental-differential psych developmental cascade model

Very interesting research that suggests a developmental (neo-Piagetian) wrinkle to the developmental cascade model, a model that has shown that Gs influences working memory (Gwm), and working memory in turn influences Gf (but Gs has no direct influencee on Gf).

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"However, the exact role of speed and working memory is still debated. Some researchers emphasize speed as a purer index of the quality of information processing in the brain (e.g., Jensen, 1998). This interpretation is based on studies which estimate the relation between speed and intelligence without involving working memory. Others emphasize working memory because it is the workspace of thinking (Kyllonen & Christal, 1990). Studies emphasizing working memory usually measure all three constructs in young adults, when working memory is the dominant predictor of Gf, according to the patterns to be described below. Finally, others assume a causal linear relation between them such that changes in speed cause changes (or differences) in working memory which, in turn, cause changes (or differences) in Gf (Case, 1985; Coyle, Pillow, Snyder, & Kochunov, 2011; Kail, 1991; Kail & Ferrer, 2007). However, this chain of relations may only reflect the fact that working memory tasks are both timed, like speed tasks, and require information management, like Gf tasks, rather than a causal sequence. In fact, there is evidence that control of attention is common to all, speed, WM, and Gf, explaining their relations (Cowan, Morey, Chen, & Bunting, 2007; Engle et al., 1999; Stankov & Roberts, 1997)"

Note. Attentional control (AC) is now proposed to represent a narrow ability under the broad CHC domain of Gwm (short-term working memory) by the authors of the forthcoming WJ IV [Conflict of interest disclosure--I am one of the coauthors of the WJ III and WJ IV). This is consistent with Schneider and McGrew's (2012) recent book chapter CHC model update.

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"Demetriou et al. (2013) showed recently that the relations between these constructs are more complicated than originally assumed, because they vary with growth. Specifically, speed increases and WM expands. Gf evolves along a reconceptuali-zation sequence (ReConceP) where changes in the nature of representations alternate with changes in the command and interlinking of representations constructed earlier."

"These patterns provide support for an integrated developmental–differential theory of intelligence that would explicate why Gf changes coalesce with speed at the beginning of developmental cycles and with WM changes at the end. Gf undergoes three types of change: representational, inferential, and complexity."

I previously presented (McGrew, 2005) support for the developmental cascade model in 5 age-differentiated WJ III norm samples (see one of the sample models below). Instead of causal models with Gf as the criterion, I specified a criterion g-factor defined by Gv, Ga, Glr, Gf, and Gc. The results strongly supported the Gwm->g link, and significant causal links from Gs to working memory. Gs did not dispaly a direct link to g in the childhood samples, but did demonstrate small significant direct paths to g in the adolescent and adult samples.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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IQ-ACH study consistent with extant CHC g+specific-->ACH and causal modeling research

I have made additional comments and provided links to relevant research as per the IQs Reading feature of IQs Corner.

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intelligence IQ tests IQ testing IQ scores CHC intelligence theory CHC theory Cattell-Horn-Carroll human cognitive abilities psychology school psychology individual differences cognitive psychology neuropsychology neuroscience psychology special education educational psychology psychometrics psychological assessment psychological measurement IQs Corner general intelligence intelligent IQ testing Dean-Woodock Neuropsychology Model WJ-R WJ III fluid intelligence working memory processing speed g+specific abilities research Gf Gsm Glr Gs

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General developmental mechanism explains growth across most personal competence domains?

I believe that this is a very intriguing and potentially important research study for all involved in research or the assessment of human development, and those working in school and learning situations in general. The study suggests a very general domain-general set of mechanisms may account for growth and change across different broad domains of personal competence. I have made it an "IQ's Reading" post (click here for explanation). The annotated article can be found here. I found it a very thought provoking article that connects some research and theoretical dots across diverse academic disciplines that study human cognitive and physical/motor development. See comments in post for more thoughts.

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Processing speed (Gs) developmental growth on two WJ tests

I just skimmed an interesting (an largely theoretical) article by Kail and Ferrer (2007; Child Development-click here to view) that fit different mathematical models of age-related (developmental) growth to the WJ-R (Woodcock-Johnson--Revised) Visual Matching and Cross Out tests. The article does not have immediate practical applications. I'm was interested in the article since it deals with two tests from the WJ III (conflict of interest note - I'm a coauthor of the WJ III). I also read the article because of the first author...Kail. He, IMHO, is one of the top researchers investigating the nature of developmental growth of cognitive processing speed (Gs).

The abstract for the article is below.

The authors concluded that quadratic and exponential models fit the growth patterns of the Visual Matching and Cross Out tests the bests. The quadratic model was the best fitting model. The authors use this finding to illustrate how such analysis might be useful in exploring the mechanisms that underlie growth in cognitive speed (Gs).

For example, the authors note that the "parameters of these quadratics are often qualitatively like those obtained here: nonlinear change is achieved from a linear increase coupled with a nonlinear (power function) decrease." They then point other physiological/cerebral functions that show similar quadratic growth patterns - e.g., total cerebral volume and total body fat all show the same pattern of quadratic change in childhood and adolescence. The authors suggest that such a finding may suggest that all might have a "common (unspecified) biological base."

Interesting stuff...but not all that practical for the field of applied intelligence testing. However, I think the finding that the two tests showed the same pattern of developmental growth might be interpreted to support the interpretation of the two Gs tests as measuring the same underlying cognitive construct.

As I've written elsewhere, John Horn frequently talked about different types of validity evidence of human ability constructs--structural (factor analytic), genetic or heritability, neurocognitive, criterion-outcome, and developmental. We know from EFA/CFA (structural) studies of the WJ-R and WJ III that Visual Matching and Cross Out load on a common broad Gs factor. Logical content analysis has suggested that they are both measures of the narrow Gs ability of P (perceptual speed). The finding that both VM and CO display the same longitudinal developmental growth pattern, when combined with the extant EFA/CFA structural research that finds these two tests always loading on a common factor, in my opinion supports the validity of the logical narrow (stratum I) classifications of both tests as measures of Gs-P.

Just my two cent applied/practical extraction of information from this largely theoretical piece of research.

Abstract

• The primary aim of the present study was to examine longitudinal models to determine the function that best describes developmental change in processing speed during childhood and adolescence. In one sample, children and adolescents (N 5503) were tested twice over an average interval of 2 years on two psychometric measures of processing speed: Visual Matching and Cross Out. In another sample, children and adolescents (N 5 277) were tested four times, every 6 months, on Cross Out. Age-related changes in performance on both tasks were examined using six longitudinal models representing different hypotheses of growth. Linear, hyperbolic, inverse regression, and transition models yielded relatively poor fit to the data; the fit of the exponential and quadratic models was substantially better. The heuristic value of these latter models is discussed.

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Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, developmental, intelligence, IQ, IQ tests, WJ-R, WJ III, CHC, CHC theory, Visual Matching, Cross Out

Processing speed (Gs) and working memory (Gsm-MW) - more support for developmental cascade hypothesis

For a number of years I've been intrigued by the empirical research that has investigated the "developmental cascade" model. This model hypothesizes that developmental increases in processing speed (Gs) results in increases in working memory abilities (Gsm-MW), which in turn has a large and direct effect on fluid reasoning (Gf) and possibly general intelligence (g).

The first I read of the developmental cascade hypothesis was a 1996 article by Fry and Hale in Psychological Science. Subsequently, I provided an overview of this research literature in my chapter in the 2005 Flanagan and Harrison Contemporary Intellectual Assessment book.My overview also included the presentation of causal models I ran that provided, IMHO, strong support for this theoretical conceptualization of cognitive growth.

A new study by Kail directly investigates Fry and Hale's developmental cascade hypotheses with a longitudinal study design. Kail's research continues to support this model and can be viewed by clicking here.

Regular readers of this blog know that I've been very interested in research regarding the role of working memory in academic and cognitive performance. Click here to view all posts to date that have dealt with working memory, cognitive load, etc.

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Age decline in reasoning - speed and executive abilities

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What may be the reasons for decreased fluid intelligence (Gf) with increasing age?

According to recent study (see reference and abstract below),two of the primary causative factors for decreased fluid intelligence (as established by recent research) are (a) decreased speed of cognitive processing (a generalized cognitive slowing mechanism) and (b) decreases in the executive functions of the frontal lobes as evidenced by decreased frontal lobe volume, alterations in frontal lobel cell morphology, and reductions in cerebral blood flow to the frontal and prefrontal brain lobes. The current study investigated the relative contributions of processing speed and frontal lobe function on decreases in Gf. The abstract for the article (together with URL link) is reproduced below. I will summarize some of the major findings plus add my two cents.

I have 2 cents worth of methodological comments. First, measures of reaction time (Gt) operationally defined cognitive processing speed in this study. According to the CHC taxonomy, these measures represent aspects of the broad domain of Gt (broad reaction time), which is NOT to be confused with broad cognitive processing speed (Gs). Thus, the current results are specific to the influence of Gt and may not be generalizable to Gs abilities. Additional research with valid Gs markers is needed.

Second, the authors continue the unfortunate tradition of using the Wechsler Block Design test as a marker for Gf. Contemporary Gf-Gc/CHC joint exploratory and confirmatory factor studies have convincly indicated that Block Design is a strong measure of visual-spatial processing (Gv)...not Gf. Luckily, the authors also use the Wechsler Matrix Reasoning test which is a valid indicator of Gf. Given the problems with Block Design, I recommend that readers of this article only pay attention to the results focused on understanding the decline in the Matrix Reasoning test (Of course, the Block Design findings can be interpreted in the context of Gv if that is what is of interest.)

Given this caveat, below are the major conclusions regarding possible explanations for age-related declines in Gf. I believe readers should only focus on the composite Gt measure as the indicator of general reaction time (Gt) and only the analyses that included the Gt, frontal function measures, and age in the analysis (as these provide the most valid and comprehensive explanations from the current study). These findings have been highlighted in red in the above table picture.

• Frontal (executive) function and Gt, collectively, account for approximately 27 % of the decrease in Gf with age. Chronological age explains and additional 15-16 % of the decline in Gf, above and beyond frontal function and Gt abilities.

• As noted by the authors, a generalized slowing of cognitive speed contributes to decreased Gf abilities...but, speed is not the entire picture. Decreased frontal functions, as well as other unaccounted for variables realated to age, also contribute to decreased Gf with age. Decreased frontal functions and Gt both contribute uniquely to age-related declines in Gf abilities.

• Declines in cognitive abilities, in this case Gf, are multiply determined. No one single mechanism can explain age-related changes in cognitive ability.

• Bottom line - age-related decreases in the ability to reason inductively/deductively and solve novel problems (Gf - fluid intelligence) appear due, in part, to age-related decreased speed of cognitive thinking and decreases in ability to think and manage (executive function) one's own thinking processes ("thinking about thinking"), plus additional factors not clearly delineated.
  

Bugg, J., Zook, N., DeLosh, E., Davalos, D. & Hasker, D, (2006). Age differences in fluid intelligence: Contributions of general slowing and frontal decline. Brain and Cognition, 62, 9–16 (click here to view)

Abstract

• The current study examined the contributions of general slowing and frontal decline to age differences in fluid intelligence. Participants aged 20–89 years completed Block Design, Matrix Reasoning, simple reaction time, choice reaction time, Wisconsin Card Sorting, and Tower of London tasks. Age-related declines in fluid intelligence, speed of processing, and frontal function were observed. Hierarchical regression analyses showed that the processing speed and frontal function measures accounted for significant variance in fluid intelligence performance, but there was also a residual effect of age after controlling for each variable individually as well as both variables. An additional analysis showed that the variance in fluid intelligence that was attributable to processing speed was not fully shared with the variance attributable to frontal function. These findings suggest that the age-related decline in fluid intelligence is due to general slowing and frontal decline, as well as other unidentified factors.

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, neuroscience, aging, developmental, fluid intelligence, Gf, visual-spatial, Gv, frontal lobes, executive function, intelligence, IQ scores, cognition

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Think fast (Gs) - be socially active as you age

Interesting article below that suggests that staying socially active and engaged helps protect against deterioration of mental processing speed (Gs-P; perceptual speed in particular). Although the methodology and technical language of this article is a bit steep, the bottom line supports the common sense notion of staying socially active during old age may protect against cognitive decline in cognitive processing speed.

Lovden, M., Ghisletta, P., & Lindenberger, U. (2005). Social participation attenuates decline in perceptual speed in old and very old age. Psychology and Aging, 20(3), 423-434.

Abstract

• Does an engaged and active lifestyle in old age alleviate cognitive decline, does high cognitive functioning in old age increase the possibility of maintaining an engaged and active lifestyle, or both? The authors approach this conundrum by applying a structural equation model for testing dynamic hypotheses, the dual change score model (J. J. McArdle & F. Hamagami, 2001), to 3-occasion longitudinal data from the Berlin Aging Study (Time 1: n = 516, age range = 70-103 years). Results reveal that within a bivariate system of perceptual speed and social participation, with age and sociobiographical status as covariates, prior scores of social participation influence subsequent changes in perceptual speed, while the opposite does not hold. Results support the hypothesis that an engaged and active lifestyle in old and very old age may alleviate decline in perceptual speed.

Other important (select) conclusions by the authors (emphasis added by blogmaster):

• Decline in some other cognitive ability than perceptual speed might have an impact on engagement in social activities because individuals experience these declines as more immediately limiting their functional capacity.

• Little is known about the exact mechanisms by which lifestyle factors such as social participation might influence cognitive decline. An engaged lifestyle might provide greater readiness for compensatory changes in response to neurophysiological decline (e.g., Schaie, 1996; Stern, 2002).
  


• Lifestyle factors might also modify or protect against potential neurophysiological changes underlying cognitive aging in more direct ways than by introducing interindividual differences in the ability to cope with them.
  

• The exact mediating mechanisms might be one or a combination of several alternatives, such as neurophysiological effects of mental stimulations (e.g., environmental complexity and learning) and reduced cardiovascular pathology as an effect of physical activity, which in turn might be associated with social participation.

Technorati tags: CHC theory. psychology. intelligence. cognition. aging. educational psychology. Gs. processing speed.

Gs, Glr, executive function and aging

I just skimmed the interesting article below. The abstract is pretty much self-explanatory. If one uses their CHC-SL (CHC as a Second Language) skills, the CHC interpretation is that the study focuses on Glr (recognition memory), Gs and executive functions (which seems to be Gf related).

Bunce, D., & Macready, A. (2005). Processing speed, executive function, and age differences in remembering and knowing. Quarterly Journal of Experimental Psychology Section A Human Experimental Psychology, 58(1), 155-168.

Abstract
A group of young ( n = 52, M = 23.27 years) and old ( n = 52, M = 68.62 years) adults studied two lists of semantically unrelated nouns. For one list a time of 2 s was allowed for encoding, and for the other, 5 s. A recognition test followed where participants classified their responses according to Gardiner's (1988) remember-know procedure. Age differences for remembering and knowing were minimal in the faster 2-s encoding condition. However, in the longer 5-s encoding condition, younger persons produced significantly more remember responses, and older adults a greater number of know responses. This dissociation suggests that in the longer encoding condition, younger adults utilized a greater level of elaborative rehearsal governed by executive processes, whereas older persons employed maintenance rehearsal involving short-term memory. Statistical control procedures, however, found that independent measures of processing speed accounted for age differences in remembering and knowing and that independent measures of executive control had little influence. The findings are discussed in the light of contrasting theoretical accounts of recollective experience in old age.