“Be and see” the #WISC-V correlation matrix: Unpublished analyses of the WISC-V #intelligence test

 I often “play around” with data sets until I satisfy my curiosity…and never submit the results for publication.  These WISC-V analyses were completed 3+ years ago.  I stumbled upon the folder today and decided to simply post the information for assessment professionals interested in the WISC-V.  These results have not been peer-reviewed.  One must know the WISC-V subtest names to decipher the test abbreviations in some of the figures.  

This is a Gv (visual; 8 slides) summary a set of exploratory structural analyses I completed with the WISC-V summary correlation matrix (Table 5.1 in WISC-V manual). View and enjoy. 

You need to click on images to enlarge and read

Research byte: Prediction of human #intelligence (#g #Gf #Gc) from #brain (#network) #connectivity - #CHC

Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity 

,  ,  ,   

PNAS Nexus, Volume 3, Issue 12, December 2024, pgae519,

Online and PDF download available at this link:  https://doi.org/10.1093/pnasnexus/pgae519

Abstract

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves statistically significant prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive brain characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modeling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: N = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, brain-wide functional connectivity characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future prediction studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive brain characteristics over maximizing prediction performance (emphasis added).

machine learning, intelligence, cognitive ability, functional brain connectivity, predictive modeling

Research Byte: Past reflections, present insights: A systematic #review and new empirical research into the #workingmemory capacity (WMC)-#fluidintelligence (#Gf) relationship

Past reflections, present insights: A systematic review and new empirical research into the working memory capacity (WMC)-fluid intelligence (Gf) relationship

Ratko Đokić, Maida Koso-Drljević, Merim Bilalić 

Click here to go to journal

Abstract

According to the capacity account, working memory capacity (WMC) is a causal factor of fluid intelligence (Gf) in that it enables simultaneous activation of multiple relevant information in the aim of reasoning. Consequently, correlation between WMC and Gf should increase as a function of capacity demands of reasoning tasks. Here we systematically review the existing literature on the connection between WMC and Gf. The review reveals conceptual incongruities, a diverse range of analytical approaches, and mixed evidence. While some studies have found a link (e.g., Little et al., 2014), the majority of others did not observe a significant increase in correlation (e.g., Burgoyne et al., 2019; Salthouse, 1993; Unsworth, 2014; Unsworth & Engle, 2005; Wiley et al., 2011). We then test the capacity hypothesis on a much larger, non-Anglo-Saxon culture sample (N = 543). Our WMC measures encompassed Operation, Reading, and Symmetry Span task, whereas Gf was based on items from Raven's Advanced Progressive Matrices (Raven). We could not confirm the capacity hypothesis either when we employed the analytical approach based on the Raven's item difficulty or when the number of rule tokens required to solve a Raven's item was used. Finally, even the use of structural equation modeling (SEM) and its variant, latent growth curve modeling (LGCM), which provide more “process-pure” latent measures of constructs, as well as an opportunity to control for all relevant interrelations among variables, could not produce support for the capacity account. Consequently, we discuss the limitations of the capacity hypothesis in explaining the WMC-Gf relationship, highlighting both theoretical and methodological challenges, particularly the shortcomings of information processing models in accounting for human cognitive abilities.

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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White matter matters—Gf and white matter connectivity

A neuromarker of individual general fluid intelligence from the white-matter functional connectome.  Link.

Jiao Li1, Bharat B. Biswal, Yao Meng, Siqi Yang, Xujun Duan, Qian Cui, Huafu Chen, and Wei Liao

Abstract

Neuroimaging studies have uncovered the neural roots of individual differences in human general fluid intelligence (Gf). Gf is characterized by the function of specific neural circuits in brain gray-matter; however, the association between Gf and neural function in brain white-matter (WM) remains unclear. Given reliable detection of blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals in WM, we used a functional, rather than an anatomical, neuromarker in WM to identify individual Gf. We collected longitudinal BOLD-fMRI data (in total three times, ~11 months between time 1 and time 2, and ~29 months between time 1 and time 3) in normal volunteers at rest, and identified WM functional connectomes that predicted the individual Gf at time 1 (n = 326). From internal validation analyses, we demonstrated that the constructed predictive model at time 1 predicted an individual's Gf from WM functional connectomes at time 2 (time 1 ∩ time 2: n = 105) and further at time 3 (time 1 ∩ time 3: n = 83). From external validation analyses, we demonstrated that the predictive model from time 1 was generalized to unseen individuals from another center (n = 53). From anatomical aspects, WM functional connectivity showing high predictive power predominantly included the superior longitudinal fasciculus system, deep frontal WM, and ventral frontoparietal tracts. These results thus demonstrated that WM functional connectomes offer a novel applicable neuromarker of Gf and supplement the gray-matter connectomes to explore brain–behavior relationships.

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Attentional control has indirect effect on Gf via working memory (Gwm)

Another study  supporting attentional control (AC) as having an indirect causal effect on Gf mediated via working memory (Gwm).

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 ahierarchical cognitive basis of intelligence that is stable from childhood into adulthood.

study

Spatial ability (Gv) and math (Gq; Gf-RQ): A meta-analysis

Is Spatial Ability Related to Mathematical Ability: a Meta-analysis

Fang Xie & Li Zhang  & Xu Chen & Ziqiang Xin

Abstract

The relationship between spatial and mathematical ability is controversial. Thus, the current study conducted a meta-analysis of 73 studies, with 263 effect sizes to explore the relationship between spatial and mathematical ability. Furthermore, we explored potential factors that moderate this relationship. Results showed that the relationship between mathematical and spatial ability was not simply linear. Specifically, logical reasoning had a stronger association with spatial ability than numerical or arithmetic ability with spatial ability. Intrinsic-dynamic, intrinsic-static, extrinsic-dynamic, extrinsic-static spatial ability, and visual–spatial memory showed comparable associations with mathematical ability. The association between spatial and mathematical ability showed no differences between children, adolescents, and adults and no differences between typically developing individuals and individuals with developmental disabilities. The implications of these findings for theory and practice are discussed.

Keywords Spatial ability . Mathematical ability . Meta-analysis . robumeta package . Spatial training.

Implications for Theory and Practice

“Our study can shed light on our understanding of the relationship between spatial and mathematical abilities. The relationship between spatial and mathematical abilities is not simply linear. Our moderation analyses suggested that logical reasoning was more strongly associated with spatial ability than numerical and arithmetical ability. As such, when examin-ing the mechanism of the association between spatial and mathematical ability, each domain of mathematical ability should be separately examined. The current study has important educational implications. Although we did not prove the causal relationship between spatial and mathematical ability, our findings might provide some pedagogical suggestions about how to train spatial ability to improve children's mathematical abilities. Notably, a recent intervention study by Sorby et al. (2018) demonstrated the positive effect of spatial interventions on STEM-related skills, and several studies have shown that spatial training can improve mathematical achievement (Cheng and Mix 2014; Clements et al. 2011; Sorby and Baartmans 2000). Firstly, our findings shed light on what kind of spatial ability training should be chosen. The current study indicated that different domains of spatial ability are associated with mathemat-ical ability to a similar degree. Therefore, training in other domains of spatial ability, not just intrinsic-dynamic spatial abilities (Cheng and Mix 2014; Clements et al. 2011; Taylor and Hutton 2013), should be encouraged in educational practice. Further, our findings shed light on when to begin spatial ability training. This study showed that the close association between spatial and mathematical abilities exists in childhood and adolescence. Therefore, spatial training can be beneficial for both children and adolescents. For children, spatial training can be rooted in the real world to develop direct experience by using regular activities such as paper folding, paper cutting (Burte et al. 2017), and Lego construction (Nath and Szücs 2014). For adoles-cents, it is better to carry out spatial training through comprehensive courses involving theory and practice in a series of spatial skills (Miller and Halpern 2013; Patkin and Dayan 2013; Sorby et al. 2013).”

Educational Psychology Review

Longitudinal Analysis of Associations between 3-D Mental Rotation and Mathematics Reasoning Skills during Middle School: Across and within Genders

File under Gv and Gq/Gf as per CHC model of intelligence

Longitudinal Analysis of Associations between 3-D Mental Rotation and Mathematics Reasoning Skills during Middle School: Across and within Genders

Caitlin McPherran Lombardia, Beth M. Caseyb, Elizabeth Pezarisb, Maryam Shadmehrb, and Margeau Jong

JOURNAL OF COGNITION AND DEVELOPMENT 2019, VOL. 20, NO. 4, 487–509 

https://doi.org/10.1080/15248372.2019.1614592

ABSTRACT

The development of math reasoning and 3-d mental rotation skills are intertwined. However, it is currently not understood how these cognitive processes develop and interact longitudinally at the within-person level – either within or across genders. In this study, 553 students (52% girls) were assessed from fifth to seventh grades on 3-d mental rotation spatial skills (assessed each fall) and numerical and algebraic math reasoning skills (assessed each spring). Boys outperformed girls on mental rotation tests across all three grades, and on fifth and seventh grade math reasoning tests. Consistent with the literature on between-person comparisons, there was a positive correlation between mental rotation and math reasoning skills in the full sample and for both genders. A random inter-cept cross-lagged panel model was used to control for these confounding group-level differences in order to isolate within-person associations between earlier and later performance. Initially in fifth grade, math reasoning predicted subsequent sixth grade mental rotation skills. By seventh grade, more advanced mental rotation skills were associated with subsequent math reasoning skills while math reasoning skills were no longer predictive of mental rotation skills. An examination of gender differences revealed that this pattern was driven by boys while girls experienced less within-person change. These findings suggest that boys may initially rely in part on their math reasoning skills to solve 3-d mental rotation tasks. However, as their 3-d mental rotation skills mature, they begin to primarily depend upon these developing spatial skills to solve math reasoning problems rather than the reverse

Mental rotation and fluid intelligence: A brain potential analysis

File under Gv and Gf

Mental rotation and fluid intelligence: A brain potential analysis
Intelligence 69 (2018) 146–157. Article link.

Vincenzo Varrialea, Maurits W. van der Molenb, Vilfredo De Pascalis


ABSTRACT

The current study examined the relation between mental rotation and fluid intelligence using performance measures augmented with brain potential indices. Participants took a Raven's Progressive Matrices Test and performed on a mental rotation task presenting upright and rotated letter stimuli (60°, 120° or 180°) in normal and mirror image requiring a response execution or inhibition depending on instructions. The performance results showed that the linear slope relating performance accuracy, but not speed, to the angular rotation of the stimuli was related to individual differences in fluid intelligence. For upright stimuli, P3 amplitude recorded at frontal and central areas was positively associated with fluid intelligence scores. The mental rotation process was related to a negative shift of the brain potential recorded over the parietal cortex. The linear function relating the amplitude of the rotation-related negativity to rotation angle was associated with fluid intelligence. The slope was more pronounced for high- relative to low-ability participants suggesting that the former flexibly adjust their expenditure of mental effort to the mental rotation demands while the latter ones are less proficient in doing so.


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Visualization, inductive reasoning, and memory span as components of fluid intelligence: Implications for technology education

File under CHC domains of Gf, Gwm, Gc and STEM

Visualization, inductive reasoning, and memory span as components of fluid intelligence: Implications for technology education. Article link.

Jeffrey Buckleya, Niall Seerya, Donal Cantyc, Lena Gumaelius

International Journal of Educational Research, 90 (2018) 64–77

ABSTRACT

The philosophy and epistemology of technology education are relatively unique as the subject largely focusses on acquiring task specific relevant knowledge rather than having an explicit epistemological discipline boundary. Additionally, there is a paucity of intelligence research in technology education. To support research on learning in technology education, this paper describes two studies which aimed to identify cognitive factors which are components of fluid intelligence. The results identify that a synthesis of visualization, short-term memory span and inductive reasoning can account for approximately 28% to 43% of the variance in fluid intelligence. A theoretical rationale for the importance of these factors in technology education is provided with a discussion for their future consideration in cognitive interventions.


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Higher intelligence related to more efficiently organized brains-bigger/larger/more not always better

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Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Article link.

Erhan Genç, Christoph Fraenz, Caroline Schlüter, Patrick Friedrich, Rüdiger Hossiep, Manuel C. Voelkle, Josef M. Ling, Onur Güntürkün, & Rex E. Jung

Abstract

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.

From discussion

Taken together, the results of the present study contribute to our understanding of human intelligence differences in two ways. First, our findings confirm an important observation from previous research, namely, that bigger brains with a higher number of neurons are associated with higher intelligence. Second, we demonstrate that higher intelligence is associated with cortical mantles with sparsely and well-organized dendritic arbor, thereby increasing processing speed and network efficiency. Importantly, the findings obtained from our experimental sample were confirmed by the analysis of an independent validation sample from the Human Connectome Project25



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Meta-analytic SEM of literacy and language development relations

Using Meta-analytic Structural Equation Modeling to Study Developmental Change in Relations Between Language and Literacy. Article link.

Jamie M. Quinn Richard K. Wagner

The purpose of this review was to introduce readers of Child Development to the meta-analytic structural equa-tion modeling (MASEM) technique. Provided are a background to the MASEM approach, a discussion of its utility in the study of child development, and an application of this technique in the study of reading compre-hension (RC) development. MASEM uses a two-stage approach: first, it provides a composite correlation matrix across included variables, and second, it fits hypothesized a priori models. The provided MASEM application used a large sample (N = 1,205,581) of students (ages 3.5–46.225) from 155 studies to investigate the factor structure and relations among components of RC. The practical implications of using this technique to study development are discussed.

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Possible Gf subprocesses

Interesting conceptual framework for understanding performance on Gf tasks. However, it is Important to note that factor analysis studies have suggested a number of subprocesses that do not necessarily fit in this task-analysis based model.

Signatures of multiple processes contributing to fluid reasoning performance (article link)

Ehsan Shokri-Kojoria and Daniel C. Krawczyk


A R T I C L E I N F O

Keywords: Fluid intelligence Individual differences Multi-process Raven's progressive matrices

A B S T R A C T

We aimed to achieve a better understanding of the cognitive processes of fluid reasoning (or fluid intelligence; Gf), the ability to reason in novel conditions. While fluid reasoning has often been considered a unitary con-struct, multiple cognitive processes are expected to affect fluid reasoning performance. Yet, the contribution of various cognitive processes in fluid reasoning performance remains under-explored. We hypothesized that in-dividual differences in fluid intelligence can be viewed as a composite of individual differences in performance in various processes of Gf. Change detection, rule verification, and rule generation were the three processes-of-interest that were additively recruited in a novel visuospatial reasoning task. We observed decreases in accuracy and increases in response time as the processing requirements increased across task conditions. Hierarchical multiple linear regression analyses showed that individual differences in the likelihood of success and speed of each of these processes, accounted for different aspects of individual differences in accuracy and response time in fluid reasoning performance, as measured by Raven's Progressive Matrices. Change detection was a significant contributor to performance in problems with higher visuospatial demand, however, rule verification and rule generation consistently contributed to performance for all problem types. Our findings support the position that individual differences in fluid intelligence emerge as a composite of performance on separable cognitive op-erations, with rule processing being important for differentiating performance on high difficulty problem.

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Research Byte: The Role of Visuospatial Ability in the Raven's Progressive Matrices

File under Gf and Gv as per CHC theory.

The Role of Visuospatial Ability in the Raven's Progressive Matrices

Nicolette A. Waschl, Ted Nettelbeck, and Nicholas R. Burns

School of Psychology, University of Adelaide, SA, Australia

Abstract:

Debate surrounding the role of visuospatial ability in performance on the Raven's Progressive Matrices (RPM) has existed since their conception. This issue has yet to be adequately resolved, and may have implications regarding sex differences in scores. Therefore, this study aimed to examine the relationship between RPM performance, visuospatial ability and fluid ability, and any sex differences in these relationships. Data were obtained from three samples: two University samples completed the Advanced RPM and one population-based sample of men completed the Standard RPM. All samples additionally completed an alternative measure of fluid ability, and one or more measures of visuospatial ability. Structural equation modeling was used to examine the relationships between performance on the visuospatial and fluid ability tests and performance on the RPM. Visuospatial ability was found to significantly contribute to performance on the RPM, over and above fluid ability, supporting the contention that visuospatial ability is involved in RPM performance. No sex differences were found in this relationship, although sex differences in visuospatial ability may explain sex differences in RPM scores.

Keywords: Raven's Progressive Matrices, fluid ability, visuospatial ability, sex differences

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Research Byte: Is General Intelligence Little More Than the Speed of Higher-Order Processing?

Although a small sample, this is still and interesting study. The results are consistent with the continued nexus of the g, Gf, Gwm, attentional control and speed of higher order processing (especially P300 in ERP’s), white matter tract integrity and the PFIT model of intelligence as well as the recent process overlap theory (POT) of g.

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Article link.

Anna-Lena Schubert, Dirk Hagemann, and Gidon T. Frischkorn Heidelberg University

ABSTRACT

Individual differences in the speed of information processing have been hypothesized to give rise to individual differences in general intelligence. Consistent with this hypothesis, reaction times (RTs) and latencies of event-related potential have been shown to be moderately associated with intelligence. These associations have been explained either in terms of individual differences in some brain-wide property such as myelination, the speed of neural oscillations, or white-matter tract integrity, or in terms of individual differences in specific processes such as the signal-to-noise ratio in evidence accumulation, executive control, or the cholinergic system. Here we show in a sample of 122 participants, who completed a battery of RT tasks at 2 laboratory sessions while an EEG was recorded, that more intelligent individuals have a higher speed of higher-order information processing that explains about 80% of the variance in general intelligence. Our results do not support the notion that individuals with higher levels of general intelligence show advantages in some brain-wide property. Instead, they suggest that more intelligent individuals benefit from a more efficient transmission of information from frontal attention and working memory processes to temporal-parietal processes of memory storage.

Keywords: ERP latencies, event-related potentials, intelligence, processing speed, reaction times



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Mathematical (Gq) giftedness: Review of cognitive, conative and neural variables

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Article link.




ABSTRACT

Most mathematical cognition research has focused on understanding normal adult function and child development as well as mildly and moderately impaired mathematical skill, often labeled developmental dyscalculia and/or mathematical learning disability. In contrast, much less research is available on cognitive and neural correlates of gifted/excellent mathematical knowledge in adults and children. In order to facilitate further inquiry into this area, here we review 40 available studies, which examine the cognitive and neural basis of gifted mathematics. Studies associated a large number of cognitive factors with gifted mathematics, with spatial processing and working memory being the most frequently identified contributors. However, the current literature suffers
from low statistical power, which most probably contributes to variability across findings. Other major shortcomings include failing to establish domain and stimulus specificity of findings, suggesting causation without sufficient evidence and the frequent use of invalid backward inference in neuro-imaging studies. Future studies must increase statistical power and neuro-imaging studies must rely on supporting behavioral data when interpreting findings. Studies should investigate the factors shown to correlate with math giftedness in a more specific manner and determine exactly how individual factors may contribute to gifted math ability.


SELECTIVE SUMMARY CONCLUSION STATEMENTS

In line with the heterogeneous nature of mathematical disabilities (e.g., Rubinsten and Henik, 2009; Fias et al., 2013), mathematical giftedness also seems to correlate with numerous factors—(see Appendix A for which factors were found in each study). These factors roughly fall into social, motivational, and cognitive domains. Specifically, in the social and motivational domains, motivation, high drive, and interest to learn mathematics, practice time, lack of involvement in social interpersonal, or religious issues, authoritarian attitudes, and high socio-economic status have all been related to high levels of mathematical achievement. Speculatively, it is interesting to ask whether some of these factors may be related to the so-called Spontaneous Focusing on Numerosity (SFON) concept which appears early in life and means that some children have a high tendency to pay attention to numerical information (Hannula and Lehtinen, 2005). To clarify this question, longitudinal studies could investigate whether high SFON at an early age is associated with high levels of mathematical expertise in later life. Better assessment of individual variability is also important, for example, Albert Einstein (who was a gifted even if sometimes “lazy” mathematician; see e.g., Isaacson, 2008) was famously anti-authoritarian.

In terms of cognitive variables, we found that spatial processing, working memory, motivation/practice time, reasoning, general IQ, speed of information processing, short-term memory, efficient switching from working memory to episodic memory, pattern recognition, inhibition, fluid intelligence, associative memory, and motor functions were all associated with mathematical giftedness. As a caveat it is important to point out that mere “significance counting” (i.e., just considering studies with statistical significant results regarding a concept) can be very misleading especially in the typically underpowered context of psychology and neuro-imaging research (see e.g., Szucs and Ioannidis, 2017). However, considering the patchy research, this is the best we can do at the moment. In addition, even if meta-analyses were possible, these also typically only take into account published research, so they usually (highly) overestimate effect sizes especially from small scale studies (see Szucs and Ioannidis, 2017).


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Research byte: Executive functions and relations with Gf and creativity (Gr)

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Research byte: Verbal ability (Gc) and knowledge (Gkn) highly related but still separate constructs

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Research Byte: Short-term memory for faces is related to general intelligence: A possible new CHC narrow ability taxonomy candidate?

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Intelligence

Available online 21 May 2016

In Press, Corrected Proof — Note to users

Short-term memory for faces relates to general intelligence moderately ^☆

• Gilles E. Gignac^, ,
• Mahesh Shankaralingam,
• Kipling Walker,
• Philippe Kilpatrick

 Show more

doi:10.1016/j.intell.2016.05.001

Get rights and content

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Highlights

•

Short-term memory for faces correlated positively with several stratum II factors.

•

Short-term memory for faces was associated with general intelligence at .34.

•

Short-term memory for faces should not be considered “special” (i.e., independent of g).

•

Prosopagnosia may be best characterised as a learning disability.

---

Abstract

The results associated with a small number of investigations suggest that individual differences in memory for faces, as measured by the Cambridge Face Memory Test (CFMT), are independent of intelligence. Consequently, memory for faces has been suggested to be a special construct, unlike other cognitive abilities. However, previous investigations have measured intelligence with only one or two subtests. Additionally, the sample sizes upon which previous investigations were based were relatively small (N = 45 to 80). Consequently, in this investigation, a battery of eight cognitive ability tests and the CFMT were administered to a relatively large number of participants (N = 211). Based on a correlated-factor model, memory for faces was found to be correlated positively with fluid intelligence (.29), short-term memory (.23) and lexical knowledge ability (.19). Additionally, based on a higher-order model, memory for faces was found to be associated with g at .34. The results are interpreted to suggest that memory for faces, as measured by the CFMT, may be characterised as a relatively typical narrow cognitive ability within the Cattell–Horn–Carroll (CHC) model of intelligence, rather than a special ability (i.e., independent of other abilities). Future research with a greater diversity in the measurement of face recognition ability is encouraged (e.g., long-term memory), as the CFMT is a measure of short-term face memory ability.

Keywords

• Intelligence;
• CHC theory;
• Face identity recognition;
• Prosopagnosia

Research Byte: Multivariate Associations of Fluid Intelligence (Gf) and NAA--more P-FIT model support

When it rains--it pours.  Second posting today of research study reinforcing the importance of the P-FIT neuro-model of intelligence.

 

Multivariate Associations of Fluid Intelligence and NAA

1. Aki Nikolaidis1,2,
2. Pauline L. Baniqued1,3,4,
3. Michael B. Kranz1,3,
4. Claire J. Scavuzzo2,5,
5. Aron K. Barbey1,
6. Arthur F. Kramer1,2,3 and
7. Ryan J. Larsen1

+ Author Affiliations

1. ¹Beckman Institute for Advanced Science and Technology
2. ²Neuroscience Program and
3. ³Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
4. ⁴Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
5. ⁵Psychology Department, University of Alberta, Edmonton, Alberta, Canada

1. Address correspondence to Aki Nikolaidis. Email: g.aki.nikolaidis@gmail.com

Abstract

Understanding the neural and metabolic correlates of fluid intelligence not only aids scientists in characterizing cognitive processes involved in intelligence, but it also offers insight into intervention methods to improve fluid intelligence. Here we use magnetic resonance spectroscopic imaging (MRSI) to measure N-acetyl aspartate (NAA), a biochemical marker of neural energy production and efficiency. We use principal components analysis (PCA) to examine how the distribution of NAA in the frontal and parietal lobes relates to fluid intelligence. We find that a left lateralized frontal-parietal component predicts fluid intelligence, and it does so independently of brain size, another significant predictor of fluid intelligence. These results suggest that the left motor regions play a key role in the visualization and planning necessary for spatial cognition and reasoning, and we discuss these findings in the context of the Parieto-Frontal Integration Theory of intelligence.