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.

Research Byte: Frontal and Parietal Cortices Show Different Spatiotemporal Dynamics across Problem-solving Stages--Is the P-FIT it?

Yet another study supporting the P-FIT neuro model of intelligence. Overview of P-FIT here. https://en.m.wikipedia.org/wiki/Parieto-frontal_integration_theory

I have previously provided an overview of the P-FIT model of intelligence at the Interactive Metronome-Home blog.

Frontal and Parietal Cortices Show Different Spatiotemporal Dynamics across Problem-solving Stages. - PubMed

Arithmetic problem-solving can be conceptualized as a multistage process ranging from task…

Read it on Flipboard

Read it on ncbi.nlm.nih.gov

Executive function of "peacekeeping" has a role in fluid intelligence (Gf)

Click on images to enlarge

CHC Theory: Fluid reasoning or intelligence (Gf) definition

Fluid Reasoning (Gf): The deliberate but flexible control of attention to solve novel “on the spot” problems that cannot be performed by relying exclusively on previously learned habits, schemas, and scripts. Fluid reasoning is a multi-dimensional construct but its parts are unified in their purpose: solving unfamiliar problems. Fluid reasoning is most evident in abstract reasoning that depends less on prior learning. However, it is also present in day-to-day problem solving. Fluid reasoning is typically employed in concert with background knowledge and automatized responses. That is, fluid reasoning is employed, even if for the briefest of moments, whenever current habits, scripts, and schemas are insufficient to meet the demands of a new situation. Fluid reasoning is also evident in inferential reasoning, concept formation, classification of unfamiliar stimuli, generalization of old solutions to new problems and contexts, hypothesis generation and confirmation, identification of relevant similarities,differences, and relationship among diverse objects and ideas, the perception of relevant consequences of newly acquired knowledge, and extrapolation of reasonable estimates in ambiguous situations.

• Induction (I). The ability to observe a phenomenon and discover the underlying principles or rules that determine its behavior.
• General Sequential Reasoning (RG). The ability to reason logically using known premises and principles. This ability is also known as deductive reasoning or rule application.
• Quantitative Reasoning (RQ): The ability to reason, either with induction or deduction, with numbers, mathematical relations, and operators.

The above definitions were abstracted from Schneider and McGrew's (2012) contemporary CHC theory chapter in the form of a special CHC v2.0 publication. See the chapter for more in depth information regarding this ability domain and contemporary CHC theory.

Prior definitions in this series can be found here.


Thanks to Dr. Scott Barry Kaufman for permission to to use the above graphic depiction of this CHC ability. These CHC icons are part of Dr. Kaufman's book, Ungifted: Intelligence Redefined, and are the creative work of George Doutsiopoulos.

Posted with Blogsy

CHC Theory: Comprehension-knowledge or crystallized intelligence (Gc) definition

Comprehension-Knowledge (Gc):  Depth and breadth of knowledge and skills that are valued by one’s culture. Every culture values certain skills and knowledge over others.  Gc reflects the degree to which a person has learned practically useful knowledge and mastered valued skills. Thus, by definition it is impossible to measure Gc independent of culture. Gc is theoretically broader than what is measured by any existing cognitive battery.

• General Verbal Information (K0). Breadth and depth of knowledge that one’s culture deems essential, practical, or otherwise worthwhile for everyone to know.

• Language Development (LD). General understanding of spoken language at the level of words, idioms, and sentences. In the same way that Induction is at the core of Gf, Language Development is at the core of Gc. Although listed as a distinct narrow ability in Carroll’s model, his description of his analyses make it clear that he meant Language Development as an intermediate category between Gc and more specific language-related abilities such as Lexical Knowledge, Grammatical Sensitivity, and Listening Ability. Language development It appears to be a label for all language abilities working together in concert.

• Lexical Knowledge (VL). Knowledge of the definitions of words and the concepts that underlie them. Whereas Language Development is more about understanding words in context, Lexical Knowledge is more about understanding the definitions of words in isolation.

• Listening Ability (LS). Ability to understand speech.  Tests of listening ability typically have simple vocabulary but increasingly complex syntax or increasingly long speech samples to listen to.

• Communication Ability (CM). Ability to use speech to communicate one’s thoughts clearly. This ability is comparable to Listening Ability except that it is productive (expressive) rather than receptive.

• Grammatical Sensitivity (MY). Awareness of the formal rules of grammar and morphology of words in speech. This factor is distinguished from English Usage in that it is manifest in oral language instead of written language and that it measures more the awareness of grammar rules rather than correct usage.

The above definitions were abstracted from Schneider and McGrew's (2012) contemporary CHC theory chapter in the form of a special CHC v2.0 publication. See the chapter for more indepth information regarding this ability domain and contemporary CHC theory.

Prior definitions in this series can be found here.

Thanks to Dr. Scott Barry Kaufman for permission to to use the above graphic depiction of this CHC ability. These CHC icons are part of Dr. Kaufman's book, Ungifted: Intelligence Redefined, and are the creative work of George Doutsiopoulos.

Posted with Blogsy

Research Bytes: Brain complexity, predicting job success, neuroscience/creativity, fluid IQ and personality

Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200-209.

Although the ultimate aim of neuroscientific enquiry is to gain an understanding of the brain and how its workings relate to the mind, the majority of current efforts are largely focused on small questions using increasingly detailed data. However, it might be possible to successfully address the larger question of mind–brain mechanisms if the cumulative findings from these neuroscientific studies are coupled with complementary approaches from physics and philosophy. The brain, we argue, can be understood as a complex system or network, in which mental states emerge from the interaction between multiple physical and functional levels. Achieving further conceptual progress will crucially depend on broad-scale discussions regarding the properties of cognition and the tools that are currently available or must be developed in order to study mind–brain mechanisms.
Article Outline



Ziegler, M., Dietl, E., Danay, E., Vogel, M., & Buhner, M. (2011). Predicting Training Success with General Mental Ability, Specific Ability Tests, and (Un)Structured Interviews: A meta-analysis with unique samples. International Journal of Selection and Assessment, 19(2), 170-182.


Several meta-analyses combine an extensive amount of research concerned with predicting training success. General mental ability is regarded as the best predictor with specific abilities or tests explaining little additional variance. However, only few studies measured all predictors within one sample. Thus, intercorrelations were often estimated based on other studies. Moreover, new methods for correcting range restriction are now available. The present meta-analyses used samples derived from a German company in which applicants for different apprenticeships were tested with an intelligence structure test, specific ability tests as well as a structured and an unstructured interview. Therefore, intercorrelations between different assessment tools did not have to be estimated from other data. Results in the final examination, taking place at least 2 years after the original assessment, served as criterion variable. The dominant role of general mental ability was confirmed. However, specific abilities were identified that can be used as valuable additions. Job complexity moderated some of the relationships. Structured interviews were found to have good incremental validity over and above general mental ability. Unstructured interviews, on the other hand, performed poorly. Practical implications are discussed.


Sawyer, K. (2011). The Cognitive Neuroscience of Creativity: A Critical Review. Creativity Research Journal, 23(2), 137-154.

Cognitive neuroscience studies of creativity have appeared with increasing frequently in recent years. Yet to date, no comprehensive and critical review of these studies has yet been published. The first part of this article presents a quick overview of the 3 primary methodologies used by cognitive neuroscientists: electroencephalography (EEG), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI). The second part provides a comprehensive review of cognitive neuroscience studies of creativity-related cognitive processes. The third part critically examines these studies; the goal is to be extremely clear about exactly what interpretations can appropriately be made of these studies. The conclusion provides recommendations for future research collaborations between creativity researchers and cognitive neuroscientists.


Djapo, N., KolenovicDjapo, J., Djokic, R., & Fako, I. (2011). Relationship between Cattell's 16PF and fluid and crystallized intelligence. Personality and Individual Differences, 51(1), 63-67.

The aim of the study was to explore the relationship between the five global factors and 16 dimensions of Cattell’s personality model and fluid and crystallized intelligence. A total of 105 third graders (45.7% males) of three high schools participated in the research. Fluid intelligence was measured by Raven’s Advanced Progressive Matrices and crystallized intelligence was measured by the Mill Hill Vocabulary Scale. Personality traits were measured by the Sixteen Personality Factor Questionnaire. Anxiety is correlated neither with fluid nor with crystallized intelligence. Extraversion and Self-Control are negatively correlated with fluid intelligence whereas Tough-Mindedness is positively correlated with it. Independence is positively correlated with crystallized intelligence and Tough-Mindedness is negatively correlated with it. Regression analysis reveals that all broad personality factors, except anxiety, are significant predictors of fluid intelligence. When combined together, these factors account for 25% of the variance of fluid intelligence scores. The regression model with crystallized intelligence as a criterion variable is not statistically significant. The study results are consistent with the Chamorro


- iPost using BlogPress from my Kevin McGrew's iPad

Hot and cold CHC intelligence abilities--Gf,Gc,Gv hot--Ga,Glr cold

Interesting article in the journal Intelligence reviewing the state-of-the-art of factor analysis practices for identifying the g (general intelligence) factors. Abstract is below. Of interest is the use of the CHC framework to classify the type of broad CHC factor indicators found in the research synthesis.

Not unexpectedly, Gf, Gc, and Gv were found most often in IQ factor analysis research, followed by Gq, Gs and Gsm. Abilities that appear underrepresented in IQ factor analysis g research are the domains of Glr and Ga.

However, a couple of major caveats. The literature review was primarily adult samples. There has been considerable factor analysis activity with tests in childhood and adolescent samples that might increase the proportion of Glr and Ga indicators. Also, the authors did not include journals favored by those doing research in school psychology, special education, and speech and language---fields of study that most likely have published more studies in the under-represented CHC domains.

Never-the-less......the general trends are not surprising.

Clicking on images should take you toe larger versions.






- iPost using BlogPress from my Kevin McGrew's iPad

intelligence IQ tests IQ scores CHC theory Cattell-Horn-Carroll human cognitive abilities psychology school psychology individual differences cognitive psychology neuropsychology special education educational psychology psychometrics psychological assessment psychological measurement IQs Corner neuroscience neurocognitive cognitive abilities cognition Gq quantitative abilities quantitative knowledge quantitative reasoning Gf fluid intelligence Gc crystallized intelligence Gsm Short-term memory Gll long-term retrieval Ga auditory processing Gv visual processing visual-spatial Gs processing speed factor analysis general intelligence g

Research bytes: Cognitive employment testing--aging strategies--cognitive thresholds

Three interesting articles from one of my favorite journals--Current Directions in Psychological Science.

As per usual when I make a research byte/brief post, if anyone would like to read the original article, I can share via email---with the understanding that the article is provided in exchange for a brief guest post about it's contents. :) (contact me at iap@earthlink.net if interested). Also, if figure/images are included in the post, they can usually be made larger by clicking on the image.












- iPost using BlogPress from my Kevin McGrew's iPad

intelligence IQ tests IQ scores CHC theory Cattell-Horn-Carroll human cognitive abilities psychology school psychology individual differences cognitive psychology neuropsychology special education educational psychology psychometrics psychological assessment psychological measurement IQs Corner neuroscience neurocognitive cognitive abilities cognition gifted aging developmental psychology cognitive thresholds employment testing

Research byte: Relationship between working memory, memory span and fluid intelligence (Gf)

As per usual when I make a research byte/brief post, if anyone would like to read the original article, I can share via email---with the understanding that the article is provided in exchange for a brief guest post about it's contents. :) (contact me at iap@earthlink.net if interested). Also, if figure/images are included in the post, they can usually be made larger by clicking on the image.


Pascale M.J. Engel de Abreu, Andrew R.A. Conway, Susan E. Gathercole. Working memory and fluid intelligence in young children. Intelligence 38 (2010) 552–561

Abstract

The present study investigates how working memory and fluid intelligence are related in young children and how these links develop over time. The major aim is to determine which aspect of the working memory system—short-term storage or cognitive control—drives the relationship with fluid intelligence. A sample of 119 children was followed from kindergarten to second grade and completed multiple assessments of working memory, short-term memory, and fluid intelligence. The data showed that working memory, short-term memory, and fluid intelligence were highly related but separate constructs in young children. The results further showed that when the common variance between working memory and short-term memory was controlled, the residual working memory factor manifested significant links with fluid intelligence whereas the residual short-term memory factor did not. These findings suggest that in young children cognitive control mechanisms rather than the storage component of working memory span tasks are the source of their link with fluid intelligence.





The findings of this study are very similar to a series of SEM models I ran with indicators from the WJ III. The WJ III CHC-based models also showed that memory span (Gms-MS) was a causal factor for working memory (Gsm-MW) which in turn had a significant causal effect on g (and not just Gf). The primary difference was that these WJ III based analyses also included processing speed (Gs) as a causal influence on MS and MW but, consistent with the developmental cascade hypothesis Gs did not have a direct causal effect on Gf (it was mediated thru Gsm-MS-MW). In addition, these models included a much broader array of indicators of g, inclusive of Gc, Gv, Ga, Glr, and Gf.


- iPost using BlogPress from my Kevin McGrew's iPad

intelligence IQ tests IQ scores CHC theory Cattell-Horn-Carroll human cognitive abilities psychology school psychology individual differences cognitive psychology neuropsychology special education educational psychology psychometrics psychological assessment psychological measurement IQs Corner neuroscience neurocognitive cognitive abilities cognition Gv Ga Glr Gf Gc Gsm working memory developmental cascade hypothesis cognitive control fluid intelligence general intelligence WJ III

More support for working memory (Gsm-MW) and fluid intelligence (Gf)

The relationships of working memory, secondary memory, and general fluid intelligence: Working memory is special. By Shelton, Jill Talley; Elliott, Emily M.; Matthews, Russell A.; Hill, B. D.; Gouvier, Wm. Drew
Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol 36(3), May 2010, 813-820.

Abstract

Recent efforts have been made to elucidate the commonly observed link between working memory and reasoning ability. The results have been inconsistent, with some work suggesting that the emphasis placed on retrieval from secondary memory by working memory tests is the driving force behind this association (Mogle, Lovett, Stawski, & Sliwinski, 2008), whereas other research suggests retrieval from secondary memory is only partly responsible for the observed link between working memory and reasoning (Unsworth & Engle, 2006, 2007). In the present study, we investigated the relationship between processing speed, working memory, secondary memory, primary memory, and fluid intelligence. Although our findings show that all constructs are significantly correlated with fluid intelligence, working memory—but not secondary memory—accounts for significant unique variance in fluid intelligence. Our data support predictions made by Unsworth and Engle (2006, 2007) and suggest that the combined need for maintenance and retrieval processes present in working memory tests makes them special in their prediction of higher order cognition.

Traing working memory increases fluid intelligence (Gf): New research

Training working memory can increase fluid intelligence (Gf). Wow. Hmmmmm?

I've had a number of people forward the following abstract to me. After reading the article I now see why. The article in the Proceedings of the National Academy of Sciences (PNAS) reports that a working memory training intervention produced positive transfer effects in fluid intelligence (Gf). Cognitive ability training research suffers from a paucity of studies that demonstrate positive transfer to other tasks/domains that differ from the training medium. This study also adds additional strong evidence to the link between working memory and Gf.

Cool stuff. A must read. Much has been written about the link between working memory and Gf. Here are some prior related posts touching on the topics of working memory and Gf.

I need to take time to read this study in depth before commenting more. I've only skimmed the abstract at this point in time. Maybe others can read and comment.

Jaeggi, S., Buschkuehl, M., Jonides, J. & Perrig, W. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academic of Sciences, 105 (19), 6829-6833. (click to read)


Abstract

• Fluid intelligence (Gf) refers to the ability to reason and to solve new problems independently of previously acquired knowledge. Gf is critical for a wide variety of cognitive tasks, and it is considered one of the most important factors in learning. Moreover, Gf is closely related to professional and educational success, especially in complex and demanding environments. Although performance on tests of Gf can be improved through direct practice on the tests themselves, there is no evidence that training on any other regimen yields increased Gf in adults. Furthermore, there is a long history of research into cognitive training showing that, although performance on trained tasks can increase dramatically, transfer of this learning to other tasks remains poor. Here, we present evidence for transfer from training on a demanding working memory task to measures of Gf. This transfer results even though the trained task is entirely different from the intelligence test itself. Furthermore, we demonstrate that the extent of gain in intelligence critically depends on the amount of training: the more training, the more improvement in Gf. That is, the training effect is dosage-dependent. Thus, in contrast to many previous studies, we conclude that it is possible to improve Gf without practicing the testing tasks themselves, opening a wide range of applications.

Can we train Gf (fluid IQ)?

Check out the following from one of my favorite blogs- Sharp Brains.:

"A recent scientific study is being welcomed as a landmark that shows how fluid intelligence can be improved through training. I interviewed one of the researchers recently (Can Intelligence Be Trained? Martin Buschkuehl shows how), and contributor Dr. Pascale Michelon adds her own take with the great article that follows. Enjoy!"

Sent from KMcGrew iPhone

Working memory, Gf and task switching article

Database: PsycARTICLES

[Journal Article]

Speed and accuracy of accessing information in working memory: An individual differences investigation of focus switching.

Unsworth, Nash; Engle, Randall W.

Journal of Experimental Psychology: Learning, Memory, and Cognition. 2008 May Vol 34(3) 616-630

Abstract

Three experiments examined the nature of individual differences in switching the focus of attention in working memory. Participants performed 3 versions of a continuous counting task that required successive updating and switching between counts. Across all 3 experiments, individual differences in working memory span and fluid intelligence were related to the accuracy of the counts, but not to the time cost associated with switching between counts. The authors suggest that working memory span and fluid intelligence measures partially index the ability to accurately switch information in and out of the focus of attention, but this variation is not related to the speed of switching. (PsycINFO Database Record (c) 2008 APA, all rights reserved)

Sent from KMcGrew iPhone

Prediction of WJ III reading/math ach by cognitive and language tests

This is an update to my CHC cognitive abilities and reading and math research projects (please visit prior posts for background information).

I recently ran multiple regression analysis in the WJ III norm data [conflict of interest note - I'm a coauthor of the WJ III] where I used the complete set of WJ III cognitive tests (the original WJ III and WJ III Diagnostic Supplement tests) and WJ III oral language tests to predict the WJ III (a) individual reading tests, (b) reading clusters, (c) individual math tests, and (d) math clusters.

Summary tables of the results are now included in the CHC reading and math summary visual-graphic mindmaps posted previously. As noted in the summary documents, I ran step-wise multiple regression models (with backward stepping) at three different age groups in the WJ III norm sample (ages 6-8; 9-13; 14-19). I specified that the models include five predictor tests. Due to possible predictor-criterion contamination, the WJ III Number Series test was excluded from the predictor (IV) pool in the prediction of the Math Reasoning and Quantitative Concepts test (half of the QC test, which is part of the MR cluster, includes number series type items). Finally, the regression models were run on correlation matrices that were calculated in each age group with age variance removed (age-based standard scores were used). The EM missing data algorithm was invoked during the calculation of the matrices. These matrices where then used for the multiple regression analysis.

Descriptions and explanations of the WJ III tests are available via a link in a prior post (first link under "The Results: Phase I" section of prior post)

The summary tables include the final standardized regression weights as well as the coding of tests that, although not ending up in the final regression models, were often close to entering the regression model at some steps (based on inspection of the partial correlations at each step)--call them bridesmaid tests.

This material is being incorporated into a presentation I'm completing as part of a NASP 2008 workshop. The interpretation of these results, combined with a select review and synthesis of CHC- and non-CHC-organized COG-ACH correlates research, will be presented at that workshop, as well as the eventual posting of select PPT slides from that workshop (watch for announcements at this blog).

Please recognize that these results have NOT been peer-reviewed. The results are being presented "as is" with no interpretation. As noted above, interpretation of this material will be part of the NASP workshop. Additionally, this material will be included in the next update to the WJ III Evolving Web of Knowledge (EWOK), which I plan to update prior to (or immediately after) this workshop.

Enjoy running your fingers through the analysis summaries. I hope the posting of this information stimulates hypothesis generation and discussion by other CHC/WJ III scholars and assessment professionals. I urge interested individuals to make comments on the CHC listserv...as the CHC listserv provides for a more dynamic give-and-take learning experience than available via static blog "comment" posts.

You can go directly past go and download the reports at the following two links.

• WJ III reading achievement multiple regression summary
• WJ III math achievement multiple regression summary
  

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, Riverside Publishing, Woodcock-Johnson, WJ III, WJ III NU, normative update, reading, math, Grw, Gq, CHC, CHC theory, multiple regression

Powered by ScribeFire.

Is it possible to be too smart?

Is it possible that too much (high) fluid intelligence (Gf) can be detrimental to some forms of cognitive performance? Possibly, according to Shamosh and Gray's (2007) article "The relation between fluid intelligence and resource depletion" published in Cognition and Emotion. Although a small and select sample (the ubiquitous sample of undergraduate students), this study suggested the possibility that individuals with higher Gf may "deplete" their self-regulation resources more than individuals with lower Gf. Maybe this is why people who engage in strenuous cognitive performance over time often end up feeling drained...and in need of a break. At least this is going to give me a valid excuse when I feel I simply can't copy anymore after "thinking too hard."

• Abstract: Self-regulation depends on a limited resource that can be depleted temporarily, but little is known about how this resource relates to individual differences in cognitive ability. We investigated whether self-regulatory depletion would vary with individual differences in fluid intelligence (gF), a stable index of cognitive ability with ties to executive function. Participants performed an emotion regulation task varying in self-regulatory demand, followed by the Multi-Source Interference Task to assess depletion. On a separate day, participants completed Raven’s Advanced Progressive Matrices to assess gF. Emotion suppression led to impairment on the interference task, indicating self-regulatory depletion. Critically, higher gF was associated with greater depletion. Controlling for variables reflecting susceptibility to task demands and trait motivation did not influence this effect. The results have implications for theories of the relation between self-regulatory and cognitive abilities, and the mechanisms supporting the control of behaviour.

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, fluid intelligence, Gf, intelliigence, IQ, IQ scores, IQ tests, self-regulation.executive function

Powered by ScribeFire.