Showing posts with label cognitive efficiency. Show all posts
Showing posts with label cognitive efficiency. Show all posts

Thursday, December 29, 2016

Research Byte: A closer look at who "chokes under pressure" - importance of attentional control (AC)

Volume 5, Issue 4, December 2016, Pages 470–477
Working Memory in the Wild: Applied Research in Working Memory

A Closer Look at Who “Chokes Under Pressure” ☆ ☆



Highlights

•
High pressure settings compromise working memory and decrease cognitive performance.
•
Those with higher working memory show greatest pressure-induced cognitive deficits.
•
Attentional control alters relation of working memory to performance under pressure.

Previous research has shown that the higher one's working memory capacity, the more likely his/her performance is to be negatively impacted by performance pressure. In the current research we examined potential explanations for this finding by assessing the relation between pressure-induced performance deficits (i.e. “choking under pressure”) in math-based problem solving and individual differences in both working memory (as assessed via complex span tasks) and attentional control (as assessed via two measures from an Eriksen Flanker task). We find higher working memory only relates to “choking under pressure” when individuals were low in attentional control. These results further elucidate the mechanism by which high-pressure scenarios can lead to errors in performance and carry implications for developing effective intervention strategies to prevent poor performance in high-stakes situations.

Tuesday, September 02, 2014

Working memory training (n-back task) improves fluid intelligence (Gf) 3-4 IQ points

Interesting meta-analysis suggestingthat working memory training, via the n-back task, over a relatively short period of time, can improve fluid intelligence/reasoning (Gf). Conservative estimate of 3-4 Gf IQ points improvement. Click on images to enlarge.









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Saturday, July 05, 2014

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.

Click on images to enlarge














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Thursday, March 08, 2012

Research Bytes: What is cognitive efficiency--conceptual and methodological models




Cognitive efficiency has become a hot construct in the psychometric measurement of intelligence. However, not much conceptual or methodological foundation work has addressed the issue of "what is CE" and "what methodological model should be used?"

Hoffman and associates have written some interesting conceptual articles that we in the area of intelligence testing may want to review to add some clarity to the measurement and interpretation of our CE measures on IQ batteries. Below are two recent articles.

Click on images to enlarge.









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Friday, July 01, 2011

Cognitive efficiency measures as the brain's cognitive thermometer




Yet on more study linking poor global processing speed (Gs) with a clinical disorder, this time reading (again). It is very clear that during the past decade one of the most robust research findings (based on the relations between psychometric measures of abilities and all kinds of clinical disorders) is the importance of "cognitive efficiency" in identifying individuals with a wide variety of disorders.

The CHC domains of processing speed (Gs) and working memory (Gsm-WM) have repeatedly been found to be strong indicators that something is wrong in cognitive function, across many clinical disorders. I like to describe measures of cognitive efficiency (Gs+Gsm) as brain thermometers. They can tell you that the cognitive system is not operating efficiently, but they lack specificity to make specific differential diagnoses. Cognitive efficiency markers are domain-general re: Dx.

Click on image to enlarge.



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Tuesday, November 24, 2009

Dissertation dish: New insights on the subdomains of Gs (processing speed)


Exploring the relationships among various measures of processing speed in a sample of children referred for psychological assessments by Nelson, Megan A., Ph.D., University of Virginia, 2009 , 102 pages; AAT 3348732

Abstract

Processing speed is a robust psychometric factor in modern tests of cognitive ability (Carroll, 1993), but the common factors underlying mental speed and its contributions to individual differences in functioning are not well understood. The goal of the current study was to further explore mental speed by conducting a confirmatory factor analysis (CFA) on 11 speeded subtest scores. It was hypothesized that the 11 subtests would be best represented by a four-factor model. These four factors were then submitted to a cluster analysis to identify whether certain patterns of factor scores were related to different demographic characteristics, diagnoses, or referral questions. It was hypothesized that Learning Disorder, Attention-Deficit/Hyperactive Disorder, and comorbid LD/ADHD diagnoses would be most likely to have unique processing speed factor patterns.

Participants were 186 children (ages 6 - 18 years old) referred to a university-based clinic for a comprehensive psychological evaluation. The CFA indicated that although the 11 measures are all speeded, they are best represented as four distinct constructs, labeled perceptual speed, naming facility, academic facility, and reaction time in this study. The clusters produced in this study appeared to be most highly differentiated by level (likely influenced by intelligence level) and by pattern only in respect to reaction time factor scores. Therefore, both the CFA and cluster analyses lend support to Cattell-Horn-Carroll cognitive theory's distinction between cognitive processing speed (Gs) and decision/reaction time (Gt). Additionally, the CFA results suggest that Gs may be multifaceted, but the cluster analysis did not differentiate clusters based on the processing speed factors. Although the results of this study have important implications for both assessment clinicians and cognitive theory, further research is needed to clarify the constructs of processing speed and reaction time as well as to identify the clinical implications of different processing speed pattern
s

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Thursday, April 09, 2009

Brain rhythm? Do you have it? How can we get it?

Does your brain have good rhythm?

[Made this post at IQ Brain Clock sister blog...but thought IQs Corner readers might find interesting]

Very interesting research (Thut & Miniussi, 2009; Trends in Cognitive Sciences) being reported on the synchronization of brain oscillation behavior (brain rhythms) and neurotechnology to stimulate brain rhythm to enhance cognitive and motor performance.

The whole concept of neurological or brain rhythm has permeated a number of strands of research related to the internal mind or brain clock (mental interval timing). Also, if you've viewed my two on-line PPT presentations on (a) mental timing (IQ Brain Clock) and (b) trying to explain the positive effects of Interactive Metronome on a variety of cognitive and motor outcomes, you will see mention of hypothesized mechanisms dealing with synchrony of brain circuits, coordination of brain regions, increased neural efficiency, etc...all that seem to possibly relate to what researchers are now calling brain rhytyms.

Very interesting stuff. Although electronically or magnetically stimluating the brain to increase neural syncrhonization and rhythm is interesting, as noted in the two PPT slides linked above, I've hypothesized that the positive effect of less expensive technologies (e.g., Interactive Metronome ; conflict of interest - I'm on the IM Scientific Advisory Board) might be accomplishing similar effects by "fine-tunning brain rhythms."

Below is the abstract for the above linked article. Warning..it is a very technical article and not an easy read. Make sure your brain rhythms are at peak performance before trying to read the article.

There is renewed interest in the functional role of oscillatory brain activity in specific frequency bands, investigated in humans through electroencephalography (EEG) and magnetoencephalography (MEG) recordings. In parallel, there is a growing body of research on noninvasive direct stimulation of the human brain via repetitive (rhythmic) transcranial magnetic stimulation (TMS), and on those frequencies that have the strongest behavioural impact. There is, therefore, great potential in combining these two lines of research to foster knowledge on brain rhythms, in addition to potential therapeutic applications of rhythmic brain stimulation. Here, we review findings from this rapidly evolving field linking intrinsic brain oscillations to distinct sensory, motor and cognitive operations. The findings emphasize that brain rhythms are causally implicated in cognitive functions.
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Friday, April 03, 2009

WISC-IV cognitive proficiency index

Just learned that the WISC-IV now has a way to provide a "cognitive proficiency" index.  See Technical Report 6.

Interesting.  Composition is conceptually identical to WJ III Cognitive Efficiency cluster (Gsm+Gs), one of the most sensitive clusters for all kinds of deficits and disorders.  Welcome aboard WISC-IV.  Imitation is the best form of flattery [conflict of interest - I'm a coauthor of the WJ III)

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Monday, October 20, 2008

WJ III: 2-D MDS analysis ages 6-18

As promised, this is a follow-up to my posting of a 3-D Guttman Radex MDS model of WJ III tests. I'm now presenting a 2-D Radex model based on the analysis of all WJ III norm subjects from ages 6-18 (using the WJ III NU norms). You can view/download the pdf file by clicking here.

I could write an entire chapter on implications, hypotheses, etc. Instead, I'm going to make just a few comments and post a few questions in hopes that this approach to examining the characteristics of tests generates some interest. IMHO, MDS is an excellent analytic tool that provides a unique lens by which to augment our factor-analytic based understanding of cognitive ability tests. I wish more of us would complete these analyses with all major intelligence batteries.

A few thoughts/comments/questions:
  • Note that Concept Formation is near the middle of the circle. This whole round of MDS analyses I've been posting is based on a concern (see J. Schneider comments) whether the CF test was a good measure of Gf....and if it was strongly related to g. As per MDS interpretation, the proximity of CF to the middle cross-hairs suggests it is one of the more "cognitively complex" tests in the entire WJ III battery. This would support its interpretation as a strong indicator of Gf and g.
  • Notice that Sound Awareness (Ga/Gsm), Understanding Directions (Gsm/Gc), Applied Problems (Gq/Gf), Quantiative Concepts (Gq/Gf), and Verbal Comprehension (Gc) are also close to the middle - suggesting that they are all cognitively demanding measures in terms of the concept of cogntive complexity. And...interestingly they come from different CHC broad factors. I'm convinced that the reason Sound Awareness and Understanding Directions are cognitively complex is the major working memory load placed on subjects during these tasks. This should serve to remind us that cognitive complexity does NOT necessarily need to be associated with abstract "thinking" (Gf-ish) type tasks. Further notice that Auditory Working Memory is not that far away either. Do these findings support the research that suggest a strong relation between working memory (Gsm-MW) and Gf or g?
  • Notice how "tight" or "cohesive" some of the respective CHC factor tests are. Clearly the Grw, Gq, Gc, Gf, and Gsm (except for MS) tests all tend to hang in the same proximity. In contrast notice the wide degree of distance between the WJ III Gv and Ga tests. Does this suggest that some broad CHC domains are more tight/cohesive while other domains are much broader (lower domain cohesion). What does this mean for test interpretation? What does this mean for understanding the theoretical nature of the different CHC factor domains?
  • Notice the cool cognitive efficency (CE) quandrant. Isn't it sweet how most all the Gs and Gsm tests can be circumscribed in one area. Yet...there is distance between the CE tsts which probably is very informative in understanding differences in the characteristic process/content demands placed on subjects. Isn't this exciting?
  • Is the fact that most Gv tests are far from the cognitive complexity center (as were most of the Wechsler Gv tests in the enclosed slide in the file) helping us understand why traditonal Gv tests are repeatedly found to be unrelated (statistically) to school achievement (esp. mathematics), when we know that considerable research indicates that Gv is important for mathematics. Does this tell us that we have yet, in the world of applied test development, failed to develop sufficiency complex and cognitively demanding Gv tests that would relate more to school achievement (e.g., visual-spatial working memory tests). Curious minds want to know.
  • Like Gv, notice the distances between the Ga tests (which do form a nice psychometric factor when using traditonal factor methods). Incomplete Words is quite far away from Sound Blending, which in turn is closer to the acquired knowledge tests. Does this suggest that IW may be the more "pure" phonetic coding measure while SB is potentially influenced by training and education? Further note the location of Auditory Attention --- I've included it among the cognitive efficiency area. Is this telling us that the sound discrimination (Ga component) of the AA test is minimal while the selective attention (under distraction--ability to resist distractions) component is greater?
  • I'm not comfortable with the interpretation of quandrant 4 in the model. Can others suggest ideas? I think part of the problem is that a 3-D model (like the one I posted the other day) may required to better account for the dimensionality of the complete set of WJ III tests.
I could stare at this forever and generate more thoughts, hypotheses, questions, etc. I'd like to leave that to others. Please feel free to start a thread discussing the potential benefits of examing cognitive and achievement tests via the lens of MDS analysis. It clearly is an under-utilized methodology that can help us better understand our measures. A problem is that most quantoids (myself include) have become seduced by the more sexy contemporary SEM (CFA) methods. Maybe it is time we go "back to the future."


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Wednesday, October 08, 2008

Dissertation Dish: Processing Speed (Gs) Working Memory (Gsm-M) and math LD

Yet another study supporting the critical importance of cognitive efficiency variables (Gs-processing speed; Gsm-MW - working memory) and academic achievement (math), this time in young adults.

Working memory and processing speed as correlates of math skills in adults with learning disabilities by Zimmermann, Ute, Ph.D., Alliant International University, Los Angeles, 2008, 122 pages; AAT 3317770

Abstract

Math skills are required in every walk of life throughout a lifespan. The foundation for mathematical performance is innate and there appears to be a natural tendency for humans to build upon this foundation from simple arithmetic to more complex algorithms. Until recently the underlying mechanisms for math functioning have only been sparsely studied, especially in adults. The purpose of this correlational study was to identify the relationship between specific neuropsychological abilities and math skills in adults identified with a learning disability through the use of well established, rigorously standardized, and widely used test batteries, namely the WAIS-III and the Woodcock Johnson-III Tests of Achievement. Furthermore the Intake Self Report and the Academic Attribute Survey II (AAS-II) were utilized to investigate if students tend to over-report their difficulties in math performance when compared to their actual ability level.

This study utilized archival data from a community college in an suburban area of southern California. One hundred fifty participants met eligibility criteria for inclusion in the study. The participant sample consisted of 76 male and 74 females, with ethnic diverse backgrounds. All participants met eligibility criteria for learning disabilities as described by Title 5 regulations that govern the California Community Colleges in accordance with the State Education Code and Federal legislative guidelines. Analysis of data revealed that students tend to over-estimate their perceived difficulties in math when compared to their actual ability level. Correlational analysis indicated significant correlations between the WAIS-III Processing Speed Index and each of the three subtests of the WJ-III Broad Math Cluster. Significant correlations were also revealed between WAIS-III Working Memory Index and each of the three subtests of the WJ-III Broad Math Cluster. A comparison in magnitude of correlations revealed that working memory was the dominant factor for math performance in adults, with processing speed secondary to working memory. Exploratory analyses were performed to investigate the role of gender. No significant correlations emerged between the Processing Speed Index and any of the three Broad Math Cluster subtests for the female participants. Findings are discussed with recommendations for future research.

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Friday, April 18, 2008

WJ III Cognitive clusters: Concurrent validity with a neuropsychological battery

I just stumbled across an interesting study in The Clinical Neuropsychologist that investigated the concurrent validity of the Automated Neuropsychological Assessment Metrics (ANAM) and the Woodcock-Johnson III Tests of Cognitive Ability (WJ III) [Conflict of interest disclosure - I'm a co-author of the WJ III). Below is the abstract. Following the abstract are a few select findings and comments from the blogmaster. As appropriately noted by the authors (Jones et al., 2008), the results should be viewed with caution and need replication in larger and more diverse samples. The current sample was a relatively small (n=77) and homogenous/select sample of young adults (colleage undergraduate students).

Abstract
  • This study examines the relationship between a computerized neuropsychological assessment battery, the Automated Neuropsychological Assessment Metrics (ANAM) and a widely used ability measure, Woodcock-Johnson III Tests of Cognitive Ability (WJ-III). Results indicated substantial relationship between the ANAM throughput (accuracy /response time) scores and the WJ-III Cognitive Efficiency cluster. An unexpectedly strong relationship was evident between accuracy scores on the ANAM Logical Reasoning scale and the WJ-III General Intellectual Ability score, purporting to measure the g factor. The findings support the viability of the ANAM as a time- and cost-effective tool for appraisal of cognitive function.

Although the focus of the study was validating the ANAM, given the blogmaster's interests (and most readers of this blog), I'm more interested in the validity evidence from the perspective of the WJ III measures.

First, what is the ANAM? As described by the authors, the ANAM " is a clinical subset of instruments originally developed by the Office of Military Performance Assessment Technology. The ANAM battery is comprised of a series of cognitive tasks administered and scored with a computer...the common feature in the ANAM scales is highly structured, repetitive information processing tasks with well-defined stimuli and simple response modes."

The most intriguing results, from the perspective of the WJ III, are the significant and consistent moderate to moderately high correlations between the ANAM throughput scores and the WJ-III Cognitive Efficiency (Gsm+Gs) cluster (comprised of the Visual Matching and Numbers Reversed tests). Six of these seven correlation coefficients were statistically significant (ranging from approximately .30 to .52). The authors suggest that the respective ANAM measures "appear to be tapping an underlying construct of effective initial processing of input." This finding is also consistent with the WJ III authors suggested interpretation of the Cognitive Efficiency cluster (click here for a prior post that will eventually take you, if you follow some additional links, to a more detailed discussion of cognitive efficiency and some WJ III/CHC organized studies that investigated the relations between cognitive efficiency and psychometric g)

The next intriguing finding was a .58 (when corrected for range restriction) correlation between the WJ III Fluid Reasoning (Gf) and the NAM Matching to Sample measure. As described on the official ANAM web page, the MSP test "assesses spatial processing and visuo-spatial working memory. The user views a pattern produced by eight shaded cells in a 4x4 sample grid. The sample is then removed and two comparison patterns are displayed side by side. One grid is identical to the sample grid and the other grid differs by one shaded cell. The user is instructed to press a designated button to select the grid that matches the sample." Thus, the .58 correlation, which suggests approximately 34% shared variance, suggests that performance on the WJ III Fluid Reasoning (Gf) cluster may require a significant amount of visual-spatial working memory. This would be consistent with a large body of contemporary research that suggests a strong relation between working memory and higher-order cognitive functions (often Gf and or g, general intelligence).

The third major finding of interest were the range-restricted corrected correlations of .56 and .77 bewteen the WJ III GIA (g) cluster and the ANAM Matching to Sample (MSP)and Logical Reasoning (LR) scales. According to the offical ANAM web page, the LR test "assesses abstract reasoning and verbal syntax ability by asking the user to evaluate the truth of a statement (e.g., "& comes after #") describing the order of two symbols displayed on the display (e.g., "& #"). The user presses designated buttons to indicate whether the statement is true or false."

The article also presents correlations between select individual WJ III tests and ANAM measures. I was disappointed that the authors only presented select WJ III test with ANAM measure correlations (also only select WJ III cluster with ANAM measure correlations). Additional analysis focused on the prediction of WJ III variables from the ANAM variables using multiple regression. You can read this on your own, particularly if you are interested in the ANAM.

Supplementary exploratory factor analysis (EFA) of the ANAM measures were also included. I was disappointed that the authors reported only one EFA with any WJ III measures, and then, with only the Fluid Reasoning cluster. It would have been more informative to see a joint cross-battery factor analysis that included all of the WJ III individual tests that were administered. Combined with the small and select sample size, I don't see much value in discussing the limited ANAM/WJ III EFA analysis.



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Thursday, December 27, 2007

IQ Bytes # 3 - cognitive efficiency definition elaboration

I've previously referenced the concept of "cognitive efficiency" in the context of the CHC Theory of Intelligence (also check here). Briefly, this construct references the interaction of cognitive processing speed (Gs) and working memory (Gsm-MW).

Today I read what I think is a nice definition for "efficiency" in the context of human cognitive abilities. Charles Perfetti (2007), in an a recent article ("Reading Ability: Lexical Quality to Comprehension") published in the Scientific Studies of Reading (11-4; 357-383), stated:
  • "Efficiency is not the same as speed. Efficiency is a ratio of outcome to effort, with time as a proxy for effort."(p. 359)

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