Pushing the edge of the contemporary cognitive (CHC) theory: New directions for psychologists

Pushing the edge of the contemporary cognitive (CHC) theory: New directions for psychologists
Kevin S. McGrew, PhD
Woodcock-Muñoz Foundation
16th Annual APS College of Clinical Neuropsychologists Conference
From East to West: New directions in Neuropsychology
30 September - 2 October 2010
Notre Dame University, Fremantle, Western Australia

Or…..what an inquisitive applied intelligence scholar/psychometrician constructed/discovered from playing almost a decade in his data, literature, and theoretical sandbox

“Intelligent” testing and interpretation
requires…knowing thy instruments
Neuropsych. interpretation
Error variance (reliability)
External criterion relevance
Uniqueness (specificity)
g loading
Degree of cognitive complexity
CHC Ability factor classifications
Degree of cultural loading
Degree of linguistic demand
Metric scale
Information processing & stimulus/response characteristics
Ability domain cohesion

“ If you give a monkey a stradivarius violin and you get bad music……..you don’t blame the violin”
McGrew (circa 1986)

Three things (or major steps) completed that have resulted in the intelligence model(s) to be presented today

Things 1 and 2:
Will be covered quickly to provide context and background for primary content of today – Thing 3

Psychometric vs. neuropsychological conception/model assessment gap
“It is notable that there is a gap between neuropsychological measures and evolving conceptualizations of intelligence. That is, for as seemingly related as the instruments and concepts are, they have strikingly different historical backgrounds.”
(Hoelzle, 2008)

Psychometric vs. neuropsychological assessment gap: Select reasons why (Hoelzle, 2008)
Singular concept of intelligence (g) has hadminimal clinical utilityin neuropsychological assessment
NP assessment has been traditionally non-theoretical---popular models of intelligence and cognitive abilities have been derived via statistical procedures
NP measures traditionally selected on ability to differentiate between neurological and normal conditions---psychometric frameworks derived with factor analytic techniques to synthesize theories that were similarly derived

Verticalfactor analysis (trait) model
Gf
Gc
Glr
G..
Gsm
Gv
etc
Attn
Psychometric approaches have had primary (but not sole) focus/goal on internal/structural validity within each construct domain --- Vertical models

Horizontalmultiple regression (aptitude/functional/pragmatic) model
Criterion DVs
Gf
Gc
Glr
G..
Gsm
Gv
etc
Attn
TBI ?
Brain Area/function
Neuropsychological approaches have had primary (but not sole) focus/goal on external/predictive (Dx) validity – Horizontal models
Result has been many NP measures are mixture measures of multiple CHC domain abilities (which abilities and in what amount [weighting] best predict criterion variables?)

My primary goal
Present a different (yet compatible value-added) psychometric theory of intelligence perspective for thinking about testing cognitive abilities

Importance Of Classification
Taxonomies In All Sciences
Classification is arguably one of the most central and generic of all our conceptual exercises…without classification, there could be no advanced conceptualization, reasoning, language, data analysis, or for that matter, social science research (K.D. Bailey, 1994).
A specialized science of classification of empirical entities known astaxonomy(Bailey, 1994; Prentky, 1994) is ubiquitous in all fields of study because it guides our search for information or truth.

?
Reliable variance
(reliability)
Error variance
-individual/situational variables (e.g., distractibility)
-item variables (e.g., item sampling and item gradients;
test floor and ceiling)
-examiner variables (e.g., rapport, scoring and
administration errors)
-testing environment variables (e.g., noise, comfort)
Unique abilitiesnot shared
in common with other CHC factor indicators (specificity)
We have been searching for an empirically/theoretically-based cognitive taxonomyto interpret the reliable variance of cognitive tests

The Cattell-Horn-Carroll (CHC) theory of cognitive
abilities is the contemporary consensus
psychometric model of the structure of human intelligence
The CHC Timeline Project (and detailed information re: CHC theory/model)can be found at IQ’s Corner blog
www.iqscorner.com

g
T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
PMA1
PMA2
PMA3
PMA4
PMA1
PMA2
PMA3
PMA4
PMA1
PMA2
PMA3
PMA4
PMA1
PMA2
PMA3
PMA4
…etc
(1b) Thurston’s Multiple Factor (Primary Mental Abilities) Model
…etc
(1a) Spearman’s general Factor model
G1
G2
G3
…etc
…etc
…etc
g ?
…etc
G1
G2
G3
…etc
…etc
(1e) Consensus Cattell-Horn-Carroll Hierarchical Three-Stratum Model
Arrows from g to each test
(rectangle) have been omitted for readability
Stratum III
g
G1
Stratum II
G2
…etc
Stratum I
…etc
…etc
(1d) Carroll’s Schmid-Leiman Hierarchical Three-Stratum Model
(1c) Cattell-Horn Gf-Gc Hierarchical Model
Stratum III
Note: Circles represent
latent factors. Squares represent manifest measures (tests; T1..). Single-headed path arrows designate factor loadings. Double headed arrows designate latent factor correlations
Stratum II
Stratum I
Figure 1: Major stages in the evolution of psychometric theories from Spearman’s g to Cattell-Horn-Carroll (CHC) theory

CHC theory has entered the mainstream neuropsychological assessment literature

A landmark event in understanding the structure of human cognitive abilities - 1993

THE SCOPE OF CARROLL’S FACTOR ANALYTIC REVIEW
Reviewed factor analytic research of the past 50-60 years
Includes nearly all of the more important and classic factor analytic investigations
Started with 1,500 references
Final pool of 461 data sets that meet specific criteria
Reanalyzed all or nearly all of the data sets
Used exploratory methods in order to “let the data speak for themselves”

The verdict is unanimous re: the importance of Carroll’s (1993) work
Richard Snow (1993):
“John Carroll has done a magnificent thing. He has reviewed and reanalyzed the world’s literature on individual differences in cognitive abilities…no one else could have done it… it defines the taxonomy of cognitive differential psychology for many years to come.”
Burns (1994):
Carroll’s book “is simply the finest work of research and scholarship I have read and is destined to be the classic study and referencework on human abilities for decades to come” (p. 35).

John Horn (1998):
A “tour de force summary and integration” that is the “definitive foundation for current theory” (p. 58). Horn compared Carroll’s summary to “Mendelyev’s first presentation of a periodic table of elements in chemistry” (p. 58).
Arthur Jensen (2004):
“…on my first reading this tome, in 1993, I was reminded of the conductor Hans von Bülow’s exclamation on first reading the full orchestral score of Wagner’s Die Meistersinger, ‘‘It’s impossible, but there it is!’’
“Carroll’s magnum opus thus distills and synthesizes the results of a century of factor analyses of mental tests. It is virtually the grand finale of the era of psychometric description and taxonomy of human cognitive abilities. It is unlikely that his monumental feat will ever be attempted again by anyone, or that it could be much improved on. It will long be the key reference point and a solid foundation for the explanatory era of differential psychology that we now see burgeoning in genetics and the brain sciences” (p. 5).

Contemporary psychometric research has converged on
the Cattell-Horn-Carroll (CHC) theory of cognitive abilities as the consensusworking taxonomy of human intelligence
McGrew, K. (2009). Editorial: CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research, Intelligence, 37, 1-10.

T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
PMA1
PMA2
PMA3
PMA4
g ?
…etc
G1
G2
G3
…etc
…etc
CHC as the consensus psychometric model of intelligence
Because the Carroll model is largely consistent with the model originally proposed by Cattell (1971), McGrew (2009) has proposed an integration of the two models which he calls the Cattell-Horn-Carroll (C-H-C) Integration model….Because of the inclusiveness of this model, it is becoming the standard typology for human ability. It is certainly the culmination of exploratory factor analysis.
The Science of Intelligence
(Doug Detterman, 2010; book manuscript in preparation)

T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
PMA1
PMA2
PMA3
PMA4
g ?
…etc
G1
G2
G3
…etc
…etc
“The Cattell–Horn–Carroll (CHC) theory of cognitive abilities is the best validated model of human cognitive abilities”
[Ackerman, P. L. & Lohman D. F. (2006). Individual differences in cognitive functions. In P. A. Alexander, P. Winne (Eds.), Handbook of educational psychology, 2nd edition (pp. 139-161). Mahwah, NJ: Erlbaum.]

T2
T3
T4
T5
T6
T7
T8
T9
T1
T12
T10
T11
PMA1
PMA2
PMA3
PMA4
g ?
…etc
G1
G2
G3
…etc
…etc
A significant number of Australian intelligence scholars have framed (and/or continue to frame) their research as per the extended Gf-Gc (aka. CHC) model of intelligence. Many have made foundational contributions to building the model.
N. R. Burns
T. Nettlebeck
L. Stankov
R. Roberts
S. Bowden

Importance Of Classification
Taxonomies In All Sciences
Classification is arguably one of the most central and generic of all our conceptual exercises…without classification, there could be no advanced conceptualization, reasoning, language, data analysis, or for that matter, social science research (K.D. Bailey, 1994).
A specialized science of classification of empirical entities known astaxonomy(Bailey, 1994; Prentky, 1994) is ubiquitous in all fields of study because it guides our search for information or truth.

Gf
Broad
RG
RP
Narrow
I
RQ
RE
CHC theory classifies abilities according to three levels or strata
g
All CHC narrow abilities and their definitions can be found at www.IAPsych.com
General
RG = Gen Sequential (deductive) Reasoning
I = Induction
RQ = Quantitative Reasoning
RP = Piagetian Reasoning
RE = Speed of Reasoning

...most disciplines have a common set of terms and definitions (i.e., a standard nomenclature) that facilitates communication among professionals and guards against misinterpretations. In chemistry, this standard nomenclature is reflected in the ‘Table of Periodic Elements’. Carroll (1993a) has provided an analogous table for intelligence…..
(Flanagan & McGrew, 1998)

g
Gv
Gf
Glr
Gs
Gsm
Gc
Ga
Reliable variance
(reliability)
Error variance
CHC Theory is the best available empirically and theoretically sound cognitive ability taxonomy available today

This is where the field of psychometric intellectual
assessment is at..and a bandwagon has formed
g
Gf
Gc
Induction
(I)
Lang.
Develpmt
(LD) (LD)
General Seq.
Reasoning
(RG)
Quantitative
Reasoning
(RQ)
Listening
Ability
(LS)
General
Information
(K0)
Lexical
Knowledge
(VL)
Speed of
Reasoning
(RE)
Primary ability
Reliable variance
(reliability)
Secondary ability
Error variance

Published WJ III CHC model (McGrew & Woodcock, 2001
CFA analysis of 50+ cognitive and achievement tests

Starting point
Ages 6-adult CFA Broad CHC Model in WJ III Technical Manual
(McGrew & Woodcock, 2001)
g
.55
.91
.88
.73
.87
.88
.79
.82
.93
Gs
Gsm
Gv
Gc
Gq
Ga
Glr
Gf
Grw
First order measurement model omitted for readability purposes

Deconstruction: The validated/published WJ III CHC structure was “torn down”
Psychologists need a healthy degree of positive skepticism

Reconstruction: New structural models specified based on insights from large variety of statistical analysis of the WJ III norm data since 2001.

Stage (Thing 2) approach
Theoretical considerations (Berlin BIS model; dual-processing cognitive models; etc.) also served as guides during exploratory model specification.
Important caution: The final models demonstrated near identical model fit statistics (e.g., some equivalent models). Also, the large amount of exploratory model specification employed has the potential to capitalize on "random chance factors"- thus rendering statistical model evaluation comparisons useless.
Thegoal of these analyses were to "push the edge of the envelope" of the WJ three data via SEM-based model generation procedures. Thelaw of parsimony was deliberately discarded.
Cross validation of proposed final models in independent samples is needed.

Variety of Exploratory Data Analyses with Variety of Datasets
Data Sets
WJ III norm data
WJ III+ other batteries
(WISC-R; WAIS-III/WMS-III/KAIT)
WAIS-IV subtest correlations
Methods
Cluster analysis
Multidimensional scaling analysis (MDS) – 2D and 3D
Standard and Carroll EFA+CFA exploratory factor analysis
Model-generation CFA (SEM)
CHC cognitive causal SEM models

Cluster analysis of WJ III and WJ III + other batteries (joint analysis) + other batteries alone (WAIS-IV)

Cluster Analysis
Cluster analysis is an set of exploratory (structure discovering) data analysis tools for solving classification problems. Sometimes it has been called a “poor mans” factor analysis. Its object is to sort cases (people, things, events, tests, etc) into groups, or clusters, so that the degree of association is strong between members of the same cluster and weak between members of different clusters. Each cluster thus describes, in terms of the data collected, the class to which its members belong; and this description may be abstracted through use from the particular to the general class or type.
CA often helps confirm EFA results and similar to MDS (multidimensional scaling), can spatially represent the degree of similarity of tests measuring a common dimension (dimension cohesion). Its hierarchical sequential structure is often useful in suggesting higher-order dimensions/factors.

Cluster Analysis
The strength of cluster analysis (discovering structure in data with more relaxed statistical assumptions and mathematics than data reduction methods such as exploratory factor analysis) is also one of its major limitations. CA will find groups or clusters in random data. The algorithms are designed to find any structure, even if structure is not present. As a result, the later clusters in a hierarchical approach are often “necessary evils or by products”--CA must end with one grand cluster. Thus, often in CA a point is reached where the further collapsing of meaningful groupings ceases to make substantive sense. It is important to recognize this in the resultant cluster dendogram.
Also, given the above, tests (objects, etc.) that share little in common with other measures need to be assigned to some grouping and cluster. Thus, often “loner” type tests will appear in very meaningful clusters but will not be consistent with the underlying interpretation of the grouping/cluster. Sometimes this suggests new insights regarding the test. Other times these “I’ve got to be grouped with some cluster somewhere in the process” tests are best ignored and should not interpreted as discounting the strong communality of a grouping or clustering of tests

AS
CF
SA
UD
Gf (language-based)
AP
Clusters beyond this point not easily interpretable – see limitations of CA method
NS
Complex lang. processing/
reasoning?
QC
NM
Gf
CAL
RQ
MF
Gf (numeric-based)
RDF
WF
? = no apparent current CHC ability classification
Red font = CHC factors
Blue font = possible new abilities at different strata to consider
PSC
RV
Gs (achievement)
ED
?
LW
SP
RC
WS
SOS
Orthographic
processing?
Grw (words,
sent, con. disc)
WA
PV
VC
Grw
GI
Grw (phonemes)
AK
OC
LD/VL
STR
DRS
K0
RPN
?
REF
Gc
DS
LS
PC
VM
NA/R4 (RAN?)
CO
Gs (cognitive)
VCL
R9
PR
BR
Ppr
SPR
Pc
SNP
PLN
MV
NR
Gv
Cluster analysis (Wards method) of 50 WJ III cognitive and achievement tests (ages 6-18; NU norms)
Kevin McGrew
11-13-09
AWM
SR/Vz
MS
?
MW
SR/Vz+
IW
SB
MW
AA
Gsm
DRV
MS
VAL
Temporal Processing or Tracking /
Aud. Sequential Processing
MN
PC
DRM
Ga
Glr-MA
Distances
2.5
0.5
1.0
1.5
2.0
0.0

Cluster analysis (Ward method) for all WJ3 tests across all ages (K. McGrew 12-7-03)
VISCLO
MV
Gv (content facet—visual/figural)
PICREC
SNDPTV
SPAREL
SR/Vz
BLKROT
SR/Vz
PLAN
RPCNAM
NA
RETFLU
AUDATN
Ga
SNDBLN
PC
INCWRD
Ga+Gsm (content facet–auditory)
MEMWRD
System 2 (controlled) cognitive processing
MS
MEMSEN
AWKMEM
Gsm
NUMREV
SPELL
LWIDNT
[ phoneme/grapheme knowledge ]
WRDATK
SPLSND
Grw (content facet –
Language; read or written)
WRTSMP
EDIT
SNDAWR
V
RDGVOC
PSGCMP
RC
ORLCMP
VERBANL
LS
Gc (content facet – words
& connected discourse)
STYREC
ORALVOC
GENINF
LD/VL
PICVOC
AKHUM
AKSOC
AKSCI
K0
CALC
QCCONC
A3/KM
APPROB
NUMSER
Gq (content facet – numerical)
NUMMAT
RQ
ANLSYN
UNDDIR
Gf
CONFRM
(Shading designates stratum II abilities)
VAL
(content facet – figural/visual)
MEMNAM
MA
WRTFLU
g (stratum III)
RDGFL
Gs (ach/content)
MTHFLU
CRSOUT
Gs System 1 (automatic) cognitive processing
VISMAT
PAIRCN
P
DECSPD
Gs (cog/process)
Distances
0.0
0.5
1.0
1.5
2.0
2.5
© Institute for Applied Psychometrics llc 12-07-03

WAIS-IV test Cluster Tree (Wards method)
of WAIS-IV subtest intercorrelations
Verbal know & comp (Gc)
IN
CO
VC
Level (unspeeded) cognitive abilities
SI
Short-term & working memory (Gsm)
LN
DS
AR
Fluid Reasoning (Gf)
FW
MR
Visual-Spatial Proc.(Gv)
BD
VP
General Intelligence (g) as per WAIS-IV
?
PCM
CD
Processing Speed (Gs)
(rate cognitive abilities)
SS
CA
0.0
0.5
1.0
1.5
Distances

PS
PCSS
Gv
BLKROTWR
SR
BDSS
SR/VZ
OS
SPAREL
CONFRM
UNDDIR
RQ
Thinking Abilities /
Controlled Processing
NUREAWR
Gq/Gf
QNTCON
APPROB
A3
ARITHSS
CALC
MA
MEMNAMWR
Glr
VAL
ORLCMP
ANLSYN
Gf/MW?
AWKMEM
MW
NUMREV
DSSS
PLAN
COMPSS
VL
SIMSS
LD
Gc
VOCSS
Acquired Knowledge
INFOSS
VRBCMP
ACKNOWWR
GENINF
STYREC
NA/R4/RAN
RETFLU
Cog Fluency/ Efficiency (speeded)
RPCNAM
Gs
DECSPD
SSSS
VISMAT
P/R9
CODSS
Cognitive Efficiency /
Automatic Processing
MS
MEMSENWR
Succ Proc/ Gsm / Temp Tracking ?
MEMWRD
PC
INCWRD
BLND
Cog Fluency/ Efficiency (unspeeded)
US/UR
AUDATN
SNDPATWR
PICREC
VISCLOWR
McGrew-Evans WJ III/WISC-III Cluster Analysis Interpretation Worksheet
Phelps WJ III Technical Manual
Validity sample – n=150 grade 3-5
© Institute for Applied Psychometrics, llc 02-05-02

WJR Visual Matching
P (Gs)
WJR Cross Out
RG
WJR Analysis-Synthesis
Gf
KAIT Logical Steps
I
KAIT Mystery Codes
WJR Concept Formation
CS
WJR Visual Closure
Gv
WISC3 Object Assembly
MV
WJR Picture Recognition
KAIT Memory for Block Designs
PC (Ga)
WJR Incomplete Words
WJR Sound Blending
LD
KAIT Double Meanings
KAIT Famous Faces
WJR Picture Vocabulary
Gc
VL
WJR Oral Vocabulary
KAIT Definitions
Gc/Grw
WJR Letter-Word ID
WJR-Reading Vocabulary
WJR Visual-Auditory Learning
WJR Memory for Names
MA
KAIT Rebus Learning
KAIT Rebus Learning-Delayed
Glr
KAIT Auditory Comprehension-Delayed
LS/MM
Gy
KAIT Auditory Comprehension
WJR Memory for Sentences
MS (Gsm)
WJR Memory for Words
Distances
0
1
2
3
4
Flanagan & McGrew (1998) WJ-R/KAIT joint cluster analysis

Late Career Carroll-EFA+CFA Method (e.g., Carroll, 2003) of WJ III

Traditional EFA of WJ III at
various age levels

CHC CFA (SEM) of WJ III and WJ III + other batteries (joint analysis)

Gregg/Hoy College Sample-WJ III + WAIS-III + WMS-III
(LD/Non-LD; n=200)
(McGrew et al., 2001)

SEM Causal Information Processing Models of WJ III

Note: Ovals represent
latent factors. Rectangles represent manifest measures (tests). Single-headed arrows to tests from ovals designate factor loadings. Single headed arrows between ovals represent causal paths (effects). Test and factor residuals omitted for readability purposes.
Visual Matching
Mem for Sentences
.78
.78
Decision Speed
.49
.66
MS
Gs
Mem for Words
.75
.81
Cross Out
.80
.27
Aud Working Mem
.72
MW
Numbers Reversed
.72
Block Rotation
.07
.53
.07
Verbal Comp
Spatial Relations
Picture Recognition
.69
Gv
.94
.47
.82
Oral Comp
Memory for Names
.74
Gc
.83
.88
General Information
.87
.59
Retrieval Fluency
Analysis-Synthesis
.50
.92
g
Glr
.74
Del Recall-VAL
.69
.95
Concept Formation
.72
.78
Gf
76 % of g variance explained
.77
.78
Vis-Aud Lrng (VAL)
Numerical Reas
Sound Blending
Incomplete Words
.74
.58
Ga
Figure 4: WJ III CHC information processing  g causal model (ages 14-19)
.40
Sound Patterns
Cognitive Efficiency

Guttman’s Radex Theory
Ability tests can be classified by:
Differences in kind of content
Differences in type of processes
UsesMDS (multidimensional scaling)

Example of MDS (Radex Model)
The closer a test is to the center of the figure, the more it is related to the underlying general dimension of the battery. Also, the center represents the most cognitively complex (i.e., have the largest number of performance components) tests.
Tests that group together are interpreted as sharing common stimulus content or cognitive processing characteristics

MDS (Guttman Radex model) of WAIS-IV subtest intercorrelations
3
Short-term memory /working memory (Gsm)
1
Processing speed (Gs)
LN
DS
CD
Verbal know &
comp (Gc)
VC
CO
Dimension-2
Fluid
reasoning (Gf)
AR
MR
CA
SS
SI
IN
FW
BD
VP
-1
PCM
Visual-spatial processing (Gv)
-3
-3
-1
1
3
Dimension-1

MDS (Guttman Radex model) of WAIS-IV subtest intercorrelations
It is a common practice in MDS analysis to visually partition the MDS spatial configuration into broader dimensions and consider interpretation at a higher-order level.
The current WAIS-IV MDS revealed the following hypothesized higher-order structure
Note – similar to hand rotation of factors in early days of EFA, K. McGrew took the cross-hair lines and hand rotated them (simultaneosly) until a meaningful pattern emerged. The four-broad dimensions are interpreted as being very similar to the four cognitive domains of Woodcock’s Cognitive Performance Model (CPM) – see next two slides
Short-term memory /working memory (Gsm) – Cognitive Efficiency unspeeded/memory
3
Verbal know &
comp (Gc) – Acquired Knowledge or “Product” dominant abilities?
1
LN
DS
CD
VC
AR
CA
CO
MR
SS
Processing speed (Gs) -
Cognitive Efficiency speeded
SI
IN
FW
BD
VP
-1
PCM
Fluid Reasoning (Gf) and Visual-spatial processing (Gv) –Thinking or “Process” dominant abilities?
-3
-3
-1
1
3

Thinking abilities
Process-dominant “level” abilities
Visual-spatial/figural (low linguistic) stimuli (Gv,Gf,Glr)
Controlled cognitive processing
Acquired knowledge abilities
Product -dominant “level” abilities
Language (aud-linquistic) and symbolic stimuli (Ga,Gc,Grw,Gq)
Controlled cognitive processing
2
WJ III Radex Model
IW
1
Broad CHC factor
ability font key legend
(based on CFA studies)
drm
mn
SB
PV
drs
MS
OC
STR
BR
Gf
SNP
Gsm
GI
NM
AK
drv
RV
PSC
val
glr
VC
WA
LW
DIM(2)
ED
SA
0
SOS
AP
REF
CF
SPL
Ga
WS
QC
AS
RDF
UD
NS
Gv
CAL
WF
RPN
AWM
SPR
Gs
MW
MF
NR
Cognitive efficiency (speeded-Gs) rate/fluency abilities
Automatic cognitive processing
Gc
VCL
VM
CO
Grw
-1
Gq
PC
DS
AA
PR
PLN
Cognitive efficiency (unspeeded-Gsm) abilities
Automatic cognitive processing
-2
The grand “big picture model” --- probably requires a subsequent 3-D MDS analysis to see clearly….more to come
-3
-2
-1
0
1
2
DIM(1)

Gv
2
PL
BR
PR
More
visual-spatial
& figural
Gf
SR
DIM(3)
VC
Gq
AS
MN
SPV
VAL
CF
NM
AP
CO
NS
DS
CNC
CA
PC
VM
Gsm
AA
More auditory
& linguistic
SA
RDF
-3
MF
WF
BL
NR
RF
AWM
IW
More process-
dominant
MW
RPN
DIM(1)
2
More System 2
(controlled) cognitive
processes
Red font = Gs
DIM(2)
Note – all Gc and
Grw unspeeded tests
are omitted and are
located within
dashed area in center
0
0
Blue font = Ga
More System 1
(automatic) cognitive
processes
More product-
dominant
2
-2
WJ III 3-D MDS Model

g loading
Metric scale

Food for thought: Are the MDS quadrants or partitions reflecting content “facets” or a combination of content“facets and “operations” as per the BIS model of intelligence….see next slide

BIS: Berlin Model of
Intelligence Structure
Gs
Gsm + Glr (level abilities)
Carroll’s Gy
Glr(fluency abilities)
Gf
Note difference in term in different versions:
Processing capacity defined as complex reasoning

Unveiling of preliminary new models in WJ III norm data

Alternative Model 1
g
.39
Gs
(Cognitive speed)
.82
.88
.71
.87
.86
.79
.84
1.0
.64
.55
.62
.59
.36
.49
Gs (Grw)
.54
Gsm
Gv
Gs (Gv)
Gs (Gq)
Gs (Gc)
.62
Gc
Gq
Ga
Glr
Gf
Grw
First order measurement model and other lower-order latent factors (below smallest oval latent factors) omitted for readability purposes. Thicker path arrow with bold font 1.0 parameter designates path that had to be constrained (fixed) to 1.0

Alternative Model 2
.86
.99
.93
g
.36
1.0
Cognitive efficiency
(More automatic & effortless)
Cog. knowledge domains/systems
(product/content abilities)
Lang/linguistic./symbolic abilities
Cognitive operations
(process/operations/analytic/rule-based abilities) figural-spatial, lower-linguistic abilities
Gs
(Cognitive speed)
.89
.76
.91
.85
1.0
.83
.82
.64
.52
.60
.58
.41
.52
Gs (Grw)
.58
Gsm
Gv
Gs (Gv)
Gs (Gq)
Gs (Gc)
.67
Gc
Gq
Ga
Glr
Gf
Grw

Close inspection of the evidence suggests that generic dual-system theory is currently oversimplified andmisleading
We might be better off talking about type 1 and type 2 processes since all theories seem to contrast fast, automatic, or unconscious processes with those that are slow, effortful, and conscious (Samuels 2006). Such terminology does not commit use to a two-system view. However, it would then be helpful to have some clear basis for this distinction
My suggestion is that type 2 processes are those that require access to a single, capacity-limited central working memory, while type 1 processes do notrequire such access. This implies that the core features of type 2 processes are that they are slow, sequential, and capacity limited. The last feature implies also that their functioning will correlate with individual differences in cognitive capacity and be disrupted by concurrent working memory load. Depending upon what else is assumed about working memory, there may be a rationale for describing such type 2 processes as registering in consciousness and having properties associated with executive processes and intentional, higher-order control.

Alternative Model 2b
.93
.99
1.0
.86
g
.36
1.0
Type I cognitive processing
(Cognitive efficiency):
More automatic & effortless
Type II cognitive processing:
More cognitively controlled & deliberate
Gs
(Cognitive speed)
.89
.76
.91
.85
1.0
.83
.82
.64
.50
.60
.58
.41
.52
Gs (Grw)
.58
Gsm
Gv
Gs (Gv)
Gs (Gq)
Gs (Gc)
.67
Gc
Gq
Ga
Glr
Gf
Grw

Alternative Model 3
.95
.99
.94
.21
g
Auditory temporal (serial) Proc.
Visual/figural (parallel?) Proc.
Cog. knowledge
domains/systems
Gs
(Cognitive speed)
.89
.77
.91
.85
1.0
.82
.86
.90
.63
.45
.60
.54
.48
.65
Gs (Grw)
.64
Gsm
Ga
Gs (Gv)
Gs (Gq)
Gs (Gc)
.73
Gc
Gq
Gv
Glr
Gf
Grw

1.0
.94
.21
(process/operations/
analytic/rule-based abilities)
g
.95
1.0
Auditory temporal (serial) Proc.
Visual/figural (parallel?) Proc.
Cog. knowledge
domains/systems
Gs
(Cognitive speed)
.89
.77
.91
.85
1.0
.82
.86
.90
.63
.45
.60
.54
.48
.66
Gs (Grw)
.64
Gsm
Ga
Gs (Gv)
Gs (Gq)
Gs (Gc)
.74
Gc
Gq
Gv
Glr
Gf
Grw

Pushing the edge of the envelope of CHC theory and the WJ III measurement model: Part IIThe first-order measurement model and implications for interpretation of WJ III tests

Glr and Gsm measurement models were similar to
those originally reported by McGrew & Woodcock (2001)

See next slide for other
indicators
Vis. Clos. (.41)
Blk. Rot. (.52)
Spat. Rel. (.66)
Pic. Rec (.43)
Planning (.43)
Alternative Models: WJ III Measurement model for speed factors
Gq
Gv
.54
Gs(Gq)
.36
.62
Gs(Gv)
GGs
(Cog Spd)
.64
.55
Gs(Gc)
.59
Gc
.49
Gs(Grw)
See next slide for other
indicators
Grw
.62
Wrd. Atk. (.78) Edit. (.78)
Psg. Cmp.* (.55) Wrt. Smp, (.76)
Rdg. Voc.* (.34) Spelling (.86)
LWrdID (.89)
* Dual loading on Gc on next slide

Alternative Models: WJ III Measurement model for possible new Gf factor structure
Calculation (.75)
Gq
.34
.51
.27
Gf (RQ)
.66
.17
Gf
.99
Gf *
.70
Gc
Gen. Info .(.89)
Acd. Knw. (.89)
Orl. Cmp. (.77)
Psg. Cmp. (.30) (.55-Grw)
Rdg. Voc. (.54) (.34-Grw)
Mem. Sen. (.36) (.38- Gsm)
Story Rec. (.29) (.39-Glr)
Sound Awareness and Understanding Directions did not load on any other factors
Gf * = complex language-based working memory and reasoning?

Iteration 1:
CHC-based
Intelligence model of WJ III battery
Kevin McGrew
8-18-2010
See handouts for clear copy

Gf (RQ)
.66
Gf
.99
Gf *
Hmmmm…???

It is time to look at some non-CHC/Gf-Gc research on reasoning (Gf): Alternative lenses

The distinction between inductive and deductive reasoning (i.e., CHC/Gf-Gc Carroll-type model) may be outdated (Wilhelm, 2005)
Most established reasoning tests confound the direction of inference with deductive and inductive reasoning task
(Whilhelm, 2005)

Whilhelm tested Gf model’s as per CHC (I, RQ, RG) and BIS (verbal, quant, figural) structures, and various model interactions. The following was the best fitting model

CFA using dual indicators (split-half—odd/even item sets) for each test:
Conclusion: WJ III RG, I, RQ tests are highly correlated but do measure different aspects of Gf

WJ III CHC Gf model
Fit for this and prior model (prior slide) more-or-less equivalent

Lets add in more CHC domain indicators

f8
r12
2
8
.
PICVOCER
.
7
PicVoc
4
r13
4
f1
PICVOCOR
9
.
r0
.
9
4
ANLSYNER
6
9
.
r14
GENINFOR
AnlSyn
7
GenInf
9
.
9
5
.
r1
4
9
ANLSYNOR
.
r15
GENINFER
f9
Gc
f2
r16
.
0
9
9
8
.
r2
ACKNOWOR
.
CONFRMER
9
5
AcdKnw
.
9
1
ConFrm
r17
ACKNOWER
r3
5
9
f10
.
.
CONFRMOR
7
r18
7
.
1
9
2
.
1
ORLVOCER
OrlVoc
.
9
2
f3
r19
r4
Gf
ORLVOCOR
.
9
4
NUMSERER
f11
r20
NumSer
5
1
8
.
2
VERBANLER
.
.
r5
9
VrbAnl
9
4
NUMSEROR
9
.
r21
VERBANLOR
.
9
0
3
7
f4
.
f12
Gf
.
9
r6
5
f14
NUMMATER
(lang)
r22
NumMat
9
8
.
SNDAWRER
r7
SndAwr
f13
4
9
f5
NUMMATOR
.
r23
SNDAWROR
.
8
8
r8
.
r24
6
APPROBER
5
AppPrb
0
9
UNDDIRER
.
Gq
r9
r25
UndDir
APPROBOR
f6
UNDDIROR
3
9
r10
.
.
8
8
CALCER
f7
f15
Calc
r11
CALCOR
7
8
.
Gf sub-abilities differentiated by content/stimulus features (Wilhelm model)
f16
.
5
f18
8
Gf
(vis)
2
.
9
3
.
3
8
8
.
.
9
2
f19
.
9
g
9
3
9
.
.
9
4
8
7
.
Gf
.
9
6
.
(qnt)
8
9
.
5
9
6
3
.
f17
.
9
7
6
9
.
.
9
6
Although some fit stats are slightly better for this model (when compared to
model on prior slide) using practical criteria they are more-or-less equivalent

Important Reminder: All statistical methods, such
as factor analysis (EFA or CFA) have limitations and constraints.
It only provides evidence of structural/internal validity and typically nothing about external, developmental, heritability, neurocognitive validity evidence
Need to examine other sources of evidence and use other methods – looking/thinking outside the factor analysis box

Additional support for differentiation of Gf by type of content or stimulus features
3
Note which tests are near the center: More cognitively complex
Snd Awareness
Under. Directions.
#/quant.
Lang (aud-verbal)
1
Calc
ApPrb
AcdKn
NumMat
PicVoc
GenInf
NumSer
SndAwr
Reasoning (procedural/Gf)-------Recall (declarative/Gc/Gq))
OrlVoc
UndDir
AnlSyn
VrbAnl
-1
Visual-figural
ConFrm
-3
-3
-1
1
3
Language (verbal/aud.)--------Nonverbal (#’s,visual)
Guttman Radex MDS model of WJ III Gf, Gc, and Gq test indicators

Thing 3 – attempt to integrate Thing 1 and Thing 2 with neuropsychological
assessment models

The First Commandment of Neuropsychological Assessment
"If one writes a book on neuropsychological assessment, thou shall not write a book that is less than 3 inches thick or less than 3 lbs in weight“ (McGrew, August 13, 2010)

Lets look at the pieces one by one – blow them up

Arm-chair factor analysis of neuropsych. assessment domains [and CHC construct mapping] (K. McGrew; 8-18-10) [I of 3]
g
Gf
Gc
Grw
Gq

Gv
Ga
Gsm
Glr

Gs
Gsm
AC
??
Gp
Gps
Go
Gh
Gk

Hypothesized (“working”) CHC-based intelligence model (iteration 2)
Kevin McGrew (8-26-2010)

Mapping of current CHC domains with hypothesized new CHC-based intelligence model
Kevin McGrew
8-18-2010
Lets look at the pieces one by one – blow them up
Motor functions (including motor speed) - Expressive across domains?

Empirical examples of Gkn domains
From Carroll (1993)

Empirical examples of Gkn domains
Ackerman et al. research group

Gp and Gps across domains
Motor functions (including motor speed) - Expressive across domains?

.
The somatosensory system
The cortical homunculus was discovered by Wilder Penfield

Olfactory abilities/functioning (Go)

Olfactory abilities/functioning (Go): Possible
sub-abilities mentioned in the literature
Olfactory memory (OM)
Odor-evoked memories
Episodic odor memory
Olfactory store in working memory
Olfactory sensitivity (OS) /detection
Odor specific abilities (O1, O2, O3, O4)
Odor identification/recognition/detection
/discrimination
Olfactory thresholds (and reaction time)
Olfactory acuity
Semantic odor networks/odor naming
Olfactory imagery
Odor discrimination
Odor awareness
Sexual role of odors
Ecological odor sensitivity

Olfactory abilities/functioning (Go): Dx importance
(Doty, 2001)

This is research/work in progress: Suggested research that needs to be explored and integrated. Go from here to……………..

g loading
Metric scale

This is NOT a model of human functioning – it is a “working” heuristic of Kevin McGrew’s current hypothesized thinking (iteration 3?) regarding the important dimensions that may be important in the development and interpretation of measures of human abilities …………. (not a Guilford SOI model where all cells are believed to exist)
Content/stimulus dimension
Language (aud.-verb.)
Numerical/quant.
Somatasensory
Visual-figural
Olfactory
?: Is the low-how cog. complexity continuum simply a continuous representation of the Type 1/I processing distinction ?
Cognitive knowledge
domains/systems
Type II
Processing
Cognitive control
High
Abilty domain dimension
Cognitive efficiency
Sensory functions
Low
Type I
Processing
Motor functions
Cognitve complexity
dimension
Note: CHC taxonomy is embedded in the ability domain dimension (see prior slides)

g – speed ?
Stratum III
(General)
Stratum II
(Broad)
Stratum I
(Narrow)
Gp
Broad Psycho-
Motor Ability
Gt
Broad Decision
Speed
Gs
Broad Cognitive
Speed
Gps
Broad Psycho-
Motor Speed
RT
Reaction
Time
R9
Rate-of-test
Taking *
P
Perceptual
Speed *
MT
Movement
Time
Speed of Limb Movement (R3)
Wrist-finger Speed (P5)
Speed of Articulation (PT)
Speed of Writing (WS) ****
Numerical Fluency (N)
Speed of Reasoning (RE) **
Reading Speed (RS) ***
Pattern Recognition (Ppr)
Scanning (Ps)
Memory (Pm)
Complex (Pc)
Simple Reaction Time (R1)
Choice Reaction Time (R2)
Semantic Processing Speed (R4)
Mental Comparison Speed (R7)
Static Strength (P3)
Multilimb Coordination (P6)
Finger Dexterity (P2)
Manual Dexterity (P1)
Arm-hand Steadiness (P7)
Control Precision (P8)
Aiming (AI)
[ Narrow P abilities suggested by Ackerman et al. (2002) ]
* Carroll classified P and R9 as narrow abilities
under Gs/Gv and Gt, respectively
** Classified as speed and level (Gf) ability by Carroll
*** Classified as a speed and level (Gc) ability by Carroll
Also classified under Grw by the current author
**** Classified as Psychomotor Ability by Carroll. Also
classified under Grw by current author
Figure 2: Hypothesized speed hierarchy based on integration of Carroll (1993) speed abilities with recent research
(Ackerman, Beier & Boyle, 2002; O’Connor & Burns, 2003; McGrew & Woodcock, 2001; Roberts & Stankov,
1998; Stankov, 2000; Stankov & Roberts, 1997)
Integrate proposed g-speed hierarchy (McGrew & Evans, 2004; McGrew, 2005)

g – speed ?
.93
.99
1.0
.86
g
.36
1.0
Type I cognitive processing
(Cognitive efficiency):
More automatic & effortless
Type II cognitive processing:
More cognitively controlled & deliberate
Gs
(Cognitive speed)
.89
.76
.91
.85
1.0
.83
.82
.64
.50
.60
.58
.41
.52
Gs (Grw)
.58
Gsm
Gv
Gs (Gv)
Gs (Gq)
Gs (Gc)
.67
Gc
Gq
Ga
Glr
Gf
Grw

f8
r12
2
8
.
PICVOCER
.
7
PicVoc
4
r13
4
f1
PICVOCOR
9
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r0
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9
4
ANLSYNER
6
9
.
r14
GENINFOR
AnlSyn
7
GenInf
9
.
9
5
.
r1
4
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ANLSYNOR
.
r15
GENINFER
f9
Gc
f2
r16
.
0
9
9
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r2
ACKNOWOR
.
CONFRMER
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AcdKnw
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ConFrm
r17
ACKNOWER
r3
5
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f10
.
.
CONFRMOR
7
r18
7
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1
9
2
.
1
ORLVOCER
OrlVoc
.
9
2
f3
r19
r4
Gf
ORLVOCOR
.
9
4
NUMSERER
f11
r20
NumSer
5
1
8
.
2
VERBANLER
.
.
r5
9
VrbAnl
9
4
NUMSEROR
9
.
r21
VERBANLOR
.
9
0
3
7
f4
.
f12
Gf
.
9
r6
5
f14
NUMMATER
(lang)
r22
NumMat
9
8
.
SNDAWRER
r7
SndAwr
f13
4
9
f5
NUMMATOR
.
r23
SNDAWROR
.
8
8
r8
.
r24
6
APPROBER
5
AppPrb
0
9
UNDDIRER
.
Gq
r9
r25
UndDir
APPROBOR
f6
UNDDIROR
3
9
r10
.
.
8
8
CALCER
f7
f15
Calc
r11
CALCOR
7
8
.
Gf sub-abilities differentiated by content/stimulus features (Wilhelm model)
Additional dual-indicator modeling of WJ III data in other domains (e.g., Gsm, Glr, Ga, Gv, Gq, Grw)
f16
.
5
f18
8
Gf
(vis)
2
.
9
3
.
3
8
8
.
.
9
2
f19
.
9
g
9
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Gf
.
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(qnt)
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f17
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9
6

Reconcile and integrate Johnson & Bouchard VPR (Verbal-Perceptual-Image Rotation) psychometric model of intelligence with working CHC model

Integrate and conceptualize working model within information processing models

Note: Ovals represent
latent factors. Rectangles represent manifest measures (tests). Single-headed arrows to tests from ovals designate factor loadings. Single headed arrows between ovals represent causal paths (effects). Test and factor residuals omitted for readability purposes.
Visual Matching
Mem for Sentences
.78
.78
Decision Speed
.49
.66
MS
Gs
Mem for Words
.75
.81
Cross Out
.80
.27
Aud Working Mem
.72
MW
Numbers Reversed
.72
Block Rotation
.07
.53
.07
Verbal Comp
Spatial Relations
Picture Recognition
.69
Gv
.94
.47
.82
Oral Comp
Memory for Names
.74
Gc
.83
.88
General Information
.87
.59
Retrieval Fluency
Analysis-Synthesis
.50
.92
g
Glr
.74
Del Recall-VAL
.69
.95
Concept Formation
.72
.78
Gf
76 % of g variance explained
.77
.78
Vis-Aud Lrng (VAL)
Numerical Reas
Sound Blending
Incomplete Words
.74
.58
Ga
Figure 4: WJ III CHC information processing  g causal model (ages 14-19)
.40
Sound Patterns
Cognitive Efficiency

And…..the state-of-the art research being conducted on working memory
I particularly favor the models and research of:
Conway, Engle and Kane group– Human working memory lab – Princeton, NJ.
Controlled executive attention model
Torkel Klingberg group - Karolinska Institute-Stockholm Brain Institute

Integrate and conceptualize working model within dual-processing neuro-cognitive research and models

Integrate working model with Haier and colleagues parieto-frontal integration theory (P-FIT)

P-FIT model

P-FIT model researchers are mapping brain areas to CHC domain constructs

Timescales of temporal processing
(Mauk & Buonomano, 2004)
Humans process
temporal information over scales of at least 10-12 orders of magnitude that have been categorized into 3-4 major timescale groups

Research suggests common dopamine link (e.g., dopaminergic disorders)
Mental Timing Research:
Has been implicated as important in human learning and understanding a variety of clinical disorders. Examples include:
Parkinson’s
Huntington’s
Schizophrenia
ADHD
Reading development and disorders (dyslexia/reading disabilities)
Speech and language development and related disorders
Analogy – auditory processing of Morse code
Musical abilities and performance
Motor timing disorders
Aspergers???
(See IQ Brain
Clock EWOK
for research)

Integrate working model with temporal g (brain clock) research
Temporal information processing models (Creelman, 1962; Gibbon, 1991; Rammsayer & Ulrich, 2001; Treisman et al., 1990; see Grondin, 2001 for review) are based on the central assumption ofneural oscilliations(note – same central feature of Jensen’s neural efficiency theory of g) as a major determinant of timing performance.
The higher the frequency (higher speed) of neural oscillations the finer the temporal resolution of the internal clock = greater timing accuracy (Rammsayer & Brandler; 2007)

Temporal g ?
Analyses suggested a unitary timing mechanism, referred to as temporal g.
Performance on temporal information processing provided a more valid predictor of psychometric g than traditional reaction time measures
r (with psychometric g) = .56 (temporal g) vs .34 (reaction time g)
Findings suggest that temporal resolution capacity of the brain (as assessed with psychophysical temporal tasks) reflects aspects of neural efficiency associated with general intelligence.
Rammsayer & Brandler (2007)

Two primary mental timing circuits
(Buhusi & Meck, 2005; Lewis & Miall, 2006)
Automatictiming system
Works in the millisecond range
Discrete-event (discontinuous) timing,
esp. movement/motor tasks
Involves the cerebellum
Cognitively-controlled timing system
Continuous-event timing
Requires attention and involvement of
working memory
Involves the basal ganglia and related cortical structures
It is the “constellation of task characteristics that dictate which timing “circuits” of brain “systems”are invoked in a particular task performance (Lewis & Miall, 2006)

In conclusion.....

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Pushing the edge of the contemporary cognitive (CHC) theory: New directions for psychologists

by Kevin McGrew

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Pushing the edge of the contemporary cognitive (CHC) theory: New directions for psychologists Presentation Transcript