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During
the past decade, significant factor analytic research has been limited primarily to five well-
established CHC domains—Gv, Ga, Gsm, Gc, and Gs. |
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Gv
abilities have been cursed by with a schizophrenic relationship with intelligence researchers.
Despite inclusion in most all models of human cognitive abilities, and being one of the more
studied domains of human cognitive functioning (Carroll, 1993), “spatial abilities have long been
relegated to a secondary status in accounts of human intelligence” (Lohman, 1996, p.97).
According to Lohman (1996), Gv’s second class status is due, in part, to the fact
that: (1) beyond
a minimum level of proficiency, Gv abilities do not consistently predict success in school or
work,
(2) the relations between Gv and outcome criteria are dwarfed when other more powerful
predictors (e.g., Gc, Gf) are included in prediction studies, (3) the typical criterion variables
used
in prediction studies tend to be biased in favor of verbal and language- based measures, (4)
existing Gv measures used in studies may be poor measures of visual-spatial functioning, and
(5)
the typical practice of first entering a g proxy in prediction studies may mask potential important
Gv-outcome relations. The love-hate status Gv has experienced in the intelligence
research is
also due to the fact that Gv has concurrently been associated with both highly acclaimed and
prestigious achievements in demanding professions such as engineering, architecture, physics,
chemistry and mathematics, as well as more pedestrian trades such as carpentry, auto mechanics,
and technical/industrial occupations (Lohman, 1996; Shea et al., 2001).
[Note.
However, according to Lohman (1994), tests of spatial abilities are: (1) among some of the best
predictors training of machine and bench workers, and air crews, (2) have been moderately associated
with
grades in engineering and trade schools, and (3) are listed among 84 job categories from the United
States
Employment Service (1957).]
Recently, Gv has
enjoyed a renaissance in status due to the linkage of high Gv abilities with: (1)
higher- order thinking in science, math and the achievement, (2) incremental prediction of
performance (above and beyond verbal and quantitative abilities) in gifted and talented
populations (Shea, Lubinski & Benbow, 2001), and (3) the observation of creative “insights
by
means of thought experiments on visualized systems of waves and physical bodies in states of
relative motion” (Lohman, 1996, p. 99). For example, high spatial ability, particularly the
ability to
visual complex dynamic systems, has been reported to play a prominent role in the
accomplishments of such imminent scientists and inventors as Albert Einstein, Michael Faraday,
Herman Von Helmholtz, Benjamin Frankin, Francis Galton and Leonardo da Vinci (Lohman,
1996; West, 1991). As an example, “on several occasions Albert Einstein reported that verbal
processes seemed not to play a role in his creative thought. Rather, he claimed that he achieved
insights by means of thought experiments on visualized systems of waves and physical bodies in
states of relative motion” (Lohman, 1994, p. 1000).
With
the exception of studies investigating the role of Gv in information processing/working
memory models (Lohman, 1996), only a handful of investigations have studied the structural
characteristics of the Gv domain during the past decade. Juhel (1991), using Carroll-based
exploratory factor methods and procedures in a sample of college students, confirmed the
existence of the well documented Visualization (Vz), Spatial Relations (SR), and Visual Memory
(MV) abilities (see Carroll, 1993; Lohman, 1979; 1996). More importantly, contrasts of
subgroups categorized as high or low on Vz and MV reinforced the notion that Gv tasks vary as
a function of cognitive complexity. Vz abilities were reported to require the most complex
cognitive processing (i.e., require greater complex mental manipulations and transformations; load
highest on Gv). MV was found, in a relative sense, to be a lower level (less complex) ability
in
the domain of visual-spatial abilities. This finding is consistent with Lohman’s (1979)
statement,
regarding the narrow Gv abilities of MV, P, and CS, that these “factors consistently fall
near the
periphery of scaling representations, or at the bottom of a hierarchical model” (p. 126-127).
Although MV may be viewed as an ability of lower stature within the Gv domain (e.g., relatively
lower g-loadings than Vz and SR), Juhel’s (1991) research suggested that the more complex Gv
abilities are partially dependent on, and supported by, MV.
Miyake,
Friedman, Rettinger, Shah, Hegarty’s (2001) structural equation-based investigation of
the relations between measures of information processing and psychometric measures of Gv (Vz,
SR, P) reinforces the findings of Juhel (1991). These investigators hypothesized (and confirmed)
that Vz, SR, and P abilities differed as a function of the relative demands each placed on the
working memory system, particularly the visuo-spatial sketchpad and executive function
components. Similar to prior research (Carroll, 1993; Lohman, 1979), Miyake et al. (2001) found
it difficult to structurally differentiate the Vz and SR factors. However, the Vz and SR factors, as
well as the P factor, were clearly differentiated as a function of degree of information processing
demands. Miyake et al. (2001) concluded that Vz, SR and P differed in the degree of executive
involvement, with Vz requiring the most and P the least. It is possible that the higher complexity
attributed to Vz and SR tasks is due to a greater use of verbal analytic processing during these
task (Justin & Carpenter, 1985). Furthermore, the “three spatial abilities require a substantial
degree of visuo-spatial storage, but the maintenance of visuo-spatial representations involved in
the performance on these spatial ability tests (particularly the Spatial Visualization and Spatial
Relations tests) may be strongly tied to executive functioning or controlled attention. Finally, these
relations between the WM-related constructs and the spatial ability factors are substantial. In
fact, they are so substantial that, together, the Executive Functioning and Visuo-spatial STM-WM
variables were able to essentially fully explain the pattern of the intercorrelations between the
three spatial ability factors” (Miyake et al. 2001. p 637).
“Imagery
refers to the mental depiction or recreation of people, objects, and events that are not
actually present” (Finke & Freyd, 1994, p. 561). Visual imagery has been linked to a
variety of
abilities such as: (1) Gf--thinking hypothetically; constructing mental models of complex
conceptual systems; “seeing” relationships and solutions to problems, (2) efficient retrieval
of Gc
information, (3) Gv--mental rotation of objects or patterns, and (4) mental extrapolations involved
in complex motor activities (e.g., driving a car; athletic performance) (Finke & Freyd, 1994).
According to Carroll (1993), imagery was not clearly defined by the factor studies available at the
time of his review. Following Carroll’s (1993) recommendation for further IM research, Burton
and Fogarty (2003) reported exploratory and confirmatory factor analysis of 26 cognitive ability
measures, 5 self-report visual imagery inventories, 7 experimental imagery tasks, and 2 tasks
requiring creative imagery. Consistent with the extant Gv structural literature (Carroll,
1993;
Lohman, 1979), support was found for the narrow abilities of Vz, SR, MV, and CS. In addition,
three first-order IM factors (quality, self- report, and speed) were suggested. The IM (quality)
and IM (speed) factors shared moderate amounts of variance with the Vz, SR, MV, and CS
factors, while the IM (self- report) factor did not. [Note. It is very likely that the IM (self-report)
factor represents a “method” factor as it was the only factor defined in this investigation
by the subjects
self-reports.] Burton
and Fogarty’s (2003) findings consistent with research that suggests that IM
may be a multidimensional construct characterized along the dimensions of generation, vividness,
clarity, controllability, transformation and/or maintenance (Kosslyn, 1980; Poltrock & Agnoli,
1986). Burton and Fogerty’s study reinforces the specification of the visual IM (i.e., the
IM
quality factor) ability alongside the other major Gv narrow abilities. Left unanswered by this
study
is whether the IM (quality) and IM (speed) abilities represent the level and rate aspects
of the
imagery domain. |
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In
the long history of research on individual differences in human cognitive abilities, Ga has
played the role of “stepchild” to its elder visual-spatial processing (Gv) sibling.
According to
Carroll (1993), auditory abilities have “received little attention in the factor-analytic literature”
(p.
364). Fortunately, interest and research in the Ga domain has increased due to: (1)
technological
advances that have made research in the Ga domain easier (Stankov, 1994), (2) an increased
interest in the psychophysics of auditory perception (Hirsh & Watson, 1996), (3) an explosion
of
research focused on the relations between the Ga abilities of phonological processing or
phonemic awareness (Phonetic Coding-PC as per Carroll, 1993) and early reading development
and reading disabilities (see Bus & van IJzendoorn,1999; Ehri, Nunes, Willows, Schuster,
Yoghoub-Zadeh & Shanahan, 2001; McBride-Chang, Chang, & Wagner, 1997; McBride-Chang,
1995, 1996; Metsala, Stanovich, & Brown, 1998 Stahl & Murray, 1994; Stone & Brady,1995;
Torgesen, Wagner, Rashotte, Rose, Lindamood, Conway & Garvan, 1999 Wagner, Torgesen,
Laughon, Simmons & Rashotte, 1993; Wagner, Torgesen & Rashotte, 1994), (4) the increased
interest in Central Auditory Processing Disorder (CAPD; Ricco & Hynd, 1996) in the professions
of speech-language pathology and audiology, and (5) the inclusion of the Ga abilities
of pitch,
rhythm and sound discrimination in Howard Gardner’s (1983) musical and linguistic intelligences.
In
1998, Flanagan and I (McGrew & Flanagan, 1998) were persuaded by a small number of
exploratory and confirmatory research studies with young children (Wagner, Torgesen, Laughon,
Simmons & Rashotte, 1993; Yopp, 1988) that Phonetic Coding (PC) should be split into two
narrow PC abilities—PC:Analysis (PC:A) and PC:Synthesis (PC:S). Research during the
past
decade, which includes an “about face” by Wagner et al. (1993), now largely supports a
unidimensional PC ability.
In
a longitudinal study of 244 young children (grades K-2), Wagner, Torgesen and Rashotte
(1994) specified their previously hypothesized PC:A and PC:S factors in a confirmatory modeling
research study. However, the high latent factor correlations between the two PC abilities proved
problematic (high multi-colinearity) when both were included in prediction models. In a
subsequent longitudinal investigation of 216 kindergarten thru fourth grade students, Wagner,
Torgesen, Rashotte, Hecht, Barker, Burgess, Donahue & Garon (1997) again reported very high
PC:A/PC:S latent factor correlations, with the actual correlation approaching a perfect 1.0 at third
grade. Wagner et al. (1997) concluded that the two factors were representing the same
construct and subsequently respecified their model to include a single PC (phonemic awareness)
ability.
The
unidimensional interpretation of PC was recently echoed by Van Bon and Van Leeuwe
(2003) who may have provided the most comprehensive listing of studies (viz., de Jong & van der
Leij, 1999; Høien et al., 1995; Holopainen et al., 2000; Lundberg Frost & Petersen, 1988;
Mommers, 1987; Muter, Hulme, Snowling, & Taylor, 1997; Schatschneider et al., 1999;
Stahl &
Murray, 1994; Stanovich et al., 1984; Valtin, 1984; Wagner & Torgesen, 1987; Wagner et al.,
1997) in support of a unidimensional PC ability. Van Bon and Van Leewe (2003) further report
that their independent reanalysis of Yopp’s (1988) data supported a single PC ability. The
one
exception noted in the literature by Van Bon and Van Leeuwe (2003) was the tendency for
rhyming abilities to stand separate from PC. Consistent with this conclusion, Hatcher and
Hulme’s (1999) exploratory factor analysis revealed separate rhyming and phonemic manipuation
(PC) factors derived from the five phonological measures used in their study of 124 children
experiencing reading difficulties. In their own longitudinal study of 171 Dutch students in the
primary grades, Van Bon and Van Leeuwe’s (2003) exploratory and confirmatory analysis of
measures of phoneme recognition, blending, counting, deletion, segmentation, and pseudoword
repetition and rhyme judgment reinforced the presence of a unidimensional PC ability.
In
a welcome contribution to the internal (structural) and external Ga validity literature,
Anvari, Trainor, Woodside and Levy (2002) explored the relations between phonological
awareness (PC), music perception, and early reading in a sample of 100 four- and five-year old
children. Consistent with the above reviewed literature, factor analyses of the four Anvari et
al.
(2002) PC measures (rhyme generation, oddity, blending, and the Rosner task) revealed a single
factor at both age levels. Exploratory analysis of the music tasks (same/different melody,
same/different chord, chord analysis, same/different rhythm, and rhythm production tasks)
revealed a single music factor for four-year olds and two factors (pitch perception; rhythm
perception) for five-year olds. The musical factors appear to measures aspects of the Musical
Discrimination and Judgment (U1, U9) and Sound- Frequency Discrimination (U5) reported by
Carroll (1993). Moderate factor correlations (.33 to .59) supported the independence of the
music perception and PC ability factors. Further support for separate music perception and PC
abilities was the intriguing finding that “music perception skill predicts reading even after the
variance shared with phonemic awareness is removed. This suggests that phonemic awareness
and music perception ability tap some of the same basic auditory and/or cognitive skills needed
for reading but that they each also tap unique processing skills” (Anvari et al., 2002, p.127).
In
the final structurally relevant Ga study, Schatschneider, Francis, Foorman, Fletcher and Mehta
(1999) investigated the dimensionality (via factor and IRT analyses) of a battery of seven
phonological awareness measures in a large kindergarten to second grade sample (n = 945).
Results of a confirmatory factor analysis supported the unidimensionality of the PC tasks. A test
of the Wagner et al. (1993) PC:A/PC:S two-factor correlated model produced an extremely high
correlation (r = .95), again suggesting a single PC construct. The most intriguing finding
came
from the IRT analysis of a combined pool of all the items. IRT analyses of the PC items
revealed a wide range of variation in item difficulty that appeared to be a function of item tasks
demands. Schatschneider et al., (1999) suggested that the different types of PC tasks commonly
used in most reading research differ not so much in the measurement of different underlying traits
or constructs, but instead, represent different tasks that vary developmentally along a common
single latent trait ability continuum.
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Are
Memory Span (MS) and Working Memory (MW) the same or different? Ever since
Kyllonen and Christal (1990) published “Reasoning Ability is (little more than) working- memory
capacity?!” intelligence scholars have been enamored with the construct of working memory
(MW; see Table 3). Before summarizing the glamorous MWàGf or MWàg research (later in
this chapter), a more fundamental question is whether MS is different from MW. Three studies
during the past four years suggest that MS and MW are distinct constructs.
In
a sample of 133 university students, Engle, Tuholski, Laughlin and Conway (1999) used
confirmatory factor analytic methods to test if three simple short-term storage tasks and three
tasks requiring complex processing and storage were best represented by a single memory factor
or an alternative two-factor (i.e., MS and MW) model. The two- factor model provided a better
fit to the data and also suggested a MS/MW latent factor correlation of .68. Also using CFA
methods in two childhood samples (ages 7-13 years; n = 155, 132), Kail and Hall (2001) found
support for separate MS and MW factors, with latent factor correlations of .32 and .36. Finally,
in a sample of 120 young adults who were administered four simple MS storage tests and three
complex MW tests, Conway, Cowan, Bunting, Therriault and Minkoff’s (2002) CFA supported
the existence of separate, but highly correlated (.82) MS and MW tests.
The
wide range of latent factor MS/MW correlations (.32 to .82) reported across these three
studies is difficult to interpret given the differences in the study samples and measures used. To
minimize the effect of sampling error and measurement differences, I returned to the WJ III
three-stratum CFA model studies reported in the WJ III technical manual (McGrew &
Woodcock, 2001) and respecified separate MS and MW correlated factors. MS was
operationally defined by the WJ III Memory for Words and Memory for Sentences tests, while
MW was defined by the WJ III Auditory Working Memory, Numbers Reversed, Understanding
Directions, and Sound Awareness tests. [Note. Memory for Sentences and Understanding Directions
were also specified to have loadings on Gc. Sound Awareness had a loading on Ga.] The latent factor
correltions, across five large nationally representative samples that differed by age (6-8; 9-13; 14-
19; 20-29; 40- 90+ years of age) were .67, .79, .82, .84, and .80. These findings mirror the age
trend patterns in the other three studies (i.e., children displayed lower MS/MW correlations than
adults), but differ in absolute magnitude. It appears that MS and MW are strongly correlated,
yet
separate constructs that become more highly correlated with increasing age. |
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.According
to Baddeley (2001), the construct of working memory (MW) was first proposed in
1960 by Miller, Galanter and Pribram. The first multiple component conceptualization was
provided by Baddeley and Hitch (1974) who proposed that MW consisted of three components:
the visuo-spatial sketchpad, phonological loop and central executive. MW has been referred to
as
the “mind’s scratchpad” (Jensen, 1998, p. 220) and most models postulate that it consists
of a
number of subsystems or temporary “buffers.” The phonological or articulatory loop processes
auditory- linguistic information while the visuo-spatial sketch/scratchpad is the temporary buffer
for visually processed information. Most models hypothesize that the central executive
mechanism coordinates and manages the activities and processes in working memory. Baddelely
(2000, 2001) has recently proposed the addition of a fourth component, namely, the episodic
buffer.
[Note. See Baddeley (2001) for an overview of the history and evolution of the Baddeley working
memory model.]
Space limitations do not allow for a detailed description and definition of the
working memory model and its components, nor is such understanding necessary in the current
context.The research literature regarding the MW construct is voluminous
and attests to the
importance of MW as an important psychological construct.
Although
Flanagan and I (McGrew & Flanagan, 1998; Flanagan et al. 2000) previously argued
for MW’s preliminary “membership” status in the CHC taxonomy, this recommendation was
based primarily on logical and rational considerations. Our recommendation was tempered by
Carroll’s (1993) skepticism toward the working memory construct. Carroll (1993) stated that
“although some evidence supports such a speculation, one must be cautious in accepting it
because as yet there has not been sufficient work on measuring working memory, and the validity
and generality of the concept have not yet been well established in the individual differences
research” (p. 647).
Although
MW is undeniably a valid and important psychological construct, this does not
necessarily mean MW is a factor analytic, latent trait, individual differences type construct similar
to the 60+ narrow cognitive abilities that are the cornerstone of the CHC taxonomy (see Table
3).
According to Carroll (1993), “evidence for the existence of a latent trait derives from a
demonstration that a number of similar task sets are highly correlated, or in factor- analytic terms,
have weights on the same factor. A factor, if it is well established in a number of empirical
investigations, is in essence a latent trait reflecting differences over individuals in ability
characteristics or potentials” (p. 22). According to Carroll’s definition, the trait-factor
evidence
for MW is still questionable.
First,
the three studies (Conway et al., 2002; Engle et al., 1999; Kail & Hall; 2001) cited in
support of separate MS and MW factors either restricted their variable pool to only MS and MW
test indicators or used confirmatory methods that specified apriori MS and MW factors. In a
variety of unpublished exploratory factor analyses of the variables described for the WJ III CFA
studies (McGrew & Woodcock, 2001), as well as an exploratory analyses using Carroll’s EFA
software, the current author never found the two primary WJ III MW tests (Numbers Reversed
and Auditory Working Memory) to form a factor distinct from the MS tests (Memory for Words
and Memory for Sentences). Instead, in all analyses across all ages, a clear MS factor is defined
primarily by high loadings by the MS tests. The MW tests were consistently factorial complex.
For example, in a Carroll EFA of 50 WJ III tests and subtests, at the first-order level this author
found an MS factor defined primarily by Memory for Words (.80) and Memory for Sentences
(.47; also .35 on Gc). Numbers Reversed loaded on two factors (MS = .13; Quantitative
Reasoning-RQ = .21). The best WJ III operational measure of MW, Auditory Working Memory,
also loaded on RQ (defined primarily by Number Series and Number Matrices tests) as well as
MS (.26) and Ga (.27). When a complete set of CHC indicators are present in an EFA study,
it
appears that MW measures do not represent a distinct trait-like MW factor construct, but instead
are factorially complex mixtures of abilities. This should not be unexpected given the
multicomponent conceptualizatioin of MW. In the case of the WJ III Auditory Working Memory
test, one could speculate that the RQ component reflects the manipulation of stimuli (numbers) in
the visuo-spatial sketchpad (or use of part of the executive function component that is typically
associated with Gf abilities), MS the memory span component, and Ga the use of the
phonological loop to facilitate performance.
Based
on the theoretical, logical, and empirical evidence, this author concludes that working
memory (MW) is indeed a multicomponent cognitive construct of significant importance, but, it
should not be considered to be similar to the other 60+ narrow factor-based trait-like individual
difference CHC constructs identified in the psychometric literature. This conclusion is consistent
with Kyllonen (1996) who stated that “the working memory capacity construct does not depend
on factor analysis for its identification. The working memory system was developed theoretically
not as a label for an individual-differences factor, but rather as a constuct to explain experimental
results in the memory literature” (p.73), This conclusion does not negate the practical and
theoretical importance of measures of working memory. Obviously, how amalgam constructs like
working memory are integrated in the CHC taxonomy needs further deliberation and discussion.
In order to decompose and measure the various processes underlying working memory, additional
research focused on the subcomponents of working memory is needed (e.g., see the research of
Süß, Oberauer, Wittmann, Wilhelm & Schulze, 2002; Oberauer, Süß, Schulze, Wilhelm
&
Wittmann, 2000). |
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One of the most frequent methods used to assess Gc
is to ask individuals to define or solve
problems (e.g., antonyms) that require the use and understanding of words that vary in frequency
in the general culture. Vocabulary, general information, and concepts associated with specialized
occupations/professions and knowledge domains are typically avoided in Gc assessment or
research. High performance in one or more of the narrow specialized knowledge domains is
instead associated with the constructs of wisdom or expertise (Hunt, 2000). Typically
a dark and
deep line is drawn in the psychometric sand between the measurement of generalized (general
cultural) and specialized domain-specific knowledge.
Research regarding the nature and structure of the Gc
domain has languished as researchers
have focused on “romancing the domains” with more desirable appeal, such as Gf and Gv.
According to Hunt (2000):
Gc is the wallflower of the intellectual trio. Researchers
want to go dancing (or to be
less lyrical) to understand Gf and Gv. After all, is it not more important to study things
that are fluid and dynamic than to study something that is crystallized and just sits there
in memory? Besides, if Gf and g are identical, studying Gf kills two birds with one
stone. Studies of Gv can be justified by dramatic examples of its importance in
glamorous situations (e.g., aviation) or because of its fairly close ties to biology, and
especially male-female differences. Like the wallflower it is, Gc languishes in the
corner. How can you start a controversy about who acquires and uses culturally
defined problem-solving methods? Who, but a few educators, are interested in such
nearsighted, bookish behavior? (p. 124)
Anticipating Hunt’s (2000) admonition to researchers “to ask Gc to put away the horn-rimmed
glasses, put on a party dress, and take turn on the dance floor” (p.124), Rolfhus and Ackerman
(1999) investigated the relations between traditional measures of Gc, a large collection (n
= 20)
of specialized knowledge tests, and traditional cognitive measures of spatial and numerical
abilities. [Note.
The Rolfhus and Ackerman (1999) knowledge tests were in the domains of American
government, history, and literature and art, astronomy, biology, business/management, chemistry,
economics, electronics, geography, law, music, physics, psychology, statistics, technology, tools/shop,
western civilization, and world literature.] In a university sample,
these researchers first factored the
20 knowledge tests with the software and procedures employed by Carroll (1993). Four stratum
I knowledge ability factors were found (Humanities, Science, Civics and Mechanical) which
were, in turn, subsumed by a broad second- order General Knowledge (Gkn) factor.
The
Rolfhus and Ackerman narrow knowledge factors are very similar to the Information about
Culture (K2), Science (K1), and Mechanical and Technical Knowledge (MK) narrow abilities
reported in Chapter 12 (Abilities in the Domain of Knowledge and Achievement) of Carroll
(1993).
Of particular interest was the finding that the broad verbal
(Gc) and knowledge (Gkn) factors
correlated at a moderate level, a level which indicated that Gc, as typically assessed, was related
to, but was independent of Gkn. It is doubtful that a separate broad Gkn
ability would be
present at younger developmental levels given the large source of common educational variance
shared by children vis-à-vis the cultural homogenizing mechanism of schooling. However, at least
by young adulthood, and possibly during high school, a Gc/Gkn distinction appears viable.
The
Gc/Gkn distinction is consistent with Cattell’s (1971/1987) notions regarding Gc
where he wrote
that crystallized intelligence (Rolfus & Ackerman, 1999):
must become different for different people. If [individuals’
learning experiences] are
sufficiently varied and lack any common core, the very concept of general intelligence
begins to disappear. An effort to measure Gc in practice might amount to producing as
many tests as there are occupations. (p. 144)
Collectively, the knowledge abilities catalogued by Carroll
(1993), the research of Rolfhus and
Ackerman (1999), and Hunt’s (2000) wisdom (i.e., domain- specific knowledge in intelligence
theory and research), argues for the inclusion of a broad Gkn domain that emerges and “breaks
off” from Gc during adulthood.
[Note. Although prior to the time frame for the current review, Kyllonen
and Christal (1990), in four
separate samples, previously established the validity of a general knowledge domain with confirmatory
factor analysis methods. Both Kyllonen and Christal (1990) and the Ackerman research group have
used
Gk as the abbreviation for broad general knowledge. Given that Gk has also been
used to designate a
broad general kinesthetic ability (discussed later in this chapter), one of the two abbreviations needed
further specification. Gkn was choosen to replace Gk for general knowledge. Gk
was selected to remain
“as is” for general kinesthetic ability]
General Knowledge (Gkn) can be defined as an individual’s
breadth and depth of acquired
knowledge in specialized domains that do not represent the general universal experiences of
individuals in a culture. In our highly specialized society, knowledge is not a unitary entity,
especially at the higher levels of functioning and mature adulthood (Hunt, 2000). Gkn abilities
result from domain-specific experiences and training and typically “depend on regular, frequent,
and systematic practice and training over at least a decade” (Gilhooly, 1994, p. 638). The
primary distinction between Gc and Gkn is the extent to which acquired knowledge is a
function
of degree of general cultural universality. Gc primarily reflects general knowledge accumulated
via the experience of cultural universals. Gkn reflects deep specialized domain- specific
knowledge developed through intensive practice (over an extended period of time) and the
maintenance of the knowledge base through regular practice and motivated effort (Horn, 1998;
Horn & Masunaga (2000).
Similar to Gc, Gkn abilities can be categorized
as both declarative (static) and procedural
(dynamic) knowledge. Declarative or explicit knowledge refers to knowledge "that something
is
the case, whereas procedural or implicit knowledge is knowledge of how to do something"
(Gagne, 1985, p. 48). Declarative knowledge is consciously known and can typically be
communicated by the “knower” (via spoken or written language, or, a specialized code, such
as
music notation). Although procedural knowledge can be demonstrabled in behavior, it is often
difficult to explictly communicate (is not at a conscious level of awareness) (Gilhooly, 1994, p.
637). One manner in which procedural knowledge is conceptualized is in the form of schemas,
which can be thought of as well “organized methods of problem solving” (Hunt, 1999, p. 21)
that
emerge from cumulative experience. A psychologist’s knowledge of the definitions of the broad
CHC abilties (see Table 3) would reflect declarative knowledge, while the psychologist’s ability
to
instantly recognize and interpret the meaning of a specific pattern of CHC ability scores from an
intellectual assessment would require procedural knowledge (i.e., CHC intelligence interpretation
schema). The empirically identified narrow Gkn abilities are listed in Table
3. [Note. The current
author took the liberty, based on a review of Carroll (1993) and the recent Gkn research literature,
to add
certain narrow abilities previously reported by Carroll (but which have not been included in most
contemporary publications), and to move some that have previously been listed under Gc.] The
degree of
correlation between Gkn narrow abilities will likely be a function of the extent to which expertise
within one domain overlaps with expertise in another.
The positing of a broad Gkn ability separate from Gc
(during adulthood) may facilitate the bridge
between CHC and information processing theories vis-à-vis a common focus on the development
of expertise. Expertise involves the acquisition, storage, and utilization of both the
implicit (tacit)
and explicit knowledge in a field “where domain refers to a knowledge base and field
to the
social organization of that knowledge base” (Sternberg, 1998). For example, a psychologist
with
expertise in psychometrics would likely have a well developed explicit knowledge of the facts,
formulas, principles, statistics, and major ideas of the domain of psychometrics (e.g., SEM and
IRT theory and methods). The person’s implicit or tacit knowledge might constitute unspoken
information regarding whom to consult for a specific technical question, which federal agency is
likely to have the most relevant grant monies, and which professional conferences provide the
best networking for acquiring new project contracts.
Horn and Masunaga (2000) have recently studied the construct
of expertise from the perspective
of CHC theory. For example, Horn and Masunaga (2000) hypothesized that the “reasoning
involved in exercise of expertise is largely knowledge based and deductive, in contrast to
reasoning that characterizes Gf, which is inductive” (p. 145). Furthermore, reflecting a
new
CHC-based perspective from which to conceptualize expert “performance,” Horn and Masunaga
(2000) concluded that:
The superior performance of experts is characterized by a
form of long-term working
memory (LTWM). Within a circumscribed domain of knowledge, LTWM provides the
expert with much more information in the immediate situation than is available in the
system for short-term retention that has been found to decline with age in adulthoood.
LTWM appears to sublimate a form of deductive reasoning that utilizes a complex store
of information to effectively anticipate, predict, evaluate, check, analyse, and monitor in
problem- solving within the knowledge domain. These abilities appear to characterize
mature expressions of intelligence ( p. 152).
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Mental
quickness as an indicator of a bright or intelligent person has stood front- and-center in the
study of human cognitive abilities for decades (Nettelbeck, 1992, 1994; Nyborg, 2003; Stankov &
Roberts, 1997). According to Nettelbeck, two different perspectives have dominated the study of
mental processing speed. The “speediness” perspective is that most associated with applied
intelligence batteries and is defined by the quickness in performing tasks of trivial difficulty or
tasks that have been over-learned. Broad cognitive processing speed (Gs) can be defined
as the
ability to automatically and fluently perform relatively easy or over-learned cognitive tasks,
especially when high mental efficiency (i.e., attention and focused concentration) is required
(McGrew, 1993). Woodcock (1993) has likened Gs to the opening or closing of a valve in
a
water pipe. When the valve is wide open, the rate of flow increases (high cognitive processing
speed). When the valve is only partially open, the rate of flow is lessened (lower Gs).
The
narrow (stratum I) abilities subsumed by Gs, which reflect the integration of recent factor
analytic research (discussed below), are listed in Table 3.
The
second mental speed perspective is associated with experimental paradigms that employ
chronometric measures of reaction and inspection time (Deary, 2003; Nettelbeck, 1994, 2003).
The chronometric approach is based on the idea that “progress can be made in understanding
differences in human intelligence if it can be shown that there are individual differences in basic
cognitive processes that are correlated with higher level abilities as measured by mental tests”
(Deary & Stough, 1996, p. 599). Carroll and Horn both recognized this second aspect of mental
quickness in their respective models. Carroll (1993) included reaction and decision time abilities
under a broad Decision/Reaction Time or Speed (Gt) ability. Horn’s (Horn &
Masunaga, 2000)
analogous ability is called Correct Decision Speed (CDS), and is typically measured by recording
the time an individual needs to provide an answer (either correct or incorrect) to a variety of
tasks. Conceptually, Horn’s CDS appears to represent a narrower ability than Carroll’s
more
encompassing Gt. Conceptually, CDS, as defined by Horn, could easily fit under Carroll’s Gt.
The narrow (stratum I) abilities subsumed by Gt, which reflect the integration of recent factor
analytic research (discussed below), are listed in Table 3.
More
recently, both Gs and Gt have been investigated as key variables in explaining higher-level
complex cognitive processing (e.g., Gf, g) (Kail, 1991; Lohman, 1989). A pivotal concept
in
information processing models is that human cognition is constrained by a limited amount of
processing resources, particulary in working memory. "Many cognitive activities require a
person's deliberate efforts and that people are limited in the amount of effort they can allocate.
In the face of limited processing resources, the speed of processing is critical because it
determines in part how rapidly limited resources can be reallocated to other cognitive tasks" (Kail,
1991, p. 152). In other words, faster processing of information permits reasoning to reach
completion before the requisite information is lost.
Although
a plethora of research studies have studied mental speed during the past decade,
“factor analytic evidence concerning the status of a range of time-dependent constructs has been
either piecemeal or nonexistant” (Roberts et al., 2000, p. 346). Unanswered questions remain
such as "how many different speed abilities exist...what is their position in the hierarchy-that
is,
are they at the same stratum as broad organizations of the Gf/Gc theory or should they be placed
at different strata?" (Stankov, 2000, p. 39). Attempts to answer these questions have been
the
focus of the largest number of in-depth CHC- related factor analysis investigations during the last
decade. This focus is appropriate given the "general lack of clarity regarding different
aspects of
speeded processing" (Ackerman, Beier, & Boyle, 2002, p. 569). Also contributing to this
lack of
clarity has been the nearly universal omission (by most authors since 1993) of one of the three
different broad speed factors presented in Carroll’s seminal treatise. |
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In
addition to Gs and Gt, Carroll (1993) reported "a third category of second- order
speed factors
is what will henceforth be symbolized as Gp or 2P, interpreted as General Psychomotor Speed,
in
that it is primarily concerned with the speed of finger, hand, and arm movements, relatively
independent of cognitive control" (p. 618). The influence of psychomotor speed in speeded
tests
in intelligence batteries (e.g., Wechsler Coding/Digit Symbol; WJ III Visual Matching; Flanagan
et al., 2000) and the importance of sensory-motor functions in neuropsychological assessment
(see Lezak, 1995 and Dean & Woodcock, 2003) dictates the need to recognize the complete
speed trilogy (Gs, Gt, Gps), as well as to include the broad domain of psychomotor abilities
(Psychomotor Ability; Gp). All speed and psychomotor abilities summarized by Carroll are
represented in Figure 2.
[Note.
Gps is used to designate the Broad Psychomotor Speed domain instead of Gp as used by Carroll
(1993). This modification is due to the addition of a broad Psychomotor Ability domain (presented
in a
special chapter by Carroll) in the current chapter. Gps (General psychomotor speed) and Gp (General
psychomotor ability) are more logical factor codes.
It
is important to note that the psychometric abilities presented by Carroll, which are included in Figure
2,
most likely represent only a small portion of the complete domain of psychomotor abilities. Carroll’s
review
did not deliberately attempt to review the extant psychomotor ability literature. See Carroll
(1993) for his
discussion and references to other sources.]
In
1979, Ekstrom, et al., (1979), as part of the historical factor reference kit research, questioned
whether more than one perceptual speed factor existed. Approximately 20 years later, a series of
studies by Ackerman and colleagues (Ackerman et al., 2002; Ackerman & Cianciolo, 2000;
Ackerman & Kanfer, 1993) suggest that Ekstrom et al. (1979) were correct. The Ackerman
group has demonstrated that the traditional Perceptual (clerical) Speed (P) ability may rest at the
apex of hierarchy that includes a number of lower-order perceptual speed abilities. The
Ackerman group has presented evidence for four different P factors, including the ability to: (a)
quickly recognize simple visual patterns (Pattern Recognition; Ppr), (b) scan, compare, and lookup
stimuli (Scanning; Ps), (c) perform tasks that place significant demands on immediate short-term
memory (Memory; Pm), and (d) perform pattern recognition tasks that impose additional
cognitive demands such as spatial visualization, estimating and interpolating, and heightened
memory span loads (Complex; Pc). [Note. The abbreviations used for the Ackerman Perceptual
Speed factors were developed for this document by the current author]
Although
using different factor names than the Ackerman group, O'Connor and Burns (2003)
presented factorial evidence for Perceptual Speed and Visualization (the time needed to complete
tasks that included complex visualization of stimuli) factors that bear resemblance to Ackerman's
Pattern Recognition (Ppr) and Complex (Pc) factors, respectively. Additionally, Stankov and
colleagues (Stankov, 2000; Stankov & Roberts, 1997) reported an ability to perform speeded
visual or auditory perceptual tasks (Tv/a) that resembles components of the Ackerman Pattern
Recognition (Ppr) and Complex (Pc) Perceptual Speed abilities. In the model presented in Figure
2, these findings are reflected vis-à-vis the reclassification of Perceptual Speed (P) as an
intermediate ability lying between the broad (stratum II) and narrow (stratum I) abilities. The
four lower-order perceptual speed abilities (Ppr, Pm, Ps, Pc) are placed at the narrow ability level.
An
additional proposed revision to the CHC speed hierarchy is the movement of the Rate-of-test
Taking (R9) narrow ability to an intermediate level between stratum I and II. The rational for
this
reclassification (see Figure 2) is twofold. First, when attempting to classify the speeded
psychometric tests in most intelligence batteries, McGrew and colleagues (McGrew, 1997;
McGrew & Flanagan, 1998; Flanagan et al., 2000) found it difficult not to classify all speeded
tests as measures of R9. Second, a closer reading of Carroll (1993) suggests that R9 is an ability
that cuts across speeded tasks in multiple domains. Carroll (1993) stated that the R9 factor
did
"not appear to be associated with any type of test content" (p. 475) and "the speed factors
associated with the major dimensions of level abilities may be thought of as factors of 'rate of test
taking' " (p. 508). Furthermore, Stankov and colleagues (Stankov, 2000) identified a similarly
described higher-order factor (Psychometric Time; PT) that subsumed a number of lower-order
factors that varied across other broad CHC ability domains (e.g., time spent in working on
inductive reasoning tasks; time spent in working on visual and auditory perceptual tasks). In
their
empirically-based speed hierarchy, Roberts and Stankov (1998) located the PT factor, a factor
which has also been interpreted by others (O’Connor & Burns, 2003) as a test-taking speed
ability, between the broad and narrow strata. Support for the general Stankov speed hierarchy
has been provided by O’Connor and Burns (2003) who, based on the factor analysis of 18
speeded variables, concluded that the “data presented here are highly supportive of the model
of
mental speed proposed by Roberts and Stankov (1999) (p. 722). The similarity of Carroll's R9
and Stankov’s higher- order PT factors argues for the placement of R9 as an intermediate ability
between broad and narrow stratum (see Figure 2).
An
additional proposed modification to Carroll's 1993 model is the listing of the Speed of
Reasoning (RE) narrow ability under both Gf and Gs. The finding of a "time
spent on inductive
reasoning tasks" (Ti) factor under the Roberts and Stankov (1998) higher-order PT factor
suggests that RE may tap more Gs than originally suggested. Evidence supporting the
influence
of speeded variables during complex task performance (e.g., Gf) has been provided by a diverse
array of intelligence researchers (Sternberg, 1977; Jensen, 1987; Reed & Jensen, 1991, 1992;
Vernon, 1987). A recent example is represented by Verguts, De Boeck and Maris’ (1999)
experimental investigation of performance on the Ravens Advanced Progressive Matrices
(APM). Verguts et al. (1999) presented evidence in support of rule generation speed in
solving
complex reasoning tasks.
Rule generation process plays a crucial role in solving the
APM items. If (APM) rules
are compared with balls in an urn, this means that people sample balls from an urn.
Individual differences in the generation process can be thought of as sampling from
different urns (qualitative differences) or at different rates (quantitative
differences)…Given a limited time to solve the test, and given that the ‘different urns
effect’ is cancelled out, this implies that fast persons (fast in the sense of generating
many possible rules in a limited time) have a higher probability to solve a particular item
correctly (p. 330).
The similarity between Carroll’s RE ability, Stankov
and Roberts Ti ability, and Verguts et al.
(1999) rule generation speed ability, collectively suggest that Speed of Reasoning (RE) should
play a more prominent role in contemporary CHC research and practice. Findings parallel to
the Gf/RE pairing are also present in the domain of Grw. Carroll (1993), who categorized
most
all reading and writing abilities as Gc, placed Writing Speed (WS) under Gps due to the
obvious
speeded motor component. In well designed confirmatory studies conducted on large nationally
representative samples (McGrew & Woodcock, 2001), tests requiring simple speeded writing
(WJ III Writing Fluency) and reading (WJ III Reading Fluency) demonstrate dual factor
loadings on both broad Grw and Gs factors. These findings are consistent with
Carroll’s
(2003) subsequent interpretation of a combined exploratory and confirmatory factor analysis of
the WJ-R norm data where Carroll reports that the WJ-R Writing Fluency test demonstrated
salient loadings on Gs and a “Language” factor (similar to Grw). As a
result, both Reading
Speed (RS) and Writing Speed (WS) are included in the speed hierarchy presented in Figure
2.
The
above proposed revisions to the CHC model are echoed by O’Connors and Burns (2003)
who stated that “the inference drawn is that if a diverse battery was administered, there may be
speed factors associated with each of the second order factors defined in Gf–Gc theory” (p.
722). Stankov and Roberts (1997) suggested the same hypothesis when they concluded that “the
possibility cannot be ruled out that there may be as many disparate mental speed factors as there
are factors among measures based on accuracy scores” (p. 73). Bates and Shieles (2003) arrived
at the same conclusion when they stated:
just as general effects on computational speed underping g,
these additional group factors
are also explained by variance in speed, but that the particular groups factors such as
verbal and visuo-spatial reflect parcelated speed effects: speed variance reflected not
across the whole brain but in a restricted set of processing modules. Some support for
this notion is found in the already demonstrated finding that most or all of the abilities
identified by Carroll (1993) are correlated with speed measures. Thus, for
instance,‘‘fluid’’ ability is related to speed of reasoning, ‘‘crystallized’’
intelligence to
reading speed, visual perception/spatial ability to perceptual speed, ideational fluency with
retrieval ability, test-taking speed with cognitive speed, and, of course, reaction time with
processing speed. The two abilities without a named speed correlate are memory and
learning, and auditory perception. But of course working memory is associated with
speed of rehearsal…and a growing literature supports a direct auditory analogue of IT
for auditory stimuli (p. 284).
[Note. See Parker, Crawford, & Stephen (1999) for an example of
research regarding auditory inspection time.]
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There
is little doubt that intelligence scholars have been enamored with the measurement of
speed of basic information processing as measured by Reaction Time (RT) and Inspection Time
(IT) paradigms (Roberts & Stankov, 1997; Nyborg, 2003). [Note. The reaction and inspection time
literature is too voluminous to treat in this chapter. See Deary, (2003) and Nettelbeck, (2003) for
recent
summaries.] Literature reviews (Grudnik & Kranzler, 2001; Kranzler & Jensen, 1989;
Nettelbeck,
1987) have established the relationship between IT and psychometric g in a range for .30 to .50
(Stankov & Roberts, 1997). Despite this wide interest, researchers have been unable to
reach a
consensus on what RT and IT are measuring (Deary, 2000) and what implications these
measures have for intelligence theory (Stankov & Roberts, 1997) and for applied practice (e.g.,
education) (Ackerman & Lohman, 2003).
When
traditional speeded psychometric measures are factored together with measures of
reaction and inspection time (RT/IT), relatively robust and separate reaction time (RT) and
movement time (MT) factors emerge (O'Connor & Burns, 2003; Roberts & Stankov, 1998;
Stankov, 2000; Stankov & Roberts, 1997). The RT/MT dichotomy reflects the two phases of
reaction time as measured by various elementary cognitive tasks (ECTs) (see summaries by
Deary, 2003; Nettelbeck, 2003).
Given
the robust finding of separate RT and MT components across different reaction time
paradigms, and the emergence of distinct higher-order RT and MT factors that subsume lower-
order reaction time factors in empirical studies (see Roberts & Stankov, 1999; Stankov, 2000), a
logical, theoretical, and operational decision was made to classify the RT and MT factors as
intermediate factors between the broad and narrow ability strata (see Figure 2). An intriguing
set
of findings warranting future research are the reported significant correlations between DT and
broad memory (Gy as per Carroll, 1993) and reasoning (Gf), and MT with Ga
(Roberts et al.,
2000). The Ga/MT correlation “is most interesting because it suggests possible links
among
natural tempo, psychomotor performance, and audition” (Roberts et al., 2000, p. 351).
In
a study designed specifically to evaluate the role of IT in structural models of intelligence,
Burns and Nettelbeck (2003) conducted a Carroll-type exploratory factor analyses of
chronometric IT measures together with select tests of Gsm, Gs, Gv, and Gf from the WJ-R
and
WAIS-R. These exploratory analyses were followed by confirmatory factor methods. The
results unambiguously found that IT loaded on the psychometric broad Gs processing speed
factor. IT did not load on any other first-order CHC factors. Burns and Nettelbeck (2003)
concluded that “IT, to be sure, somehow taps the same processes as those that contribute to
performance on tests of clerical speed” (p. 249).
Finally,
although using different terminology, the research of Stankov and colleagues (Roberts &
Stankov, 1998; Stankov & Roberts, 1997; Stankov, 2000) suggests that a model of human
cognitive abilities that includes a general speed ability (g-speed; see Figure 2) at the same
level as
g is plausible. According to Stankov (2000), “the structure of mental speed may be as complex
as
the structure of all other cognitive abilities and the Gt factor may analogous to a putative general
factor based on accuracy scores (i.e., psychometric g)” (p. 41). |
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1. 1. Introduction
