Fluid Reasoning and the Developing Brain

Abstract

Fluid reasoning is the cornerstone of human cognition, both during development and in adulthood. Despite this, the neural mechanisms underlying the development of fluid reasoning are largely unknown. In this review, we provide an overview of this important cognitive ability, the method of measurement, its changes over the childhood and adolescence of an individual, and its underlying neurobiological underpinnings. We review important findings from psychometric, cognitive, and neuroscientific literatures, and outline important future directions for this interdisciplinary research.

Full text

Frontiers in Neuroscience www.frontiersin.org May 2009 | Volume 3 | Issue 1 | 46
FOCUSED REVIEW
published: 1 May 2009
doi: 10.3389/neuro.01.003.2009
Fluid reasoning and the developing brain
Emilio Ferrer
1
, Elizabeth D. O’Hare
2
and Silvia A. Bunge
2,3
*
1
Department of Psychology, University of California, Davis, CA, USA
2
Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
3
Department of Psychology, University of California, Berkeley, CA, USA
Fluid reasoning is the cornerstone of human cognition, both during development and in adulthood.
Despite this, the neural mechanisms underlying the development of fluid reasoning are largely
unknown. In this review, we provide an overview of this important cognitive ability, the method
of measurement, its changes over the childhood and adolescence of an individual, and its
underlying neurobiological underpinnings. We review important findings from psychometric,
cognitive, and neuroscientific literatures, and outline important future directions for this
interdisciplinary research.
Keywords: problem-solving, intelligence, prefrontal cortex, rostrolateral parietal cortex, individual differences
Edited by:
Robert T. Knight, University
of California, Berkeley, CA, USA
Reviewed by:
Anna C. Nobre, University of Oxford, UK
Donald T. Stuss, University of Toronto,
Canada; Baycrest Centre for Geriatric
Care, Canada
* Correspondence:
Silvia A. Bunge collaborated with
Professor Emilio Ferrer at UC Davis
to launch a longitudinal study on the
normative development of fluid reasoning
between the ages 6 and 18. Post-doctoral
Fellow Elizabeth O’Hare and others
have so far collected behavioral and brain
imaging data pertaining to over 90
participants. Initial cross-sectional
fMRI analyses for this project were
reported in Wright et al., FiHN, 2007,
and in Crone et al., Dev. Sci., 2009.
Structural analyses relating reasoning
ability to cortical thickness and white
matter coherence were presented
at the Society for Neuroscience Annual
Meeting in 2008.
sbunge@berkeley.edu
INTRODUCTION
Fluid reasoning (FR) is the capacity to think logi-
cally and solve problems in novel situations, inde-
pendent of acquired knowledge (Cattell, 1987). It
is an essential component of cognitive develop-
ment (Goswami, 1992), as this capacity serves as
a scaffold for children, in helping them acquire
other abilities (Blair, 2006; Cattell, 1971, 1987).
FR, in childhood, accurately predicts performance
in school, university, and cognitively demanding
occupations (Gottfredson, 1997). Despite this
knowledge, we do not yet fully understand the
cause for individual differences in fluid intelli-
gence. This review examines the construct of FR,
its development over childhood and adolescence,
and its known underlying neurobiological mecha-
nisms. We conclude by outlining the important
challenges associated with this line of inquiry, and
offering recommendations for future research.
MEASUREMENT OF FR
The term “fluid reasoning” was originally
described in the Cattell’s theory of fluid and
crystallized intelligences. According to Cattell,
FR – or fluid intelligence – referred to a general
cognitive ability that emerges early in life and
is applied by the child during any information
retrieval process. Furthermore, FR greatly influ-
ences the way, in which children learn tasks that
require complex spatial, numerical, or conceptual
relations. Children add perceptual, discrimina-
tory, and executive skills to their cognitive reper-
toire through experience. The complex abilities
acquired are attached to particular perceptual and
motor areas of the brain and become hardened,
or “crystallized”, abilities.
There are various measures adopted to assess FR.
Of these measures, perhaps the most commonly
used is the Raven’s Progressive Matrices (RPM)
test The RPM test requires participants to identify
relevant features based on the spatial organization
of an array of objects, and then select the object
that matches one or more of the identified features
(Figure 1A). The test measures relational reasoning,
or the ability to consider one or more relationships
between mental representations. As the number of
relations increases in the RPM, participants tend
to respond more slowly and less accurately. Like
matrix reasoning tests, propositional analogy tests
(Figure 1B) also evaluate relational reasoning, as it
is necessary to determine whether the semantic rela-
tionship existing between two entities is the same as
the relationship between two other, often completely
different, entities.

Frontiers in Neuroscience www.frontiersin.org May 2009 | Volume 3 | Issue 1 | 47
Ferrer et al. Fluid reasoning and brain development
FR DEVELOPMENT
AND INDIVIDUAL DIFFERENCES
DEVELOPMENTAL
TRAJECTORY
FR is believed to emerge in the first 2 or 3 years
of life, after the development of general, per-
ceptual, attentional and motoric capabilities
(Cattell, 1987). Notably, FR follows a different
developmental trajectory than crystallized abili-
ties (McArdle et al., 2002), supporting the idea
of separable cognitive functions (Horn, 1991;
Schaie, 1996). The psychometric literature indi-
cates that FR advances rapidly in early and mid-
dle childhood, continues to increase, though at a
slower rate, until early adolescence, and reaches
asymptotic values in the mid-adolescence to
late-adolescence stage, after which it begins to
decline (McArdle et al., 2002). Although age-
related changes in FR ability have been well-
characterized, the mechanisms of such changes,
especially the structure and function of brain
areas underlying FR, are unknown.
FR AND RELATED
COGNITIVE ABILITIES
FR has been linked to other important cognitive
abilities. For example, cross-sectional behavioral
studies indicate that FR is related to working
memory and executive functioning (Engle et al.,
1999), and to secondary memory (Mogle et al.,
2008). Such studies provide information about
the time-independent covariation between FR
and other cognitive abilities. However, they do
not reveal information about within-person
changes and, more importantly, an empirical
understanding of possible mechanisms underly-
ing such covariation.
In longitudinal studies with adults, FR has been
related to changes in crystallized abilities, short-
term memory, and processing speed (McArdle
et al., 2000). Furthermore, among children and
adolescents, FR has been identified to be a lead-
ing indicator of changes over time in crystallized
abilities (McArdle, 2001) and changes in quanti-
tative ability, academic knowledge, and reading
(Ferrer and McArdle, 2004; Ferrer et al., 2007).
In addition to these bottom-up and top-down
influences, there are possible synergistic influ-
ences that involve working memory (Demetriou,
2002). This longitudinal research suggests that FR
is most closely related to processing speed and
working memory, although other studies focused
on the simultaneous changes of these constructs
over time indicate a complex pattern of inter-
relations among variables, eliminating a simple
interpretation of a single leading indicator of
changes (McArdle et al., 2000).
NEURAL BASIS OF FR
An important endeavor for understanding FR
is to identify the neural substrates that underlie
such cognitive ability and its development (e.g.,
Duncan et al., 2000). Achieving the goal requires
the usage of measures that directly map onto the
theoretical construct of FR, such as the RPM
task (Figure 1A).
Studies have demonstrated the importance
of the frontal lobe in fluid intelligence (e.g.,
Duncan, 2005; Duncan et al., 1995). More
specifically, functional Magnetic Resonance
Imaging (fMRI) studies involving the RPM task
in adults have demonstrated that a region in the
anterior prefrontal cortex, known as the rost-
rolateral prefrontal cortex (RLPFC), is acti-
vated when participants engage in relational
integration during RPM tasks (Christoff et al.,
2001; Kroger et al., 2002). As RLPFC is acti-
vated more for 2-relational problems than 1- or
0-relational problems, it appears that RLPFC
is specifically engaged when participants must
integrate across multiple mental representa-
tions (Ramnani and Owen, 2004). fMRI stud-
ies involving other visuospatial reasoning tasks
have also linked RLPFC to the process of rela-
tional integration (Christoff et al., 2003; Smith
et al., 2007). Additionally, in verbal proposi-
tional analogy tasks, RLPFC is preferentially
engaged when participants must consider an
analogy (identical in structure to the proposi-
tional analogy shown in Figure 1B, with words
rather than pictures), as opposed to when par-
ticipants must evaluate two individual semantic
relationships (Bunge et al., 2005; Green et al.,
2006; Wendelken et al., 2008).
In addition to RLPFC, the parietal cortex
has been implicated in relational reasoning.
Parietal activation has been shown to mediate
the relationship between FR and performance
during a demanding working memory task
(Gray et al., 2003). Individuals with superior
IQ scores rely more heavily on parietal cor-
tex during relational integration tasks, com-
pared to individuals with average IQ scores
(Lee et al., 2006). Thus, it appears that while
RLPFC is critical for relational integration
during relational reasoning, the parietal cortex
is essential for the identification and repre-
sentation of visual–spatial relations that are
fundamental to overall relational reasoning.
The notion of parietal cortex as the “work-
horse” of relational reasoning is consistent
with a recent lab study, in which adults showed
a higher degree of inferior parietal activation
compared to children, during an RPM task
(Crone et al., 2009).
Fluid reasoning (FR)
The capacity to think logically
and solve problems in novel situations
independent of acquired knowledge.
This construct is central to theories
of human intelligence.
Relational reasoning
A form of fluid reasoning consisting
of identifying correspondences
between the structures of distinct
mental representations.
Propositional analogy
A form of relational reasoning that
entails the abstraction of a relationship
between a familiar representation
and mapping it to a novel
representation (see Figure 1B).
Solving such a problem requires (1)
the abstraction of the relationship
between the base items (a bike moves
on the road), and (2) mapping
the relationship to the target pair
(a canoe moves on water).
Rostrolateral prefrontal cortex
(RLPFC)
Brain region corresponding to the
lateral Brodmann area 10, which
has been implicated in fluid reasoning,
and in particular, relational integration.
Relational integration (or second-
order relational processing)
The cognitive process, by which
several relations between mental
representations are combined or
compared, as in a 2-relational RPM
problem, a propositional analogy,
or a transitive inference problem.
This is a critical component
of relational reasoning that shows
delayed maturation.

Frontiers in Neuroscience www.frontiersin.org May 2009 | Volume 3 | Issue 1 | 48
Ferrer et al. Fluid reasoning and brain development
CHANGES IN THE NEURAL SUBSTRATES
OF FR OVER CHILDHOOD
DEVELOPMENTAL CHANGES IN BRAIN STRUCTURE
Structural brain development during late child-
hood and adolescence consists of concomitant
reductions in synaptic density and increases in
axonal myelination that proceeds along spe-
cific spatio-temporal patterns. Longitudinal
MRI research confirms and extends prior post-
mortem work by demonstrating that in general,
brain loss occurs first in the dorsal parietal lobes
during childhood and then spreads anteriorly
to dorsal frontal regions during adolescent and
post-adolescent years (Gogtay et al., 2004; Shaw
et al., 2008; Sowell et al., 2004). During this stage,
RLPFC exhibits cortical thinning until the age of
20 years (O’Donnell et al., 2005).
In adults, IQ has been observed to correlate pos-
itively with cortical thickness in bilateral RLPFC
(Narr et al., 2007). However, a longitudinal MRI
study found that it was the trajectory of cortical
thickness in aPFC in individuals from ages 4–29
rather than the actual values that distinguished
highly intelligent individuals from others (Shaw
et al., 2006). By this measure, the most intelligent
children displayed a protracted increase in corti-
cal thickness, followed by adolescents, in whom
cortical thickness was observed to have under-
gone significant thinning (Shaw et al., 2006). If
the authors had compared these individuals only
at ages 8, 10, or 12, they would have concluded
that individuals of superior IQ exhibited lower,
greater, or equivalent cortical thickness in aPFC,
respectively, when compared with average or
high-IQ individuals. In fact, their longitudinal
data reveal that any one of these accounts would
have been an incomplete and potentially mislead-
ing characterization of the differences between
these groups of children. Similarly, we expect that
the large, ongoing longitudinal study will shed
additional light on the relationship between age,
individual differences in FR ability, and cortical
thickness.
AGE-RELATED CHANGES IN BRAIN ACTIVATION
ASSOCIATED WITH FR
Three studies have examined the neural basis
of FR in a pediatric sample (Crone et al., 2009;
Eslinger et al., 2008; Wright et al., 2007). In the
first of fMRI studies, the group (Wright et al.,
2007) tested children (ages 6–13) and adults (ages
19–26) on a visual analogy task with semantic
(1-relational problems) and analogy (2-relational
problems) conditions. In semantic trials, partici-
pants were presented with one target image (e.g.,
a baseball) and five response images. They had
to select the response image that best matched
the target image (e.g., a baseball bat). In anal-
ogy problems, participants were presented with
three target images and had to select one of the
four response figures that completed the array
(Figure 1B).
Among children, it was observed that RLPFC
activation increases from the age of 6–13, bilater-–13, bilater-13, bilater-
Figure 1 | (A) Sample matrix reasoning problem adapted from the RPM. Participants simply need to complete the
array with the matching figure for 0-relational problems. Participants must identify a vertical or horizontal relationship
between items in the array for 1-relational problems. For 2-relational problems, participants must jointly consider
horizontal and vertical relations and hence, this task is considered to require relational integration abilities. The correct
answers to the featured problems are choices 2, 3, and 1, respectively. (B) Sample propositional analogy task adapted
from the Kaufman Brief Intelligence Test (KBIT). The correct answer is (A).

Frontiers in Neuroscience www.frontiersin.org May 2009 | Volume 3 | Issue 1 | 49
Ferrer et al. Fluid reasoning and brain development
ally for 1-relational problems and in left RLPFC
for 2-relational problems (i.e., analogy problems).
Among adults, it was found that individuals per-
forming analogy problems with the greatest accu-
racy showed the largest differential recruitment
of RLPFC during relational integration prob-
lems as compared with 1-relational problems.
The findings suggest that RLPFC involvement
in analogical reasoning may go through several
developmental stages. During middle childhood,
RLPFC is recruited during the performance of
visual analogy tasks, but is not distinguished
between 1-relational and 2-relational prob-
lems. In early adulthood, RLPFC shows selective
engagement for the processing and integration
of multiple relations (e.g., relational integration).
Furthermore, time series analyses have revealed
delayed RLPFC activation in children, compared
to adults (Figure 2).
Arguing against the possibility that chil-
dren merely display sluggish hemodynamic
response in RLPFC, similar time courses have
been observed in the region in the age group
of 8–12 years – as well as in young adults, in
the context of a RPM task (Crone et al., 2009).
Furthermore, a study comparing the hemody-
namic response between children and adults has
not revealed consistent differences in timing
between these groups (Kang et al., 2003). Hence,
the shift in the timing of RLPFC activation is
task-specific: children engage RLPFC in a timely
manner on the RPM task, but not on the visual
analogy task. It is speculated that children rely
on their knowledge about the objects depicted
in the visual analogy problems, rather than
approaching the problems analytically. A line
of research shows that sometimes, even adults
endorse illogical lines of reasoning if the content,
about which the adults are asked to reason, is
familiar and plausible (Braine, 1978). Similarly,
it may be true that children approach visual anal-
ogy problems in the inappropriate way because
they can access semantic memory for objects
comprising the problem. However, this is not
probable for RPM problems, which are largely
devoid of semantic information.
These observations are consistent with the pos-
sibility that children tend to respond too hastily,
and that the performance of children can benefit
from training on the tasks requiring response
inhibition. Additionally or alternatively, training
on FR tasks might lead to more efficient relational
integration.
The idea of a developmental shift in the net-
works of the brain regions involved in relational
integration, across childhood and adolescence,
receives additional support from the study
involving an RPM-type task (Crone et al., 2009).
In adults, RLPFC did not discriminate between
0-relational and 1-relational problems and was
Figure 2 | During the performance of visual analogy problems, RLPFC activation (surface rendering shown on
left) in children peaked after motor cortex. In other words, RLPFC was not engaged in time to influence the behavioral
response on 2-relational problems.

Frontiers in Neuroscience www.frontiersin.org May 2009 | Volume 3 | Issue 1 | 50
Ferrer et al. Fluid reasoning and brain development
specifically recruited with dorsolateral prefrontal
cortex (DLPFC) and posterior parietal cortex for
2-relational problems. In contrast, children aged
8–12 recruited both RLPFC and DLPFC to similar
extents for 1- and 2-relational problems. These
findings are also consistent with a recent report
that indicates decreased activation with age, in
bilateral RLPFC and DLPFC, during 1-relational
problem solving (Eslinger et al., 2008).
Altogether, the results indicate that while
regions that support relational reasoning are
already engaged by middle childhood, the precise
ways in which the regions contribute to reasoning
are fine-tuned via structural brain changes during
adolescence (see Figure 3).
CURRENT AND FUTURE DIRECTIONS
Neuroscientific research has begun to provide
clues about the changes in brain function under-
lying developmental changes in FR. However,
there are still many unknown changes. First, it is
necessary to measure the reasoning-related pat-
terns of brain activation when reasoning ability
first begins to emerge in young childhood and
changes most rapidly (e.g., Goswami, 1992).
Marked changes have been observed in perform-
ance between the age groups 4–6 and 7–10 on
the visual analogy task (B. Matlen, unpublished
undergraduate honors thesis). By the age of
6 years, the youngest age at which fMRI data
were successfully collected on the reasoning tasks,
children had already begun to reason – although
this ability improved further over subsequent
development.
Second, longitudinal fMRI data are needed
to examine within-subject changes in brain
function, which underlie improvements in FR.
Indeed, the longitudinal structural MRI data
from Shaw et al. (2006) revealed that it was the
trajectory of cortical thickening and its thinning
over time that distinguished individuals on the
basis of IQ, rather than the thickness values
themselves. Similarly, the ongoing longitudinal
fMRI study is expected to reveal new insights
into the developmental changes in brain func-
tion underlying FR.
Third, a longitudinal approach enables mod-
eling of the complex patterns of interrelations
between the cognitive abilities that contribute to
FR, including processing speed, working memory,
and specific executive functions. These behavio-
ral measures, as well as brain measures, can be
evaluated with regard to their predictive value –
the extent to which a combination of these meas-
ures at one timepoint allows the prediction of
an individual’s FR ability at a later time (Hoeft
et al., 2007)
Fourth, it is important to examine the
implications of the research for school achieve-
ment. It is necessary to determine whether a
deeper understanding of the mechanisms under-
lying FR development will enable the develop-
ment of an effective intervention for children
who struggle to perform well in school as a result
of low FR ability. Encouraging preliminary evi-
dence from the laboratory indicates that 8 weeks
of training on FR – but not on processing speed
– leads to improved performance on standard
reasoning tasks in children aged 7–9 years, some
of whom had low IQ scores at the outset of train-
ing. It will be necessary to replicate these findings
in a larger sample, and test whether FR training
has a positive and lasting influence on school
performance.
ACKNOWLEDGMENTS
The authors thank Samantha Wright, Bryan
Matlen, and Carol Baym for their contributions
to the empirical paper in Frontiers in Human
Neuroscience, on which this focused review was
based. They also thank Brian Johnson, Kirstie
Whitaker, Zdena Op de Macks, Allyson Mackey,
Susanna Hill, Ori Elis, Eveline Crone, Carter
Wendelken, and other current and former mem-
bers of the Bunge laboratory for their work on
the longitudinal study of reasoning development,
funded by NIMH grant R01NS57146-01.
Figure 3 | Schematic illustration of some of the key regions that support FR, and data
in relation to their change in the regions over middle childhood and adolescence phases.

Frontiers in Neuroscience www.frontiersin.org May 2009 | Volume 3 | Issue 1 | 51
Ferrer et al. Fluid reasoning and brain development
REFERENCES
Blair, C. (2006). How similar are fluid
cognition and general intelligence? A
developmental neuroscience perspec-
tive on fluid cognition as an aspect of
human cognitive ability. Behav. Brain
Sci. 26, 109–160.
Braine, M. D. S. (1978). On the relations
between the natural logic of reason-
ing and standard logic. Psychol. Rev.
85, 1–21.
Bunge, S. A., Wendelken, C., Badre, D.,
and Wagner, A. D. (2005). Analogical
reasoning and prefrontal cortex: evi-
dence for separable retrieval and inte-
gration mechanisms. Cereb. Cortex 15,
239–249.
Cattell, R. B. (1971). Abilities: Their
Structure, Growth and Action. Boston,
Houghton-Mifflin.
Cattell, R. B. (1987). Intelligence: Its
Structure, Growth and Action.
Amsterdam, North-Holland.
Christoff, K., Prabhakaran, V.,
Dorfman, J., Zhao, Z., Kroger, J. K.,
Holyoak, K. J., and Gabrieli, J. D.
(2001). Rostrolateral prefrontal cor-
tex involvement in relational integra-
tion during reasoning. Neuroimage
14, 1136–1149.
Christoff, K., Ream, J. M., Geddes, L. P.,
and Gabrieli, J. D. (2003). Evaluating
self-generated information: anterior
prefrontal contributions to human
cognition. Behav. Neurosci. 117,
1161–1168.
Crone, E. A., Wendelken, C., van
Leijenhorst, L., Honomichl, R.
D., Christoff, K., and Bunge, S. A.
(2009). Neurocognitive development
of relational reasoning. Dev. Sci. 12,
55–66.
Demetriou, A. (2002). The Development
of Mental Processing: Efficiency,
Working Memory, and Thinking.
Monographs of the Society for
Research in Child Developent 67,
Serial No. 268.
Duncan, J. (2005). Frontal lobe function
and general intelligence: why it mat-
ters. Cortex 41, 215–217.
Duncan, J., Burgess, P., and Emslie, H.
(1995). Fluid intelligence after fron-
tal lobe lesions. Neuropsychologia 33,
261–268.
Duncan, J., Seitz, R. J., Kolodny, J., Bor,
D., Herzog, H., Ahmed, A., Newell, F.
N., and Emslie, H. (2000). A neural
basis for general intelligence. Science
289, 457–460.
Engle, R. W., Kane, M. J., and Tuholski, S. W.
(1999). Individual differences in work-
ing memory capacity and what they
tell us about controlled attention, gen-
eral fluid intelligence, and functions
of the prefrontal cortex. In Models
of Working Memory, A. Miyake and
P. Shah, eds (New York, Cambridge
University Press), pp. 102–134.
Eslinger, P. J., Blair, C., Wang, J., Lipovsky, B.,
Realmuto, J., Baker, D., Thorne, S.,
Gamson, D., Zimmerman, E.,
Rohrer, L., and Yang, Q. X. (2008).
Developmental shifts in fMRI activa-
tions during visuospatial relational
reasoning. Brain Cogn. 69, 1–10.
Ferrer, E., and McArdle, J. J. (2004). An
experimental analysis of dynamic
hypotheses about cognitive abilities
and achievement from childhood
to early adulthood. Dev. Psychol. 40,
935–952.
Ferrer, E., McArdle, J. J., Shaywitz, B. A.,
Holahan, J. N., Marchione, K., and
Shaywitz, S. E. (2007). Longitudinal
models of developmental dynam-
ics between reading and cognition
from childhood to adolescence. Dev.
Psychol. 43, 1460–1473.
Gogtay, N., Giedd, J. N., Lusk, L.,
Hayashi, K. M., Greenstein, D.,
Vaituzis, A. C., Nugent, T. F., 3rd,
Herman, D. H., Clasen, L. S.,
Toga, A. W., Rapoport, J. L., and
Thompson, P. M. (2004). Dynamic
mapping of human cortical develop-
ment during childhood through early
adulthood. Proc. Natl. Acad. Sci. U.S.A.
101, 8174–8179.
Goswami, U. (1992). Analogical Reasoning
in Children. Hillsdale, NJ, Lawrence
Erlbaum.
Gottfredson, L. S. (1997). Why g mat-
ters: the complexity of everyday life.
Intelligence 24, 79–132.
Gray, J. R., Chabris, C. F., and Braver, T. S.
(2003). Neural mechanisms of gen-
eral fluid intelligence. Nat. Neurosci.
6, 316–322.
Green, A. E., Fugelsang, J. A., Kraemer, D. J.,
Shamosh, N. A., and Dunbar, K. N.
(2006). Frontopolar cortex mediates
abstract integration in analogy. Brain
Res. 1096, 125–137.
Hoeft, F., Ueno, T., Reiss, A. L., Meyler, A.,
Whitfield-Gabrieli, S., Glover, G. H.,
Keller, T. A., Kobayashi, N., Mazaika, P.,
Jo, B., Just, M. A., and Gabrieli, J. D.
(2007). Prediction of children’s read-
ing skills using behavioral, functional,
and structural neuroimaging meas-
ures. Behav. Neurosci. 121, 602–613.
Horn, J. L. (1991). Measurement of intel-
lectual capabilities: a review of theory.
In: Woodcock-Johnson Technical
Manual, K. S. McGrew, J. K. Werder, and
R. W. Woodcock, eds (Allen, TX, DLM
Teaching Resources), pp. 197–246.
Kang, H. C., Burgund, E. D., Lugar, H. M.,
Petersen, S. E., and Schlaggar, B. L.
(2003). Comparison of functional
activation foci in children and adults
using a common stereotactic space.
Neuroimage 19, 16–28.
Kroger, J. K., Sabb, F. W., Fales, C. L.,
Bookheimer, S. Y., Cohen, M. S., and
Holyoak, K. J. (2002). Recruitment of
anterior dorsolateral prefrontal cor-
tex in human reasoning: a parametric
study of relational complexity. Cereb.
Cortex 12, 477–485.
Lee, K. H., Choi, Y. Y., Gray, J. R., Cho, S. H.,
Chae, J. H., Lee, S., and Kim, K. (2006).
Neural correlates of superior intelli-
gence: stronger recruitment of pos-
terior parietal cortex. Neuroimage 29,
578–586.
McArdle, J. J. (2001). A latent differ-
ence score approach to longitudi-
nal dynamic structural analysis. In
Structural Equation Modeling: Present
and Future. A Festschrift in Honor of
Karl Joreskog, R. Cudeck, S. du Toit,
and D. Sorbom, eds (Lincolnwood,
IL, Scientific Software International),
pp. 341–380.
McArdle, J. J., Ferrer-Caja, E.,
Hamagami, F., and Woodcock, R. W.
(2002). Comparative longitudinal
structural analysis of growth and
decline of multiple intellectual abili-
ties over the lifespan. Dev. Psychol. 38,
113–142.
McArdle, J. J., Hamagami, F., Meredith, W.,
and Bradway, K. P. (2000). Modeling
the dynamic hypotheses of Gf-Gc
theory using longitudinal life-span
data. Learn. Individ. Differ. 12, 53–79.
Mogle, J. A., Lovett, B. J., Stawski, R. S.,
and Sliwinski, M. J. (2008). What’s
so special about working memory?
An examination of the relationships
among working memory, second-
ary memory, and fluid intelligence.
Psychol. Sci. 19, 1071–1077.
Narr, K. L., Woods, R. P., Thompson, P. M.,
Szeszko, P., Robinson, D., Dimtcheva, T.,
Gurbani, M., Toga, A. W., and
Bilder, R. M. (2007). Relationships
between IQ and regional cortical gray
matter thickness in healthy adults.
Cereb. Cortex 17, 2163–2171.
O’Donnell, S., Noseworthy, M. D.,
Levine, B., and Dennis, M. (2005).
Cortical thickness of the frontopolar
area in typically developing children
and adolescents. Neuroimage 24,
948–954.
Ramnani, N., and Owen, A. M. (2004).
Anterior prefrontal cortex: insights
into function from anatomy and
neuroimaging. Nat. Rev. Neurosci. 5,
184–194.
Schaie, K. W. (1996). Intellectual
Development in Adulthood: The
Seattle Longitudinal Study. New York,
Cambridge University Press.
Shaw, P., Greenstein, D., Lerch, J.,
Clasen, L., Lenroot, R., Gogtay, N.,
Evans, A., Rapoport, J., and Giedd, J.
(2006). Intellectual ability and cortical
development in children and adoles-
cents. Nature 440, 676–679.
Shaw, P., Kabani, N. J., Lerch, J. P.,
Eckstrand, K., Lenroot, R., Gogtay, N.,
Greenstein, D., Clasen, L., Evans, A.,
Rapoport, J. L., Giedd, J. N., and
Wise, S. P. (2008). Neurodevelopmental
trajectories of the human cerebral cor-
tex. J. Neurosci. 28, 3586–3594.
Smith, R., Keramatian, K., and Christoff, K.
(2007). Localizing the rostrolateral
prefrontal cortex at the individual
level. Neuroimage 36, 1387–1396.
Sowell, E. R., Thompson, P. M.,
Leonard, C. M., Welcome, S. E., Kan, E.,
and Toga, A. W. (2004). Longitudinal
mapping of cortical thickness and
brain growth in normal children.
J. Neurosci. 24, 8223–8231.
Wendelken, C., Nakhabenko, D.,
Donohue, S. E., Carter, C. S., and
Bunge, S. A. (2008). “Brain is to
thought as stomach is to?”: investigat-
ing the role of rostrolateral prefrontal
cortex in relational reasoning. J. Cogn.
Neurosci. 20, 682–693.
Wright, S. B., Matlen, B. J., Baym, C. L.,
Ferrer, E., and Bunge, S. A. (2007).
Neural correlates of fluid reasoning
in children and adults. Front. Hum.
Neurosci. 1, 8.
Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any com-
mercial or financial relationships that
could be construed as a potential conflict
of interest.
Received: 15 January 2009; paper pending
published: 09 February 2009; accepted:
26 February 2009; published: 01 May
2009.
Citation: Front. Neurosci. (2009) 3,1:
46–51. doi: 10.3389/neuro.01.003.2009
Copyright © 2009 Ferrer, O’Hare and
Bunge. This is an open-access article subject
to an exclusive license agreement between
the authors and the Frontiers Research
Foundation, which permits unrestricted
use, distribution, and reproduction in any
medium, provided the original authors and
source are credited.