Noble KG, McCandliss BD, Farah MJ. Socioeconomic gradients predict individual differences in neurocognitive abilities. Dev Sci 10: 464-480

Abstract

Socioeconomic status (SES) is associated with childhood cognitive achievement. In previous research we found that this association shows neural specificity; specifically we found that groups of low and middle SES children differed disproportionately in perisylvian/language and prefrontal/executive abilities relative to other neurocognitive abilities. Here we address several new questions: To what extent does this disparity between groups reflect a gradient of SES-related individual differences in neurocognitive development, as opposed to a more categorical difference? What other neurocognitive systems differ across individuals as a function of SES? Does linguistic ability mediate SES differences in other systems? And how do specific prefrontal/executive subsystems vary with SES? One hundred and fifty healthy, socioeconomically diverse first-graders were administered tasks tapping language, visuospatial skills, memory, working memory, cognitive control, and reward processing. SES explained over 30% of the variance in language, and a smaller but highly significant portion of the variance in most other systems. Statistically mediating factors and possible interventional approaches are discussed.

Full text

Developmental Science 10:4 (2007), pp 464–480 DOI: 10.1111/j.1467-7687.2007.00600.x
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and
350 Main Street, Malden, MA 02148, USA.
Blackwell Publishing Ltd
PAPER
Socioeconomic gradients predict individual differences in
neurocognitive abilities
Kimberly G. Noble,
1,2
Bruce D. McCandliss
2
and Martha J. Farah
1
1. University of Pennsylvania Center for Cognitive Neuroscience, USA
2. Sackler Institute for Developmental Psychobiology of Weill Medical College of Cornell University, USA
Abstract
Socioeconomic status (SES) is associated with childhood cognitive achievement. In previous research we found that this asso-
ciation shows neural specificity; specifically we found that groups of low and middle SES children differed disproportionately
in perisylvian/language and prefrontal/executive abilities relative to other neurocognitive abilities. Here we address several new
questions: To what extent does this disparity between groups reflect a gradient of SES-related individual differences in neuro-
cognitive development, as opposed to a more categorical difference? What other neurocognitive systems differ across individuals
as a function of SES? Does linguistic ability mediate SES differences in other systems? And how do specific prefrontal/executive
subsystems vary with SES? One hundred and fifty healthy, socioeconomically diverse first-graders were administered tasks tap-
ping language, visuospatial skills, memory, working memory, cognitive control, and reward processing. SES explained over 30%
of the variance in language, and a smaller but highly significant portion of the variance in most other systems. Statistically
mediating factors and possible interventional approaches are discussed.
Introduction
SES is strongly associated with a number of indices of
children’s cognitive ability and achievement, including IQ,
achievement tests, grade retentions and literacy (Baydar,
Brooks-Gunn & Furstenberg, 1993; Brooks-Gunn, Guo
& Furstenberg, 1993; Liaw & Brooks-Gunn, 1994; Smith,
Brooks-Gunn & Klebanov, 1997). These associations are
typically quite large (Gottfried, Gottfried, Bathurst, Guerin
& Parramore, 2003), and are observed throughout devel-
opment, from infancy through adolescence and into
adulthood (Bradley & Corwyn, 2002). Many decades of
research have sought to characterize the mediators and
moderators of socioeconomic effects on cognitive ability
(Bradley & Corwyn, 2002; McLoyd, 1998).
The traditional measures of cognitive performance
used in this research have been broad-based, lacking
specificity regarding the underlying cognitive abilities
involved. Standardized tests and school achievement
generally measure the combined functioning of multiple
neurocognitive systems. With the advent of cognitive
neuroscience, it has become possible to assess specific
neurocognitive systems more selectively.
Recently, we applied this approach in two preliminary
investigations of the developmental relationships between
SES and certain cognitive functions associated with
specific brain systems (Noble, Norman & Farah, 2005;
Farah, Shera, Savage, Betancourt, Gianetta, Brodsky,
Malmud & Hurt, 2006). Hypothesizing that brain sys-
tems with protracted postnatal development would have
greater susceptibility to environmental influences, we
proposed that perisylvian regions underlying language
processing (Giedd, Blumenthal, Jeffries, Castellanos, Liu,
Zijdenbos, Paus, Evans & Rapoport, 1999; Paus, Zijdenbos,
Worsley, Collins, Blumenthal, Giedd, Rapoport &
Evans, 1999; Sowell, Peterson, Thompson, Welcome,
Henkenius & Toga, 2003; Sowell, Thompson, Rex,
Kornsand, Tessner, Jernigan & Toga, 2002) and prefrontal
regions underlying executive functioning (Casey, Giedd
& Thomas, 2000; Giedd
et al.
, 1999; Huttenlocher, 1997)
would show the strongest associations with SES. Sup-
porting this, we found that children from low SES back-
grounds tended to perform below their middle SES peers
on most measures of the language and executive systems.
The effect sizes were striking: group means were on the
order of a standard deviation apart on composites of
Address for correspondence: Kimberly Noble, Department of Pediatrics, Columbia University, Children’s Hospital of New York, New York, NY
10032, USA; e-mail: kimnoble2007@gmail.com

SES and individual differences 465
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
language skills, and on the order of half a standard
deviation apart on executive function tasks. The studies
produced differing results on the relation of SES to
medial temporal memory function. In the first study, of
kindergarten-aged children, no SES difference was
found in memory ability, whereas in the second study, of
middle school-aged children, a substantial difference, on
the order of two-thirds of a standard deviation, was
observed. In the present study, the relationship between
SES and neurocognitive outcome is further explored,
addressing three new issues.
First, our previous research concerned groups of low
SES and middle SES children. In contrast, here we study
SES as a continuous variable across the broad range
represented in our sample, increasing both our statistical
power and ecological validity. Second, the previous
studies left several open questions about SES disparities
in neurocognitive systems other than language and
executive function. Although no SES differences were
observed in kindergarteners’ performance on memory
tasks, this may have been due to the extremely brief
retention interval used in that study. In addition, non-
significant trends were observed for SES differences in visual
and spatial cognition in both of the earlier studies. An
additional goal of the present study is to provide more
powerful tests of these observed but unreliable differences
using a larger sample and a new set of tasks. Finally,
only the middle school study assessed individual sub-
systems of executive ability, and found some, but not all,
correlating with SES. Both ‘working memory’ and ‘cog-
nitive control’ (associated with lateral prefrontal and
anterior cingulate cortex, respectively) were found to
vary with SES, whereas ‘reward processing’ (associated
with ventromedial prefrontal cortex) was not. A final
goal of the present study is to assess the subsystems of
prefrontal/executive function in a larger group of children
with new tasks.
In the present study, New York City first-graders from
a wide range of socioeconomic backgrounds were
administered a set of tasks drawn from the cognitive
neuroscience literature, to assess relatively specific neuro-
cognitive systems: the left perisylvian/language system,
the parietal/spatial cognition system, the medial tem-
poral/memory system, the lateral prefrontal/working
memory system, the anterior cingulate/cognitive control
system, and the ventromedial/reward processing system.
SES was estimated by parental education, occupation,
and income, the three most frequently used indices of
SES (Ensminger & Fothergill, 2003). These can be
considered proxies for the many other factors that vary
systematically with SES and are likely to influence child
development, including physical health, home environ-
ment, early education, and neighborhood characteristics
(Bornstein & Bradley, 2003). In addition, parents responded
to a questionnaire concerning the children’s home environ-
ment and parenting practices.
The hypotheses to be tested include the degree to
which SES accounts for individual differences in the
neurocognitive systems listed, including a finer-grained
analysis of prefrontal/executive function than was previ-
ously carried out, and more powerful assessments of
visual/spatial and memory functions. In addition, we
constrain possible causal hypotheses concerning the
association of SES and neurocognitive development, by
examining the relations among the systems in mediating
the observed effects of SES, and the relations among
parent-reported aspects of the children’s home lives and
neurocognitive development.
Method
Subjects
One hundred and sixty-eight first-graders were recruited
from nine New York City public schools that serve fam-
ilies from a wide range of socioeconomic backgrounds.
Parents of participants signed IRB-approved permission
slips for their children to engage in a short in-school
battery of cognitive tests for a research study, for which
their children would receive a free book. The parents of
150 of these children (80 boys, 70 girls) were able to be
reached by telephone to answer a 5-minute question-
naire that included items on socioeconomic background,
the child’s medical and psychiatric history, and activities
engaged in at home. These 150 children constitute the
subjects in the analyses presented below.
Thirty-four per cent of children were identified by their
parents as African-American; 6.7% were Asian; 22.7%
were Latino; 22.7% were white; and 14.0% of children
were identified as mixed or other. All children were
native English speakers. Although English was the pri-
mary language spoken in the home, 68 children (45%)
grew up in a family in which another language was also
spoken by at least one family member part of the time.
Results of relevant analyses accounting for second lan-
guage exposure are presented below.
Seventeen children’s parents reported some type of
significant medical or psychiatric history. Of these, five
children weighed less than 1500 g at birth. Three children
were reported to have diagnoses of ADHD, and one child
was taking Ritalin. No other children were taking psy-
chotropic medications of any kind. No child had been
diagnosed with a learning disability, although 14 children’s
parents reported a history of some other type of psychiatric
or developmental problem. One child was reported to have

466 Kimberly G. Noble
et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
suffered a head injury involving loss of consciousness for
several hours. Analyses are presented both including
and excluding children with significant medical and/or
psychiatric histories. Although parental report is always
subject to error, it was previously found that children’s
medical histories as reported by parents were reasonably
accurate, and that results from the subset verified by
children’s pediatricians were the same as those from the
whole sample (Noble
et al
., 2005).
Procedures
A battery of tasks parsed cognition into six broad neuro-
cognitive systems: language, visuospatial processing,
memory, working memory, cognitive control, and reward
processing. The six systems cover a range of cognitive
abilities, grouped into broad categories whose validity is
supported by anatomical and information-processing
considerations, discussed below.
Each neurocognitive system was assessed using two
tasks that were superficially different, but that were
designed to predominantly tax that system. The level of
functioning of each of the six neurocognitive systems
was measured by a composite score derived from that
system’s tasks. Although no task is pure, and some tasks
undoubtedly engage multiple systems, the tasks were
chosen to be relatively selective measures of particular
neurocognitive systems in that they tax one system and
place relatively light demands on the others, as based on
evidence from the cognitive neuroscience literature.
Whenever possible, we provide neurocognitive evidence
from pediatric populations. Of course, behavioral measures
alone cannot tell us with complete confidence whether
a particular task engaged the hypothesized neural
circuitry in our subject population.
The battery consisted of paper-and-pencil and com-
puterized tasks, each lasting approximately 5–10 minutes,
with the complete battery requiring two 45-minute sessions.
Subjects were tested in a quiet room or hallway in school.
Each session included tasks from multiple systems and the
order of sessions was counterbalanced between subjects.
Data collection also included a questionnaire for
parents documenting the education level, income, and
occupation for all adults in the home (McLoyd, 1998).
Parental education was defined as the average education
of any parents, step-parents, or guardians in the home.
The income-to-needs ratio was calculated for each family,
defined as the total family income divided by the official
poverty threshold for a family of that size, such that a
family with an income-to-needs ratio of 1 is living at the
poverty line (McLoyd, 1998). Finally, parental occupa-
tion was defined as the highest occupational score of any
parent, step-parent, or guardian in the home, according
to the 9-point Hollingshead Index Occupational Status
Scale (Hollingshead, 1975, as cited in Bornstein, Hahn,
Suwalsky & Haynes, 2003). Although the Hollingshead
is frequently criticized for being oversimplified and out-
of-date (Duncan & Magnuson, 2003), it is nonetheless
the best-known and most widely used measure (Bornstein
et al.
, 2003). Occupations of all adults in the home were
assigned to one of the nine categories, ranging from ‘farm
laborers/menial service workers’ to ‘higher executives/
proprietors of large businesses, and major professionals’.
Modern-day urban occupations were assigned as seemed
reasonable, with the best efforts made to stay true to the
original scale. For instance, ‘tailor’ was assigned to the
category of ‘skilled manual workers, craftsmen, and tenant
farmers’, for a score of 4; ‘office manager’ was assigned
to the category of ‘smaller business owners, farm owners,
managers, and minor professionals’, for a score of 7; and
‘teacher’ was assigned to the category of ‘administrators,
lesser professionals, and proprietors of medium-sized
businesses’, for a score of 8. To ensure consistency, all
assignments were made by one author (K.G.N.).
Parents were also asked to report the number of hours
per week the child had spent in preschool and/or daycare
prior to kindergarten, the frequency with which parents
currently engage in pro-academic activities with children
(reading at home, talking about what was learned in
school that day, talking about numbers in everyday
activities, and practicing writing letters or words), the
frequency with which they themselves read books or the
newspaper, and the frequency of physical punishment.
For each activity, they were asked to choose whether
they had engaged in the activity ‘within the last week,
month, six months, or less frequently’. Activity frequencies
were coded as 1, 2, 3, or 4, respectively, such that a higher
score indicated spending less time engaging in that activity.
Left perisylvian/language system
Language acquisition is crucial for many aspects of cog-
nition as well as communication. SES associations have
been found in all domains of linguistic competence, but
especially in lexical-semantic knowledge and phonologi-
cal awareness (Whitehurst, 1997).
Peabody Picture Vocabulary Test (PPVT).
This is a
standardized test of lexical-semantic knowledge, used in
our previous studies of SES and neurocognitive develop-
ment. On each trial the child hears a word and must select
the corresponding picture from among four choices.
Certain forms of aphasia (Goodglass & Kaplan, 1982)
and semantic memory impairments (McCarthy &
Warrington, 1990), both of which involve damage to left
perisylvian cortex, produce impairments in this task.
Similar word–picture matching tasks used in functional

SES and individual differences 467
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
neuroimaging studies also implicate left perisylvian cortex
(Thompson-Schill, D’Esposito, Aguirre & Farah, 1997).
CTOPP – Blending words subtest.
This 20-item subtest
was taken from the Comprehensive Test of Phonological
Processing (CTOPP; Wagner, Torgesen & Rashotte, 1999)
and measures the child’s ability to combine sounds to
form words. The child listens to a series of tape-recorded
sounds and puts the sounds together to make a whole
word. Phonological processing is often compromised after
perisylvian damage (Blumstein, 1994) and has been linked
to a left perisylvian network in neuroimaging studies in
children (Shaywitz, Shaywitz, Pugh, Mencl, Fulbright, Skud-
larski, Constable, Marchione, Fletcher, Lyon & Gore, 2002).
Parietal/spatial cognition system
Spatial cognition involves the perception and mental
manipulation of spatial relations (Macaluso & Driver,
2003), and plays a role in mathematics and technical
subjects (Zago & Tzourio-Mazoyer, 2002) as well as artistic
endeavors (Kirk & Kertesz, 1989).
Developmental Neuropsychological Assessment (NEPSY)
(Korkman, Kirk & Kemp, 1998) arrows line orientation
task.
In this standardized test of spatial perception and
cognition, the experimenter initially shows the subject a
concentric target with two arrows, and demonstrates
how the two arrows point to the center of the target. The
subject is then shown another target with eight sur-
rounding arrows, and is asked, ‘Which two arrows point
straight to the middle of the target?’ The task continues
for 15 trials, and the total score is the sum of points
earned on all items, for a maximum of 30. Line orienta-
tion judgment is most impaired by lesions to the parietal
cortex in humans (Walsh, 1987).
Mental rotation task.
In this task, the experimenter uses
laminated line-drawings of hands, similar to those used
by Parsons, Gabrieli, Phelps and Gazzaniga (1998), to
demonstrate that two identical right hands can be super-
imposed, but that a right hand and a left hand cannot
be superimposed no matter how they are rotated. The
child is then told that two hands will appear on the com-
puter screen, and that as quickly as possible he is to
press one button if the two hands are the same and
another button if they are different. The buttons are
marked with stickers, such that two of the same stickers
(two stars) indicate the ‘same’ button, and two different
stickers (a circle and a heart) indicate the ‘different’ but-
ton. Five practice trials with feedback ensue, followed by
30 test trials without feedback. The hand on the left is
always a non-rotated right hand. The hand on the right
is rotated either 0, 45 or 90 degrees clockwise from the
reference hand on the left, and was a left hand in 50% of
trials. Because of the speed–accuracy tradeoff involved
in this task, the relevant score represents an average of the
z
-scores of accuracy and reaction time of correct trials.
Both patient data (Ratcliff, 1979) and pediatric fMRI (Booth,
MacWhinney, Thulborn, Sacco, Voyvodic & Feldman, 1999)
have linked mental rotation to the parietal lobes.
Medial temporal/declarative memory system
The ability to form new memories is essential to success
in school and most other aspects of life. The memory
tasks used here were tests of incidental memory, in that
the children were not aware that their memory would be
tested at the time they were exposed to the stimuli. Inci-
dental memory is unaffected by differences in strategy or
intention to learn. It affords a relatively pure measure of
medial temporal memory processing, independent of
prefrontally mediated strategy (Rugg, Fletcher, Frith,
Frackowiak & Dolan, 1997).
NEPSY delayed memory for faces.
In this standardized,
incidental learning task, the child is presented with 16
children’s faces, presented individually, each of which the
child must classify as a boy or girl. During the test
phase, presented about 20 minutes later, the child is pre-
sented with sets of three faces, and must choose which
of the three faces she has seen before. Medial temporal
damage impairs incidental learning of faces (Mayes,
Meudell & Neary, 1980), and face learning is known to
activate medial temporal regions of normal humans
(Haxby, Hoffman & Gobbini, 2002).
Incidental picture pair learning task.
In this task involving
the incidental learning of arbitrarily paired associates,
the child is shown 10 pairs of line-drawings from the
Snodgrass and Vanderwart (1980) corpus (e.g. a book
and a clock), and is asked to indicate which picture
answers a simple question (e.g. ‘Which one has pages?’
with the correct answer being the book). Each set of
paired associates is presented twice, once with the question
referring to one picture of the pair, and once referring to
the other. During the test phase, about 10 minutes later,
the child is shown three pictures on a page, and is asked
to indicate which two of the three had previously been
paired. The task continues for 20 trials. Position of each
picture on the page was randomized. Patients with medial
temporal damage are impaired at recognition memory
and their impairment is evident in incidental learning
tasks (Mayes, Meudell & Neary, 1978). Functional
neuroimaging studies support this localization (Squire,
Ojemann, Miezin, Petersen, Videen & Raichle, 1992).
Lateral prefrontal/working memory system
Working memory involves the ability to retain and
manipulate information over a short duration. It is

468 Kimberly G. Noble
et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
essential for complex reasoning and problem solving
(Klingberg, Forssberg & Westerberg, 2002).
Spatial working memory task.
This computerized task,
adapted from Klingberg
et al
. (2002), involves a story-
line in which the child is told she has to ‘help the
squirrel find his acorns’. Acorns are serially presented in
pseudo-random positions on a four-by-four grid. After
the last acorn is presented, the screen turns green to
indicate that the subject should point to the positions of
the respective acorns in the order they appeared. The
number of acorns in the sequence is successively increased
after two trials at a given span level. The task proceeds
until the child misses two trials at the same span level.
Spatial working memory has been linked to prefrontal
cortex function, particularly dorsolateral PFC, in lesion
studies (Shimamura, 1994) and in functional neuroimag-
ing, including fMRI of pediatric populations (Thomas,
King, Franzen, Welsh, Berkowitz, Noll, Birmaher & Casey,
1999).
Delayed nonmatch to sample.
This computerized task,
adapted from Marks, Cyrulnik, Berwid, Santra, Curko
and Halperin (2001), requires children to hold simple
nonverbalizable figural stimuli in working memory. In
the experimental condition, the child is presented with a
single shape for 4 seconds, followed by a 1-second delay.
A response screen containing the original figure and one
new figure is then presented. The child is asked to ‘point
to the shape that is different from the one you just saw’.
The task difficulty increases incrementally every three
trials, such that the first three trials contain stimuli with
a single figure and a response screen containing two
figures; the second level contains two figural stimuli and
three response options, and so on. In the control condi-
tion, the child must perform the same task without delay,
such that the child views the stimulus and response
figures simultaneously, and does not need to engage
working memory. In both conditions, the task continues
until the child gets fewer than two out of three trials
correct within a given level. The score is the difference
between the total correct in the experimental and control
conditions. Since most children answer all items cor-
rectly on the control task but fewer items correctly on
the experimental task, scores are generally negative.
Similar tasks are impaired in rats with prefrontal lesions
(Porter, Burk & Mair, 2000), and have been linked to
ventrolateral prefrontal cortex in humans using fMRI
(de Zubicaray, McMahon, Wilson & Muthiah, 2001).
Anterior cingulate/cognitive control system
The ability to suppress or override competing atten-
tional or behavioral responses, and the ability to adjust
the effort required to do so, are key components in the
performance of many cognitive processes (Casey, Tottenham
& Fossella, 2002). The ability to ignore competing sources
of attention is crucial for success in the classroom
environment.
Go/no-go task.
In this task, also used in our study of
SES in kindergarteners, the child is told that he will see
pictures of different animals on the computer screen,
and that he should press the space bar every time he sees
an animal, but never when he sees the cat. Items are
pseudo-randomized, and the cat appears on 10 out of
60 trials. This task assesses the child’s ability to inhibit
a prepotent response, by measuring the number of false
alarms made to the cat. This ability has been linked to
the anterior cingulate in both lesion studies (Drewe, 1975)
and pediatric and adult fMRI (Casey, Trainor, Orendi,
Schubert, Nystrom, Cohen, Noll, Giedd, Castellanos,
Haxby, Forman, Dahl & Rapoport, 1997).
NEPSY auditory attention and response set.
The first part
of this standardized task was used to set up a prepotent
response. Red, yellow, and blue squares are placed
alongside an empty box. An audiotape plays a list of
words at the rate of one word per second. The child is
told that every time he hears the word
red
, he is to place
a red square in the box. The second part of this task is
similar to the Stroop task, in that the child must inhibit
a prepotent response while shifting to and maintaining
a new set of contrasting instructions. Here, the child is
told that he will hear some more words on the audio-
tape. However, this time, the child is to place a yellow
square in the box every time he hears the word
red
, a red
square in the box every time he hears the word
yellow
,
and a blue square in the box every time he hears the
word
blue
. Standard scores represent performance on
the second half of the task, and take into account both
accuracy and reaction time. Lesion studies (Swick &
Jovanovic, 2002) and neuroimaging (Peterson, Kane,
Alexander, Lacadie, Skudlarski, Leung, May & Gore,
2002) have implicated anterior cingulate cortex during
performance of Stroop and Stroop-like tasks.
Orbitofrontal/reward processing system
The ability to learn the reward value of stimuli is essen-
tial for flexibly adapting to changing situations in the
world. Orbitofrontal cortex has been shown to encode
the context-specific reward value of stimuli, and has
been linked to impulse control and stimulus–reward
learning (Mesulam, 2002).
Reversal learning task.
In this task, adapted from
Fellows and Farah (2003), subjects play a computerized
card game, in which they are dealt two cards at a time
from decks of different colors. One deck consistently
conceals a 5-point win, the other a 5-point loss. Subjects

SES and individual differences 469
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
are told to pick a card from one deck, and to try to get
as many points as possible. Feedback is provided after
each trial. After the learning criterion of eight consecu-
tive cards chosen from the winning deck is met, the con-
tingencies are switched, constituting the reversal phase
of the task. If eight cards are again picked consecutively
from the winning deck, the contingencies are switched
again, for a total of 40 trials, allowing for up to five
reversals. The number of errors following each reversal
is scored. Both lesion (Fellows & Farah, 2003) and neuro-
imaging (Elliott, Dolan & Frith, 2000) data suggest
that this aspect of reward processing involves orbitofron-
tal cortex.
Delay of gratification.
In this task, the child is told that
the experimenter has a present for her, but that she has
to remain turned around while it is wrapped, similar to
Carlson and Moses (2001). The experimenter then
proceeds to noisily wrap the present for up to 5 minutes.
Starting at 2 minutes, the experimenter makes a noise
with a noisemaker every 30 seconds. The child’s score is
the amount of time elapsed before she turns around,
with a maximum of 300 seconds. The ability to delay
gratification has been shown to be decreased in rats with
lesions to the orbital PFC (Newman, Gorenstein &
Kelsey, 1983), and is noted clinically in patients with
damage to this brain area (Stuss & Benson, 1984).
Results
SES index
A stable measure of SES incorporates education, occu-
pation, and income (McLoyd, 1998). Although ideally
all parents would provide data pertaining to all three
components of SES, in reality, parents are often more
willing to provide education and occupation than income
data (Bornstein & Bradley, 2003). In our sample, 150
parents provided education and occupation information,
whereas only 130 of these were willing to disclose income.
So as to avoid discarding the data from the remaining
20 children (and therefore potentially biasing our results
towards those families who, for whatever reason, were
willing to provide income information), a regression
equation was constructed to predict the income-to-needs
ratio from the other two variables in the subjects for
whom all three variables were available. However, because
of the nature of the income-to-needs ratio, there tend to
be positive, but not negative, outliers: that is, all families
living below the poverty line are distributed between
values of zero and one, whereas very wealthy families may,
in theory, be extreme outliers with very high income-to-
needs ratios. To illustrate this point, the mean income-
to-needs ratio in our sample of 130 parents who pro-
vided this information was 3.36 (SD 3.78); however,
whereas the minimum ratio was only 0.23 (less than one
standard deviation from the mean), the maximum was
19.5 (over 4 standard deviations from the mean). To
eliminate the skewing effect that these positive outliers
would have on predicting the missing data, the nine fam-
ilies who had income-to-needs ratios greater than 10 were
eliminated, at which point the standardized residuals
displayed a normal distribution. A regression equation was
then calculated from the remaining 121 families (income-
to-needs
=
0.358 (parental education)
+
0.344 (parental
occupation) – 4.097;
R
2
=
.545;
p
<
.0001), and this equation
was used to impute the income-to-needs scores for the 20
children whose parents did not provide income information,
using the education and occupation information that those
children’s parents provided. An SES index score was then
determined for each child by entering the three variables
(parental education, occupation, and income-to-needs or
imputed income-to-needs) into a factor analysis, using
the maximum likelihood method of extraction. A single
factor was extracted, explaining 73.5% of the variance
across the three variables. This factor loading was then
used as the SES index score for each child.
Cognitive measures
For all cognitive tasks in all subjects, a total of 13 indi-
vidual scores fell more than 3 standard deviations on
either side of the mean of the sample and were elimi-
nated from the data set. These included one high
performance on PPVT, one low performance on NEPSY-
arrows, four outliers on hand rotation (two with low
accuracy and two with high reaction time), five low per-
formances on reversal learning (including three subjects
who scored a high number of reversal errors, plus two
additional children who never reached the learning
criterion of eight in a row correct), as well as one low
performance each on memory-pictures and go/no-go. In
addition to the outliers, other data points were elimi-
nated or missing, either because the child refused to
participate in a particular game (one data point for go/
no-go), the child was unable to complete the testing
session (one data point each for arrows, hand rotation,
memory-faces, memory-pictures, and present wrapping),
or due to computer difficulties (6 data points each for
go/no-go and reversal learning, 4 data points for acorns,
and 2 data points for delayed non-match to sample). The
number of remaining participants for each task, as well
as the means and standard deviations of the analyzed
scores for each task are shown in Table 1.
Scores were converted to
z
-scores relative to the entire
distribution of 150 children, thus putting all task

470 Kimberly G. Noble
et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
performances on a common scale. A composite score for
each neurocognitive system was then constructed by
averaging the relevant
z
-scores. Regression analyses of
the relation between the SES index and neurocognitive
systems were conducted, as well as further regressions
including these measures and measures of early child-
hood experience.
Regression analyses
The SES index was entered into regressions to examine
whether SES could statistically account for the variance
in performance of each system. Results are presented in
Figure 1. SES statistically accounted for a portion of the
variance in each system except for reward processing.
Specifically, SES accounted for 32.0% of the variance in
the language composite (Beta
=
.556;
p
<
.0001); 16.7%
of the variance in the visuospatial composite (Beta
=
.409;
p
<
.0001); 10.2% of the variance in the memory com-
posite (Beta
=
.32;
p
<
.0001); 5.5% of the variance in the
working memory composite (Beta
=
.239;
p
<
.002), and
5.5% of the variance in the cognitive control composite
(Beta
=
.234;
p
<
.004). Using the test for correlated
correlations (Meng, Rubin & Rosenthal, 1992), it was
found that SES accounted for statistically more variance
in the language composite than in the next highest system
composite (
p
<
.03). The variance explained in the other
systems did not significantly differ from one another,
though the difference in the variance accounted for in
the visuospatial system as compared to that in both the
cognitive control and working memory systems was border-
line significant (
p
<
.06).
SES is generally considered to comprise education,
occupation and income (McLoyd, 1998). However, because
Table 1 Means and standard deviations of tasks
System Task N Mean SD
Language PPVTa (standard score) 149 93.7 15.0
CTOPP blendsb (standard score) 150 8.9 2.3
Visuospatial Arrows (line orientation)b (standard score) 148 9.4 3.0
Hand rotation-accuracy (# correct) 145 26.4 4.2
Hand rotation-reaction time (ms) 145 2104.7 646.7
Declarative memory Memory-facesb (standard score) 149 10.4 3.2
Memory-picture pairs (# correct) 148 17.6 2.4
Executive-working memory Acorns (spatial working memory) (span) 146 4.3 1.6
Delayed non-match to sample (span, experimental condition minus control condition) 148 −5.4 2.69
Executive-cognitive control Go/no-go (# false alarms) 142 2.1 1.4
Auditory attention and response setb (standard score) 150 7.8 3.1
Executive-reward Reversal learning (reversal errors) 139 6.1 1.0
Present wrapping (ms) 149 197.3 107.1
Note: See text for maximum and minimum possible scores on non-standardized tasks. PPVT = Peabody Picture Vocabulary Test. CTOPP = Comprehensive Test of
Phonological Processing.
a Nationally normed mean standard score of 100, with a standard deviation of 15.
b Nationally normed mean standard score of 10, with a standard deviation of 3.
Figure 1 SES accounts for variance in all neurocognitive
composites except reward processing. SES accounts for
statistically more variance in the language composite than in
all other composites, which do not statistically differ from each
other.

SES and individual differences 471
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
our SES index used imputed income-to-needs ratios for
the 20 subjects whose families declined to provide income
data, we verified our results by re-running the above regres-
sion analyses using several other proxies for SES for
which we had information from all families surveyed,
including (1) maternal education, and (2) both parents’
education and occupational status. Results in both cases
were nearly identical to those found when using the SES
index. For the language composite, maternal education
accounted for 27.0% of the variance (Beta
=
.520;
p
<
.0001). When both parental education and occupation
were included in the language model, 30.6% of the vari-
ance was accounted for, and both education (Beta
=
.360;
p
<
.0001) and occupation (Beta
=
.256;
p
<
.003) accounted
for unique variance. For the visuospatial composite,
maternal education accounted for 15.0% of the variance
(Beta
=
.387;
p
<
.0001). Parental education and occupa-
tion together accounted for 15.7% of the variance in the
model; education contributed unique variance (Beta
=
.263;
p
<
.007), while occupation did not (Beta
=
.177;
p
<
.066).
In the memory composite, maternal education accounted
for 5.7% of the variance (Beta
=
.238;
p
<
.004). Together,
parental education and occupation accounted for 9.9%
of the variance; in this case, only occupation was signi-
ficant (Beta
=
.240;
p
<
.016), while education was not
(Beta
=
.104;
p
<
.294). For working memory, maternal
education accounted for 4.7% of the variance (Beta
= .216;
p < .009). Parental education and occupation together
accounted for 6.1% of the variance; education was sig-
nificant (Beta = .225; p < .029) and occupation was not
(Beta = .032; p < .758). Finally, in the cognitive control
composite, 8.7% of the variance was accounted for by
maternal education (Beta = .294; p < .0001). Parental
education and occupation together accounted for 5.9%
of the variance; once again, education was significant
(Beta = .261; p < .010) while occupation was not (Beta =
−.034; p < .737). As above, neither maternal education
nor the combination of parental education and occupa-
tion accounted for variance in reward processing.
These results are generally consistent with our previ-
ous findings. As before, language shows the strongest
association with SES. Also as before, executive functions
are related to SES. The present results indicate that both
working memory and cognitive control are associated
with SES, whereas reward processing is not. This is also
consistent with our previous findings, although the
ages of the children and the tasks used to measure these
abilities were different. In the study of middle schoolers,
working memory and cognitive control were assessed
with different tasks from those used here, but neverthe-
less showed significant SES disparities. That study also
operationalized reward processing using different tasks
and, in agreement with the present study, found no SES
disparity. The executive function task most relevant to reward
processing in the study of kindergarteners, a delayed
gratification task requiring the children to choose between
one sticker now and more stickers later, was also per-
formed equivalently well by low and middle SES children.
The present study clarifies two ambiguous findings from
previous research on SES and neurocognitive develop-
ment. The first concerns spatial cognition. In both of the
two previous studies, low and middle SES children dif-
fered in this ability, but in each case the difference failed
to reach statistical significance. Our hope was that, with
a larger sample size, we would disambiguate the relation
between SES and spatial cognition, and indeed the present
study found a clearly significant association. Recently,
Levine, Vasilyeva, Lourenco, Newcombe and Huttenlocher
(2005) also reported SES disparities in spatial cognition.
The second finding clarified by the present study con-
cerns new learning and memory. In our previous study
of kindergarteners, memory ability was equivalent across
SES groups, but this may have been due to the short
interval between exposure and test in that study. When
memory was assessed in low and middle SES middle
schoolers using a longer retention interval, it was found
to differ substantially between groups. The results of this
study confirm that, when tested after a sufficient interval,
SES is associated with recognition memory performance.
So far we have examined the relation between SES
and neurocognitive systems, rather than performance on
specific tasks. By focusing on the neurocognitive system
composite measures, which are based on pairs of tasks
that differ from one another as much as possible, we can
sample the functioning of each system somewhat more
broadly. For example, the memory composite assesses
both face memory and memory for verbally described
pictures, and the language composite assesses both
lexical-semantic and phonological aspects of language
ability. Nevertheless, it is also of interest to measure the
strength of association between SES and performance
on individual tasks. Table 2 shows these results. Among
tasks within systems that showed a significant statistical
influence of SES, only the go/no-go and delayed non-
match to sample tasks did not themselves demonstrate
variance accounted for by SES. SES did not account for
variance in either of the two reward tasks.
Although one might expect high correlations between
the tasks within a system, our attempt to sample different
aspects of each system nonredundantly would be expected
to reduce intrasystem correlations. Table 3 shows that
the two tasks in each of the language, visuospatial, mem-
ory, and working memory systems are correlated with
each other, whereas the cognitive control and reward tasks
were not. Further, several tasks correlate with members
of different systems.

472 Kimberly G. Noble et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
To better assess the specificity of our a priori group-
ings of tasks, whose mean standardized scores were
termed ‘system composites’, we conducted a post-hoc
principal components analysis with varimax rotation on
the subset of tasks that showed intra-system correlation
(i.e. eight tasks in the language, visuospatial, memory
and working memory composites). This allowed us to
extract loading scores on four principal components,
which we term ‘factors’. Table 4 shows the results of this
analysis. The two visuospatial skills loaded most strongly
on factor 1 (which we will term the ‘visuospatial factor’)
and the two language tasks loaded most strongly on
factor two (‘linguistic factor’). Factor 3 (‘declarative
memory factor’) shows strong loadings from both the
memory tasks, and factor 4 (‘working memory factor’)
is nearly exclusively representative of the working mem-
ory tasks. Though the specificity is not perfect – factors
1 and 3 show some contamination from other tasks – it
can nonetheless be seen that four of the six a priori
systems show reasonable orthogonality. Further, the SES
index shows a similar relationship to each post-hoc factor
loading as described previously for the a priori system
composites. Specifically, SES accounted for 20% of the
variance of the linguistic principal component (Beta = .442;
p < .0001). SES also accounted for a significant but sub-
stantially smaller portion of the variance for the visuospatial
(R2 = .12, Beta = .350, p < .0001) and declarative memory
(R2 = .07, Beta = .271, p < .001) principal components.
Interestingly, SES did not significantly account for any
unique variance in the working memory principal component.
Potential mediating factors
In a preliminary attempt to unpack the relationship
between SES and the neurocognitive measures examined
here, we statistically controlled for several potential SES-
related health, cognitive and environmental influences.
Such factors could represent the underlying mechanisms
by which SES is associated with cognitive outcome. Although
prospective experimental studies are necessary to test such
hypotheses directly, the following analyses can put useful
constraints on the likely mechanisms. Note that when a
factor reduces or eliminates the association between SES
and a particular cognitive ability, this does not imply
that the factor is an alternative to SES; rather, the factor
may be part of the complex construct of SES, for which
the easily quantifiable measures of parental education,
occupation and income serve as a proxy.
Physical and mental health
Because health factors vary systematically with SES
(Klein, Hack & Breslau, 1989; Needleman, Schell,
Bellinger, Leviton & Allred, 1990), and are likely to play a
role in creating and sustaining the SES gap in cognitive
performance and achievement (Bornstein & Bradley,
2003), we reanalyzed the data excluding the 17 children
who had any significant medical or psychiatric history.
The results were essentially unchanged. The SES index
accounted for 33.7% of the variance in the language
composite (Beta = .581; p < .0001); 15.5% of the variance
in the visuospatial composite (Beta = .394; p < .0001); 9.2%
of the variance in the memory composite (Beta = .303;
p < .0001); 6.3% of the variance in the working memory
composite (Beta = .251; p < .004); and 6.0% of the vari-
ance in the cognitive control composite (Beta = .245;
p < .004). Again, SES did not significantly account for
any variance in reward processing performance. It is
notable that the variance accounted for by SES in the
language, working memory, and cognitive control composites
actually increased after excluding children with a signi-
ficant medical or psychiatric history, suggesting that health
factors are unlikely to be mediating the associations
between SES and these systems.
Table 2 SES variance in individual tasks
System Task R2p
Language PPVT .439 .0001
CTOPP blends .091 .0001
Visuospatial Arrows (line orientation) .182 .0001
Hand rotation-accuracy .084 .0001
Hand rotation-reaction time .000 .857
Declarative memory Memory-faces .074 .001
Memory-picture pairs .049 .007
Executive-working memory Acorns (spatial working memory) .081 .001
Delayed non-match to sample .012 .194
Executive-cognitive control Go/no-go false alarms .009 .274
Auditory attention and response set .157 .0001
Executive-reward Reversal learning (reversal errors) .009 .262
Present wrapping .003 .490
Note: SES accounts for variance in all tasks other than Go/no-go, Delayed non-match to sample, and the two reward system tasks.

SES and individual differences 473
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
We found similar results when applying the same
exclusionary criteria and regressions to the post-hoc
factors, as well. The SES index accounted for 22% of the
variance in the linguistic factor (Beta = .464; p < .0001),
10% of the visuospatial factor (Beta = .316; p < .0001)
and 6.8% of the variance in the declarative memory
factor (Beta = .260; p < .004). Once again, SES did not
account for unique variance in the working memory
factor. Together, these results suggest that physical and
mental health factors do not play a large role in mediat-
ing the associations found between SES and certain cog-
nitive skills in our sample.
Language
Although all children were native English speakers, a
large number had at least some exposure to a second
language in the home. We therefore examined what
effect, if any, this may have on the development of cog-
nitive abilities. Second language exposure itself did not
account for variance in any system. Furthermore, after
controlling for second language exposure, the associations
with SES were quite similar to those reported above.
Specifically, when the SES index is added to the model,
33.3% of the variance in the language composite is explained
(Beta = .589; p < .0001), as well as 16.9% of the variance
in the visuospatial composite (Beta = .418; p < .0001);
10.2% of the variance in the memory composite (Beta =
.319; p < .0001); 6.7% of the variance in the working
memory composite (Beta = .235; p < .005); and 5.9% of
the variance in the cognitive control composite (Beta =
.249; p < .003). Again, SES did not significantly account
for any of the variance in reward processing. Thus, the
presence or absence of second language exposure does
not appear to be mediating the associations between SES
and cognitive abilities.
When controlling for second language exposure in the
post-hoc principal components, results were again quite
similar to those reported earlier. After accounting for
second language exposure, the SES index explained 21%
of the variance in the linguistic factor (Beta = .467;
p < .0001); 12% of the variance in the visuospatial factor
(Beta = .349; p < .0001); and 7% of the variance in the
declarative memory factor (Beta = .293; p < .001). Again,
SES did not account for unique variance in the working
memory factor. Together, these results suggest that the
presence or absence of second language exposure does
not appear to be mediating the associations between
SES and the neurocognitive abilities examined here.
Previously, we reported that language abilities statistic-
ally mediated the association between SES and executive
function (Noble et al., 2005). In the present study, the
language composite accounts for 10.6% of the variance
Table 3 Correlations between tasks
System Tasks PPVT CTOPP Arrows Hand
rotation Mem-
faces Mem-
pictures Acorns DNMS GNG AARS Present
wrap Reversal
learning
Language PPVT – .423** .485** .185* .326** .306** .347** .014 .158 .538** .028 −.181*
CTOPP – .284** .195* .106 .054 .174* −.058 .122 .428** .046 −.176*
Visuo-spatial Arrows – .243** .326** .141 .374** .043 .068 .396** −.039 −.171*
Hand rotation – .070 .097 .401** .154 −.035 .327** .199* −1.44
Declarative memory Memory-faces – .192*.165* .140 .084 .200* .238** .002
Memory-picture pairs – .139 −.009 .105 .117 −.093 −.044
Working memory Acorns –.274** .015 .394** .078 −.115
Delayed non-match to sample – .098 .022 .028 −.147
Cognitive control Go/no-go – .031 −.139 −.028
Aud. attn. and response – .172* −.147
Reward Present wrapping –−.009
** p < .01.
* p < .05.

474 Kimberly G. Noble et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
in the cognitive control composite (p < .0001). Similar to
the results of the previous study, the SES index does not
account for any unique variance in cognitive control
after controlling for language ability (R2 change = .004;
p < .436), although language ability retains significance
(Beta = .284; p < .003). To explore this phenomenon
further, the effects of the semantic and phonological sub-
components of the language composite were examined
separately. Table 2 shows that the SES index is much
more strongly correlated with the PPVT (semantic
processing) than with the CTOPP (phonological process-
ing). Interestingly, however, both tasks are individually
nearly identically correlated with the cognitive control
composite (PPVT R2 = .079; p < .001, and CTOPP R2 =
.074; p < .001). This represents non-overlapping variance
when both tasks are entered into the model together.
(PPVT Beta = .201; p < .021 and CTOPP Beta = .189;
p < .030). The question is then raised as to whether
one or both tasks are primarily accounting for the
SES effect on cognitive control. When controlling for
CTOPP performance, the SES index continues to account
for some residual variance in cognitive control (R2 change
= .026; p < .043). On the other hand, when controlling
for PPVT performance, SES no longer accounts for
variance in the cognitive control composite (R2 change =
.007; p < .281). Further, when the two language tasks
and the SES index are entered in the model together,
only the CTOPP displays unique variance (Beta = .187;
p < .032). Thus, though both semantic and phonological
processing are correlated with cognitive control perform-
ance, it is semantic processing that mediates the majority
of the association between SES and cognitive control.
Although the language composite score also accounts
for some variance in performance in visuospatial skills
(R2 = .225; p < .0001), memory (R2 = .095; p < .0001), and
working memory (R2 = .039; p < .017), the SES index
accounts for unique variance in these systems after con-
trolling for language ability (for visuospatial, memory,
and working memory systems respectively, R2 change =
.044; p < .003; R2 change = .031; p < .024; and R2 change
= .028; p < .042). Language skills significantly account
for a portion of the variance in reward processing (R2 =
.033; p < .016); however, SES continues not to account
for variance in this skill.
Of course, principal components are by definition
orthogonal to one another, and so the linguistic factor
is, in effect, already covaried from the other factors.
However, when entering the language composite score
into the regressions, we find that the SES index still
accounts for variance in the visuospatial (R2 change =
.06; p < .003) and memory (R2 change = .04; p < .022)
factors, but not the working memory factor.
Home and school environment
A great deal of work has examined how home and
school effects mediate socioeconomic differences in aca-
demic achievement. In order to begin investigating the
relationship between these mediating factors and specific
neurocognitive systems, participants’ parents were asked
a number of questions about the home environment,
including the frequency of literacy-related activities
(parental reading of newspapers and books, reading with
the child, and practicing writing letters or words with
the child), and frequency of physical punishment.
Responses were subjected to principal components analysis
with Varimax rotation. Two factors with Eigenvalues
greater than 1 were extracted, together accounting
for 57.3% of the variance of the initial variables. The first
loaded predominantly on the factors related to the home
literacy environment, and the second loaded heavily on
reported frequency of physical punishment.
In addition, a number of variables pertaining to the
quality of children’s early education were examined. Based
on information made publicly available by the New York
City Department of Education, the average attendance,
average dollar allotment per student, and percent of
students meeting New York State and City English Lan-
guage Arts standards was collected for each school from
which participants were recruited. We also calculated the
number of hours per week each child spent in preschool
or daycare prior to kindergarten, as reported by parents.
Table 4 Principal components analysis of tasks
Task Factor 1
(Visuospatial) Factor 2
(Language) Factor 3
(Memory) Factor 4
(Working memory)
Language tasks PPVT .250 .700 .427 .039
CTOPP-blends .125 .890 −.086 −.026
Visuospatial tasks Hand rotation .739 .199 −.186 .022
Arrows (line orientation) .668 .270 .185 −.050
Declarative memory tasks Memory-faces .621 −.108 .332 .009
Memory-picture pairs .046 .077 .889 −.001
Executive-working memory tasks Acorns (spatial w.m.) .693 .151 .003 .318
Delayed non-match to sample .092 −.020 .004 .973

SES and individual differences 475
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
We then entered each of these early education variables
into a principal components analysis with Varimax
rotation, which revealed two factors with Eigenvalues
greater than one, together accounting for 84.3% of the
total variance. One factor loaded heavily on all school
environment factors, while the other loaded heavily on
time spent in daycare or preschool.
Because we did not have a priori hypotheses about
which home or school environment variables would
account for variance in different cognitive systems, we
entered all four factor loadings into a single regression
step, followed by the SES index, for each system com-
posite. The final step of each regression, presented in
Table 5, indicates that in many cases, these variables
significantly reduced or eliminated the unique variance
accounted for in cognitive performance by the SES index.
In the first step of the language composite regression,
home and school environment variables together account
for 24% of the variance in performance (p < .0001). In
this step, elementary school environment accounts for a
unique portion of the variance in language skills (Beta =
.432; p < .0001). As seen in Table 5, after adding the SES
index in the second step, 34.7% total variance is
accounted for, such that the SES index accounts for an
additional 10.7% unique variance (p < .0001). That is,
home and school variables together account for over
20% of the variance in language performance previously
accounted for by the SES index. Both elementary school
environment and the SES index account for unique
variance, suggesting that school environment partially
mediates the association between SES and language
abilities, and that school environment accounts for some
residual portion of language performance even after
controlling for SES.
Similar results are found when examining the post-hoc
linguistic factor. In this case, home and school variables
together account for 16.4% of the variance in the factor
loading score (p < .0001), while the SES index accounts
for an additional 5.7% of the variance (p < .003). Thus,
home and school environment appear to largely mediate
the association between SES and performance on tasks
relying on linguistic skill.
A similar pattern emerges when visuospatial skills are
examined. In the first step of the regression, home and
school environment variables together account for 16.9%
of the variance in the visuospatial composite (p < .0001),
with time spent in daycare or preschool accounting for
unique variance (Beta = .133; p < .0001). When the SES
index is added in the next step, 23% total variance is
accounted for, such that the SES index accounts for
6.1% unique variance (p < .001) – a marked reduction
from the 17% of variance originally accounted for by the
SES index alone. This reduction is largely statistically
mediated by time spent in daycare or preschool, and
Table 5 shows that this variable does not account for
unique variance once the SES index is controlled in the
second step. Similarly, the principal components analysis
reveals that home and school variables account for
10.7% of the variance in the visuospatial factor (p < .006),
with the SES index contributing an additional 3.8% (p <
.019).
In the memory composite, home and school variables
account for 7% of the variance in performance (p < .04),
with elementary school environment accounting for unique
variance (Beta = .223; p < .008). In the second step, the
SES index accounts for an additional 4.3% of variance
in memory skill (p < .011). Elementary school environ-
ment is thus partially mediating the association with
SES, although when all variables are entered in the
second step, only the SES index explains unique residual
variance. Home and school variables do not significantly
account for any variance in the memory factor, and when
these variables are controlled the SES index continues to
account for 6.4% unique variance (p < .004).
Table 5 Final regression steps: home and school environment
and SES
System Model R2 Beta p
Language composite Home – lit, .347 −.083 .243
Home – punish, −.019 .785
School – elem., .219 .009
School – day/pre, .034 .634
SES .410 .0001
Visuospatial Home – lit, .230 −.037 .631
composite Home – punish, .034 .653
School – elem., .203 .026
School – day/pre, .065 .408
SES .310 .001
Declarative Home – lit, .113 .081 .331
memory Home – punish, −.050 .535
composite School – elem., .087 .371
School – day/pre, .085 .311
SES .261 .011
Working Home – lit, .128 −.162 .055
memory Home – punish, −.112 .179
composite School – elem., .133 .176
School – day/pre, .163 .055
SES .107 .291
Cognitive Home – lit, .076 −.083 .326
control Home – punish, .105 .204
composite School – elem., −.030 .760
School – day/pre, −.037 .670
SES .238 .022
Reward Home – lit, .017 .122 .163
processing Home – punish, −.014 .871
School – elem., .021 .834
School – day/pre, .043 .629
SES .038 .719
Note: Environmental factors mediate some variance accounted for by
SES in language, visuospatial skills, memory and working memory. See
text for definitions of variables.

476 Kimberly G. Noble et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
Finally, some interesting dissociations can be observed
among the three executive measures. First, home and
school variables together account for 12.1% of the vari-
ance in the working memory composite (p < .002). Home
literacy environment (Beta = −.179; p < .03), school environ-
ment (Beta = .190; p < .022), and time in daycare/
preschool (Beta = .186; p < .024) each account for
unique variance. Table 5 shows that, in the second step,
the SES index no longer statistically accounts for variance
in this composite. Further, no variables account for unique
variance in the second step, suggesting that home and
school environment variables entirely mediate the asso-
ciation between SES and working memory abilities. As
stated above, SES does not account for variance in the
post-hoc working memory factor.
In contrast to working memory, none of the home or
school environment variables significantly accounts for
variance in the cognitive control composite. In the second
step, the SES index continues to account for approxi-
mately the same variance as originally reported (R2
change = 3.6%; p < .022). Finally, neither the home and
school environment variables nor the more global SES
index statistically account for unique variance in the
reward composite.
Discussion
Socioeconomic background has long been associated
with large differences in cognitive achievement. We asked
which specific neurocognitive systems underlie this asso-
ciation, and found that language, spatial cognition, memory,
and some but not all executive abilities vary continu-
ously with SES in our sample of first-grade children.
SES and neurocognitive performance
SES accounted for over 30% of the variance in perform-
ance in language tasks, with a statistically smaller por-
tion of the variance accounted for in other systems. One
possible explanation of the strong association between
SES and language is that the perisylvian brain regions
involved in language processing have been shown to
undergo a more protracted course of maturation in vivo
than any other neural region (Sowell et al., 2003). It is
thus possible that a longer period of development leaves
the language system more susceptible to the myriad
environmental influences that covary with SES (though
it should be noted that some postmortem studies have
implicated other regions of protracted development,
including prefrontal regions; e.g. Huttenlocher, 1997).
Alternatively, different systems may be differentially
reliant on the types of enculturation processes that differ
across SES; variation in language exposure relating to
cognitive development may be particularly tied to differ-
ences in SES (Whitehurst, 1997; Hart & Risley, 1995).
Notably, SES was more strongly associated with the
measure of receptive vocabulary than with the measure
of phonological processing. It is possible that this is
related to differences in the amount of cultural know-
ledge necessary to perform the two tests. Future work
could further explore this by including additional meas-
ures of the semantic and phonological subsystems for a
finer-grained analysis. In addition, other areas of language
development, such as syntax, might be explored as well.
A post-hoc principal components analysis of a subset
of the composites revealed four orthogonal factors among
our tasks, comprising linguistic skills, visuospatial skills,
declarative memory, and working memory. SES once
again accounted for unique variance in the linguistic
factor, and to a lesser extent, in the visuospatial and
declarative memory factors, providing additional evidence
that linguistic processes are particularly susceptible to
SES differences.
Mediating factors
Characterizing the associations between SES and different
neurocognitive systems is not an end in itself, but rather
an intermediate step toward longer range goals includ-
ing a more mechanistic understanding of the relation
between SES and neurocognitive development. A pre-
liminary extension of the present study in this direction
involved the examination of various SES-related factors
as possible mediators for SES-cognition associations.
Our analyses suggested that none of the associations
between SES and cognitive performance were mediated
by the physical health factors we tested or by exposure
to a second language. In contrast, several other linguistic
and environmental factors did statistically mediate such
effects. Previously we reported that language ability
statistically mediated the association between SES and
executive function (Noble et al., 2005). Here, controlling
for language ability eliminated the association between
SES and cognitive control, and reduced the association
between SES and all other systems. Thus, language abil-
ity may mediate the association between SES and cogni-
tive control, and may partially mediate the association
between SES and visuospatial skills, memory, and work-
ing memory. On further probing, it was found that the
SES association with cognitive control was largely accounted
for by semantic as opposed to phonological abilities. Future
neuroimaging studies could explore this further to
understand the mechanism underlying this association.
Several home and school environment variables also
statistically mediated associations between SES and

SES and individual differences 477
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
cognitive performance. In particular, school environment
accounted for variance in language, memory, and work-
ing memory performance. In addition, attendance in
daycare or preschool accounted for variance in visuospatial
skills. Finally, home literacy environment accounted for
additional variance in working memory performance. It
should be noted that a portion of the school environ-
ment loading score consisted of a report on the percent-
age of children who passed city- or state-wide Language
Arts tests. Thus, strictly speaking, we do not have evi-
dence that school quality per se influences individual
differences in cognitive development. We could argue,
however, that a school’s success and ability to provide
resources is associated with individual differences in
language, memory and working memory.
After controlling for all home and school environment
factors, the association between SES and working memory
was eliminated, whereas the respective associations between
SES and language and visuospatial skills were markedly
reduced. In contrast, these factors did not account for
variance in cognitive control or in reward processing.
Dissociation of executive functions
Previously, we found that SES differences were associated
with differences in several measures of executive func-
tion. Consistent with our earlier work, we found that
SES accounts for significant and statistically indistin-
guishable amounts of variance in the working memory
and cognitive control composites, whereas no associa-
tion was found between SES and reward processing.
However, although SES is associated with both cognitive
control and working memory, the factors that statistically
mediate these associations are quite different. Working
memory skill was statistically mediated by a variety of
home and school variables, including home literacy environ-
ment, daycare/preschool attendance, and elementary
school quality. This suggests the possibility that target-
ing these factors may lead to an improvement in the
ability to store and manipulate ‘online’ information, though
again, a prospective study is necessary to make formal
claims about causation. In contrast to working memory,
language abilities (particularly receptive vocabulary)
accounted for the association between SES and cognitive
control, in line with our previous report (Noble et al., 2005).
This suggests the possibility of a causal pathway in
which differences in SES influence language development
(Whitehurst, 1997; Hart & Risley, 1995), which then
independently drives cognitive control. This too could
be tested by a prospective, experimentally designed inter-
vention study. Interestingly, SES did not account for any
variance in reward processing. This finding is in line with
previous work that did not find SES differences in the
ability to delay gratification (Noble et al., 2005), delay
response, or reverse stimulus–reward associations (Farah
et al., 2006). These dissociations, observed across stud-
ies, indicate the complexity of the interplay between SES
and neurocognitive development and the feasibility,
nevertheless, of generalizing about the process.
Potential implications for intervention, caveats
and conclusions
A number of randomized controlled trials have shown
that educational intervention has the potential to narrow
the performance gap across SES. For instance, the IQ of
low SES children who have participated in intensive
early education is between one-half and one full standard
deviation higher than low SES control groups (Ramey &
Ramey, 1998). Although critics often conclude that the
benefits of early intervention wane shortly after termina-
tion of the program (e.g. Haskins, 1989), other studies
have shown sustained (Brooks-Gunn, McCarton, Casey,
McCormick, Bauer, Bernbaum, Tyson, Swanson, Bennett,
Scott, Tonascia & Meinert, 1994) and cost-effective
(Barnett, 1998) effects.
An as-yet untested approach to maximizing the effi-
cacy of interventions is to focus programs on those
neurocognitive abilities that vary most steeply with SES.
In addition, neurocognitive analysis may reveal different
SES-related factors playing different mediating roles across
neurocognitive systems. By examining which underlying
factors are associated with which cognitive abilities, we
can design and test interventions with increased efficacy.
This study made a preliminary attempt at disentan-
gling the experiential factors that may mediate SES dif-
ferences in neurocognitive performance. Several factors
limit its conclusiveness, however. First, we relied heavily
on retrospective parental report. Parents may not accur-
ately remember, or may feel pressured to answer a certain
way, biasing results. Second, more detailed information
about childhood experience is needed. For instance, not
only the amount but also the type of shared literacy
activities have been shown to be important in skill develop-
ment (Evans, 2000; Raz & Bryant, 1990).
Third, our data were correlational. Experimental designs
are ultimately necessary to systematically test predictions
about the effects of various factors that may mediate
neurocognitive development. Finally, our data were
behavioral, and inferences regarding brain function were
indirect. Without imaging the subjects as they are per-
forming the tasks, it is impossible to infer from behavior
alone whether tasks within a composite are truly involving
the same system. Such ambiguities are partially resolved
by choosing task types for which both the lesion and
neuroimaging literatures have demonstrated a good

478 Kimberly G. Noble et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
degree of neural specificity. Additionally, post-hoc analyses
showed reasonable correlations between tasks within
four of the composites, and orthogonal principal com-
ponents behaved much like their counterparts in the
simple composite structure. Importantly, however, behav-
ioral correlation alone is not necessarily the best predic-
tor of neural specificity, as it is possible for two tasks to
use different systems but to be highly correlated (i.e. if
system A drives system B); alternatively, it is possible for
two tasks to engage the same system in different ways,
such that behavioral correlation of performance between
tasks is actually quite low. Ultimately, hypotheses regarding
neural function must be investigated by incorporating
assessments of SES into functional neuroimaging studies
using pediatric populations.
Finally, an assumption was made that the neural sys-
tems engaged during certain cognitive tasks are consistent
across SES. In fact, the cognitive neuroscience literature
is largely based on studies of subjects of average to high
socioeconomic background. A rigorous examination of
the degree to which SES plays into the relationships
between cognitive processing and neural function is
therefore necessary. It is possible or even likely that dif-
ferences in cognitive performance and/or associated brain
activity are influenced by cultural and educational factors
like familiarity, knowledge, practice, and test-taking skills
that vary with SES.
In sum, SES accounts for individual differences in
performance on a variety of tasks that were designed to
tap particular neurocognitive systems, with a particu-
larly strong association with language abilities. These
associations are statistically mediated by different cogni-
tive, home and school factors. By more precisely under-
standing the associations between SES and cognitive
achievement, we may ultimately develop more specific
interventions, with educational strategies targeted at
cognitive outcomes and social strategies targeted at
underlying mediating factors.
Acknowledgements
Support for this work was provided by NIH grants R01-
HD043078 (M.J.F.), R21-DA015856 (M.J.F.), R01-DA20011
(M .J.F.), R01-DA014129 (M.J.F.), P50-HD25802-13 (B.D.M.),
T32-MH17168 (K.G.N.), NSF grant REC-0337715 (B.D.M.),
and the John Merck Scholars Program in the Biology of
Developmental Disabilities in Children (B.D.M.). We
gratefully acknowledge the helpful suggestions of Frank
Furstenberg and Andy Leon. This work was conducted
in partial fulfillment of the requirements for a doctoral
dissertation in the Neuroscience graduate program at the
University of Pennsylvania.
References
Barnett, W.S. (1998). Long-term cognitive and academic effects
of early childhood education on children in poverty. Preventive
Medicine, 27, 204–207.
Baydar, N., Brooks-Gunn, J., & Furstenberg, F. (1993). Early
warning signs of functional illiteracy: predictors in child-
hood and adolescence. Child Development, 64, 815–829.
Blumstein, S.E. (1994). Impairments of speech production and
speech perception in aphasia. Philosophical Transactions of
the Royal Society of London – Series B: Biological Sciences,
346 (1315), 29–36.
Booth, J., MacWhinney, B., Thulborn, K., Sacco, K., Voyvodic, J.,
& Feldman, H. (1999). Functional organization of activation
patterns in children: fMRI during three different cognitive
tasks. Progress in Neuro-Psychopharmacology and Biological
Psychiatry, 23, 669–682.
Bornstein, M.H., & Bradley, R.H. (2003). Socioeconomic status,
parenting, and child development. Mahwah, NJ: Lawrence
Erlbaum Associates.
Bornstein, M.H., Hahn, C.-S., Suwalsky, J.T.D., & Haynes, O.M.
(2003). Socioeconomic status, parenting and child develop-
ment: the Hollingshead Four-Factor Index of Social Status
and the Socioeconomic Index of Occupations. In M.H.
Bornstein & R.H. Bradley (Eds.), Socioeconomic status,
parenting and child development (pp. 29–82). Mahwah, NJ:
Lawrence Erlbaum Associates.
Bradley, R.H., & Corwyn, R.F. (2002). Socioeconomic status
and child development. Annual Review of Psychology, 53,
371–399.
Brooks-Gunn, J., Guo, G., & Furstenberg, F. (1993). Who drops
out of and who continues beyond high school? Journal of
Research on Adolescence, 3, 271–294.
Brooks-Gunn, J., McCarton, C., Casey, P., McCormick, C.,
Bauer, J., Bernbaum, J., Tyson, M., Swanson, M., Bennett, F.,
Scott, D., Tonascia, J., & Meinert, C. (1994). Early inter-
vention in low birthweight, premature infants. Journal of the
American Medical Association, 272, 1257–1262.
Carlson, S.M., & Moses, L.J. (2001). Individual differences
in inhibitory control and children’s theory of mind. Child
Development, 72 (4), 1032–1053.
Casey, B.J., Giedd, J.N., & Thomas, K.M. (2000). Structural
and functional brain development and its relation to cogni-
tive development. Biological Psychology, 54 (1–3), 241–257.
Casey, B.J., Tottenham, N., & Fossella, J. (2002). Clinical,
imaging, lesion and genetic approaches toward a model of
cognitive control. Developmental Psychobiology, 40, 237–254.
Casey, B.J., Trainor, R., Orendi, J.L., Schubert, A., Nystrom, L.,
Cohen, J.D., Noll, D.C., Giedd, J., Castellanos, X., Haxby, J.,
Forman, S.D., Dahl, R.E., & Rapoport, J.L. (1997). A develop-
mental fMRI study of prefrontal activation during perform-
ance of a go-no-go task. Journal of Cognitive Neuroscience, 9,
835–847.
de Zubicaray, G.I., McMahon, K., Wilson, S.J., & Muthiah, S.
(2001). Brain activity during the encoding, retention, and
retrieval of stimulus representations. Learning and Memory,
8 (5), 243–251.

SES and individual differences 479
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
Drewe, E.A. (1975). Go-no-go learning after frontal lobe
lesions in humans. Cortex, 11, 8–16.
Duncan, G.J., & Magnuson, K.A. (2003). Off with Hollings-
head: socioeconomic resources, parenting and child develop-
ment. In M.H. Bornstein (Ed.), Socioeconomic status,
parenting and child development (pp. 83–106). Mahwah, NJ:
Erlbaum Associates.
Elliott, R., Dolan, R.J., & Frith, C.D. (2000). Dissociable func-
tions in the medial and lateral orbitofrontal cortex: evidence
from human neuroimaging studies. Cerebral Cortex, 10,
308–317.
Ensminger, M.E., & Fothergill, K.E. (2003). A decade of
measuring SES: what it tells us and where to go from here.
In M.H. Bornstein & R.H. Bradley (Eds.), Socioeconomic
status, parenting and child development (pp. 13–27). Mahwah,
NJ: Lawrence Erlbuaum Associates.
Evans, M.A. (2000). Home literacy activities and their influ-
ence on early literacy skills. Canadian Journal of Experimental
Psychology, 54 (2), 65–75.
Farah, M.J., Shera, D.M., Savage, J.H., Betancourt, L.,
Giannetta, J.M., Brodsky, N.L., Malmud, E.K., & Hurt, H.
(2006). Childhood poverty: specific associations with neuro-
cognitive development. Brain Research, 1110, 166–174.
Fellows, L.K., & Farah, M.J. (2003). Ventromedial frontal
cortex mediates affective shifting in humans: evidence from
a reversal learning paradigm. Brain, 126 (Pt 8), 1830–1837.
Giedd, J.N., Blumenthal, J., Jeffries, N.O., Castellanos, F.X.,
Liu, H., Zijdenbos, A., Paus, T., Evans, T., & Rapoport, J.
(1999). Brain development during childhood and adolescence:
a longitudinal MRI study. Nature Neuroscience, 2 (10), 861–
863.
Goodglass, H., & Kaplan, E. (1982). Assessment of aphasia
and related disorders. Philadelphia, PA: Lea & Febiger.
Gottfried, A.W., Gottfried, A.E., Bathurst, K., Guerin, D.W.,
& Parramore, M.M. (2003). Socioeconomic status in chil-
dren’s development and family environment: infancy through
adolescence. In M.H. Bornstein & R.H. Bradley (Eds.),
Socioeconomic status, parenting and child development
(pp. 189–207). Mahwah, NJ: Lawrence Erlbaum Associates.
Hart, B., & Risley, T. (1995). Meaningful differences in the
everyday experience of young American children. Baltimore,
MD: Brookes.
Haskins, R. (1989). Beyond metaphor: the efficacy of early
childhood education. American Psychologist, 44, 274–282.
Haxby, J.V., Hoffman, E.A., & Gobbini, M.I. (2002). Human
neural systems for face recognition and social communication.
Biological Psychiatry, 51 (1), 59–67.
Huttenlocher, P.R. (1997). Regional differences in synapto-
genesis in human cerebral cortex. Journal of Comparative Neuro-
logy, 387, 167–178.
Kirk, A., & Kertesz, A. (1989). Hemispheric contributions to
drawing. Neuropsychologia, 27 (6), 881–886.
Klein, N., Hack, M., & Breslau, N. (1989). Children who were
very low birthweight: development and academic achieve-
ment at nine years of age. Journal of Developmental and
Behavioral Pediatrics, 10, 32–37.
Klingberg, T., Forssberg, H., & Westerberg, H. (2002).
Increased brain activity in frontal and parietal cortex under-
lies the development of visuospatial working memory capacity
during childhood. Journal of Cognitive Neuroscience, 14 (1),
1–10.
Korkman, M., Kirk, U., & Kemp, S.L. (1998). NEPSY – a
developmental neuropsychological assessment. San Antonio,
TX: The Psychological Corporation.
Levine, S.C., Vasilyeva, M., Lourenco, S.F., Newcombe, N.S.,
& Huttenlocher, J. (2005). Socioeconomic status modifies
the sex difference in spatial skill. Psychological Science, 16
(11), 841–845.
Liaw, F.-R., & Brooks-Gunn, J. (1994). Cumulative familial
risks and low-birthweight children’s cognitive and behavioral
development. Journal of Clinical Child Psychology, 23 (4),
360–372.
Macaluso, E., & Driver, J. (2003). Multimodal spatial repre-
sentations in the human parietal cortex: evidence from func-
tional imaging. Advances in Neurology, 93, 219–233.
McCarthy, R.A., & Warrington, E.K. (1990). Cognitive
neuropsychology: A Clinical introduction. New York: Academic
Press.
McLoyd, V.C. (1998). Socioeconomic disadvantage and child
development. American Psychologist, 53 (2), 185–204.
Marks, D.J., Cyrulnik, S.E., Berwid, O.G., Santra, A., Curko, E.A.,
& Halperin, J.M. (2001). Relationship between ADHD rat-
ings and working memory in preschool children. Journal of
the International Neuropsychological Society, 8 (2), 302–303.
Mayes, A.R., Meudell, P., & Neary, D. (1978). Must amnesia
be caused by either encoding or retrieval disorders? In
E.M.M. Guenberg (Ed.), Practical aspects of memory. London:
Academic Press.
Mayes, A.R., Meudell, P.R., & Neary, D. (1980). Do amnesiacs
adopt inefficient encoding strategies with faces and random
shapes? Neuropsychologia, 18, 527–541.
Meng, X.-L., Rubin, D.B., & Rosenthal, R. (1992). Comparing
correlated correlation coefficients. Psychological Bulletin,
111 (1), 172–175.
Mesulam, M.M. (2002). The human frontal lobes: transcend-
ing the default mode through contingent encoding. In D.T.
Stuss & R.T. Knight (Eds.), Principles of frontal lobe function
(pp. 8–30). Oxford: Oxford University Press.
Needleman, H.L., Schell, A., Bellinger, D., Leviton, A., &
Allred, E. (1990). The long term effects of low doses of lead
in childhood: an eleven-year followup report. New England
Journal of Medicine, 322, 83–88.
Newman, J., Gorenstein, E., & Kelsey, J. (1983). Failure to
delay gratification following septal lesions in rats. Personality
and Individual Differences, 4, 147–156.
Noble, K.G., Norman, M.F., & Farah, M.J. (2005). Neuro-
cognitive correlates of socioeconomic status in kindergarten
children. Developmental Science, 8 (1), 74–87.
Parsons, L.M., Gabrieli, J.D.E., Phelps, E.A., & Gazzaniga, M.S.
(1998). Cerebrally lateralized mental representations of hand
shape and movement. Journal of Neuroscience, 18 (16),
6539–6548.
Paus, T., Zijdenbos, A., Worsley, K., Collins, D.L., Blumen-
thal, J., Giedd, J.N., Rapoport, J.L., & Evans, A.C. (1999).
Structural maturation of neural pathways in children and
adolescents: in vivo study. Science, 283, 1908–1911.

480 Kimberly G. Noble et al.
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.
Peterson, B.S., Kane, M.J., Alexander, G.M., Lacadie, C.,
Skudlarski, P., Leung, H.-C., May, J., & Gore, J.C. (2002).
An event-related functional MRI study comparing interfer-
ence effects in the Simon and Stroop tasks. Cognitive Brain
Research, 13, 427–440.
Porter, M.C., Burk, J.A., & Mair, R.G. (2000). A comparison
of the effects of hippocampal or prefrontal cortical lesions
on three versions of the delayed non-matching-to-sample
based on positional or spatial cues. Behavioural Brain Research,
109, 69–81.
Ramey, C., & Ramey, S. (1998). Prevention of intellectual
disabilities: early interventions to improve cognitive develop-
ment. Preventive Medicine, 27, 224 –232.
Ratcliff, G. (1979). Spatial thought, mental rotation and the
right cerebral hemisphere. Neuropsychologia, 17, 49–54.
Raz, I.S., & Bryant, P. (1990). Social background, phonological
awareness and children’s reading. British Journal of Develop-
mental Psychology, 8 (3), 209–225.
Rugg, M.D., Fletcher, P.C., Frith, C.D., Frackowiak, R.S., &
Dolan, R.J. (1997). Brain regions supporting intentional and
incidental memory: a PET study. Neuroreport, 8 (5), 1283–
1287.
Shaywitz, B.A., Shaywitz, S.E., Pugh, K., Mencl, W.E.,
Fulbright, R.K., Skudlarski, P., Constable, R.T., Marchione,
K.E., Fletcher, J.M., Lyon, G.R., & Gore, J.C. (2002). Dis-
ruption of posterior brain systems for reading in children with
developmental dyslexia. Biological Psychiatry, 52, 101–110.
Shimamura, A. (1994). Memory and frontal lobe function. In
M. Gazzaniga (Ed.), The cognitive neurosciences (pp. 803–815).
Cambridge, MA: MIT Press.
Smith, J., Brooks-Gunn, J., & Klebanov, P. (1997). Conse-
quences of living in poverty for young children’s cognitive
and verbal ability and early school achievement. In G. Duncan
& J. Brooks-Gunn (Eds.), Consequences of growing up
poor. New York: Russell Sage.
Snodgrass, J.G., & Vanderwart, M. (1980). A standardized
set of 260 pictures: norms for name agreement, image agreement,
familiarity, and visual complexity. Journal of Experimental
Psychology: Human Learning and Memory, 6 (2), 174–215.
Sowell, E.R., Peterson, B.S., Thompson, P.M., Welcome, S.E.,
Henkenius, A.L., & Toga, A.W. (2003). Mapping cortical
change across the human life span. Nature Neuroscience, 6
(3), 309–315.
Sowell, E.R., Thompson, P.M., Rex, D., Kornsand, D.,
Tessner, K.D., Jernigan, T.L., & Toga, A.W. (2002). Mapping
sulcal pattern asymmetry and local cortical surface gray
matter distribution in vivo: maturation in perisylvian cortices.
Cerebral Cortex, 12, 17–26.
Squire, L., Ojemann, J., Miezin, F., Petersen, S., Videen, T., &
Raichle, M. (1992). Activation of the hippocampus in normal
humans. Proceedings of the National Academy of Sciences,
USA, 89, 1837–1841.
Stuss, D.T., & Benson, D.F. (1984). Neuropsychological studies
of the frontal lobes. Psychological Bulletin, 95 (1), 3–28.
Swick, D., & Jovanovic, J. (2002). Anterior cingulate cortex
and the Stroop task: neuropsychological evidence for topo-
graphic specificity. Neuropsychologia, 40 (8), 1240 –1253.
Thomas, K., King, S., Franzen, P., Welsh, T., Berkowitz, A.,
Noll, D.C., Birmaher, V., & Casey, B.J. (1999). A develop-
mental functional MRI study of spatial working memory.
NeuroImage, 10, 327–338.
Thompson-Schill, S.L., D’Esposito, M., Aguirre, G.K., &
Farah, M.J. (1997). The role of left prefrontal cortex in
semantic retrieval: a re-evaluation. Proceedings of the National
Academy of Sciences, USA, 94, 14792–14797.
Wagner, R.K., Torgesen, J.K., & Rashotte, C.A. (1999).
Comprehensive test of phonological processing. Austin, TX:
Pro-Ed.
Walsh, K.W. (1987). Neuropsychology: A clinical approach.
New York: Churchill-Livingstone.
Whitehurst, G.J. (1997). Language processes in context:
language learning in children reared in poverty. In L.B.
Adamson & M.A. Romski (Eds.), Research on communica-
tion and language disorders: Contribution to theories of lan-
guage development (pp. 233–266). Baltimore, MD: Brookes.
Zago, L., & Tzourio-Mazoyer, N. (2002). Distinguishing visuo-
spatial working memory and complex mental calculation
areas within the parietal lobes. Neuroscience Letters, 331 (1),
45–49.
Received: 12 July 2005
Accepted: 6 June 2006