Fine Motor Skills and Early Comprehension of the World: Two New School Readiness Indicators

Abstract

Duncan et al. (2007) presented a new methodology for identifying kindergarten readiness factors and quantifying their importance by determining which of children's developing skills measured around kindergarten entrance would predict later reading and math achievement. This article extends Duncan et al.'s work to identify kindergarten readiness factors with 6 longitudinal data sets. Their results identified kindergarten math and reading readiness and attention as the primary long-term predictors but found no effects from social skills or internalizing and externalizing behavior. We incorporated motor skills measures from 3 of the data sets and found that fine motor skills are an additional strong predictor of later achievement. Using one of the data sets, we also predicted later science scores and incorporated an additional early test of general knowledge of the social and physical world as a predictor. We found that the test of general knowledge was by far the strongest predictor of science and reading and also contributed significantly to predicting later math, making the content of this test another important kindergarten readiness indicator. Together, attention, fine motor skills, and general knowledge are much stronger overall predictors of later math, reading, and science scores than early math and reading scores alone.

Full text

Fine Motor Skills and Early Comprehension of the World:
Two New School Readiness Indicators
David Grissmer
University of Virginia Kevin J. Grimm
University of California, Davis
Sophie M. Aiyer and William M. Murrah
University of Virginia Joel S. Steele
University of California, Davis
Duncan et al. (2007) presented a new methodology for identifying kindergarten readiness factors and
quantifying their importance by determining which of children’s developing skills measured around
kindergarten entrance would predict later reading and math achievement. This article extends Duncan et
al.’s work to identify kindergarten readiness factors with 6 longitudinal data sets. Their results identified
kindergarten math and reading readiness and attention as the primary long-term predictors but found no
effects from social skills or internalizing and externalizing behavior. We incorporated motor skills
measures from 3 of the data sets and found that fine motor skills are an additional strong predictor of later
achievement. Using one of the data sets, we also predicted later science scores and incorporated an
additional early test of general knowledge of the social and physical world as a predictor. We found that
the test of general knowledge was by far the strongest predictor of science and reading and also
contributed significantly to predicting later math, making the content of this test another important
kindergarten readiness indicator. Together, attention, fine motor skills, and general knowledge are much
stronger overall predictors of later math, reading, and science scores than early math and reading scores
alone.
Keywords: school readiness, fine motor skills, general knowledge, achievement
Supplemental materials: http://dx.doi.org/10.1037/a0020104.supp
Duncan et al. (2007) presented a new methodology for identi-
fying school readiness factors and quantifying their importance by
determining which of children’s developing skills measured
around kindergarten entrance would predict much later reading
and math achievement. This research utilized six international
longitudinal data sets that collected data between birth and kin-
dergarten entry and followed children at least through third grade.
School readiness factors tested in Duncan et al.’s analysis included
measures of early reading and math, attention, internalizing and
externalizing behavior, and social skills. Their regressions in-
cluded as controls a large set of diverse family and child character-
istics measured from birth to kindergarten entry. A meta-analysis of
their results suggested that measures of early math and reading and
attention were significant predictors of later math and reading
achievement and that internalizing and externalizing behavior and
social skills were not significant predictors. Earlier math measures
predicted both later math and reading scores, but early reading mea-
sures predicted only later reading scores. Early math scores predicted
later reading as strongly as early reading scores.
The results suggest that early math skills should receive more
attention in research and curriculum because they predict both later
math and reading scores. The results also suggest that not all
socioemotional skills may be equal in boosting later achievement.
Attention measures appear to be consistently linked to later scores,
but social skills and internalizing and externalizing behavior were
not. Duncan et al. (2007) provided several caveats for these find-
ings. First, the results measured only the association with later
achievement, and including a broader range of long-term devel-
opmental outcomes could show different results. Second, socio-
emotional behavior may affect other students’ achievement more
than one’s own achievement. Third, problem behaviors may
emerge more strongly after school entry, so later measures may
show different results. For instance, class sizes are smaller in
kindergarten compared with later grades, and problem behavior
may be more frequent when children enter larger classes where
teachers have less control.
David Grissmer, Sophie M. Aiyer, William M. Murrah, Center for the
Advanced Study of Teaching and Learning, University of Virginia; Kevin
J. Grimm and Joel S. Steele, Department of Psychology, University of
California, Davis.
This research was supported by National Science Foundation Research
and Evaluation on Education in Science and Engineering Program Grant
(DRL-0815787) and the National Center for Research on Early Childhood
Education, Institute of Education Sciences, U.S. Department of Education
(R305A06021). The opinions and views expressed in this article are those
of the authors and do not necessarily represent the views and opinions of
the U.S. Department of Education.
Correspondence concerning this article should be addressed to David
Grissmer, Center for the Advanced Study of Teaching and Learning,
University of Virginia, 350 Old Ivy Way, Suite 100, Charlottesville, VA
22903. E-mail: dwg7u@virginia.edu
Developmental Psychology © 2010 American Psychological Association
2010, Vol. 46, No. 5, 1008–1017 0012-1649/10/$12.00 DOI: 10.1037/a0020104
1008

The main objectives of this article are threefold: (a) provide new
empirical evidence that fine motor skills, a developmental skill
measured at school entry but not included in Duncan’s et al.’s
(2007) analysis, is strongly predictive of later scores, (b) present
several sensitivity analyses that extend Duncan et al.’s findings,
including assessing the predictive power of a child’s knowledge of
the world, and (c) review the developmental and neuroscience
literature to assess and suggest mechanisms for a link between
early motor skills and later achievement. For the first objective, we
used three longitudinal data sets that measured early fine motor
skills and were also used in Duncan et al. We added these mea-
sures to similarly specified and estimated equations to test whether
fine motor skills were predictive of later achievement and, if so, its
relative strength compared with attention. For the second objec-
tive, we utilized the Early Childhood Longitudinal Survey–
Kindergarten Cohort (ECLS-K) to (a) test whether the null effects
of social skills, internalizing behavior, and externalizing behavior
are sensitive to different specifications, (b) test whether the relative
strengths of kindergarten math and reading scores in predicting
later math and reading scores are sensitive to inclusion of the
general knowledge score, (c) explore the relative strength of the
motor and attention measures in predicting fifth-grade scores with-
out kindergarten math and reading measures, (d) add results for
later science scores and compare with math and reading results,
and (e) interpret the changing predictive effect of child’s age on
scores from kindergarten to fifth grade to suggest that the key
developmental skills linked to school readiness may be captured
by this analysis.
The link between attention and cognitive development is not
conceptually difficult because of research on executive function as
well as personal experience in maintaining attention when doing
cognitive tasks. However, the link between motor skills and cog-
nitive skills is less apparent. For the third objective, we review the
neuroscience and developmental literature that has linked motor
and cognitive development to provide additional empirical and
theoretical support for the linkage and make this linkage easier to
grasp.
Empirical Evidence From Three Longitudinal Data
Sets With Motor Measures
The ECLS-K, the British Birth Cohort Study (BCS), and the
National Longitudinal Survey of Youth (NLSY) included mea-
sures of motor skills that were not included in Duncan et al.
(2007). These data sets were described in Duncan et al. and the
associated supplemental materials. We describe only the motor
measures used in each of these data sets.
Motor Measures in the ECLS-K
Two measures of psychomotor assessment were obtained in the
ECLS-K: gross and fine motor skills. The ECLS-K uses motor
items adapted from the Early Screening Inventory–Revised
(Meisels, Marsden, Wiske, & Henderson, 1997). This inventory is
a well-standardized multidomain screening test widely used to
identify preschool and kindergarten children at risk for school
failure (Kimmel, 2001; Paget, 2001). For the assessment of fine
motor skills, participants used building blocks to replicate a model,
copied five figures on paper, and drew a person. For the assess-
ment of gross motor skills, participants skipped, hopped on one
foot, walked backward, and stood on one foot. Interitem reliability
(alpha coefficient) was .57 for the fine motor scale and .51 for the
gross motor scale. The low reliabilities are partially a function of
the binary scoring system for the items that comprised the scales
and the small number of items.
Motor Measures in the NLSY
The motor skills measure for the NLSY was developed at the
National Center for Health Statistics and comprised items based on
standard and reliable measures of child motor and social develop-
ment (including the Bayley, Gesell, and Denver methods). Age
appropriate sets of dichotomous items were determined on the
basis of previous analyses of 2,714 U.S. children (Peterson &
Moore, 1987). On the basis of the determined age ranges, the
instrument completed by the child’s mother contained eight com-
ponents designed to assess children from ages 22 to 47 months.
Standard scores were created that are comparable for children of
different ages. More detailed information regarding the reliability
and validity of these measures is available in the discussion of
motor skills development in the NLSY79 Child Handbook (Baker,
Keck, Mott, & Quinlan, 1993) and the NLSY Children, 1992 (Mott,
Baker, Ball, Keck, & Lenhart, 1995).
Motor Skills in the BCS
In the BCS, motor skills at age 5 were measured with three
drawing tasks: design copying, human figure drawing, and profile
drawing. The copying task required children to copy eight basic
designs. Children were allowed two attempts with no assessor
assistance. This test was used in previous studies to assess fine
motor and visual control (Davie, Butler, & Goldstein, 1972; Rut-
ter, Tizard, & Whitemore, 1970). In the human figure drawing
task, children were asked to create a full body drawing of a person.
Children received no instructor help during drawing, but clarifi-
cation of aspects of the drawing was allowed after completion. In
the profile drawing test, children completed the profile of a basic
head shape from a test booklet with no assessor help. These tests
have a reported reliability of .94 and good discrimination proper-
ties (Harris, 1963; Koppitz, 1968).
Estimation
In all three data sets, estimation was done with weighted ordi-
nary least squares. Missing data were handled in the ECLS-K with
multiple imputation (20 imputations) and in the NLSY through full
information maximum likelihood. Results reported here for the
BCS are taken from the supplemental materials in Duncan et al.
(2007) because the motor variables were included in the original
analysis. Missing data were handled in the BCS through inclusion
of missing value dummies. The full estimation results are provided
in the supplemental materials.
Results
Table 1 summarizes the results in format similar to that used by
Duncan et al. (2007). The patterns of results for early math and
reading, attention, social skills, internalizing problems, and exter-
nalizing problems were very similar to those found by Duncan et
1009
SPECIAL SECTION: TWO NEW SCHOOL READINESS INDICATORS

al. For the ECLS-K, early math scores appeared to be the best
predictors of both later math and reading scores, whereas reading
scores also added significant predictive accuracy for later reading
scores but not for later math scores. The NLSY, however, sug-
gested a stronger role for reading in the prediction of later reading
and mathematics achievement than the ECLS-K did. The attention
coefficients for the three data sets showed the same sign as in
Duncan et al., and all were statistically significant at an alpha level
of .01 or better. The coefficients of social skills, internalizing
problems, and externalizing problems also showed patterns similar
to those of Duncan et al., who found mostly nonsignificant results
or significance far less than that for attention or early reading and
math. The exception is for the measure of social skills in the
NLSY, which showed strong significance for both reading and
math. However, the measure of social skills used a rating by the
test administrator when participants were between ages 54 and 78
months. This measure was not included in Duncan et al., perhaps
because of its possible unreliability.
Measures of fine motor skills showed highly significant results
for both math and reading in all three data sets. However, the gross
motor measure in the ECLS-K was not a significant predictor. The
profile drawing test in the BCS was not significant for either
reading or math, but all other fine motor measures showed statis-
tical significance at the .01 alpha level or better. Comparisons of
statistical significance between fine motor skills and attention
measures showed that fine motor skills were almost always as
significant or more statistically significant than attention. The
comparison of the coefficients for effect sizes showed that atten-
tion was much stronger than motor skills for predicting reading in
the ECLS-K but was of similar strength in the NLSY and BCS. For
math, the coefficients for attention were somewhat greater or
approximately similar to those for fine motor skills for the
ECLS-K and NLSY, but the BCS showed stronger effects for fine
motor skills. This evidence suggests that both attention and fine
motor skills were important developmental predictors of later
achievement, controlling for family and child characteristics and
earlier math and reading.
Results From Sensitivity Analyses With the ECLS-K
We used the ECLS-K for analysis of five additional issues. The
first issue was whether the socioemotional variables that were
generally not significant would show stronger effects in the ab-
sence of the attention measure. The attention measure used in the
ECLS-K, a measure of approaches to learning, is a composite
measure that includes attention-related measures (attention, persis-
tence, and concentration) but also measures linked to eagerness to
learn, interest, curiosity, creativity, and responsibility. Such a
broad measure could mask the effects of other socioemotional
measures. Table 2 contrasts the results when the approaches to
learning measure was included and excluded in the analysis. The
results showed that social skills for math and reading became
significant, although still much less significant than attention and
Table 1
Summary of Results Predicting Later Achievement With Earlier Math and Reading Readiness and Socioemotional and Motor Skills
With Three Longitudinal Data Sets
Measure
Reading Math
ECLS-K NLSY BCS ECLS-K NLSY BCS
Earlier cognitive
Reading .08
ⴱⴱⴱⴱ
.10
ⴱⴱⴱⴱⴱ
.13
ⴱⴱⴱ
ns .11
ⴱⴱⴱⴱⴱ
.09
ⴱⴱⴱ
Math .20
ⴱⴱⴱⴱ
.10
ⴱⴱⴱⴱⴱ
.33
ⴱⴱⴱⴱ
.14
ⴱⴱⴱⴱⴱ
Socioemotional
Attention
a
.16
ⴱⴱⴱⴱ
⫺.05
ⴱⴱ
⫺.08
ⴱⴱ
.21
ⴱⴱⴱⴱ
⫺.05
ⴱⴱ
⫺.09
ⴱⴱ
Externalizing–I ns ⫺.04
ⴱ
ns ns ns ns
Externalizing–II ns ns
Internalizing .03
ⴱⴱ
ns ns ns ns ns
Social skills
b
ns .06
ⴱⴱⴱⴱⴱ
⫺.04 .04
ⴱⴱ
Motor
Gross motor ⫺.02
ⴱ
ns
Fine motor .07
ⴱⴱⴱⴱ
.14
ⴱⴱⴱⴱ
Motor/social .05
ⴱⴱⴱ
.05
ⴱⴱ
Copy 8 designs .26
ⴱⴱⴱ
.36
ⴱⴱⴱ
Human drawing .09
ⴱⴱ
.09
ⴱⴱ
Profile of head ns ns
Family/home controls X X X X X X
Early measures X X
Adjusted R
2
.54 .31 .45 .55 .30 .45
Observations 7,814 5,462 1,778 7,830 5,462 1,753
Note. Blank cells indicate that the measures were not available on a specific data set. X indicates that family/home controls or early measures were
included in the regression. All coefficients are standardized. ECLS-K ⫽Early Childhood Longitudinal Survey–Kindergarten Cohort; NLSY ⫽National
Longitudinal Survey of Youth; BCS ⫽British Birth Cohort Study.
a
The attention measures on the ECLS-K were teacher-rated attention where higher values indicate more attention. For the NLSY and BCS, the attention
measures were of attention problems, and higher values indicate poorer attention.
b
In the NLSY, the social skills measure of sociability, which was a
rating by the test administrator given at 42 months, was not included in Duncan et al. (2007) as a social skills measure.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
ⴱⴱⴱⴱ
p⬍.00001.
ⴱⴱⴱⴱⴱ
p⬍.000001.
1010 GRISSMER, GRIMM, AIYER, MURRAH, AND STEELE

fine motor skills. Internalizing and externalizing behavior re-
mained insignificant.
In addition to the early math and reading test, the ECLS-K had
a test measuring knowledge of the world or early science and
social science knowledge. The science items assessed factual
and conceptual understanding as well as the ability to formulate
and answer questions related to the natural world. Social studies
questions assessed children’s knowledge about their environment
across a broad range of categories, including history, economics,
and culture. Duncan et al.’s (2007) results and the results reported
in Table 1 included the test of general knowledge as a control
variable, but its coefficients were not included in the summary
table.
Table 3 shows the effects of including and excluding the test of
general knowledge. The results show that the general knowledge
test was a very strong predictor of later reading, far stronger than
the early reading test, and was also a strong predictor of later math
scores, although early math remained the strongest predictor. Gen-
eral knowledge was a much stronger predictor of both reading and
math than early reading. The pattern of results for the socioemo-
tional, attention, and motor skills was generally unaffected by the
inclusion of general knowledge.
One possible explanation for the strength of the general knowl-
edge test is that it captured comprehension and ability to integrate
knowledge of the external world—skills that may be needed at
fifth grade in both reading and math. Children make a key transi-
tion in reading skills between kindergarten and fifth grade from
learning to read to reading to learn. The general knowledge test
may better track this transition than the early reading test.
Another issue was the sensitivity of the attention, motor, and
socioemotional measures to the removal of the early math and
reading tests. The fifth-grade estimates that included the earlier
readiness tests can underestimate the potential effects of the motor,
attention, and socioemotional measures if these measures and early
readiness scores are correlated. In fact, attention and motor mea-
sures are also strong predictors of early math and reading, so the
effect of attention, motor, and possibly the other socioemotional
measures on fifth-grade achievement may be underestimated (e.g.,
indirect effects through early math and reading).
Table 4 shows the results including and excluding the early
measures of math, reading, and general knowledge. The results
that excluded early reading and math scores may better predict
possible effects of early interventions to improve attention and fine
motor skills. The results showed that the pattern of results for the
Table 2
Sensitivity Test of Socioemotional Variables to Absence of Approaches to Learning
Approaches to learning excluded Approaches to learning included
Math Reading Math Reading
Measure Coef tCoef tCoef tCoef t
Approaches to learning .21 14.71 .16 11.50
Social skills .06 3.86 .03 1.76 ⫺.01 ⫺0.49 ⫺.03 ⫺1.66
Externalizing behavior ⫺.02 ⫺1.24 ⫺.00 ⫺0.12 ⫺.00 ⫺0.23 .01 0.67
Internalizing behavior ⫺.00 ⫺0.45 .01 1.15 .02 1.96 .03 3.03
Self-control .00 0.23 .02 1.50 ⫺.04 ⫺2.55 ⫺.01 ⫺0.07
Gross motor .01 0.57 ⫺.01 ⫺1.57 .00 0.09 ⫺.02 ⫺1.96
Fine motor .16 15.37 .09 8.26 .14 13.42 .07 6.62
Note. Coef ⫽coefficient.
Table 3
Sensitivity Test to Inclusion of General Knowledge
General knowledge excluded General knowledge included
Math Reading Math Reading
Measure Coef tCoef tCoef tCoef t
Attention .21 15.15 .18 12.09 .21 14.71 .16 11.50
Social skills ⫺.00 ⫺0.07 ⫺.01 ⫺0.84 ⫺.01 ⫺0.49 ⫺.03 ⫺1.66
Externalizing ⫺.00 0.09 .01 0.91 ⫺.00 ⫺0.23 .01 0.67
Internalizing .02 1.69 .02 2.47 .02 1.96 .03 3.03
Self-control ⫺.05 ⫺2.74 ⫺.02 ⫺1.07 ⫺.04 ⫺2.55 ⫺.01 ⫺0.70
Gross motor .01 1.15 ⫺.00 ⫺0.05 .00 0.09 ⫺.02 ⫺1.96
Fine motor .15 14.25 .09 8.03 .14 13.42 .07 6.62
Math .37 25.27 .28 18.60 .33 21.66 .20 13.27
Reading .02 1.76 .11 7.72 .01 0.69 .08 6.04
General knowledge .16 12.30 .30 22.18
R
2
.55 .51 .56 .55
Note. Coef ⫽coefficient.
1011
SPECIAL SECTION: TWO NEW SCHOOL READINESS INDICATORS

other socioemotional measures remained similar, mostly insignif-
icant or of far lower significance than the attention and motor
measures. However, the coefficients of attention and fine motor
skills increased markedly with greater statistical significance. The
attention coefficients were larger in both reading and math compared
with the fine motor coefficients. The estimated effect sizes for atten-
tion and fine motor skills were around .3 and .2, respectively Perhaps
more important is that interventions that combine attention and fine
motor skills might predict effect sizes of around .5.
We also studied the sensitivity of early and later math and
reading scores to the child’s age. Children in the United States start
kindergarten with a range of ages that can span 18 months. The
large age range arises from the normal 1-year entry window for
kindergarten combined with differences between states in the
lowest kindergarten entry age. Table 5 contains regression coeffi-
cients controlled for family characteristics and for age of child at
testing in equations predicting reading and math achievement at
kindergarten entrance and in the spring of first, third, and fifth
grade. Table 5 also contrasts the age coefficients when the socio-
emotional, attention, and motor variables are included and ex-
cluded. With socioemotional, attention, and motor skills excluded,
the results suggest that differences in age when children were
tested were very significant predictors of kindergarten entry and
spring first-, third-, and fifth-grade scores, but their significance
declined markedly by grade. However, when socioemotional, at-
tention, and motor skills were included, the effect of age com-
pletely faded by third grade.
Sensitivity of scores to age can indicate that a developmental
variable dependent on age was missing from the analysis. That is,
age itself is not a causative mechanism, but rather the significance
of age indicates that developmental differences were present at
different ages that need to be specified in the equations. For
instance, Table 5 illustrates that the absence of attention, motor,
and socioemotional variables at any given grade causes declines in
the strength and statistical significance of the age variable. Thus,
the inclusion of developmental measures reduces the significance
of the age variable. When age becomes insignificant at third grade,
it might suggest that developmental variables measured at kinder-
garten have been accounted for.
Another issue was whether fifth-grade science scores showed
patterns similar to those for math and reading. Table 6 shows that
later science scores were much more strongly predicted by the
general knowledge test, with some contribution from math and
little from reading. The effect of fine motor skills on later science
Table 4
Sensitivity Test to Exclusion of Early Math, Reading, and General Knowledge Scores
Early math, reading,
and general knowledge
excluded
Early math, reading,
and general knowledge
included
Math Reading Math Reading
Measure Coef tCoef tCoef tCoef t
Attention .34 23.13 .30 19.85 .21 14.71 .16 11.50
Social skills ⫺.02 ⫺1.27 ⫺.03 ⫺1.81 ⫺.01 ⫺0.49 ⫺.03 ⫺1.66
Externalizing behavior .01 0.49 .02 1.35 ⫺.00 0.23 .01 0.67
Internalizing behavior ⫺.00 ⫺0.19 .01 0.78 .02 1.96 .03 3.03
Self-control ⫺.06 ⫺3.52 ⫺.04 ⫺2.06 ⫺.04 ⫺2.55 ⫺.01 ⫺0.07
Gross motor .03 2.88 .02 1.56 .00 0.09 ⫺.02 ⫺1.96
Fine motor .22 19.71 .15 13.27 .14 13.42 .07 6.62
Math score .33 21.66 .20 13.27
Reading score .01 0.69 .08 6.06
General knowledge score .16 12.30 .30 22.18
R
2
.46 .43 .56 .55
Note. Coef ⫽coefficient.
Table 5
Coefficients of Age for Different Grades and Different Specifications
Grade and
semester
Coefficients of age for reading Coefficients of age for math
Excluding SE and
motor
Including SE and
motor
Excluding SE and
motor
Including SE and
motor
Coef tCoef tCoef tCoef t
Kindergarten, fall .19 21.63 .12 13.43 .27 31.55 .17 20.70
First grade, spring .13 11.08 .04 3.70 .19 15.98 .08 7.23
Third grade, spring .10 8.83 .01 1.00 .11 9.39 ⫺.00 ⫺0.20
Fifth grade, spring .08 7.43 .00 ⫺0.09 .06 4.94 ⫺.05 ⫺4.98
Note. SE ⫽socioemotional; Coef ⫽coefficient.
1012 GRISSMER, GRIMM, AIYER, MURRAH, AND STEELE

was stronger than the effect for reading but weaker than the effect
for math. Attention was a somewhat weaker predictor of science
than either later reading or math. However, the general knowledge
test had effect sizes of .2, .3, and .4, respectively, for math,
reading, and science. General knowledge was a much stronger
combined predictor of all three tests than either early math or
reading. Similar to later reading and math, none of the other
socioemotional variables showed statistical significance or they
showed much weaker significance than attention and fine motor
skills.
Neuroscience and Developmental Evidence for a
Motor–Cognitive Link
One possibility that might partially account for a motor–
cognitive causal link is that most activities that build or display
cognitive skills also involve the use of fine motor skills. Writing
requires fine motor skills with the hands as well as hand–eye
coordination. Speaking requires fine motor skills that control the
production of sound. Reading requires the use of fine motor skills
controlling eye movement for word tracking. Poor fine motor skills
can make cognitive learning and performance more difficult be-
cause of the simultaneous need for fine motor skills in cognitive
activities. However, evidence from neuroscience and recent child
development research presents a much more complex relation
between early motor and later cognitive development. Evidence
suggests that even if cognitive development required no simulta-
neous usage of motor and cognitive skills, earlier motor skill
development could have a significant impact on later cognitive
development. In this section, we summarize some of this evidence.
This review is not comprehensive but summarizes some of the
evidence and current thinking about how early motor skills might
be linked to later cognitive development.
Diamond (2000) summarized the links between motor and cog-
nitive skills, using evidence from neuroimaging and neuroanat-
omy, from co-occurrence of effects from brain damage or brain
abnormalities, and from correlations between deficits in motor and
cognitive skills in developmental disorders. Diamond found sig-
nificant evidence for a motor–cognition association in each of
these areas. Historically, cognitive and motor activities were as-
signed to separate brain areas. Diamond’s neuroimaging evidence
suggests that some of the primary brain regions previously thought
to be involved only in motor activities (cerebellum and basal
ganglia) or cognitive activities (prefrontal cortex) are coactivated
when doing certain motor or cognitive tasks. Neuroanatomy also
provides evidence for two-way neural communication linkages
between these motor and cognitive areas. Diamond also proposed
that executive function, primarily located in the prefrontal areas,
may coordinate complex activities requiring several parts of the
brain regardless of whether the task is motor, emotional, or cog-
nitive.
Diamond (2000) also provided evidence for a prevalent co-
occurrence of both motor and cognitive deficits in many developmen-
tal disorders, in movement disorders, and in brain damaged persons.
Individuals with brain damage to either the primary motor or primary
cognitive areas often show impairment in both skill areas. Also,
ADHD and dyslexia, both traditionally classified as childhood cog-
nitive disorders, are often characterized by poor motor coordination.
Individuals with Parkinson’s disease, which is primarily thought of as
a movement disorder, often show areas of significant cognitive im-
pairment. Although these linkages involve primarily disorders outside
the normal variation of motor and cognitive skills in the general
population, the evidence provided from the longitudinal surveys sug-
gests a motor–cognitive linkage due to variations found within pop-
ulations without significant disorders.
Since Diamond’s (2000) study, multidisciplinary research has sub-
stantially strengthened the evidence for the motor–cognitive link and
has elucidated the nature of the linkage. This research suggests that
motor and cognitive development are inextricably linked. Literature
suggests that the linkage occurs primarily for two reasons: (a) many
types of cognitive activities utilize specialized control and modulation
functions located in the cerebellum and basal ganglia that develop
during motor acquisition, and (b) some of the neural infrastructure
linking the prefrontal and motor areas built to adaptively control the
learning process during motor development is also used to control
learning in cognitive development. The evidence to support this view
comes from neuroimaging, neuroanatomy, studies of motor/cognitive
disorders, psychological testing, and recent child development re-
search on motor skills.
Table 6
Comparison of Results for Math, Reading, and Science
Math Reading Science
Measure Coef tCoef tCoef t
Approaches to learning .21 14.71 .16 11.50 .11 7.73
Social skills ⫺.01 ⫺0.49 ⫺.03 ⫺1.67 .01 0.82
Externalizing behavior ⫺.00 ⫺0.23 .01 0.67 .01 0.46
Internalizing behavior .02 1.96 .03 3.03 .03 3.54
Self-control ⫺.04 ⫺2.55 ⫺.01 ⫺0.70 ⫺.02 ⫺1.12
Gross motor .00 0.09 ⫺.02 ⫺1.96 ⫺.02 ⫺2.30
Fine motor .14 13.42 .07 6.62 .08 8.25
Math score .33 21.66 .20 13.27 .14 9.56
Reading score .01 0.69 .08 6.04 .04 2.70
General knowledge .16 12.30 .30 22.18 .40 30.00
Adjusted R
2
.56 .55 .57
Note. Coef ⫽coefficient.
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Doya (1999) suggested that the prefrontal cortex, basal ganglia,
and cerebellum are all specialized to different types of learning,
with the prefrontal areas more specialized to unsupervised learn-
ing, the basal ganglia more specialized to reinforcement learning,
and the cerebellum more specialized to supervised learning. Seger
(2006) suggested that two important common functions utilizing
the basal ganglia and shared by some motor and cognitive tasks are
performing a sequential, coordinated series of events over time and
complex categorization. The sequential events could be perform-
ing a coordinated motor movement, organizing grammatical ele-
ments in language, or sequencing subgoals in complex reasoning.
Subsequently, cognitive tasks attributed primarily to motor areas
include tracking and estimation of time durations, sequential learn-
ing tasks, nondeclarative and categorical learning, learning based
on implicit or explicit rewards, and the acquisition of new skills
(Ashby & Spiering, 2004; Graybiel, 2005; Nicolson, Fawcett, &
Dean, 2001; Saint-Cyr, 2003; Shohamy, Myers, Grossman, et al.,
2004; Shohamy, Myers, Kalanithi, & Gluck, 2008; Shohamy,
Myers, Onloaor, & Gluck, 2004; Toplak, Dockstader, & Tannock,
2006). For instance, Ashby and Spiering (2004) suggested three
different types of category learning that utilize specialized coor-
dinated subsystems that draw from prefrontal and motor areas
(basal ganglia). These studies provide evidence of a highly devel-
oped specialization of brain areas linked by distinct and special-
ized bidirectional loop circuitry to carry out different types of
learning. Haber (2003), Middleton (2003), and Middleton and
Strick (2000, 2002) reported anatomical evidence that the neces-
sary communication pathways are present between the cerebellum,
basal ganglia, and prefrontal areas. Paquier and Marien (2005),
Middleton (2003), and Middleton and Strick (2000, 2002) sug-
gested that the cerebellum modulates cognitive functions through
the cortico–ponto–cerebellar system and the cerebello–thalamo–
cortical pathways.
Evidence of motor–cognitive linkages from developmental dis-
orders, brain abnormalities, and brain damage has also grown
much stronger. Theories about the causal mechanisms in both
ADHD and dyslexia now involve malfunctions in the cerebellum
and/or basal ganglia (Banaschewski et al., 2005; Casey, Nigg, &
Durston, 2007; Castellanos, Sonuga-Barke, Milham, & Tannock,
2006; Castellanos & Tannock, 2002; Durston & Casey, 2006;
Durston & Konrad, 2007; Krain & Castellanos, 2006; Nigg &
Casey, 2005; Schmahmann, 2003, 2004; Sergeant, Geurts, Hui-
jbregts, Scheres, & Oosterlaan, 2003; Smith, Taylor, Warner,
Newman, & Ribia, 2002; Sonuga-Barke, 2003; Sonuga-Barke,
Auerbach, Campbell, Daley, & Thompson, 2005; Sonuga-Barke,
Dalen, & Remington, 2003; Sonuga-Barke, Sergeant, Nigg, &
Willcutt, 2008; Toplak, Dockstader, & Tannock, 2006). For in-
stance, deficits in timing associated with the basal ganglia have
been linked to children with reading problems like dyslexia and
language deficits and ADHD (Nicolson et al., 2001; Smith et al.,
2002). Evidence also suggests that damage to the cerebellum can
produce cognitive deficits, specifically called cerebellar cognitive
affective syndrome, that include impairment in executive function,
spatial cognition, linguistic processing, and verbal working mem-
ory (Ravizza et al., 2006; Schmahmann, 2003).
This research provides a compelling case for significant utiliza-
tion during cognitive development of highly specialized neural
infrastructure built during motor development in the cerebellum
and basal ganglia. The literature also suggests that motor devel-
opment contributes a second type of neural infrastructure, involv-
ing both the motor and prefrontal areas, that adaptively controls the
learning process itself whether motor or cognitive skills are being
learned. Surprisingly, motor development appears to require and
develop a quite sophisticated cognitive control capacity, later used
when learning cognitive skills.
Adolph (2005, 2008; Adolph & Berger, 2006) suggested that
researchers’ views of motor development have been naive because
they have not recognized the quite complex cognitive tasks de-
manded of infants. Adolph proposed that infants are learning to
learn as they master locomotion and subsequent gross and fine
motor skills. Infants continually have to solve complex problems
in adapting and changing each movement in response to their
perception of the current but ever changing environment, their
changing constraints on physical movement because of physical
growth of arms, limbs, and other body parts, and their current
levels of neural maturation and motor capability. In essence, no
two motor movements are ever the same but require continual
adaptation.
New theories of motor development to help explain such child
development research have addressed how children adapt to and solve
these continuing motor challenges. These theories required the pos-
tulation and testing of interplay between cognitive and motor func-
tions. The underlying conceptual model common in theoretical
models of motor and some cognitive tasks is the adaptive control
system that sequentially uses a trial-and-error, reward sensitive, or
reinforcement feedback process that iterates and hones initial rep-
resentations of cognitive or motor actions to desired objectives
(Cohen & Frank, 2008; Doya, 1999, 2000; Ito, 2005).
Ito (2005) suggested that a common function shared by certain
motor and cognitive-learning tasks is needed for control and ma-
nipulation of internal neural representations. In motor develop-
ment, this representation involves a model of the body in the
external environment that predicts, iteratively in a trial-and-error
process, how to control physical movement in a way to achieve the
desired motor action. For some cognitive tasks, the representation
is often of abstract symbols for similar trial and error and adaptive
manipulation toward a solution.
Adolph’s (2005, 2008; Adolph & Berger, 2006) learning to
learn analogy suggests that the neural infrastructure built to control
and guide the adaptive control process for motor skills is also used
when solving cognitive problems. This control process appears
to be orchestrated from the prefrontal areas but also involves the
cerebellum and basal ganglia. This research suggests that virtually
no new motor development or action can occur without an increas-
ingly sophisticated cognitive capacity that adaptively controls and
habituates motor actions. By the time children reach preschool age,
they have developed, during motor development, quite a sophisti-
cated cognitive capacity to initiate learning actions and use exec-
utive function skills in pursuit of motor learning. They have also
developed a significant capacity to orchestrate activities among
prefrontal, cerebellum, and basal ganglia that manipulates increas-
ingly complex representational models to achieve desired motor
adaptations.
The sophistication of this cognitive capacity built during motor
development may depend on the challenges encountered during
motor development. An important part of motor development is a
spiraling process whereby newly developed motor skills provide
expanding opportunity for children to experience more diverse and
1014 GRISSMER, GRIMM, AIYER, MURRAH, AND STEELE

ever more challenging environments that, in turn, require more
complex cognitive maps. If diverse and more challenging motor
environments vary for children, the cognitive capacity brought to
kindergarten may also vary.
Discussion
Summary of Results
Duncan et al. (2007) used six longitudinal data sets that tracked
children from before school entry through later grades to identify
school readiness indicators—those skills known before school
entry that strongly and consistently predicted later achievement in
reading and math. The research identified early mathematical skills
and attention as strong predictors of both later math and reading,
whereas early reading was an important predictor of later reading.
The research also found that internalizing and externalizing be-
haviors and social skills were not strong predictors of later math
and reading.
This article utilized three of the six longitudinal data sets that
measured motor skills and added these measures to similarly
estimated models. The results indicated that gross motor skills
were not a significant predictor of later achievement but that fine
motor skills were a very strong and consistent predictor of later
achievement. The relative strength of fine motor skills, compared
with attention, varied across tests and data sets. In the ECLS-K,
attention was a stronger predictor of reading and math than motor
skills were, whereas the NLSY fine motor skills and attention
showed approximately equal strength in their prediction of later
achievement. In the BCS, fine motor measures were stronger
predictors than attention. Incorporating motor skills into the anal-
ysis did not change the pattern of significant effects for early math
and reading or the insignificant or weak effects for internalizing
and externalizing behavior and social skills. These results suggest
that both attention and fine motor skills measured at kindergarten
are important developmental skills that predict later achievement.
Using the ECLS-K, we also tested whether a third test in
addition to the math and reading given at kindergarten entrance
predicted later achievement. A general knowledge test measured
the child’s early comprehension of physical and social science
facts. Whereas the early math and reading tests focused mainly on
procedural knowledge, the general knowledge test focused mainly
on declarative knowledge (i.e., elementary knowledge or compre-
hension of the external world). General knowledge was the stron-
gest predictor of later reading and science and, along with earlier
math, was a strong predictor of later math. General knowledge
measured at kindergarten entrance may reflect early comprehen-
sion skills that are necessary when reading changes from a more
procedural task in early grades (learning to read) to incorporating
more comprehension around third through fifth grades (reading to
learn).
Later science scores were predicted very strongly by the general
knowledge test, with smaller contributions from early math—but
no contribution from early reading. Attention and motor skills also
predicted science scores with about equal predictive strength but
were weaker than their contribution to the prediction of later math.
Skills linked to early observation and comprehension of the social
and physical world appear to be as important as early math and
reading skills and likely should be included as an important kin-
dergarten readiness indicator.
Eliminating kindergarten math and reading measures in predict-
ing fifth-grade scores considerably increased the strength and
statistical significance of attention and fine motor skills but left the
remaining socioemotional variables as mainly insignificant or con-
siderably less significant. Predicted effect sizes at fifth grade from
improving attention and fine motor skills were around .3 and .2,
respectively. In addition, effect sizes for general knowledge were
.2, .3, and .4, respectively, for math, reading, and science.
Kindergarten-entrance reading and math scores’ sensitivity to a
child’s age is quite strong, but this effect is reduced if develop-
mental skills are present in the estimated equations. One interpre-
tation is that age is not a causative mechanism but rather that age
is a proxy for developmental differences that need to be specified
in the equations. The significance of age as a predictor of scores
declines from kindergarten entrance to fifth grade and disappears
completely when developmental skills are incorporated into the
equations. The complete elimination of age effects at fifth grade
when developmental skills were incorporated may indicate that no
other developmental skills at kindergarten entrance were missing
when predicting later achievement.
Finally, the developmental and neuroscience literatures provide
theories and evidence to support the use of the neural infrastructure
built during motor development during cognitive development.
This neural infrastructure includes highly specialized capacities in
the basal ganglia and cerebellum that are used in specific types of
learning and sophisticated adaptive control capacity that may be
essential to both motor and cognitive learning. Adolph (2008)
suggested that we learn how to learn during motor development.
Concluding Remarks
The potential discovery of two new school readiness indicators
in addition to attention, which was found by Duncan et al. (2007),
may be important for at least three reasons. The first reason is that
it might provide a new direction for intervention and experimen-
tation that can test whether the relationships are casual and actually
result in higher math and reading achievement. For instance, one
possibility for the motor relationship is an inverse one that stronger
innate cognitive skills build better fine motor skills. The second
reason, provided these results are causal, is the potential impact on
educational policies and practices and broader social policies. The
third reason is to spur new developmental and neuroscience re-
search that links these early skills through causative mechanisms
to later cognitive development.
There are few interventions directly testing whether strengthen-
ing early attention, fine motor skills, or knowledge of the world
would improve later math and reading achievement. Schellenberg
(2004) provided experimental evidence that an 8-month interven-
tion that provided first graders with musical keyboard and voice
training in small groups twice a week boosted Wechsler Intelli-
gence Scale for Children cognitive scores (effect size ⫽.35), but
providing similar structured drama training did not improve cog-
nitive scores. One main difference between the interventions was
the increased use of fine motor skills in the musical interventions
but not the drama intervention. Diamond, Barnett, Thomas, and
Munro (2007) reported an experiment with a curriculum directed
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SPECIAL SECTION: TWO NEW SCHOOL READINESS INDICATORS

toward strengthening executive function skills with promising
results on cognitive skills.
Current experimental interventions mainly focus on changing
the child’s instructional environment without understanding how
that change will influence the neural development underlying later
cognitive performance. Almost all of these interventions involve
changing the way that math and reading are taught: earlier instruc-
tion (prekindergarten), different curriculum, better classroom cli-
mate and teaching, and additional time on task. The results of our
research would certainly support improved early math and reading
because these early skills are strongly predictive of later math and
reading. As in Duncan et al. (2007), our results suggest that early
math should receive additional emphasis because reading has
already been receiving additional emphasis and math is more
predictive of later math and reading than is early reading. How-
ever, an important question is whether significant marginal gains
can be obtained in early math and reading from instructional
interventions, given the overwhelming focus of past interventions.
Our results suggest that the focus of interventions should shift
from a primary emphasis on changing the direct math and reading
instructional environment to interventions that build better foun-
dational skills of attention and fine motor skills and a better
understanding of the world outside schools. The results suggest
that current direct math and reading instruction is insufficient to
build attention and fine motor skills. Building these skills may rely
more on subjects and curricula that have been de-emphasized to
provide more math and reading instruction: the arts, music, dance,
physical education, and free play. Each of these subjects and
curricula may need to be redesigned to focus on building founda-
tional skills in the same way that math and reading have been
redesigned in recent years. Building stronger knowledge of the
external world also suggests that improving early science and
social studies curricula are important. Paradoxically, higher long-
term achievement in math and reading may require reduced direct
emphasis on math and reading and more time and stronger curric-
ula outside math and reading.
However, it is also likely that building improved attention, fine
motor skills, and knowledge of the world will require family,
parental, and societal emphasis before the start of school and
outside of school once children start school. Disadvantaged chil-
dren from all racial/ethnic groups likely have fewer opportunities
to build these skills than more advantaged children. For instance,
Grissmer and Eiseman (2008) reported that achievements gaps
found later in schooling between racial/ethnic groups are largely
present before school entry, suggesting that differences in earlier
environments and differential access to quality preschools primar-
ily cause score gaps. Grissmer and Eiseman also reported that
substantial gaps are present in attention and fine motor skills
between Black and White students at kindergarten entrance and
may account for up to 40% of the Black–White achievement gap.
The Black–White achievement gaps in reading and math have not
been closed in a sustained way over the past 25 years despite
substantial increases in school spending and the implementation in
virtually all states of standards-based accountability systems that
emphasize math and reading performance (Magnuson & Waldfo-
gel, 2008). Closing these gaps may be less about better math and
reading instruction than about building better attention and fine
motor skills and better awareness and knowledge of the external
world. It is likely that much of this work needs to be done before
kindergarten in extended preschools and in families and perhaps in
after-school programs.
In addition to suggesting new priorities for intervention, exper-
imentation, and educational and social policies, these results
should spur new developmental and neuroscience research that
links these early skills through causative mechanisms to later
achievement. This literature over the past 10 years has provided
substantial support for significant contributions of executive func-
tion and motor development to later cognitive development. Build-
ing stronger theories and knowledge about the interrelationships
among attention, fine motor skills, knowledge of the world, and the
potential causative mechanisms that might link them to later
achievement can result only in better design and increased power
and efficiency of interventions.
References
Adolph, K. (2005). Learning to learn in the development of action. In J. J.
Rieser, J. J. Lockman, & C. A. Nelson (Eds.), Action as an organizer of
learning and development (pp. 91–122). Mawah, NJ: Erlbaum.
Adolph, K. (2008). Learning to move. Current Directions in Psychological
Science, 17, 213–218.
Adolph, K., & Berger, S. A. (2006). Motor development. In W. Damon &
R. Lerner (Series Eds.) & D. Kuhn & R. S. Siegler (Vol. Eds.), Hand-
book of child psychology: Cognition, perception, and language (pp.
161–213). New York, NY: Wiley.
Ashby, F. G., & Spiering, B. J. (2004). The neurobiology of category
learning. Behavioral and Cognitive Neuroscience Reviews, 3, 101–113.
Baker, P. C., Keck, C. K., Mott, F. L., & Quinlan, S. V. (1993). NLSY child
handbook: A guide to the 1986–1990 National Longitudinal Survey of
Youth child data. Columbus, OH: Center for Human Resource Research.
Banaschewski, T., Hollis, C., Oosterlaan, J., Roeyers, H., Rubia, K.,
Willcutt, E., & Taylor, E. (2005). Towards an understanding of unique
and shared pathways in the psychopathophysiology of ADHD. Devel-
opmental Science, 8, 132–140.
Casey, B., Nigg, J. T., & Durston, S. (2007). New potential leads in the
biology and treatment of attention deficit–hyperactivity disorder. Cur-
rent Opinion in Neurology, 20, 119–124.
Castellanos, F., Sonuga-Barke, E. J. S., Milham, M. P., & Tannock, R.
(2006). Characterizing cognition in ADHD: Beyond executive dysfunc-
tion. Trends in Cognitive Sciences, 10, 117–123.
Castellanos, F., & Tannock, R. (2002). Neuroscience of attention deficit/
hyperactivity disorder: The search for endophenotypes. Nature Reviews
Neuroscience, 3, 617–628.
Cohen, M. X., & Frank, M. J. (2008). Neurocomputational models of basal
ganglia function in learning, memory and choice. Behavioural Brain
Research, 199, 141–156.
Davie, R., Butler, N. R., & Goldstein, H. (1972). From birth to seven.
London, England: Longman.
Diamond, A. (2000). Close interrelation of motor development and cog-
nitive development and of the cerebellum and prefrontal cortex. Child
Development, 71, 44–56.
Diamond, A., Barnett, S., Thomas, J., & Munro, S. (2007, November 30).
Preschool program improves cognitive control. Science, 318, 1387–
1388.
Doya, K. (1999). What are the computations of the cerebellum, the basal
ganglia and the cerebral cortex? Neural Networks, 12, 961–974.
Doya, K. (2000). Complementary roles of basal ganglia and cerebellum in
learning and motor control. Current Opinions in Neurobiology, 10,
732–739.
Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C.,
Klebanov, P.,...Japel, C. (2007). School readiness and later achieve-
ment. Developmental Psychology, 43, 1428–1446.
1016 GRISSMER, GRIMM, AIYER, MURRAH, AND STEELE

Durston, S., & Casey, B. J. (2006). What have we learned about cognitive
development from neuroimaging? Neuropsychologia, 44, 2149–2157.
Durston, S., & Konrad, K. (2007). Integrating genetic, psychopharmaco-
logical and neuroimaging studies: A converging methods approach to
understanding the neurobiology of ADHD. Developmental Review, 27,
374–395.
Graybiel, A. M. (2005). The basal ganglia: Learning new tricks and loving
it. Current Opinion in Neurobiology, 15, 638–644.
Grissmer, D. W., & Eiseman, E. (2008). Can gaps in the quality of early
environment and non-cognitive skills help explain persisting Black–
White achievement gaps? In K. Magnuson & J. Waldfogel (Eds.), Steady
gains and stalled progress: Inequality and the Black–White test score
gap. New York, NY: Sage.
Haber, S. N. (2003). Integrating cognition and motivation into the basal
ganglia pathways of action. In M. Bedard, A. Yves, S. Chouinard, S.
Fahn, A. Korczyn, & P. Lesperance (Eds.), Mental and behavioral
dysfunction in movement disorders (pp. 35–50). Totowa, NJ: Humana
Press.
Harris, D. (1963). Children’s drawings as measures of intellectual matu-
rity. New York, NY: Harcourt, Brace, & World.
Ito, M. (2005). Bases and implications of learning in the cerebellum—
Adaptive control and internal model mechanism. In C. Zeeuw & F.
Cicarita (Eds.), Progress in brain research: Creating coordination in the
cerebellum (pp. 95–109). Amsterdam, the Netherlands: Elsevier.
Kimmel, E. (2001). Test review of the Early Screening Inventory–Revised.
In B. S. Plake & J. C. Impara (Eds.), The fourteenth mental measure-
ments yearbook. Retrieved from http://www.unl.edu/buros
Koppitz, E. M. (1968). Psychological evaluation of children’s human-
figure drawings. New York, NY: Grune & Stratton.
Krain, A. L., & Castellanos, F. X. (2006). Brain development and ADHD.
Clinical Psychology Review, 26, 433–444.
Magnuson, K., & Waldfogel, J. (2008). Steady gains and stalled progress.
New York, NY: Sage.
Meisels, S., Marsden, D., Wiske, M., & Henderson, L. (1997). Early
Screening Inventory–Revised. Ann Arbor, MI: Rebus.
Middleton, F. (2003). Fundamental and clinical evidence for basal ganglia
influence on cognition. In M. Bedard, Y. Agid, S. Chouinard, S. Fahn,
A. Korczyn, & P. Lesperance (Eds.), Mental and behavioral dysfunction
in movement disorders (pp. 13–34). Totowa, NJ: Humana Press.
Middleton, F. A., & Strick, P. L. (2000). Basal ganglia output and cogni-
tion: Evidence from anatomical, behavioral, and clinical studies. Brain
and Cognition, 42, 183–200.
Middleton, F. A., & Strick, P. L. (2002). Basal-ganglia “projections” to the
prefrontal cortex of the primate. Cerebral Cortex, 12, 926–935.
Mott, F. L., Baker, P. C., Ball, D. E. Keck, C. K., & Lenhart, S. N. (1995).
The NLSY children 1992: Description and evaluation. Columbus, OH:
Ohio State University, Center for Human Resource Research.
Nicolson, R. I., Fawcett, A. J., & Dean, P. (2001). Developmental dyslexia:
The cerebellar deficit hypothesis. Trends in Neurosciences, 24, 508–
511.
Nigg, J. T., & Casey, B. J. (2005). An integrative theory of attention-
deficit/hyperactivity disorder based on the cognitive and affective neu-
rosciences. Development and Psychopathology, 17, 785–806.
Paget, K. D. (2001). Test review of the Early Screening Inventory–
Revised. In B. S. Plake & J. C. Impara (Eds.), The fourteenth mental
measurements yearbook. Retrieved from http://www.unl.edu/buros
Paquier, P., & Marien, P. (2005). A synthesis of the role of the cerebellum
in cognition. Aphasiology, 19, 3–19.
Peterson, J. L., & Moore, K. A. (1987). Motor and social development in
infancy: Some results from a national survey. Washington, DC: Child
Trends.
Ravizza, S. M., McCormick, C. A., Schlerf, J. E., Justus, T., Ivry, R. B., &
Fiez, J. A. (2006). Cerebellar damage produces selective deficits in
verbal working memory. Brain, 129, 306–320.
Rutter, M., Tizard, J., & Whitmore, K. (1970). Education, health and
behavior. London, England: Longman.
Saint-Cyr, J. A. (2003). Frontal–striatal circuit functions: Context, se-
quence, and consequence. Journal of the International Neuropsycholog-
ical Society, 9, 103–128.
Schellenberg, G. (2004). Music lessons enhance IQ. Psychological Sci-
ence, 15, 511–514.
Schmahmann, J. (2003). The role of the cerebellum in cognition and
emotion. In M. A. Be´dard (Ed.), Mental and behavioral dysfunction in
movement disorders (pp. 59–81). Totowa, NJ: Humana Press.
Schmahmann, J. (2004). Disorders of the cerebellum: Ataxia, dysmetria of
thought and the cerebellar cognitive affective syndrome. Neuropsychi-
atric Practice and Opinion, 16, 367–378.
Seger, C. A. (2006). The basal ganglia in human learning. Neuroscientist,
12, 285–290.
Sergeant, J. A., Geurts, H., Huijbregts, S., Scheres, A., & Oosterlaan, J.
(2003). The top and the bottom of ADHD: A neuropsychological per-
spective. Neuroscience and Biobehavioral Reviews, 27, 583–592.
Shohamy, D., Myers, C. E., Grossman, S., Sage, J., Gluck, M. A., &
Poldrack, R. A. (2004). Cortico-striatal contributions to feedback-based
learning: Converging data from neuroimaging and neuropsychology.
Brain, 127, 851–859.
Shohamy, D., Myers, C. E., Kalanithi, J., & Gluck, M. A. (2008). Basal
ganglia and dopamine contributions to probabilistic category learning.
Neuroscience & Biobehavioral Reviews, 32, 219–236.
Shohamy, D., Myers, C. E., Onlaor, S., & Gluck, M. A. (2004). Role of the
basal ganglia in category learning: How do patients with Parkinson’s
disease learn? Behavioral Neuroscience, 118, 676–686.
Smith, A., Taylor, E., Warner, J., Newman, S., & Ribia, K. (2002).
Evidence for a pure time perception deficit in children with ADHD.
Journal of Child Psychology and Psychiatry and Allied Disciplines, 43,
529–542.
Sonuga-Barke, E. (2003). The dual pathway model of AD/HD: An elab-
oration of neuro-developmental characteristics. Neuroscience and
Biobehavioral Reviews, 27, 593–604.
Sonuga-Barke, E., Auerbach, J., Campbell, S. B., Daley, D., & Thompson,
M. (2005). Varieties of preschool hyperactivity: Multiple pathways from
risk to disorder. Developmental Science, 8, 141–150.
Sonuga-Barke, E., Dalen, L., & Remington, B. (2003). Do executive
deficits and delay aversion make independent contributions to preschool
attention deficit/hyperactivity disorder symptoms? Journal of the Amer-
ican Academy of Child and Adolescent Psychiatry, 42, 1335–1342.
Sonuga-Barke, E., Sergeant, J. A., Nigg, J., & Willcutt, E. (2008). Exec-
utive dysfunction and delay aversion in attention deficit hyperactivity
disorder: Nosologic and diagnostic implications. Child and Adolescent
Psychiatric Clinics of North America, 17, 367–384.
Toplak, M. E., Dockstader, C., & Tannock, R. (2006). Temporal informa-
tion processing in ADHD: Findings to date and new methods. Journal of
Neuroscience Methods, 151, 15–29.
Received April 1, 2009
Revision received October 30, 2009
Accepted November 25, 2009 䡲
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