ADHD neuropsychologia.pdf
(
271 KB
)
Pobierz
92858127 UNPDF
2006, Vol. 20, No. 4, 420–429
Copyright 2006 by the American Psychological Association
Single Dissociation Findings of ADHD Deficits in Vigilance but Not
Anterior or Posterior Attention Systems
Cynthia L. Huang-Pollock
Pennsylvania State University
Joel T. Nigg
Michigan State University
Jeffrey M. Halperin
Queens College of the City University of New York
Following a distributed network model of visuospatial attention, the authors used an
A
–
X
version of the
Continuous Performance Test and a covert orienting paradigm to examine the vigilance, anterior, and
posterior attention systems. Compared with control participants without attention-deficit/hyperactivity
disorder (ADHD), children with the predominantly inattentive (ADHD-I) and combined (ADHD-C)
subtypes had lower sensitivity (
d
) to detect targets from nontargets. Children with ADHD-C, but not
ADHD-I, additionally had a highly activated response style (ln
). Performance for both subtypes
decreased to a greater extent over time in a manner consistent with problems in sustained attention.
Together, these results suggest the presence of vigilance system deficits in participants with both ADHD
subtypes. However, consistent with previous meta-analytic work, there was no evidence for anterior or
posterior system orienting dysfunctions in either subtype.
Keywords:
attention-deficit/hyperactivity disorder (ADHD), visuospatial orienting, Continuous Perfor-
mance Test
Questions remain about the nature of cognitive or neuropsycho-
logical mechanisms that may be involved in the development of
attention-deficit/hyperactivity disorder (ADHD) and its subtypes.
Although recent theories emphasize a range of processes other
than attention, attentional functioning remains an area in need of
further clarification (Douglas, 1999). In this article, we evaluate
three interconnected attentional systems in children with the two
most common subtypes of ADHD: ADHD combined type
(ADHD-C) and ADHD predominantly inattentive type (ADHD-I).
The relationship between the two subtypes and the status of their
attentional function have been issues of extensive concern and
dispute in the literature (Milich, Balentine, & Lynam, 2001). To
examine this question, we adopted as a conceptual framework a
distributed network model of attention that posits three attentional
systems (Posner & Petersen, 1990). We also drew on the concep-
tual model of Sergeant, Oosterlaan, and van der Meere (1999) and
their analysis of likely ADHD-functional deficits.
The distributed network model is among the best-known models
of visual attention. It specifies three distributed systems: (a) an
automatic posterior attention system (PAS), which involves the
superior parietal cortex, pulvinar, and superior colliculus; (b) a
voluntary anterior attention system (AAS), which involves the
anterior cingulate gyrus and supplementary motor cortex; and (c)
a vigilance system (composed of both sustained attention and
phasic alerting processes), which involves the locus coeruleus
(LC), cholinergic system of the basal forebrain, intralaminar tha-
lamic nuclei, and right prefrontal cortex. These systems are con-
ceptually separable, although as one of their many functions, they
work together to direct attention to locations in space. The strong
empirical support behind the model from lesion and imaging
studies (Posner, Choate, Rafal, & Vaughan, 1985; Posner, Walker,
Friederich, & Rafal, 1984, 1987; Rafal & Posner, 1987) combined
with ease of evaluation has made this model as well as its asso-
ciated measurement paradigm for examining attentional orienting
among the most widely used in the examination of attention
deficits in childhood ADHD (for a review, see Huang-Pollock &
Nigg, 2003).
The measurement paradigm based on reaction time (RT) is a
computer-generated target detection task in which participants are
either peripherally or centrally cued to send their attention to the
right visual field (RVF) or left visual field (LVF) in anticipation of
a target. Central cues (e.g., arrows) require voluntary cognitive
direction of attention and so probe AAS functionality. Peripheral
cues (e.g., onset of a bright light) activate reflexive orienting and
so probe the PAS. By varying the validity of the cues, it is possible
to isolate component processes such as engaging, disengaging, and
shifting of attention.
However, despite intriguing single-study findings of weaknesses
in the PAS disengage function in ADHD-C, meta-analytic effect
sizes across extant studies using this orienting paradigm have been
small to nonexistent (Huang-Pollock & Nigg, 2003). Thus, any
deficits in the PAS as conceptualized by Posner and Petersen’s
(1990) model are unlikely to be of clinical significance in
ADHD-C. Evidence for an AAS deficit is likewise weak and
mixed for ADHD-C (Huang-Pollock & Nigg, 2003), which may be
surprising in light of extensive evidence implicating anterior cor-
Cynthia L. Huang-Pollock, Department of Psychology, Pennsylvania
State University; Joel T. Nigg, Department of Psychology, Michigan State
University; Jeffrey M. Halperin, Department of Psychology, Queens Col-
lege of the City University of New York.
This work was supported in part by National Institute of Mental Health
Fellowship F31-MH12333 and Grant R01-MH59105 awarded to Cynthia
L. Huang-Pollock and Joel T. Nigg, respectively.
Correspondence concerning this article should be addressed to Cynthia
L. Huang-Pollock, 545 Moore Building, Psychology Department, The
Pennsylvania State University, University Park, PA 16802. E-mail:
clh39@psu.edu
420
Neuropsychology
0894-4105/06/$12.00 DOI: 10.1037/0894-4105.20.4.420
ADHD DEFICITS AND THE VIGILANCE SYSTEM
421
tical networks in ADHD. However, most evidence has indicated
that those anterior neural circuits are involved in multiple, perhaps
modularized, control operations of which attentional orienting in
space is only one (Fuster, 1997; Lyon & Krasnegor, 1996; Pen-
nington & Ozonoff, 1996). It is quite possible that ADHD-C
involves breakdowns in executive component functions such as
response suppression (Nigg, 2001; Schachar, Tannock, & Logan,
1993) or working memory (Martinussen, Hayden, Hogg-Johnson,
& Tannock, 2005) but not in executive spatial attention.
Even so, two main issues remain unresolved concerning atten-
tional functioning in ADHD. First, of the studies examining visuo-
spatial orienting with the aforementioned orienting paradigm, only
one included ADHD-I. Thus, the preceding conclusions mainly
pertain to ADHD-C; relatively little is known about attentional
orienting in ADHD-I. This is a key gap in the field in light of
long-standing hypotheses that the inattentive subtype may involve
PAS abnormalities (Hynd et al., 1991; Milich et al., 2001).
Second, although previous studies have examined the function-
ing of one or two attentional networks, no studies of ADHD have
examined all three networks within the same sample. This ap-
proach, because it controls for between-sample variance, would
provide a more definitive answer to the question of whether
children with ADHD demonstrate weaknesses in attentional pro-
cesses. Posner and Petersen’s (1990) model includes both phasic
alerting and sustained attention processes within the rubric of a
vigilance system. Although phasic alerting can be evaluated by
comparing RTs to uncued versus neutrally cued targets (Fan,
McCandliss, Sommer, Raz, & Posner, 2002; Rueda et al., 2004),
the orienting paradigm is not as well suited to the measurement of
sustained attention.
Therefore, to fully assess all three components of the model, one
must include a second measurement paradigm. Among the most
commonly used tests of sustained attention is the Continuous
Performance Test (CPT). Many variations of this paradigm exist
(Corkum & Siegel, 1993; Riccio, Reynolds, Lowe, & Moore,
2002), but nearly all require participants to detect an infrequently
occurring target among rapidly presented nontargets over an ex-
tended period of time. The CPT task in this study is an
A
–
X
task
modeled on those used by Rosvold, Mirsky, Sarason, Bransome,
and Beck (1956), Halperin, Sharma, Greenblatt, and Schwartz
(1991), and Halperin, Wolf, Greenblatt, and Young (1991). In
other words, children are instructed to respond when the letter
X
is
preceded by the letter
A
. The CPT has itself been extensively
applied to childhood ADHD, especially ADHD-C, with meta-
analyses indicating ADHD weakness on key parameters (Losier,
McGrath, & Klein, 1996).
Experimentally, a sustained attention deficit would lead to per-
formance worsening more rapidly over time (typically measured
by an increase in RT, error rate, or variability in RT over time)
compared with the performance of nonaffected individuals. Mirsky
and Duncan (2001) distinguished sustained attention from a sta-
bilize function that is similar in concept to phasic alertness and is
indexed by overall variability of RT. We therefore conceived of
RT variability as a separate index of alertness that is distinct from
RT change over time, which we consider an index of sustained
attention.
Methods to measure sustained attention on the CPT are not well
standardized. In particular, besides simple error rates, other argu-
ably more sophisticated methods have also been used to identify
performance deficits on the CPT. One of these entails the empir-
refers to an individual’s ability to discriminate between signal and
noise, whereas
refers to an individual’s response biases (i.e., to
respond “target present” vs. “target absent”). The cognitive–ener-
getic model (Sergeant et al., 1999) posits that the primary cogni-
tive mechanisms involved in ADHD could involve one or more of
three energetic pools (arousal, activation, and effort) as well as the
executive management of such pools. Sergeant et al. (1999) argued
that cognitive deficits in ADHD may primarily be associated with
arousal or with activation, which they indicated would be indexed
respectively by
d
and
. Under the rubric of this model,
d
is
believed to be modulated by the arousal pool that is responsible for
phasic alertness, and
is considered a measure of activation, or a
more general and tonic state of readiness to respond (Sergeant et
al., 1999). Although activation was not a primary focus of our
study (it is conceptualized as distinct from arousal and vigilance
and may be conceived as part of a broad executive function rubric;
Berger & Posner, 2000), we viewed activation as an additional
state-regulation parameter of theoretical interest in ADHD that we
were able to assess. Therefore, with the CPT, we were able to
assess activation as well as the two components of the vigilance
system: alertness/arousal and sustained attention.
Yet further methodological issues require consideration. For all
their advantages, signal detection indices may be vulnerable to
effects of temporal properties of different error types and partic-
ularly to speed–accuracy trade-off (SATO) effects (Sergeant &
Scholten, 1985). Halperin and colleagues (Halperin, Sharma, et al.,
1991; Halperin, Wolf, et al., 1991; Halperin et al., 1988; Marks,
Himelstein, Newcorn, & Halperin, 1999) developed an alternate
approach on the basis of empirically derived error scores from an
A
–
X
CPT that took into consideration both type of error and speed
of response. They found, in conjunction with RTs, that distinct
CPT error subtypes form meaningful measures of inattention and
impulsivity. Such indices have good test–retest reliability over
durations of several months and have good construct and concur-
rent validity. In their approach, inattentive and impulsive errors
likely index vigilance and activation, respectively. Dyscontrol
errors, which do not meet context or RT criteria for inattention or
impulsive errors, are posited to be nonspecific in nature. This
approach is described in more detail below. Like other CPT
measures, these error indices have adequate specificity but low
sensitivity to detect participants with ADHD (as defined in the
Diagnostic and Statistical Manual of Mental Disorders
; 3rd. ed.,
rev.) from control participants without ADHD (Matier-Sharma,
Perachio, Newcorn, Sharma, & Halperin, 1995; Nigg, Hinshaw, &
Halperin, 1996), and they are able to discriminate between chil-
dren with “pure” ADHD and other children, such as children with
anxiety disorders, children with a disruptive behavior disorder
other than ADHD, and control participants (Halperin et al., 1993;
Newcorn et al., 2001). We reasoned that an examination of vigi-
lance system responding can benefit from considering both the
signal detection and error type approaches. Table 1 describes the
cognitive processes and attentional systems under investigation in
this study as well as the indices that in our conceptualization
respectively probe each system.
In all, we sought to evaluate multiple attentional systems in the
two most common
Diagnostic and Statistical Manual of Mental
Disorders–IV
(
DSM–IV
; 4th ed., American Psychiatric Associa-
tion, 1994) subtypes of ADHD (ADHD-C and ADHD-I) with two
(Green & Swets, 1974). The index
d
ically validated and derived variables associated with Signal De-
tection Theory—
d
and
422
HUANG-POLLOCK, NIGG, AND HALPERIN
Table 1
Cognitive Processes and Systems Measured by Orienting
Paradigm (OP) and Continuous Performance Test (CPT)
present. Children with five symptoms of inattention or hyperactivity–
impulsivity by the
or
algorithm were excluded because it is difficult to
accurately determine subtype in that situation (Lahey et al., 1994). Comor-
bid oppositional defiant, conduct, anxiety, and depressive disorders were
identified with the DISC-IV, and those rates are described later. Children
were defined as having a Reading Disability if (a) their Wechsler Individ-
ual Achievement Test (WIAT; Wechsler, 1992) Basic Reading subtest
score was below 85 and (b) if the score was significantly below what would
be predicted given their estimated IQ (as determined by a four-subtest short
form of the Wechsler Intelligence Scale for Children (3rd edition; Wech-
sler, 1991). With this definition, 1 non-ADHD control participant and 4
participants with ADHD-C met criteria for reading disability. Reading
disability was infrequent because it was an exclusionary criterion during
the initial years of data collection. The simple difference method identified
the same children as the predicted-achievement method.
Because of a computer failure, we did not have data for the same number
of children on both tasks. Of the participants, 53% (
n
System
Index
Anterior attention
RT to centrally cued targets (OP)
a
Posterior attention
RT to peripherally cued targets (OP)
a
Vigilance
Sustained attention
Performance over time (CPT)
b
Inattentive errors (CPT)
c
Phasic alertness
RT to neutral/uncued targets (OP)
d
Standard deviation overall (CPT)
e
Arousal:
d
(CPT)
f
Activation
ln
(CPT)
f
Impulsive errors (CPT)
c
Note.
RT
reaction time;
d
signal detection accuracy; ln
re-
sponse bias as defined by Hochhaus (1972).
a
See Posner & Petersen (1990)
b
See van der Meere & Sergeant (1988)
c
See Halperin, Sharma, et al. (1991) and Halperin, Wolf, et al. (1991)
d
See Fan et al. (2002) and Rueda et al. (2004)
e
See Mirsky & Duncan (2001)
f
See Sergeant et al. (1999)
72) completed both
the CPT task and the orienting procedures, 40% (
n
54) had only CPT
9) had only orienting data. Table 2 describes the sample
size for each analysis. Children who completed both the CPT and orienting
task and those who were able to complete only one of the tasks did not
differ in the number or severity of parent- or teacher-reported ADHD
symptoms, IQ, reading, or mathematics achievement (all
p
s
.20). Results
did not change when analyses were restricted to children who had complete
data on both tasks. Efforts to evaluate the effects of missing data with
maximum likelihood imputation methods (the expectation–maximization
algorithm) yielded no change in conclusions, with one exception that we
detail below. However, the amount of missing data was judged too great to
rely solely on the imputed data. We therefore report all available data
(without imputation) here, adding results for the imputed data set only
when meaningful to clarify results further.
Children were excluded if they had a sensorimotor handicap, neurolog-
ical disorder, autistic disorder, mental retardation, or psychosis by parent
report, if they met criteria for Tourette’s syndrome or bipolar disorder on
the DISC-IV, if they obtained an estimated Full Scale IQ
widely used attention measures. We aimed to clarify whether
either subtype was associated with deficits in the anterior, poste-
rior, or vigilance systems, in addition to activation. On the basis of
our prior meta-analysis (Huang-Pollock & Nigg, 2003), we did not
expect to find orienting deficits in the ADHD-C type, but we did
plan to evaluate the hypothesis that the ADHD-I type would be
associated with posterior orienting deficits. Finally, on the basis of
Sergeant et al.’s (1999) study, we hypothesized that the ADHD-C
and ADHD-I types might be differentially associated with alert-
ness/arousal versus activation, if they are distinct conditions as has
been hypothesized (Milich et al., 2001).
75, or if they
were currently taking long-acting psychotropic (e.g., antidepressant) med-
ication. Children taking psychostimulant medication (45% of children with
ADHD, 0% of control participants) washed out for at least 24 hr prior to
their testing (average time off medication
Method
108 hr, range
24–552 hr).
Participants
Procedures
Participants were children aged 7–12 years diagnosed (as defined later)
as either ADHD-C (
n
68), ADHD-I (
n
21), or non-ADHD (control;
n
46). Children were recruited through advertisements in the commu-
nity, schools, clinics, and newspapers to reach as broad and representative
of a sample as possible. The sample thus was representative of the region
with regard to ethnicity: 64% Caucasian, 9% African American, 6%
Hispanic, 2% American Indian, 17% other/mixed, and 2% undisclosed. In
identifying eligible participants, we used a multistage screening procedure
to evaluate
DSM–IV
criteria on the basis of parent and teacher reports of
symptoms. Children were initially screened as possibly diagnosable with
ADHD if they (a) met or exceeded screening cutoffs on parent–teacher
ratings of the Behavioral Assessment Scale for Children (Reynolds &
Kamphaus, 1992), the Conners’ (1997) Rating Scale, and/or the ADHD
Rating Scale (DuPaul, Power, Anastopoulos, & Reid, 1998) or (b) had been
previously diagnosed by a clinician who had considered parent and teacher
reports. The Diagnostic Interview Schedule for Children, 4th edition
(DISC-IV; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000), a
computer guided, structured diagnostic interview, was then completed by
the primary caregiver, usually the mother.
Following
DSM–IV
field trial data (Lahey et al., 1994), an
or
algorithm
was used to establish final ADHD diagnosis. If children met age of onset,
severity, duration, and cross-situational impairment criteria, a symptom
was counted as present if either the parent (on DISC-IV) or teacher (on the
ADHD Rating Scale asa2ora3onthe0–3scale) endorsed it as being
CPT.
To assess vigilance, we administered a computerized
A
–
X
CPT
that was modeled after that used by Rosvold et al. (1956) and was identical
to that used by Nigg et al. (1996) and Halperin, Sharma, et al. (1991;
Halperin, Wolf, et al., 1991). On the computer screen, 11 different letters
sequentially appear in a quasi-random order over 400 trials. Total running
time was approximately 12 min that was divided into four 3-min epochs for
data analysis purposes. Each letter was presented for 200 ms, with
a 1,500-ms interstimulus interval. Children pressed a space bar as quickly
Table 2
Sample Size Available for Either the Continuous Performance
Test (CPT) or Covert Orienting Paradigm by Diagnostic
Group
Test
Control ADHD-C ADHD-I Total
Both CPT and orienting
27
38
7
72
CPT only
17
26
11
54
Orienting only
2
4
3
9
Total
46
68
21
135
Note.
ADHD-C
attention-deficit/hyperactivity disorder–combined
type; ADHD-I
attention-deficit/hyperactivity disorder–inattentive type.
data, and 7% (
n
ADHD DEFICITS AND THE VIGILANCE SYSTEM
423
as they could once they detected a target (i.e.,
A
followed by an
X
). Targets
occurred in 10% of the trials; false cues (i.e.,
A
not followed by
X
) occurred
in 17% of the trials. After instructions were given to the child, the trained
graduate or undergraduate research assistant sat quietly out of view of the
child for the duration of the task.
Examination of error type in combination with error speed allows for the
identification of three types of scores that can be conceptualized as
inat-
tention
,
impulsivity
, and
dyscontrol
errors (Halperin, Sharma, et al., 1991;
Halperin, Wolf, et al., 1991; Halperin et al., 1988). Inattention scores were
formed by summing the total number of (a) omissions, (b) slow hits
(correct hits
1700 ms), and (c) slow
X
-only commission errors (i.e.,
slower than the child’s mean RT, or MRT). Impulsivity scores were formed
by summing the total number of (a) fast
A
-not-
X
commission errors (a
keypress RT that is faster than the child’s MRT and is made after the
appearance of an
A
that is not followed by an
X
) and (b) slow
A
-only
commission errors (a keypress RT made
1,250 ms after the appearance
of an
A
but prior to the appearance of the next letter; conceptualized as
“jumping the gun”). Dyscontrol scores were formed by summing the total
number of remaining commission errors not already included in the inat-
tention or impulsivity scores, which therefore included (a) responses to any
letter other than
A
or
X
, (b) slow
A
-not-
X
responses, (c) fast
X
-only
commission errors, and (d) fast
A
-only commission errors. Measures of
perceptual sensitivity or signal detection accuracy (
d
) and response bias
(ln
) were calculated with the formulas and table provided in Hochhaus’s
(1972) article.
1
Our index of arousal was
d
. This index is a standardized measure of
how distinctly a target is perceived from a nontarget. Thus, the smaller the
value of
d
, the harder it is for a perceiver to distinguish between noise and
targets and thus the weaker is presumed to be the participants’ alertness, or
vigilance system (Pribram & McGuinness, 1976; Sergeant et al., 1999).
When
d
0, the hit rate (HR) and false alarm rate (FAR) are equal and
indicate chance performance.
To assess activation, we calculated response bias (ln
), which is an
index of whether a child tends to answer “yes,” resulting in a high HR and
FAR, or whether he or she tends to answer “no,” resulting in a high rate of
correct rejects and omission errors. If the perceiver is unbiased, then
ln
0. If ln
is negative, then the perceiver is biased toward “yes”
responses (highly activated response style). If ln
is positive, then the
perceiver favors “no” responses (underactivated response style). A positive
score, which suggests a reduced readiness to respond, is taken as an index
of a deficient activation system (Sergeant et al., 1999).
Visuospatial orienting.
To assess attentional orienting, we administered a
standard cue-detection task (Posner & Petersen, 1990). To probe the posterior,
reflexive orienting system, we administered a fast (150-ms cue–target delay)
exogenous cue condition. To probe the anterior, strategic orienting system, we
administered a slow (800 ms) endogenous cue condition. The exogenous
condition always preceded the endogenous condition to prevent contamination
of data by any carryover strategy effect that might be used during endogenous
cueing. Children completed a total of 120 trials over five blocks for the
exogenous condition, and 180 trials over five blocks for the endogenous
conditions. Total running time was 40 min. The child’s head was centered and
stabilized with a chin rest. A digital video camera, mounted behind the
computer monitor and facing the child, provided a close-up of the child’s right
eye. The experimenter observed eye movements on the video camera for each
trial and recorded eye movements that occurred within a trial by using a
response box that was synchronized with the computerized trial-by-trial ad-
ministration. Each trial was experimenter initiated once the experimenter saw
that the child’s gaze was at fixation.
In the exogenous cueing condition, the initial display consisted of a central
fixation cross, flanked 5° to the right and left by boxes, each subtending 1°
(horizontally), which appeared for 1,000 ms. A second, larger box then
appeared around one of the original boxes, which gave the appearance of a
sudden brightening to that location in space. This was the spatial cue, and it
remained present for the duration of the trial. Left-sided, right-sided, and
directionally neutral (brightening of both sides) cue conditions occurred ran-
domly and with equal probability. Single-sided cues validly predicted the
target location 50% of the time so that strategy mechanisms could not be
usefully applied in this condition. Because children cannot voluntarily allocate
spatial attention in less than 300 ms (Munoz, Hampton, Moore, & Goldring,
1999; Ross, Hommer, Breiger, Varley, & Radant, 1994; Rothlind, Posner, &
Schaughency, 1991), the 150-ms cue–target delay is not susceptible to strate-
gic or anterior system responding.
In the endogenous cueing condition, the same initial display appeared
for 1,000 ms. The cue was a centrally appearing arrow subtending 1°
(horizontally) that pointed to the right, the left, or in both directions
(neutral cues). Single-sided cues validly predicted target location 71% of
the time to maximize the benefit of strategic or planned orienting re-
sponses. A single 800-ms cue–target delay was used to allow enough time
for attention to be voluntarily allocated to the target.
In both conditions, targets were arrows subtending 0.5° (vertically) that
randomly and equiprobably pointed up or down. Targets remained present
until children made a forced-choice response indicating whether the target
arrow was pointing up or down. If target arrows were facing up, they
pressed the numeral 9 on a right-sided number pad, and if it was pointing
down, they pressed the 6. Stickers with up and down arrows were placed
over the appropriate numerals. Children were instructed to always keep
their eyes on the central fixation cross. In the exogenous cueing condition,
they were told that sometimes one of the boxes would light up to let them
know where the arrow would appear, sometimes the wrong box would light
up, and sometimes both boxes would light up. In the endogenous condition,
they were told that most of the time the central arrow would direct them to
where the target would appear. Children were told to press the appropriate
up or down button on a response box as fast as they could without making
mistakes to indicate the direction the target arrow was pointing.
Data analysis.
We used analysis of variance to decompose the factorial
design on both tasks. To convey effect size, we report the statistic partial
eta squared (
Results
Preliminary Description of Groups
Table 3 provides a description of groups. Consistent with diag-
nostic groupings, compared with control participants, children
with ADHD-I and ADHD-C had more inattentive and more hy-
peractive–impulsive symptoms. Children with ADHD-C had sig-
nificantly more hyperactive–impulsive symptoms and more inat-
tentive symptoms than did children with ADHD-I. Children with
ADHD-C also had a lower estimated IQ than did control partici-
pants or children with ADHD-I. Groups were well matched on age
and did not differ significantly in WIAT Basic Reading score.
There were no significant differences in sex distribution among
control participants and participants with ADHD subtypes. All of
these features of the sample held when restricted to the subset that
completed only the CPT or only the orienting task. Overall, be-
havioral ratings reflected significant impairment and supported the
diagnostic groupings assigned.
1
For cases in which HR
1orFAR
0,
d
and
cannot be calculated.
Therefore, following Davies & Parasuraman (1982), when HR
1or
FAR
0, HR was recalculated as 2
-1/number of targets
and FAR was recal-
culated as1–2
-1/number of non-targets
. Forty-nine children were able to
complete the task with either an HR of 1 or FAR of 0. There were no group
differences in the number of children who were able to maintain an HR
of 1,
2
(1,
N
126)
6.13,
p
.01.
2
), which is interpreted similar to
r
squared for a factor.
Results were unchanged when IQ, reading disorder, or conduct disorder
were covaried; we therefore report results without covariates for simplicity.
2
(1,
N
126)
3.14,
p
.08, although 29% of control participants
versus 8% of children with ADHD were able to maintain an FAR of 0,
424
HUANG-POLLOCK, NIGG, AND HALPERIN
Table 3
Demographic, IQ, Achievement, and Symptom Severity Information by Diagnostic Group
Control (
n
46)
ADHD-C (
n
68)
ADHD-I (
n
21)
Variable
M
SD
M
SD
M
SD
p
Age in months
115.54
13.85
115.20
13.05
114.97
13.96
.99
Estimated FSIQ
111.96
16.03
102.85
14.12
106.05
15.90
.008
WIAT Basic Reading
107.46
12.31
101.82
14.28
103.19
10.50
.08
Total no. of attention symptoms
0.87
1.15
8.47
0.94
7.90
1.00
.001
Total no. of hyper/impulsive symptoms
0.65
1.08
7.99
1.07
2.00
1.45
.001
Parent ratings (T-scores)
Conners’s ADHD index
46.85
4.17
72.85
9.11
71.05
7.98
.001
BASC Hyperactivity
41.96
7.38
71.95
15.71
47.62
9.49
.001
BASC Attention
44.93
7.15
69.28
8.58
68.95
6.24
.001
Teacher ratings (T-scores)
Conners’s ADHD index
44.43
2.67
68.00
9.20
65.48
10.75
.001
BASC Hyperactivity
43.89
5.38
63.35
12.19
52.00
10.54
.001
BASC Attention
43.16
5.42
64.56
7.99
63.71
8.86
.001
n
%
n
%
n
%
p
Ratio of boys:girls
27:19
48:20
10:11
.12
ODD diagnosis
1
2
29
44
5
24
CD diagnosis
2
4
17
25
0
0
RD
1
2
4
6
0
0
Note.
ADHD-C
attention-deficit/hyperactivity disorder–combined type; ADHD-I
attention-deficit/ hyperactivity disorder–inattentive type; FSIQ
Full Scale IQ; WIAT
Wechsler Individual Achievement Test; ADHD
attention-deficit/hyperactivity disorder; BASC
Behavioral Assessment Scale
for Children; ODD
Oppositional Defiant Disorder; CD
Conduct Disorder; RD
Reading Disorder.
Question 1. Vigilance: CPT Analysis
outcome according to some models of sustained attention perfor-
mance (Halperin et al., 1988). SATO effects likely contributed to
the lack of significant group differences in MRT on this task,
F
(2,
123)
1.82 (
Preliminary data check.
For control participants, MRT to cor-
rect hits was not significantly correlated with the number of false
alarms (
r
.16,
p
.30). Thus, there were no significant SATO
effects that could interfere with interpretation of RT scores for the
control group. This was not the case for children with ADHD-C,
however. MRT to correct hits was significantly correlated with
number of false alarms (
r
.47,
p
.001). For ADHD-I, MRT
to correct hits was moderately (but nonsignificantly) correlated
with number of false alarms (
r
0.30,
p
.23).
The negative correlation between RT to correct hits and number
of errors suggests that children with ADHD-C (and to a lesser
extent, ADHD-I) placed greater emphasis on speed over accuracy,
which is consistent with both behavioral observations of greater
impulsivity and hyperactivity in this group and is an expected
0.03,
p
.17), but statistically equivalent RTs
among groups does not imply equivalent cognitive efficiency. That
is, children with both subtypes of ADHD were still on average
slower than control participants (see Table 4), suggesting that even
a SATO strategy cannot fully account for the greater number of
errors committed.
Concerns regarding SATO effects impacting the interpretability
of RT data are further mitigated by the fact that for both ADHD
groups, MRT to correct hits was significantly and positively cor-
related with RT to false alarms (ADHD-C,
r
.48,
p
.001;
ADHD-I,
r
.67,
p
.001). That is, if children with ADHD
routinely intended to make anticipatory responses, according to the
2
Table 4
Continuous Performance Test Results by Group: Error Scores and Signal Detection Indices
Control
ADHD-C
ADHD-I
Index
M
SD
M
SD
M
SD
p
Error scores
Inattention
2.39
2.66
5.08
4.03
6.17
6.19
.001
Impulsivity
1.27
2.33
4.52
7.36
2.00
3.48
.01
Dyscontrol
1.05
1.84
4.86
8.94
3.56
5.11
.02
RT to correct hits
510.00
90.20
540.86
105.52
556.72
110.79
.17
SD
of RT
135.89
54.47
173.44
60.18
173.56
59.98
.003
Signal Detection
d
4.36
0.60
3.68
0.80
3.69
0.81
.001
ln
1.61
1.02
0.98
1.08
1.35
1.26
.01
Note.
ADHD-C
attention-deficit/hyperactivity disorder–combined type; ADHD-I
attention-deficit/hyper-
activity disorder–inattentive type; RT
reaction time.
Plik z chomika:
jardor
Inne pliki z tego folderu:
Untitled 1.jpg
(485 KB)
ZESPOL NADPOBUDLIWOSCI PSYCHORUCHOWEJ U DZIECI.doc
(70 KB)
ADHD wśród dzieci w wieku szkolnym i przedszkolnym - skany.doc
(10620 KB)
Problemy dzieci z zespołem ADHD i zaburzeniami koncentracji uwagi w przedszkolu.doc
(54 KB)
Metody pracy i terapia dzieci z zespołem ADHD.doc
(49 KB)
Inne foldery tego chomika:
0
1
1(1)
10
11
Zgłoś jeśli
naruszono regulamin