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Social Information Processing of Positive and Negative Hypothetical Events in Children With ADHD and Conduct Problems and Controls

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Journal of Attention Disorders
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Objective This study examined social information processing (SIP) of events with varied outcomes in children with ADHD and conduct problems (CPs; defined as oppositional defiant disorder [ODD] or conduct disorder [CD]) and controls. Method Participants were 64 children (46 boys, 18 girls) aged 6 to 12, including 39 with ADHD and 25 controls. Vignettes were developed that systematically varied with regard to peer intention (ambiguous, negative, positive) and event outcome (ambiguous, negative, positive), and were used to evaluate participants’ SIP abilities (cue encoding, interpretation, and response generation). Results Results showed that, after controlling for CPs, children with ADHD detected fewer positive, negative, and neutral cues; attributed more negative and less positive intent to peers; focused less on situational outcomes of vignettes; and generated fewer positive responses compared with the control group. Conclusion These results indicate that children with ADHD differ from non-ADHD children, even after controlling for CPs, in how they process positive and negative social experiences.
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Journal of Attention Disorders
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DOI: 10.1177/1087054711401346
2012 16: 491 originally published online 13 April 2011Journal of Attention Disorders
Stewart and Penny Corkum
Brendan F. Andrade, Daniel A. Waschbusch, Amelie Doucet, Sara King, Maura MacKinnon, Patrick J. McGrath, Sherry H.
Conduct Problems and Controls
Social Information Processing of Positive and Negative Hypothetical Events in Children With ADHD and
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Journal of Attention Disorders
16(6) 491 –504
© 2012 SAGE Publications
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DOI: 10.1177/1087054711401346
http://jad.sagepub.com
Social Information Processing
of Positive and Negative Hypothetical
Events in Children With ADHD and
Conduct Problems and Controls
Brendan F. Andrade1, Daniel A. Waschbusch2, Amelie Doucet3,
Sara King3, 4, Maura MacKinnon4, Patrick J. McGrath3,
Sherry H. Stewart3, and Penny Corkum3
Abstract
Objective: This study examined social information processing (SIP) of events with varied outcomes in children with
ADHD and conduct problems (CPs; defined as oppositional defiant disorder [ODD] or conduct disorder [CD]) and
controls. Method: Participants were 64 children (46 boys, 18 girls) aged 6 to 12, including 39 with ADHD and 25 controls.
Vignettes were developed that systematically varied with regard to peer intention (ambiguous, negative, positive) and event
outcome (ambiguous, negative, positive), and were used to evaluate participants’ SIP abilities (cue encoding, interpretation,
and response generation). Results: Results showed that, after controlling for CPs, children with ADHD detected fewer
positive, negative, and neutral cues; attributed more negative and less positive intent to peers; focused less on situational
outcomes of vignettes; and generated fewer positive responses compared with the control group. Conclusion: These
results indicate that children with ADHD differ from non-ADHD children, even after controlling for CPs, in how they
process positive and negative social experiences. (J. of Att. Dis. 2012; 16(6) 491-504)
Keywords
social information processing, ADHD, ODD, CD, prosocial, social cognition
Numerous studies have related information-processing def-
icits to aggressive behavior (Crick & Dodge, 1996), to
peer rejection (Coie, Dodge, & Kupersmidt, 1990; Milich
& Dodge, 1984), and to a lesser extent diagnosed disruptive
behavior disorders (DBD; Dodge, 1993; Dodge, Pettit, Bates,
& Valente, 1995; King, Waschbusch, Pelham, Frankland,
Andrade, et al., 2009; Landau & Milich, 1988; Lochman &
Dodge, 1994; Milich & Dodge, 1984; Moore, Hughes, &
Robinson, 1992). Research in this area has been spurred by
well-supported models of social cognition, including social
information processing (SIP) theory (Crick & Dodge, 1994;
Dodge, Pettit, McClasky, & Brown, 1986). This comprehen-
sive model postulates that children rapidly proceed through
six information-processing steps when they are presented
with social situations: (a) encoding of external and inter-
nal cues, (b) interpretation of attributions in relation to
self and others, (c) clarification of goal states, (d) accessing
or generating responses, (e) deciding on a response, and
(f) enacting the chosen response. These steps occur in con-
junction with a “database” of stored memories that guide
all processes.
Although each step is important, the encoding, inter-
pretation, and response-generation steps have received
considerable attention in terms of understanding social
behavior. There is now clear and consistent evidence that
these aspects of SIP are significantly associated with
aggressive behavior in children (e.g., Dodge, Laird, Lochman,
Zelli, & Conduct Problems Prevention Research Group,
2002). Specifically, research has shown that children with
high rates of aggressive behavior tend to (a) pay less
attention to relevant social cues, (b) interpret the motives
of others in a more hostile manner when the outcome of
1Centre for Addiction and Mental Health, Toronto, ON, Canada
2Center for Children and Families and the Department of Psychology,
Florida International University
3Dalhousie University, Halifax, Nova Scotia, Canada
4Mount Saint Vincent University, Halifax, Nova Scotia
Corresponding Author:
Brendan F. Andrade, Centre for Addiction and Mental Health,
250 College St., Toronto, ON, M5T 1R8, Canada
Email: brendan_andrade@camh.net
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492 Journal of Attention Disorders 16(6)
the social situation is negative and the peer’s intent is
ambiguous, and (c) generate aggressive, hostile responses
to social situations.
To date, SIP models have primarily been applied to
understand the social-cognitive abilities of children with
aggressive behavior, but several facts suggest that SIP
theory may also help in understanding children with
ADHD. First, the characteristic symptoms of ADHD (i.e.,
inattention, hyperactivity, and impulsivity) are likely
related to SIP abilities. That is, children who are inatten-
tive should attend to fewer or less-relevant social cues, and
children who are impulsive should spend less time gen-
erating possible responses to social situations. Second,
children with ADHD are often rejected by peers (Hoza,
2007; McQuade & Hoza, 2008; Pelham & Bender, 1982),
and rejected children often have impaired SIP abilities
(Dodge et al., 1986; Dodge & Price, 1994). This raises the
possibility that impaired SIP abilities account for the peer
rejection among children with ADHD. Third, and most
persuasive, available research shows that children with
ADHD have cue-encoding deficits that parallel those of
reactively aggressive children (Matthys, Cuperus, & Van
Engeland, 1999; Milich & Dodge, 1984; Moore et al.,
1992), have biased interpretations of social information
(Milich & Dodge, 1984; Moore et al., 1992; Murphy,
Pelham, & Lang, 1992), and generate more inappropriate
and fewer social responses than children without ADHD
(King, Waschbusch, Pelham, Frankland, Andrade, et al.,
2009; Matthys et al., 1999; Milich & Dodge, 1984; Murphy
et al., 1992). These facts suggest that children with ADHD
often have biased SIP.
Although much has been learned about SIP in children
with ADHD, additional research is needed. To date, research
on SIP in children with ADHD has focused exclusively on
how they process information from social situations that
have a negative valence—that is, social situations in which
the target child experiences a negative outcome. These situ-
ations are clearly important, but understanding situations
with a positive valence or with an ambiguous valence may
also be important because how children process positive
social information may be associated with the development
of prosocial behavior and positive peer relationships just as
biased negative information processing is associated with
hostile behavior (Nelson & Crick, 1999). The important
impact of prosocial behavior on child development has
long been established (Eisenberg, Fabes, & Spinrad, 2006);
however, the cognitive correlates of prosocial behavior are
not well understood. Limited evidence suggests that com-
pared with their peers, prosocial youth have less negative
and more positive social-cognitive abilities (Nelson &
Crick, 1999; Warden & MacKinnon, 2003). These findings
argue that there are important differences between positive
and negative aspects of social functioning and associated
differences in positive social cognition; however, no research
has examined whether children with ADHD differ in their
positive SIP abilities compared with controls.
Evaluating positive SIP may be especially important in
children with ADHD because they exhibit lower rates of
prosocial behavior when interacting with peers (Cunningham
& Siegel, 1987; Whalen et al., 1989). Likewise, boys with
ADHD demonstrate less empathy than boys without ADHD
(Braaten & Rosen, 2000), and attention has been described
as a key component of empathic responding in normal
developmental processes (Eisenberg et al., 2006). Finally,
there is evidence that ADHD symptoms may account for
deficits in prosocial behavior commonly reported in chil-
dren with conduct problems (CPs; Hay, Hudson, & Liang,
2010). These studies suggest that children with ADHD may
experience deficits in their ability to process social informa-
tion in situations containing positive outcomes; however,
research to date has only investigated SIP using social
vignettes that have negative outcome valences. The present
study extends current understanding of children’s SIP by
examining information-processing abilities of children with
ADHD in situations that contain combinations of positive,
negative, and ambiguous social information.
When examining how the valence (positive, negative,
ambiguous) of social situations influences children’s SIP, it
may be important to distinguish between social intention
and social outcome. We define intention as what the peer
meant (or intended) to do to the target child. Intent informa-
tion is inferred by the child based on their interpretation of
the information cues in a social situation. In contrast, out-
come information is concrete and represents what actually
happened to the target child. For instance, if a peer meant to
give the target child a cold drink but instead spilled it on his
or her shirt, the intention could be described as positive but
the outcome as negative. However, if the peer meant to
throw a cold drink on the target child’s shirt and then did so,
both the intent and outcome could be described as negative.
This example illustrates a few key points. First, the intent
and outcome of social situations can, and likely should, be
distinguished from each other when examining SIP. Second,
the valence of intentions and outcomes can independently
vary from positive to ambiguous to negative. Third, inten-
tions and outcomes may provide different information to
children and thus, result in different impacts on social
behavior. Importantly, no research has systematically varied
the valence of intention and outcome when examining chil-
dren’s SIP. Separately assessing interpretations of intent
and outcome in positive, negative, and ambiguous situations
may provide a more representative estimation of children’s
SIP in situations encountered in their daily life.
The purpose of this study was to investigate differences
in specific aspects of SIP between children with ADHD and
typically developing children using hypothetical social
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Andrade et al. 493
situations designed to systematically vary in the valence
(positive, negative, ambiguous) of intent and outcome. To
accomplish this purpose, new hypothetical social vignettes
allowing for the evaluation of different intent and outcome
valence combinations were developed and validated as a
measure of children’s SIP abilities. Next, these vignettes
were used to evaluate SIP abilities (cue detection, interpre-
tation, and response generation) in children with and
without ADHD. It was hypothesized that compared with
controls, children in the ADHD group would (a) encode
fewer total cues across all vignettes categories, (b) encode
fewer positive cues in vignettes that contain positive social
information, (c) make more negative intent attributions in
vignettes that contain ambiguous and negative social infor-
mation, (d) make less positive and more negative intent and
outcome attributions in vignettes that contain ambiguous
and positive social information, and (e) generate more neg-
ative and less positive responses in all vignettes, regardless
of valence of intent and outcome. Furthermore, these differ-
ences would remain even after controlling for co-occurring
CP. Finally, it was hypothesized that information-processing
deficits shown by children with ADHD would be robust
across different vignettes.
Method
Participants
Participants were 64 children, including 46 boys and 18 girls,
who ranged from 6 to 12 years of age (M = 9.32, SD = 1.75).
Thirty-nine children were diagnosed with ADHD and 25 were
typically developing children without ADHD, oppositional
defiant disorder (ODD), or conduct disorder (CD; controls).
Rating scale and demographic characteristics for ADHD and
control participants are summarized in Table 1.
The majority (n = 31) of children with ADHD were
recruited through a treatment program in eastern Canada,
and the remaining eight were recruited through community
advertisements. ADHD, ODD, and CD were diagnosed
using Diagnostic and Statistical Manual of Mental Disor-
ders (4th ed.; DSM-IV; American Psychiatric Association,
1994) criteria as evaluated using several sources of infor-
mation. First, symptom counts were computed for each
child and were considered present if they were endorsed by
either parent or teacher on the DBD Rating Scale or by
parent response on the DSM-IV version of the Diagnostic
Interview Schedule for Children (DISC). Next, impairment
was evaluated using parent and teacher ratings on the
Impairment Rating Scale. Finally, diagnoses were made by
doctoral-level clinicians if a sufficient number of symptoms
were endorsed (using symptom-count criteria specified in
the DSM-IV) and if there was evidence of clinically signifi-
cant impairment. Specifically, a diagnosis of ADHD-
inattentive type was assigned if at least 6 of the 9 inattention
symptoms were endorsed, a diagnosis of ADHD-
hyperactive/impulsive type was assigned if at least 6 of the
9 hyperactive/impulsive symptoms were endorsed, and a
diagnosis of ADHD-combined type was assigned if both of
these conditions were met. Likewise, a diagnosis of ODD
was assigned if at least 4 of the 8 ODD symptoms were
Table 1. Means (Standard Deviations) or Percentages for Demographic and Rating Scale Measures as a Function of Group
Measure Control (n = 25) ADHD (n = 39) Statistical test
Age (years) 9.2 (1.9) 9.4 (1.6) F = 0.24
Gender (% male) 64.0% 76.9% χ2 = 1.26
Socioeconomic statusa52.0a (14.9) 47.5 (11.8) F = 1.81
DBD symptom countsb
Inattention 0.3 (0.8) 7.7 (2.4) F = 228.26d
Hyperactive/impulsive 0.4 (1.0) 7.5 (2.0) F = 266.67d
Oppositional defiant 0.2 (0.5) 6.0 (2.3) F = 155.76d
Conduct disorder 0.0 (0.0) 2.7 (2.6) F = 25.71d
Impairment Rating Scalec
Peer relationships 0.2 (0.8) 4.8 (1.8) F = 142.08d
Getting along with adults 0.4 (0.9) 4.8 (1.6) F = 152.46d
School functioning 0.3 (1.0) 4.9 (1.6) F = 161.65d
Self-esteem 0.6 (1.5) 4.8 (1.8) F = 94.10d
Overall impairment 0.6 (1.4) 5.2 (1.4) F = 156.67d
Note: DBD = disruptive behavior disorders.
aSocioeconomic status for occupations in Canada (Blishen, Carroll, & Moore, 1987).
bNumber of symptoms endorsed by parents or teachers on the Disruptive Behavior Disorder Rating Scale (Pelham et al., 1992).
cMaximum score across parent and teacher ratings on the Impairment Rating Scale (Fabiano et al., 2006).
dADHD and controls differ at p < .05.
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494 Journal of Attention Disorders 16(6)
endorsed, and a diagnosis of CD was assigned if at least 3
of the 15 CD symptoms were endorsed. In all cases, chil-
dren also had to show evidence of impairment, although
this was always the case for children who met symptom-
count criteria. Of the children with ADHD, 84.6% (n = 33)
met criteria for ADHD-combined type, 7.7% (n = 3) met
criteria for ADHD-inattentive type, and 7.7% (n = 3) met
criteria for ADHD-hyperactive/impulsive type. All but
one child with ADHD also met criteria for ODD (n = 20,
50.3%) or CD (n = 18; 46.2%). Based on previous research
suggesting that stimulant medication may have acute effects
on SIP (King, Waschbusch, Pelham, Frankland, Andrade,
et al., 2009), all children with ADHD were unmedicated for
at least 12 hr prior to their participation.
The control children (n = 25) were recruited through
parent response to posters, public service announcements
on the radio and in the newspaper, and through a university
information service. Control children were screened for
mental health problems using information obtained from
the DISC-IV and DBD Rating Scale. In addition, parents of
control children were queried by interview to determine
whether their children had ever received intervention for
behavioral or learning difficulties. Children with evidence
of current or past behavior or learning problems were
excluded from participation.
Procedure
Procedures used in the study were approved by a university
institutional review board. Parents gave written, informed
consent and children gave assent before participating in
the study.
SIP measures were administered during a 40 to 60 min
session with a trained research assistant. Frequent breaks
were taken to reduce fatigue and to maintain a high level
of engagement. Children were rewarded for completion of
each vignette by a sticker of their choice and at the end of
the activity by a small reward of their choice (e.g., sticker
packet or toy). Parents completed diagnostic and behavior
ratings. For children recruited from the treatment program,
parent measures were completed pretreatment and child
(SIP) measures were completed during treatment. For chil-
dren recruited from the community, parent and child measures
were completed at the same time in separate rooms with
different research assistants.
Diagnostic and Screening Measures
DBD Rating Scale. The DBD Scale measures DSM-IV
symptoms of ADHD, ODD, and CD (Pelham, Gnagy,
Greenslade, & Milich, 1992) with well-supported reliability
and validity (Pillow, Pelham, Hoza, Molina, & Stultz, 1998;
Wright, Waschbusch, & Frankland, 2007). Items are rated
using Likert-type scales that range from 0 (not at all) to
3 (very much), with symptoms rated 2 or 3 considered
present in the child. Internal consistency (α) reliability
estimates for the DBD scales in this sample were ADHD-
inattention = .98, ADHD-hyperactive/impulsive = .97,
ODD = .97, and CD = .88.
National Institute of Mental Health (NIMH) DISC-IV. The
DISC-IV is a structured clinical interview designed to pro-
vide DSM-IV diagnoses of major mental health disorders in
children aged 6 to 17 (NIMH-DISC Editorial Board, 1999).
The computerized version of the DISC-IV was self-
administered by parents unless the parent had reading or
language problems, in which case, the computerized ver-
sion was administered by a clinician or trained research
assistant. A CP score was computed from the DISC-IV by
summing the number of ODD and CD symptoms that were
endorsed (M = 3.93, SD = 4.09, α = .92).
Impairment Rating Scale (IRS). The IRS measures the
child’s current functioning and need for treatment in several
developmentally important areas, including peer relation-
ships, teacher and parent relationships, academic/school
functioning, self-esteem, and overall adjustment (Fabiano
et al., 2006). Items are evaluated using visual-analogue
scales that are scored using a 0 (no problems/no need for
treatment) to 6 (severe problems/definitely needs treatment)
metric. Alphas are not reported for the IRS as each item is
scored separately, but the reliability and validity of the IRS
have been supported in several samples. For instance, in
one sample (Fabiano et al., 2006), 1-year test–retest reli-
ability correlations for teacher ratings on the IRS ranged
from .40 to .67 and interrater (parent and teacher) reliability
correlations ranged from .47 to .64 with criterion-validity
correlations ranging from .44 to .80.
Development of SIP Vignettes
Following earlier research (Dodge & Price, 1994), the SIP
vignettes used in this study were developed in three steps:
(a) vignette construction, (b) expert review, and (c) vignette
validation. These steps are described next.
Vignette construction. Thirty-five vignettes were initially
developed for examination. These vignettes described social
situations involving peers participating in activities such
as sports, game-play, sharing, cooperating, playground and
school yard accidents, and other common childhood social
scenarios. The vignettes were designed to vary with regard
to peer intent and situation outcome as follows: (a) positive
(2 vignettes)—positive intent with positive outcome. The
peer in the story meant to do something positive (had a posi-
tive intention), and his or her action produced a positive
outcome for the participant; (b) negative (2 vignettes)—
negative intent with negative outcome. The peer in the story
meant to do something negative (had a negative intention),
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Andrade et al. 495
and his or her action produced a negative outcome for
the participant; (c) ambiguous (10 vignettes)—ambiguous
intent with ambiguous outcome. It was unclear what
the peer in the story intended to do (ambiguous intent), and
his or her behavior produced a neutral outcome (neither
good nor bad) for the participant; (d) ambiguous-positive
(14 vignettes)—ambiguous intent with positive outcome.
The intention of the peer was unclear, but his or her behavior
produced a positive outcome for the participant; and
(e) ambiguous-negative (7 vignettes)—ambiguous intent
with negative outcome. The intention of the peer was unclear
but his or her behavior produced a negative outcome for the
participant. Four of the ambiguous-negative vignettes were
identical to those used in previous research (Dodge & Frame,
1982; Dodge & Price, 1994) and the remaining 31 vignettes
were novel to this study. A larger number of ambiguous and
ambiguous-positive vignettes were developed because they
were novel to this study, and we wished to maximize the
probability of obtaining valid vignettes.
Expert review. The 35 vignettes were submitted to an
expert review. Experts consisted of four PhD-level univer-
sity faculty who had expertise in child development and
developmental psychopathology. After agreeing to partici-
pate, experts were given a questionnaire containing the
35 potential vignettes and were asked to independently rate
(forced choice) the intention of the child and the outcome of
the situation as positive, negative, or ambiguous. Participants
were asked to consider intent and outcome as independent
constructs and not let one rating influence the other.
Expert ratings showed 100% agreement for 22 vignettes
on intention ratings and for 12 vignettes on outcome rat-
ings. Experts also showed 75% agreement for 9 vignettes
on intention ratings and for 10 vignettes on outcome
ratings. Less than 75% agreement was found for other
vignettes, and these were dropped from further analysis.
Vignettes with 75% or greater agreement on intention and
outcome ratings were further refined during panel discus-
sion with experts who completed the review. Additional
3 vignettes were also developed during this panel discus-
sion. After the expert review (including the discussion),
38 vignettes were produced.
Vignette validation. Finally, the validity of the 38 vignettes
was evaluated using ratings collected from 14 advanced
(4th or 5th year) psychology graduate students who were
naive to the purpose of the study. The use of student ratings
of vignette valence has been successfully used in previous
research to validate SIP measures (e.g., Dodge & Frame,
1982; MacBrayer, Milich, & Hundley, 2003). After volun-
teering to participate, graduate students completed ratings
on an electronic version of the 38 vignettes produced by the
expert review. These vignettes were broken into two parts;
the first part represented the intention of the child in the
vignette, and the second part represented the outcome of the
vignette. Students rated the valence of each part (indepen-
dently) using 5-point Likert-type scales that ranged from
1 (very much positive) to 5 (very much negative). These rat-
ings were used to select 20 vignettes that best distinguished
the valence (positive, negative, ambiguous) of the intention
and outcome (details available from the first author). The
final 20 vignettes used in the study consisted of 4 vignettes
for each of the five intention/outcome combinations
(positive, negative, ambiguous, ambiguous-positive, and
ambiguous-negative).
SIP Administration
Children were instructed to pretend that they were the pro-
tagonist in the story, following which they were to read a
vignette (described above) in which they experienced posi-
tive, negative, or neutral outcomes due to a peer’s behavior
that was clearly positive, clearly negative, or ambiguous
(not clearly positive or negative). After the story, children
were asked a series of questions that were designed to eval-
uate different aspects of their SIP. These questions were
partly derived from previous research (Crick & Ladd, 1990;
Dodge, 1980; Dodge & Frame, 1982; Rubin & Krasnor,
1983) and included the following:
1. What happened in the story?
2. How could you tell whether this was a nice way to
act or a mean way to act?
3. What could you say or do if this happened to you?
Tell me as many ways as you can.
After Question 3 was asked, children were prompted by
the examiner to “please tell me some more ways.” This
prompt was given until children clearly did not have any
more responses.
All responses were transcribed during the interview and
each interview was video recorded to verify accuracy of
written content. Interviews were conducted by one master’s
level psychology student and one advanced undergraduate
student.
SIP Response Coding
SIP responses were coded by two advanced undergraduates
who were naive to the study purpose and diagnostic status
of participants. Training involved detailed instruction of
code types and code categories, including examples of pro-
totypic responses that did and did not meet criteria for
different codes. Ongoing consultation with the primary
author was provided to resolve any questions. Coding was
used to measure the participant’s cue detection, intent and
outcome attributions, and response generation using proce-
dures described below. Following the experimental design,
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496 Journal of Attention Disorders 16(6)
codes were scored as a function of the five vignette types
(positive, negative, ambiguous, ambiguous-positive, and
ambiguous-negative) and the three code valences (positive,
negative, and neutral), resulting in 15 scores for each type
of social information (except as noted elsewhere).
Cues detected. Cues detected by participants were coded
from answers to Question 1 (i.e., “What happened in the
story?”). Coders were provided with a list of cues and asked
to indicate those that were present in the response. Cue con-
tent within vignettes was determined using a three-step
process. First, the first author developed a list of cues con-
tained within each vignette. Second, four individuals naive
to the study classified each cue as positive, negative, or
neutral. Third, these classifications were reviewed and
compared with the investigators a priori judgments. Mini-
mal discrepancies in classifications were found and these
were resolved by discussion. The total number of cues
detected (regardless of valence) and the percentage of posi-
tive, negative, and neutral cues detected (out of total cues
present) were computed for each vignette.
Intent and outcome attributions. Intent and outcome attri-
butions were derived from answers to Question 2 (“How
could you tell whether this was a nice way to act or a mean
way to act?”). Intent attribution was coded if participants
focused on the reason or the purpose for which the child
in the vignette committed an action. A response coded as an
intent attribution implied a thought process of the child in
the vignette; however, the thought process was not included
in the vignette. For example, a participant’s response coded
as intent was “Sam is mean because he does not like me” or
“Sam is mean because he is mean to a lot of kids.” These
responses refer to Sam’s thought processes that were not
described in the vignette (i.e., Sam wanting to be mean to a
lot of kids). Outcome attribution was coded if participants
focused on an action that occurred in the vignette. For
example, outcome was coded if a participant responded
“Sam was mean because he shoved me” or “Sam was nice
because he shared his Gameboy™ with me.” Intent and out-
come attributions were coded as positive, negative, or
neutral. The total number of each type of attribution (posi-
tive, negative, neutral) were summed separately for intent
and outcome and separately for each type of vignette.
Response generation. Response generation was derived
from answers to Question 3 (“What could you say or do if
this happened to you? Tell me as many ways as you can.”).
Responses generated were coded as positive, negative, or
ambiguous. The total number of responses (regardless of
valence) and the proportion of positive versus negative
versus neutral responses (out of total responses generated)
were computed for each type of vignette.
Reliability. Internal consistency was computed within each
type of vignette using Cronbach’s alpha. Results were as fol-
lows: cues detected (α = .94), intent attributions (α = .81),
outcome attributions (α = .83), and responses generated
(α = .93). Interrater reliability was evaluated by having the
two coders overlap on a randomly selected 33% of the par-
ticipants and was examined in two steps. First, paired sample
t tests were used to compare the two coders and showed that
scores did not differ between them—total cues detected,
t(19) = –0.76, p = .455; total intent attributions, t(19) =
–0.37, p = .717; total outcome attributions, t(19) = –0.58,
p = .871; total responses generated, t(19) = –2.01, p = .059.
Second, Pearson correlations were computed and were
acceptable for cue detection (total r = .97, p < .001; positive
r = .95, p < .001; negative r = .93, p < .001; neutral r = .96,
p < .001), intent attributions (total r = .64, p < .001; positive
r = .59, p < .001; negative r = .71, p < .001), outcome attribu-
tions (total r = .76, p < .001; positive r = .80, p < .001;
negative r = .69, p < .001), and response generation (total
r = .95, p < .001; positive r = .85, p < .001; negative r = .68,
p < .001). Interrater reliability for neutral intent attributions,
neutral outcome attributions, and neutral response genera-
tion were not acceptable (rs .27) most likely because of the
fact that they infrequently occurred and were thus dropped.
Analytic Plan
Data were examined using a series of repeated measures
ANCOVAs with group (ADHD vs. control) included as a
between-participants factor, vignette (positive vs. negative
vs. ambiguous vs. ambiguous-positive vs. ambiguous-
negative) included as a within-participants factor, and CPs
(defined as the total number of ODD and CD symptoms
endorsed by parent on the DISC-IV) included as a covari-
ate. Other measure-specific factors were also included in
the ANCOVAs as described below. Significant interactions
were followed up with simple effects tests and by comput-
ing standardized mean difference effect sizes (Hedges’s g)
as follows: (ADHD M – Control M)/pooled SD. Effect sizes
were computed using means adjusted for the covariate. To
save space, only significant effects involving group were
followed up.
Results
Cue Detection
Total cues detected. Total cues detected were examined
using a 2 (group) × 5 (vignette) ANCOVA and resulted
in significant main effects of vignette, F(4, 244) = 25.75,
p < .001, and group, F(1, 61) = 8.08, p = .006. After control-
ling for CPs, control children detected more cues than
children with ADHD (control: M = 13.02, SD = 3.59; ADHD:
M = 10.12, SD = 3.43; Hedges’s g = –0.83).
Valence of cues detected. The proportion of positive
versus neutral cues detected in positive vignettes was
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Andrade et al. 497
examined using a 2 (group) × 2 (cue: positive vs. neutral)
ANCOVA. These data were examined separately from
other cue data because, by design, there were no negative
cues in the positive vignettes. There was a significant main
effect of group, F(1, 61) = 10.82, p = .002, which showed
that after controlling for CPs, control children detected a
higher proportion of cues than did children with ADHD
(control: M = 0.66, SD = 0.21; ADHD: M = 0.46, SD =
0.20, Hedges’ g = –0.98). The 2 (group) × 3 (cue) × 4
(vignette) ANCOVA examining the remaining vignettes
resulted in significant main effects—vignette: F(3, 183) =
5.11, p = .002; cue: F(2, 122) = 36.87, p < .001; group:
F(1, 61) = 6.33, p = .015—and two-way interactions—
Cue × Group: F(2, 122) = 3.43, p = .036; Vignette × Cue:
F(6, 366) = 15.02, p < .001. As illustrated in Figure 1, after
adjusting for CPs, control children detected a significantly
larger proportion of positive, negative, and neutral cues
than did children with ADHD, but the difference was larg-
est for neutral cue detection.
Intent and Outcome Attributions
Intent and outcome attributions were examined using a
2 (group) × 5 (vignette) × 2 (attribution type: intent vs. out-
come) × 2 (attribution valence: positive vs. negative)
ANCOVA. There was a significant main effect of vignette,
F(4, 244) = 2.57, p = .039); significant two-way interactions,
Attribution Type × Group: F(1, 61) = 13.79, p < .001;
Attribution Type × Vignette: F(4, 244) = 16.30, p < .001;
Vignette × Attribution Valence: F(4, 244) = 132.03, p < .001;
and a significant three-way interaction Attribution Type ×
Attribution Valence × Vignette: F(4, 244) = 9.64, p < .001),
but these were qualified by a significant Group × Vignette ×
Attribution Type × Attribution Valence interaction, F(4, 244) =
7.39, p < .001. The four-way interaction was examined by
graphing intent attributions (see Figure 2) and outcome attri-
butions (see Figure 3) as a function of group, attribution
valence, and vignette. In positive and ambiguous-positive
vignettes, children with ADHD made significantly more
positive intent attributions than controls (see top half of
Figure 2); however, in these same vignettes, controls made
significantly more outcome attributions than children with
ADHD (see top half of Figure 3). Likewise, in clearly nega-
tive vignettes, children with ADHD made significantly more
negative intent attributions than controls (see bottom half
of Figure 2), whereas in these same vignettes, controls
made significantly more negative outcome attributions than
children with ADHD (see bottom half of Figure 3). Children
with ADHD also made significantly more positive intent
attributions than controls in ambiguous-negative vignettes
(see top half of Figure 2). The four-way interaction was also
followed up by comparing intent and outcome attributions
separately for each group. For children with ADHD, intent
attributions were always equal to or significantly higher than
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
Positive
# of Attributions
Vignette Type
Positive Intent Attributions
ControlADHD
p = .027
g = 0.66
p = .155
g = 0.42
p = .728
g = 0.10
p = .003
g = 0.90
p = .020
g = 0.70
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
# of Attributions
Vignette Type
Negative Intent Attributions
p = .001
g = 1.01 p = .102
g = 0.48
p = .363
g = –0.27
p = .901
g = –0.04
p = .499
g = 0.20
Ambiguous-
Negative
Ambiguous-
Positive
AmbiguousNegative
Positive Ambiguous-
Negative
Ambiguous-
Positive
AmbiguousNegative
ControlADHD
Figure 2. Number of positive (top graph) and negative (bottom
graph) intent attributions as a function of group and vignette type
Note: p = p value from simple effects tests; g = Hedges’ g effect size.
Figure 1. Proportion of positive, negative, and neutral cues
detected as a function of group
Note: p = p value from simple effects tests; g = Hedges’s g effect size.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
NeutralNegativePositive
Percent Cues Detected
Cue Type
Control ADHD
p = .045
g = – 0.60
p = .047
g = – 0.58
p = .007
g = –1.27
Valence of Cues Encoded
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498 Journal of Attention Disorders 16(6)
outcome attributions. For control children, outcome attribu-
tions were always equal to or higher than intent attributions
with the exception that negative intent attributions were
higher than negative outcome attributions in ambiguous-
positive events.
Response Generation
Total responses generated. Total responses generated were
examined using a 2 (group) × 5 (vignette) ANCOVA. There
was a significant main effect of vignette, F(4, 244) = 10.87,
p < .001, but no significant effects involving group.
Valence of responses generated. The valence of responses
generated was examined using a 2 (group) × 5 (vignette) ×
2 (response valence: positive vs. negative) ANCOVA.
There were significant main effects group: F(1, 61) = 10.20,
p = .002; response: F(1, 61) = 340.21, p < .001 and signifi-
cant two-way interactions, Response × Group: F(1, 61) = 6.24,
p = .015; Vignette × Response: F(4, 244) = 3.76, p = .005;
but these were qualified by a significant Vignette × Response
× Group interaction, F(4, 244) = 3.39, p = .010. As illus-
trated in Figure 4, after controlling for CPs, control children
generated significantly more positive responses than chil-
dren with ADHD in the clearly negative and ambiguous
vignettes, whereas children with ADHD generated signifi-
cantly more negative responses than controls in negative
vignettes.
Discussion
This study examined SIP abilities in children with ADHD,
the majority of whom also had CPs, in situations that varied
in the valence of intent and outcome. We first developed
vignettes that allowed for the separate evaluation of the
role of peer intent and situational outcome on SIP. These
vignettes were then used to evaluate several components of
Dodge’s SIP model (Crick & Dodge, 1994; Dodge et al.,
1986)—namely, cue encoding, intent and outcome attribu-
tions, and response generation—in children with and
without ADHD while controlling for CPs. It was hypothe-
sized that compared with controls, children in the ADHD
group would encode fewer cues (especially positive cues in
vignettes with a positive valence), make more negative
intent attributions in vignettes that contain ambiguous and
negative social information, make less positive and more
negative intent and outcome attributions in vignettes that
contain ambiguous and positive social information, and
generate more negative and less positive responses in all
vignettes. As described next, the results generally supported
the hypotheses.
In support of the first hypothesis, results showed that
children with ADHD encoded fewer cues than control chil-
dren. Furthermore, children with ADHD encoded a lower
percentage of positive, negative, and neutral cues in all
vignettes (see Figure 1). These results are consistent with
other studies demonstrating cue-detection deficits in chil-
dren with ADHD (Cadesky, Mota, & Schachar, 2000;
Dodge & Newman, 1981; Matthys et al., 1999; Milich &
Dodge, 1984; Moore et al., 1992). This study adds to this
literature by showing that this deficit is generally robust
with regard to the valence of the social cues, the valence of
peer intent, and the valence of the social outcome. An
important question for future research is what accounts for
these cue-detection deficits. One possibility is inattention.
It may be that children with ADHD simply do not notice the
cues when they are presented. Alternatively, it may be that
the children initially attend to the cues but fail to encode
them due to working-memory deficits. For example, chil-
dren with ADHD may not miss salient negative social cues
(e.g., being “bumped in the back”) but may miss less salient
cues and have a limited ability to “mentally manipulate” the
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
# of Attributions
Vignette Type
Positive Outcome Attributions
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
# of Attributions
Vignette Type
Negative Outcome Attributions
p = .001
g = –1.13
p = .332
g = –0.29
p = .205
g = –0.37
p = .174
g = –0.40
p = .358
g = 0.27
Ambiguous-
Negative
Ambiguous-
Positive
AmbiguousNegativePositive
Ambiguous-
Negative
Ambiguous-
Positive
AmbiguousNegativePositive
ControlADHD
p = .001
g = –1.07
p = .319
g = –0.29
p = .006
g = –0.84
ControlADHD
Figure 3. Number of positive (top graph) and negative (bottom
graph) outcome attributions as a function of group and vignette
type
Note: p = p value from simple effects tests; g = Hedges’s g effect size.
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Andrade et al. 499
information or forget details when asked. Each of these
explanations is viable as both inattention (Douglas, 1999;
Losier, McGrath, & Klein, 1996) and deficient working
memory (Shiels et al., 2008; Willcutt, Doyle, Nigg, Faraone,
& Pennington, 2005) are associated with ADHD. Regard-
less of the underlying cause, the effect is likely impairing
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent of Responses
Vignette Type
Positive Response
Control ADHD
p= .775
g= –0.08
p= .001
g= –0.99 p= .035
g= –0.63
p= .288
g= –0.31 p= .063
g= –0.56
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent of Responses
Vignette Type
Negative Response
Control ADHD
p= .009
g= 0.79
p= .158
g= 0.42 p= .798
g= 0.08
p= .655
g= 0.13
p= .852
g= –0.05
Ambiguous-NegativeAmbiguous-PositiveAmbiguousNegativePositive
Ambiguous-NegativeAmbiguous-PositiveAmbiguousNegativePositive
Figure 4. Proportion of positive (upper graph) and negative (lower graph) responses generated as a function of group and vignette
type
Note: p = p value from simple effects tests; g = Hedges’s g effect size.
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500 Journal of Attention Disorders 16(6)
because cue detection is the first step of the SIP cycle. This
suggests that children with ADHD will not have the same
amount of relevant social information at their disposal as
other children. This could have a cascading effect on other
SIP steps and may be one factor that contributes to the well-
established peer problems in children with ADHD (Hoza,
2007; Hoza et al., 2005; Pelham & Bender, 1982).
As demonstrated in Figures 2 and 3, results investigating
the second step of Dodge’s SIP model showed that ADHD
and control children differed on intent and outcome attribu-
tions. The outcomes in the stories consisted of observable
information, whereas the peer intents in the stories were not
observable. These results may suggest that children with
ADHD rely more heavily on their own opinion of what is
taking place in social situations, rather than on observable
factual information. In support of this explanation, previous
research with aggressive children—many of whom may
have had ADHD—has shown that they are significantly
biased toward using intent attributions rather than factual
information (Dodge & Somberg, 1987). This interpretation
is also consistent with the cue-encoding deficits described
earlier in that children with ADHD may have failed to
encode the observable facts in the vignettes (because of
inattention or working memory deficits or both) and thus,
had to infer the missing information. This hypothesis—that
the intent–outcome attribution differences between control
and children with ADHD reflects the fact that children
with ADHD are less likely to rely on observable social
information—could be directly evaluated by using vignettes
that separate not only intent from outcome but also observ-
able from ambiguous information. More specifically, had
we included not only ambiguous intents with clear out-
comes but also clear intents with ambiguous outcomes,
we could directly test whether it is the type of information
(intent or outcome) or the amount of observable informa-
tion (clear or ambiguous) that is most important to ADHD
versus controls when assessing social situations. Including
these stories in future research could be informative.
Results investigating the response-generation step of
Dodge’s SIP model showed that both groups of children
generated a high proportion of positive responses to all five
types of vignettes, even those with negative outcomes (see
Figure 4). However, a significantly higher portion of
the control children’s responses were positive in negative
and ambiguous situations as compared with children with
ADHD, whereas children with ADHD generated a higher
proportion of negative responses in negative situations.
Consistent with these results, past research has shown
that hyperactive/aggressive children generate significantly
more negative responses and fewer prosocial responses to
hypothetical ambiguous-negative situations than controls
(Milich & Dodge, 1984). The present results suggest that
response-generation challenges extend to situations where
both the peer intent and the outcome of the peer’s behavior
are ambiguous. Researchers have speculated that lower
rates of positive responses are a product of infrequent expo-
sure to appropriate social situations or practice with
enacting positive social behaviors (Coie et al., 1990; Crick,
1996; Wentzel & McNamara, 1999). Considering that inat-
tention is significantly associated with social problems
(Andrade, Brodeur, Waschbusch, Stewart, & McGee, 2009)
and that children with ADHD appear to miss important
social information (see Figure 1), it seems likely that they
will consequently have fewer positive social experiences.
In fact, evidence suggests that children with ADHD have
significantly higher rates of negative interactions with peers
(Hoza, 2007; Pelham & Bender, 1982) and are almost
immediately rejected by them (Erhardt & Hinshaw, 1994;
Pelham & Bender, 1982). It is noteworthy that cue
detection, attribution, and response-generation deficits
demonstrated by children with ADHD were found regard-
less of vignette valence, peer intent, or social outcome,
highlighting that children with ADHD experience SIP
deficits regardless of the content of social information.
Likewise, differences were found between the groups not
only on negative and ambiguous-negative vignettes but also
on positive and ambiguous-positive vignettes. Specifically,
the attribution results showed that children with ADHD
endorsed positive intent attributions more strongly than
controls on the positive and ambiguous-positive vignettes
but not in other vignettes (see Figure 2). Similarly, controls
endorsed positive outcome attributions more strongly than
children with ADHD in positive and ambiguous-positive
vignettes but not in other vignettes. As such, failure to
include the vignettes with positive information would have
missed detecting these effects. Past SIP research has typi-
cally focused on the association between processing of
negative social information and hostile behavior. By
including vignettes with predominantly positive social
information, this study adds to the growing body of lit-
erature highlighting the importance of also considering
prosocial information processing on children’s development.
Better understanding attribution of positive information
in situations that contain positive vignettes is a step toward
clarifying the impact of positive social cognition on proso-
cial behavior. Results of the present study support the
hypothesis that SIP biases for children with ADHD may be
prevalent for both negative and positive social information
and that these biases may vary depending on the type of
social interaction (i.e., ambiguous, positive, negative). At
the same time, caution is needed in interpreting these
results. First, although group differences emerged on
positive situations for attributions, they did not emerge
for encoding or response generation. Second, the mean-
ing of differences on positive situations remains unclear.
That is, although it is well established that SIP of
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Andrade et al. 501
ambiguous-negative situations is clinically meaningful,
predicting outcomes such as behavior with peers and social
status (Crick & Dodge, 1994; Dodge, 1986; Dodge & Coie,
1987), it is currently unknown whether the same is true for
SIP of positive situations. Additional research evaluating
these questions would be informative.
Several limitations of this study should be noted. First,
SIP was examined using hypothetical stories. This is a stan-
dard procedure for evaluating SIP abilities, but the study
would have been strengthened by including more ecologi-
cally valid measures of SIP. Second, although the present
study used rigorous diagnostic screening, including initial
phone screening for cognitive and learning difficulties, IQ
was not explicitly measured or incorporated into the analy-
ses. This is consistent with much previous research on SIP
in children, but consideration of cognitive factors, includ-
ing the potential impact of working memory abilities, may
be important for future larger scale investigations. Third,
the participants in the study were typically in lower to
middle socioeconomic status and living in a small urban
center in Canada. The results can be safely generalized to
similar populations but may not be representative of chil-
dren in other settings (e.g., rural areas, larger urban centers).
Fourth, children with ADHD were unmedicated at the time
of the study. Past research suggests that stimulant medica-
tion may have significant acute effects on the SIP abilities
of children with ADHD (King, Waschbusch, Pelham,
Frankland, Andrade, et al., 2009), suggesting that different
results may emerge for children with ADHD who are treated
with medication. Therefore, results can only be safely gen-
eralized to children with ADHD who are unmedicated.
Fifth, virtually all children with ADHD also had clinically
significant CPs. We included a measure of CPs as a covari-
ate in all analyses, thereby ensuring that differences between
the ADHD and control group were considered after taking
CPs into account. At the same time, this approach does not
allow for a test of ADHD and CPs alone and in combination
(i.e., as an interaction). To accomplish this, it is necessary to
include children with ADHD only, CPs only, and ADHD/
CPs (as well as controls), but this was not possible in the
present study because of pragmatic concerns.
Implications for Research, Policy, and Practice
Results have a number of implications for clinical and
research practice. Findings provide preliminary support for
distinguishing between positive and negative information
when evaluating social functioning, including SIP. Positive
and negative information may be processed differently. If
so, treatment programs or research studies that emphasize
only negative or positive aspects of cognition (or likely
behavior) may miss inclusion of the other valuable compo-
nent. Results of the present investigation also clearly
demonstrate the importance of taking ADHD into account
when examining SIP. This same point has been made for at
least 25 years (Milich & Dodge, 1984) but warrants repeat-
ing as ADHD continues to be neglected in many studies of
aggression and social functioning.
The results of this study suggest a number of areas for
future research. First, it would be interesting to replicate
this study using other methods of evaluating SIP, such as
videotapes or virtual-reality tasks. In addition, investiga-
tion of SIP using vignettes that separate all combinations
of positive, negative, and ambiguous intent and outcome
information would provide a more thorough understanding
of SIP abilities in children with ADHD. Second, we specu-
lated that cue-encoding deficits may be related to inattention
or working memory and that response-generation deficits
may be related to the impulsivity. Direct tests of these and
other possibilities would provide important information
about SIP in disruptive children. Finally, an important goal
of future research is to translate these findings into inter-
vention for ADHD and/or aggressive behavior. Limited
research suggests that stimulant medication may influence
SIP abilities (King, Waschbusch, Pelham, Frankland,
Andrade, et al., 2009; King, Waschbusch, Pelham, Frankland,
Corkum, et al., 2009; Murphy et al., 1992), but much more
research is needed to better understand effects of treatment—
both medication and behavior therapy—on SIP abilities.
Likewise, research is needed that examines whether treat-
ments targeting SIP abilities (e.g., Lochman, Barry, &
Pardini, 2003) have adjunctive value when added to empiri-
cally supported treatments for ADHD.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interests with
respect to the authorship and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for
the research and/or authorship of this article: This research was
conducted as part of the first author’s doctoral dissertation and was
partially supported by grants from the Nova Scotia Health Research
Foundation, the Social Sciences and Humanities Research Council
of Canada, and the IWK Health Centre.
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Bios
Brendan F. Andrade, PhD, CPsych, is a clinical psychologist and
independent clinician scientist at the Child Youth & Family Pro-
gram, Centre for Addiction and Mental Health, Toronto, Ontario,
Canada. He is assistant professor in the Department of Psychiatry
at the University of Toronto. His research focuses on the social-
cognitive factors which negatively impact behavior and peer
functioning, prosocial behavior and interventions to reduce disrup-
tive behavior.
Daniel A. Waschbusch, PhD, is a professor in the Center for
Children and Families and the Department of Psychology at
Florida International University. His research focuses on the
assessment and treatment of children with ADHD, conduct
problems and/or callous-unemotional traits. He has authored or
co-authored nearly 100 articles, has held grants in the U.S. and
Canada, and has given presentations and workshops to experts
in psychology, education, and the law.
Amelie Doucet, M.A., CPsych, is a Provisional Psychologist at
the CanLearn/Calgary Learning Centre, Calgary, Alberta, Canada.
Sara King, PhD, RPsych is an assistant professor in the Faculty of
Education at Mount Saint Vincent University, Halifax, Nova
Scotia, Canada.
Maura MacKinnon, MASP is a registered school psychologist
with the Halifax Regional School Board, Halifax, Nova Scotia,
Canada and a part-time associate of a private practice.
Patrick J. McGrath, PhD, is a professor of psychology at
Dalhousie University, Halifax, Nova Scotia.
Sherry H. Stewart, PhD, is a professor of psychology at
Dalhousie University, Halifax, Nova Scotia.
Penny Corkum, PhD, is a professor of psychology at Dalhousie
University, Halifax, Nova Scotia.
at DALHOUSIE UNIV on July 18, 2013jad.sagepub.comDownloaded from
... When emotional processing was examined across the specific emotions in MA2, significant differences were found between ADHD and controls across all emotion categories. Numerous studies have previously reported differences between ADHD and control groups in processing of positive emotions, as assessed by behavioural [11,54,62,66,68,97,104], neural [78,86] or psychophysiological measures [64]. These differences cannot be attributed to a lack of knowledge or problems retrieving emotional labels, as both groups seem to exhibit similar proficiency in emotional word fluency [113]. ...
... For example, Basile et al. [31] found no significant differences between the groups in emotion recognition performance, but they noted that easy items were intentionally selected. However, in more complex tasks involving social scenes, individuals with ADHD identified fewer relevant cues compared to controls [54,100]. In this regard, Friedman et al. [74] found that adults with ADHD used less emotional vocabulary to describe interactions between two characters they viewed in a film. ...
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... These results showed that children with ADHD have deficits in encoding and interpreting the social cues that are necessary to understand a social problem and develop appropriate social responses. These findings are also consistent with other evidence suggesting that children with ADHD display deficits in encoding and interpreting social cues when involved in social situations (Andrade et al., 2012;Dodge & Newman, 1981;Cadesky et al., 2000;. Studies clearly show that children who have better encoding skills in social situations also show better social competence and have a low level of depression (Quiggle et al., 1992). ...
... These results are consistent with the findings of the previous studies. Indeed, a large amount of existing studies showed that children with ADHD have deficits in multiple steps of social information processing compared to children in the control groups (Andrade et al., 2012;. ...
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Introduction: Examining the performance of children with Attention Deficit Hyperactivity Disorder (ADHD) in each step of the social information processing and their executive functioning behaviors while comparing them to typically developing (TD) children and determining their limitations in these processes is important for reducing the future risks that children with ADHD may face in academic and social life. In this context, the aim of the study is to comparatively examine the social information processing and executive functioning behaviors of children with ADHD and TD children. Method: The study was conducted using a general survey model, which is one of the quantitative research designs. The participants of the study included 25 children diagnosed with ADHD, aged between 8 and 10, and 25 TD children of the same gender and age range. Additionally, 25 teachers and 50 parents participated in the study. The data collection tools used in the study were the Social Information Processing Assessment Form and the Parent and Teacher Form of the Behavioral Rating Inventory of Executive Functions (BRIEF). Findings: The study findings showed significant differences between children with ADHD and TD children in all stages of the Social Information Processing Assessment Form. Similar significant differences were also found in all the sub-scales and sub-dimensions of the Parent and Teacher Form of the Behavioral Rating Inventory of Executive Functions. The relationships between social information processing skills and executive functioning skills also revealed significant associations between some sub-stages of the Social Information Processing Skills Assessment Form and some sub-dimensions of the Parent and Teacher Form of the Behavioral Rating Inventory of Executive Functions. Discussion: The findings indicate that children with ADHD experience limitations in each of the six steps of the Social Information Processing Model and in some sub-dimensions of executive functions when compared to their TD peers. The findings emphasize the significance of the relationships between social information processing and executive functioning in the development of social and academic skills in children with ADHD.
... Sustained attention and selective attention are crucial to children's success in social interactions as they enable children to continuously attend to social cues and select appropriate social responses (Andrade et al., 2012). A few studies have investigated specific aspects of social impairment and CDS in non-clinical populations and ADHD child samples. ...
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This study aimed to examine emotion dysregulation and internalizing disorders mediating the relationship between selective and sustained attention and loneliness preference in children diagnosed with attention deficit hyperactivity disorder (ADHD) and Cognitive Disengagement Syndrome (CDS). This study included 176 children and adolescents between ages 8 and 12. The solitude scale for children, Difficulties in Emotion Regulation Scale, Child Behavior Checklist, Barkley Sluggish Cognitive Tempo Scale, and CNS Vital Signs test were used. The results suggest that difficulties in emotion regulation and having an internalizing disorder had a mediating effect between difficulties in selective attention and preference for the loneliness of children with ADHD + CDS. Also, it was likely that the association between sustained attention and preference for loneliness is mediated by internalizing disorders. The results suggest that the social problems commonly exhibited by children with ADHD + CDS may be related to deficits in sustained and selective attention.
... In children and adolescents with conduct problems and co-occurring ADHD, encoding deficits may underly difficulty recognizing problematic social situations. Children with ADHD indeed tend to encode fewer social cues than control children (Andrade et al., 2012;Matthys et al., 1999). These encoding deficits seem to be related to inattention (Ferretti et al., 2019). ...
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Chapter
For many years investigators have worked toward achieving a better understanding of the causes of the impaired performance of children with Attention-Deficit/Hyperactivity Disorder (ADHD) on cognitive, information-processing, and neuropsychological tasks. These efforts have led to a recent emphasis on identifying the “core” or “primary” dysfunction responsible for ADHD children’s cognitive problems. In this chapter, I attempt an overview of the extensive and often confusing literature in this area, emphasizing findings that I believe must be encompassed in a working conceptualization of the cognitive deficits associated with ADHD. I also point to conceptual and statistical problems that I believe are impeding research progress on ADHD.
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Evaluated the social information-processing abilities of hyperactive-rejected, hyperactive-accepted, nonhyperactive-rejected, and nonhyperactive-accepted boys using Dodge's (1986) model of social competence as a framework. Hyperactivity status was ascertained using the Inattentive Overactive with Aggression (IOWA) Conners Scale (Loney & Milich, 1982). Peer nomination procedures are used to determine sociometric status. Results indicate that hyperactive-rejected boys display a unique constellation of social information-processing deficits relative to nonhyperactive-rejected boys. Specifically, nonhyperactive-rejected boys commit more attributional errors relative to hyperactive-rejected, hyperactive-accepted, and nonhyperactive-accepted boys. In addition, hyperactive-rejected boys exhibit excessive encoding and cue utilization deficiencies as compared to boys in the remaining three groups. Implications of these findings as well as suggestions for future research are discussed.