J Autism Dev Disord (2013) 43:1157–1170 DOI 10.1007/s10803-012-1657-y

A Comparison of Social Cognitive Profiles in children with Autism Spectrum Disorders and Attention-Deficit/Hyperactivity Disorder: A Matter of Quantitative but not Qualitative Difference? Carly Demopoulos • Joyce Hopkins Amy Davis

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Published online: 27 September 2012 Ó Springer Science+Business Media, LLC 2012

Abstract The aim of this study was to compare social cognitive profiles of children and adolescents with Autism Spectrum Disorders (ASD) and ADHD. Participants diagnosed with an ASD (n = 137) were compared to participants with ADHD (n = 436) on tests of facial and vocal affect recognition, social judgment and problem-solving, and parent- and teacher-report of social functioning. Both groups performed significantly worse than the normative sample on all measures. Although the ASD group had more severe deficits, the pattern of deficits was surprisingly similar between groups, suggesting that social cognitive deficit patterns may be more similar in ASD and ADHD than previously thought. Thus, like those with ASDs, individuals with ADHD may also need to be routinely considered for treatments targeting social skills. Keywords Autism
ADHD
Social skills
Facial and vocal affect recognition
Pragmatic judgment
Parent and teacher report

This paper was prepared from a predoctoral thesis submitted by the primary author as part of the degree requirements for the Ph.D. program in clinical psychology at Illinois Institute of Technology. C. Demopoulos
J. Hopkins Illinois Institute of Technology, Department of Psychology, 3105 South Dearborn, Suite 252, Chicago, IL 60616-3793, USA C. Demopoulos (&) Mind Research Network, Pete and Nancy Domenici Hall, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA e-mail: [email protected] A. Davis Alexian Brothers Neuroscience Institute, 801 Biesterfield Rd., Elk Grove Village, IL 60003, USA

Introduction A deficit in social interaction is a defining feature of Autism Spectrum Disorder (ASD; DSM-IV-TR 2000); however, social deficits are also a well-documented phenomenon in children with ADHD (Clark et al. 1999; Corbett and Constantine 2006; Hattori et al. 2006; Leyfer et al. 2006; Stormont 2001; Whalen et al. 1990). Indeed, there is a considerable body of research that has identified symptom overlap between ADHD and ASD (Corbett and Constantine 2006; Goldstein and Schwebach 2004; Holtmann et al. 2007; Leyfer et al. 2006; Sturm et al. 2004; Yerys et al. 2009; Yoshida and Uchiyama 2004). It is not clear, however, if the social deficits in each diagnostic group differ in quality or degree. Models of social information processing propose that receptive social skills (attending to, perceiving, and accurately interpreting relevant social information) are necessary to inform and execute an appropriate social response (Crick and Dodge 1994; Shapiro et al. 1993). Thus, difficulties at the level of either receptive social skills or behavioral response could adversely impact a social outcome. The hierarchical nature of these models implies that deficits in receptive social skills lead to a suboptimal behavioral response based upon misunderstanding of the social context. Alternatively, accurate social perception in the context of a limited repertoire of appropriate social problem-solving or response options may result in socially detrimental behavior as well. Thus, the specific skill deficits (i.e., social comprehension vs. social response) that lead to inappropriate social behaviors are distinct from each other and require different interventions to improve social functioning. Understanding the nature of social difficulties in children with poor social competence is essential to identifying appropriate interventions. The aim of the

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present study was to examine a range of skills relating to social comprehension and execution of social behavior in children with ASD and ADHD, and to determine if these groups differ in type or degree of social impairment.

Receptive Social Skills Facial Affect Processing in ASD Comprehension of affective cues is considered to be an essential aspect of receptive social processing (Crick and Dodge 1994; Shapiro et al. 1993). As such, affect recognition has been the focus of numerous studies of children with ASD and ADHD. Converging evidence indicates that some level of impairment in facial affect recognition is common in children with ASD (Bo¨lte and Poustka 2003; Braverman et al. 1989; Celani et al. 1999; Gross 2004; Hall et al. 2003; Mazefsky and Oswald 2007; Ozonoff et al. 1990; Piggot et al. 2004; Welchew et al. 2005), although, there are some contradictory data indicating that these children are not impaired on basic emotion recognition tasks (Baron-Cohen et al. 1997a; b; Castelli 2005; Gepner et al. 2001; Heerey et al. 2003; Prior et al. 1990; Wang et al. 2004). In two of these studies (Baron-Cohen et al. 1997a, b), the children with ASD exhibited impaired performance relative to controls when recognition of complex emotions was tested, although they were not impaired in the recognition of basic emotions. The complexity of affective stimuli, however, cannot fully account for the discrepant findings in the remainder of the studies in which intact affect recognition was reported in the ASD samples. An additional factor that may contribute to the inconsistent findings is the failure of some of the studies to control for IQ, despite evidence that performance on facial affect recognition tasks is related to general intellectual (Bo¨lte and Poustka 2003; Mazefsky and Oswald 2007), or verbal ability (Braverman et al. 1989; Ozonoff et al. 1990; Prior et al. 1990). A large number of studies, however, have identified impairments even after controlling for these variables (Bo¨lte and Poustka 2003; Celani et al. 1999; Gross 2004; Humphreys et al. 2007; Mazefsky and Oswald 2007), or have failed to find any relationship between intellectual functioning and facial affect recognition (Davies et al. 1994; Heerey et al. 2003). Results also have indicated associations between affect recognition and age (Bo¨lte and Poustka 2003), suggesting that there may be a developmental component to difficulty with emotion recognition. For example, children who experience delays in these skill areas may acquire compensatory strategies with age and experience. Null findings reported in some of these studies may be a result of failure to control for a potential age-related confound.

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Gross (2004) controlled for these potentially confounding variables by comparing children with autism to those with intellectual disability, language disorder, and typically developing controls on a multi-species facial emotionrecognition task. Results indicated that children with autism scored significantly lower than the three other groups of similarly-aged children despite the fact that two of these groups exhibited cognitive and language impairments comparable to those of the autistic group. While all characteristics were not simultaneously controlled in this study (i.e., IQ was significantly higher in the children with autism than in those with intellectual disability, but significantly lower than controls, and communication scores in the autism group were significantly lower than controls), these data support the hypothesis that children with autism are less accurate in identifying facial affect for reasons not related to language or general intellectual ability. In sum, the weight of the evidence suggests that there is an elevated frequency of deficits in facial affect comprehension among the ASD population; however, the factors impacting the variability of skills within this group remain unclear. Facial Affect Processing in ADHD Facial affect recognition in children with ADHD has not been studied as extensively as in those with ASD; however, the extant literature presents the same pattern of mixed results, with some studies identifying deficits (Cadesky et al. 2000; Corbett and Glidden 2000; Norvilitis et al. 2000; Pelc et al. 2006; Rapport et al. 2002; Shapiro et al. 1993; Singh et al. 1998), and others failing to do so (Guyer et al. 2007; Hall et al. 1999; Sprouse et al. 1998). Also consistent with findings in the autism literature, there is a trend toward impaired affect recognition in studies of younger children with ADHD (Singh et al. 1998), with one study identifying a significant group by age interaction (Shapiro et al. 1993). This age effect was not supported in a study that reported facial affect recognition impairments in 28 adults with ADHD relative to a control group, however (Rapport et al. 2002). In a study by Guyer et al. (2007), in which both age and IQ were covaried, no significant differences were found between ADHD and control groups on facial affect recognition performance. Similarly, other studies have also failed to find group differences on facial affect recognition tasks between children with ADHD and controls when matched on age and IQ (Hall et al. 1999; Sprouse et al. 1998). Two studies in which IQ alone was controlled identified deficits in facial affect recognition in children with ADHD relative to controls; however, despite all IQs being in the average range, significant IQ advantages were noted in the control groups for both studies (Cadesky et al. 2000; Corbett and Glidden 2000). In sum, the literature on facial affect recognition in ADHD is

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inconsistent. Similarly to the ASD literature, differences in study designs and participant and task characteristics make definitive conclusions difficult to draw. In general, the literature suggests that it is important to control for participant age and IQ in the study of facial affect recognition in children with both ADHD and ASD. Vocal Affect Processing in ASD Data on vocal affect recognition in children with ASD are similarly inconsistent. Specifically, several studies have indicated poor recognition of vocal affect in individuals with ASD (Golan et al. 2006; Ja¨rvinen-Palsey et al. 2008; Linder and Rosen 2006), whereas other studies have failed to find deficits. Mazefsky and Oswald (2007) found that children and adolescents with high functioning autism exhibited impaired vocal affect recognition while those with Asperger’s Disorder did not. In a study that compared affect matching (face to voice) and naming in children with ASD to those with specific language impairments (SLI), and a control group, the children with ASD scored lower than controls and higher than the SLI group on the affect matching task (Boucher et al. 2000). Surprisingly, affect naming was only impaired in the SLI group and not in the ASD group. O’Connor (2007) also failed to find differences between adult participants with Asperger’s Disorder and controls in identifying affect in face or voice, although identification of incongruent affect among face-voice paired stimuli was impaired in the Asperger’s group. This complex pattern of findings has spurred several studies that have examined auditory processing of voices in ASD. There is well-documented evidence of abnormal auditory processing in individuals with ASD (Baranek 1999; Dahlgren and Gillberg 1989; Gillberg and Coleman 1996; Lepisto¨ et al. 2005; Osterling and Dawson 1994; Rimland and Edelson 1995), specifically with regard to elements of speech that relate to understanding of emotional content, such as prosody (Ja¨rvinen-Palsey et al. 2008; Korpilahti et al. 2007; Rhea et al. 2005). Nevertheless, a direct relationship has not been established between these sensory processing issues and social cognitive deficits. Vocal Affect Processing in ADHD There are few studies that have examined vocal affect processing in children with ADHD; however, unlike the ASD literature, results provide consistent evidence of deficits in vocal affect recognition (Corbett and Glidden 2000; Norvilitis et al. 2000; Rapport et al. 2002). For example, children ages 6 to 11 with ADHD scored significantly lower than controls when required to match prosody to sentence content and facial expression (Shapiro

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et al. 1993). Abnormal auditory processing also has been demonstrated in children with ADHD (Huttunen-Scott et al. 2008; Kemner et al. 2004), potentially accounting for the difficulty in interpreting vocal affect. However, also in line with the ASD literature, no studies have directly linked auditory processing difficulty to errors in vocal affect recognition and thus, the etiology of these deficits remains unclear. Expressive Social Skills Deficits in social interaction are a defining feature of ASD (DSM-IV-TR 2000), and, therefore, there is an extensive literature focused on defining and assessing the nature of atypical social behavior in individuals on the autism spectrum. MacIntosh and Dissanayake (2006) found that children ages 4-10 with autism and Asperger’s Disorder were significantly less likely than their typically developing peers to interact socially with other children, to sustain interactions, or to interact with three or more children at a time during unstructured playground observations. Differences between clinical groups were largely due to a higher level of involvement in conversation in the children with Asperger’s compared to those with autism, which may relate to differences in expressive language, rather than social motivation. Further, when the social interaction was structured, as in the case of complementary play, both clinical groups were as frequently involved in social interaction as the typically developing children. In contrast, during periods of unstructured social play, children with ASD were less likely to participate. The authors suggested that this finding was related to difficulty understanding social expectations in children with ASD. In another study using direct observation to assess social behavior in children with ASD and typically developing peers, Murdock et al. (2007) found that children ages 5–10 with ASD demonstrated significantly fewer initiations of verbal behavior or joint attention, fewer verbal responses, and fewer total interactions than typically developing peers. Some studies have examined the quality of social interactions, rather than specific behaviors in individuals with ASD. For example, Ghaziuddin (2008) categorized social interaction of individuals ages 7–51 with autism and Asperger’s Disorder according to the three categories ‘‘aloof,’’ ‘‘passive,’’ and ‘‘active but odd.’’ Aloof participants were described as indifferent toward others in most situations, while passive participants did not initiate contact but responded appropriately without adding information to further the interaction. Finally, active but odd participants often initiated social interactions of an inappropriate nature (i.e., asking personal questions, etc.). Results indicated that most individuals with autism were categorized as aloof and passive, while those with

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Asperger’s Disorder were most often classified as active but odd. The author discussed this finding in relationship to shared symptoms of Asperger’s Disorder and ADHD. Ruble (2001) reported findings based on natural observation in their homes during structured and unstructured time of 6–10-year-old children with autism and Down syndrome, respectively. Results suggested differences in the frequency and complexity of socially-intended behaviors in children with autism, with a possible mediating effect of executive function and attention. Studies examining behavior of children with ADHD during social interaction have demonstrated a failure to modulate behavior according to the social context. In a review of the literature, Landau and Moore (1991) concluded that children with ADHD were less sensitive to more passive roles that require less activity during interaction. Further, children ages 6–12 with ADHD did not appropriately modulate communication style for different roles assigned to them in a role-playing task (Landau and Milich 1988). There is also some evidence that children with ADHD have poor social problem-solving skills, which lead to poor social judgments and behavior. In a study by Grenell et al. (1987), 7–11-year-old children with ADHD gave less suitable descriptions of an appropriate social behavior in response to a social vignette compared to peers. Comparison Studies There are a few studies that have directly compared social behavior in children with an ASD to those with ADHD. Luteijn et al. (2000) compared social deficits in 5- to 12-year-old children with ADHD and those with a Pervasive Developmental Disorder, Not Otherwise Specified (PDD-NOS). The authors reported social difficulties in both groups, differentiated by greater severity of deficits in social skills, withdrawal, relating, social interaction and communication in the PDD-NOS group. Data from this study, however, were limited to parent report measures, and the authors were, therefore, unable to control for IQ. Dyck et al. (2001) found that 9- to 16-year-old children with autism, Asperger’s Disorder and ADHD all scored lower than a control group on a battery of emotion recognition tests. When IQ was covaried, this pattern held for all groups except the Asperger’s group, who performed as well as controls. However, because analyses were performed on composite scores from a battery of tests of ‘‘empathic ability,’’ it is unclear which specific social cognitive deficits differentiated groups. In a study of 8- to 18-year-old children and adolescents, Buitelaar et al. (1999) found that an ASD group (including participants with autism and PDD-NOS) could not be differentiated from an ADHD group on theory of mind or emotion recognition tasks; although both groups performed

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significantly worse than a control group. Through their focus on the dimension of social cognition across diagnostic groups, these comparison studies have significantly added to a growing body of research investigating the nature of the deficits that lead to the poor social outcomes in children with both of these disorders. To summarize, the literature on facial affect processing suggests that there is variable performance among children with both ADHD and ASD. The factors that affect performance are not clear, although there is evidence of a relationship between affect comprehension and age (De Sonneville et al. 2002), possibly indicating that some individuals with deficits in facial affect identification may develop compensatory skills that allow them to improve their performance as they get older. Although vocal affect processing has been more frequently studied in children with ASD than in those with ADHD, the evidence of vocal affect processing deficits is more consistent in the studies of children with ADHD. To date, there are no studies that have concurrently examined facial and vocal affect processing, as well as expressive social behavior and social outcomes in children diagnosed with ASD and ADHD, as we did in this study. The shape of each group’s social cognitive profiles may offer insight into the etiological sources of social deficits in these two groups of children. Finally, information regarding the levels at which the social information processing system is disrupted in children with ASD or ADHD can be used to direct early intervention in children with these disorders.

Methods Participants Potential participants included 710 consecutive children and adolescents referred to a pediatric neuropsychology clinic in an academic medical center in the Midwestern United States who received a diagnosis of an ASD or ADHD. Inclusion criteria did not include language ability because impairment in communication is a defining feature of ASD, and the severity and type of communication impairments encompass a wide range of language abilities. Thus, excluding participants based on language ability would result in a biased sample of children on the autism spectrum. In addition, previous research has suggested that some of these skills may vary with age, and that a limited or discordant age range may account for inconsistent findings among studies with respect to facial affect processing deficits. Thus, all participants who were of appropriate age for the study measures (ages 6–17) were included. This resulted in a final sample of 573 children with a mean age of 10.54 years diagnosed with an ASD (N = 137) or ADHD (N = 436). Sample size for different

J Autism Dev Disord (2013) 43:1157–1170 Table 1 Sample size of group subtypes

Subtype

1161

Autistic disorder

49

Asperger’s disorder

39

PDD-NOS

49

ADHD, combined type

271

ADHD, inattentive type

137

ADHD-NOS

judgment incorporating all sources of information rather than on cut-off scores on the ADOS, for reasons specified in the measures section. Participants in both groups met diagnostic criteria according to DSM-IV-TR.

N

Measures

28

Diagnostic Assessment The ADHD Rating Scale (DuPaul et al. 1998) is an 18-item parent report measure of ADHD symptomatology with adequate psychometric properties. Specifically, Cronbach’s alpha values range from 0.79 to 0.84 and test–retest reliability is reportedly .85 (Zhang et al. 2005). The Childhood Autism Rating Scale (CARS; Schopler et al. 1980) is a 15-item clinician-report measure with good psychometric properties. Interrater reliability of the CARS subscales ranges from .71 to .93, with an internal consistency coefficient alpha of .94. The correlation between CARS scores and consensus clinical diagnosis is .80 (Schopler et al. 1980). The Social Communication Questionnaire (SCQ; Rutter et al. 2003) is a 40-item parent report measure with a sensitivity of .92 and specificity of .62 in classification of ASD compared to clinical diagnosis (Witwer and LeCavalier 2007). The Autism Diagnostic Observation Schedule (ADOS; Lord et al. 1989) is a semi-structured observational tool used to quantify social and communicative behavior in relation to autism symptomatology. Assessment of classification accuracy of the ADOS compared to consensus clinical diagnosis has indicated that the ADOS

subtypes are described in Table 1 and participant characteristics are presented in Table 2, with scores indicating minimal difference in ADHD symptom rating between the ASD and ADHD groups (Table 2). This finding is in line with previous research demonstrating high levels of ADHD symptomatology in individuals with ASDs (Corbett and Constantine 2006; Goldstein and Schwebach 2004; Holtmann et al. 2007; Leyfer et al. 2006; Sturm et al. 2004; Yerys et al. 2009; Yoshida and Uchiyama 2004). Diagnoses were made by a licensed, board certified, clinical neuropsychologist based on integration of developmental history, parent interview, school observation, record review, neuropsychological testing, and scores on the ADHD Rating Scale (DuPaul et al. 1998). In addition, the Childhood Autism Rating Scale (CARS; Schopler et al. 1980), the Social Communication Questionnaire (SCQ; Rutter et al. 2003), and the Autism Diagnostic Observation Schedule (ADOS; Lord et al. 1989) were administered to all children with suspected autism symptomatology. Diagnosis of an ASD was ultimately based on clinical Table 2 Group characteristics (M ± SD)

ASD group

ADHD group

Statistics/range

Age FSIQ

10.39 ± 3.49 88.33 ± 18.86

10.58 ± 3.11 98.20 ± 14.79

t(208.49) = .57a t(191.36) = 5.61**,a

VIQ

93.27 ± 18.25

103.66 ± 13.60

t(185.80) = 6.15**,a

POI/PRI

95.01 ± 17.13

100.30 ± 15.36

t(571) = 3.42*

BASC-P: hyperactivity

62.88 ± 13.12

63.48 ± 13.55

t(531) = .44

BASC-P: inattention

63.49 ± 7.58

64.81 ± 8.05

t(532) = 1.64

BASC-T: hyperactivity

60.62 ± 12.28

58.84 ± 13.15

t(414) = -1.23

BASC-T: inattention

60.96 ± 8.57

61.28 ± 8.87

t(415) = .32

ADOS (S ? C total)

12.14 ± 5.04

Range: 1–23

CARS

31.34 ± 5.58

Range: 20–47

SCQ

14.45 ± 6.92

Range: 0–30

Ethnicity (n)

* p \ .01 ** p \ .001 a

Corrected values—equal variances not assumed

Caucasian

102

316

African American

2

20

Hispanic

5

8

Asian

4

4

Other Unknown

5 19

6 82

123:14

293:143

Male:female

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effectively differentiated autism from non-spectrum disorders with reported specificities of .93–1.0 (Lord et al. 2000). The ADOS was designed to be used in the context of a larger and thorough diagnostic evaluation incorporating developmental history. Several instances in which a participant may fail to meet criteria on the ADOS while meeting criteria for an ASD are outlined in the ADOS Manual (Lord et al. 2001). Thus, clinical judgment incorporating diagnostic tools such as the ADOS is considered to be the ‘‘gold standard’’ in diagnosing an ASD, and therefore this approach was used in the present study. Intelligence IQ was assessed with the age-appropriate Wechsler test, including either the Wechsler Intelligence Scale for Children-IV (WISC-IV; Wechsler 2003) or the Wechsler Adult Intelligence Scale-III (WAIS-III; Wechsler 1997), which have been shown to be reliable measures of IQ. Reliability between WAIS-III and WISC-IV was reported to be r = .89 for Full Scale IQ, r = .86 for Verbal Comprehension Index (VCI), and r = .76 between the WAIS-III Perceptual Organization Index and the WISC-IV Perceptual Reasoning Index (PRI; Flanagan and Kaufman 2009). Affect Recognition The child and adult faces and paralanguage subtests of the Diagnostic Assessment of Nonverbal Accuracy-2 (DANVA2; Nowicki and Duke 1994) were used to measure facial and vocal affect identification abilities. This computer task presents the participant with a photographic image of an individual from the head to shoulders for 2 s for the facial affect recognition subtest. For the vocal affect recognition task the participant hears the same spoken sentence, ‘‘I’m going out of the room now, but I’ll be back later,’’ presented in a range of vocal affective tones one at a time. For each stimulus presentation the participant selects a response from a choice of four, labeled ‘‘happy,’’ ‘‘sad,’’ ‘‘angry,’’ or ‘‘fearful.’’ Stimuli range in varying levels of subtlety of emotional expression, and high- and low-intensity expressions from adult and child stimuli were combined to increase power, resulting in two variables, for facial and vocal affect, respectively. Dependent variables were standard scores derived from a table of age norms for total errors on each subtest. The DANVA-2 has been used in studies examining specificity of emotion-labeling deficits in a range of childhood psychopathology (Guyer et al. 2007) and also in studies examining social cognition and disorders of social functioning (see manual for a list of citations; Nowicki 2010). The DANVA-2 has been shown to have acceptable internal consistency and reliability (Nowicki and Carton 1993; Nowicki and Duke 1994), with reported reliabilities ranging

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from .69 to .88 and internal consistency ranging from .64 to .90 (Nowicki 2010; Nowicki and Duke 1994; Nowicki and Mitchell 1998). Further information on studies demonstrating convergent, discriminant, and other criterion-related validity measures of the DANVA-2 can be found in the test manual (Nowicki 2010). Social Problem-Solving The Test of Problem Solving 3-Elementary (TOPS-3E; Bowers et al. 2005), appropriate for children ages 6-13, and Test of Problem Solving 2-Adolescent (Bowers et al. 2007; TOPS-2A), appropriate for ages 12–18, were used to measure ability to integrate social skills to accurately read and formulate an appropriate response to picture stimuli (TOPS-3E) or written paragraphs (TOPS-2A) about interaction with others and the environment. Test–retest reliabilities range from .64 to .95 for the TOPS-3E and from .85 to .96 for the TOPS-2A. Social Judgment The pragmatic judgment subtest of the Comprehensive Assessment of Spoken Language (CASL; Carrow-Woolfolk 1999) was used as a second measure of social performance ability. This subtest evaluates the effective use of language in common, real-life social situations, asking the examinee to use contextual factors, apply mentalizing skills, and flexibly respond to contrived social situations, such as adjusting behavior during introductions to different people (i.e., peers vs. authority figures), politely declining offers, and expressing honesty with sensitivity to the feelings of another person, etc. Carrow-Woolfolk (1999) reported internal consistency reliabilities ranging from .79 to .92 across the range of age groups in the normative sample. Informant-Report of Social Competence The parent- and teacher-report on the Social Skills Scale of the Behavior Assessment Scale for Children-2nd Edition (BASC2; Reynolds and Kamphaus 2004) was used to measure parent and teacher ratings of social skills and behavior. Test–retest reliabilities range from .74 to .86 for the Social Skills scale, and interrater reliability between two parents ranges from .64 to .75 (Reynolds and Kamphaus 2004). Procedure All tests were administered and scored according to agescaled norms by a trained psychometrician and scoring was checked by a licensed, board-certified neuropsychologist. To avoid the above-mentioned concerns regarding sample bias due to limitations of age or language ability in test

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with social cognitive measures. Pearson correlation coefficients for the combined sample are reported in Table 4. Because all scores are based on age-scaled norms, performance differences due to age were not expected; however, research describing a potential interaction of age and diagnostic group on social cognitive skill development suggests that examination of correlations was warranted. Correlational analyses failed to indicate any strong correlations between age and any of the social measures (maximum r = -.11); therefore, age was not specifically controlled beyond the age-scaled test scoring. Some strong relationships were found, however, between social cognitive performance measures and measures of IQ. Examination of partial correlations indicated that full scale IQ (FSIQ) had the strongest relationship to the majority of social cognitive measures (with partial correlation values ranging from .02 for Parent Report BASC to .27 for Pragmatic Judgment) after controlling for all other IQ measures, including VIQ and PIQ. As such, FSIQ was systematically controlled in all analyses.

Table 3 Sample size with percentage missing by study task and group prior to imputation Task condition

ASD (N = 137)

ADHD (N = 436)

FSIQ

112 (18.25 %)

414 (5.05 %)

VIQ

113 (17.52 %)

414 (5.05 %)

PIQ

114 (16.79 %)

415 (4.82 %)

Facial affect

115 (16.06 %)

276 (36.70 %)

Vocal affect

113 (17.52 %)

274 (37.16 %)

95 (30.66 %)

158 (63.76 %)

Problem-solving Parent rating

74 (26.89 %) 130 (5.11 %)

163 (62.61 %) 399 (8.49 %)

Teacher rating

108 (21.17 %)

307 (29.59 %)

Pragmatic judgment

administration, data imputation was performed to replace missing data so that a representative sample of children with ADHD and ASD could be included in this study. A multiple imputation was performed on LISREL 8.8 using the EM algorithm with settings of 200 iterations, 10 repetitions, random seed, and convergence criteria of 0.00001. In multiple imputation a series of imputed datasets are created, analyzed, and ultimately combined into a final dataset. This procedure acknowledges the uncertainty due to imputation and attempts to minimize the resulting increase in error. See Shafer and Graham (2002) for a discussion of these multiple imputation methods. Convergence was reached in 18 iterations with a missing values rate of 18.71 %. Missing values for individual tests are reported in Table 3.

Analyses A series of one-sample t tests compared to the normative means were performed to determine if each group of children differed from the standardization sample. Group differences between measures were tested using a 2 9 6 mixed Analysis of Covariance (ANCOVA), with IQ entered as a covariate, to assess for between- and withinsubject main effects, as well as interactions between diagnostic group and specific skill deficit. Finally, a post hoc oneway ANCOVA was performed for each social cognitive task to explore group differences on individual tasks when IQ was covaried. Bonferroni corrections for multiple comparisons were employed for all univariate tests (corrected p value of \.0017). Results of the one-sample t-tests indicated that children in both groups scored significantly lower than expected

Results Preliminary Analyses Based on previous data indicating associations between age or IQ and social perception, correlational analyses were performed to determine which of these variables correlated Table 4 Pearson correlation coefficients (r) for combined groups Age Age

Facial affect

Vocal affect

Prag. judg.

Prob. solv.

Parent report

Teacher report

FSIQ

PIQ/PRI

–

Facial affect

.07

Vocal affect

.09

.70

Pragmatic affect

-.11

.56

.64

Problem-solving Parent report

.09 -.01

.53 .18

.63 .11

.88 .22

Teacher report

.02

.18

.14

.30

.29

.43

FSIQ

.01

.60

.67

.78

.80

.09

.12

VIQ

.02

.49

.61

.80

.83

.12

.16

.88

-.08

.55

.55

.59

.59

.04

.01

.85

POI/PRI

VIQ

– – – – .16

– – – – .65

–

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according to age-scaled scores on all social cognitive tests. On both parent- and teacher-report both groups of children scored significantly lower on the Social Skills Scale of the BASC-2 than the normative sample (see Table 5). In the ADHD group effect sizes were moderate for the CASL, TOPS, and BASC-2 parent and teacher report of social skills, and effects were small for DANVA Facial and Vocal Affect. In the ASD group effect sizes were large for the CASL, TOPS, and DANVA Facial Affect. Effects on all other measures were moderate. Box’s Test of Equality of Covariance Matrices was significant, F(21, 242,245.72) = 7.34, p \ .001, indicating that the homogeneity of covariance assumption was violated. However, considering the sufficiently large sample size (N = 573), the F-tests can be expected to be robust to this violation. Mauchly’s Test of Sphericity was significant, v2(14) = 654.81, p \ .001, indicating that the sphericity assumption was also violated. Greenhouse-Geisser corrections were used to control for violation of this assumption. Multivariate analyses were significant at the level of task condition with IQ covaried, K = .58, F(5, 566) = 83.03, p \ .001, with a moderate effect size of partial g2 = .42. The interaction between task condition and diagnostic group also yielded statistically significant results, K = .92, F(5, 566) = 10.12, p \ .001, although the effect size was much smaller, partial g2 = .08. Mixed ANCOVA results indicated significant effects of diagnostic group, F(1, 570) = 86.50, p \ .001, g2 = .13, social cognitive task condition, F(3.59, 2,048.21) = 119.56, p \ .001, g2 = .17, and the interaction between group and condition, F(3.59, 2,048.21) = 9.30, p \ .001, g2 = .02, when IQ was covaried. Table 5 One-sample t test of group performance compared to the normative sample mean

Task condition

* p \ .001 Table 6 Oneway ANCOVA results for group differences in social cognitive task standard scores with IQ as covariate

* p \ .05 ** p B .01 *** p \ .001

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Discussion Difficulty in social interaction is a defining feature and one of the criteria for diagnosing an ASD. Accordingly, there is an extensive body of research dedicated to understanding the nature, causes, and treatment of the social deficits observed in ASD. Social deficits are also common in children with ADHD, although they are not thought to be a central feature of the disorder, and are not included in the diagnostic criteria. This study was the first to concurrently compare the performance of children with ASD and ADHD on a range of social cognitive tasks and parent- and teacher-report of social skills. This allowed for the

ASD (N = 137) t

ADHD (N = 436) Cohen’s d

t

Cohen’s d

Facial affect

-8.55*

-0.86

-9.79*

-0.36

Vocal affect

-6.68*

-0.62

-5.70*

-0.18

-14.46*

-1.55

-12.56*

-0.51

Pragmatic judgment IQ was not controlled in these analyses

Univariate ANCOVA analyses indicated that the ADHD group performed significantly better than the ASD group on all social cognitive tasks (Table 6), but with generally small effect sizes. Figure 1 illustrates the interaction between group and task. Performance differences on the social perception measures were minimal compared to the discrepancy between groups on the measures of social response and outcome ratings. Skill profiles were strikingly similar overall, however, with the difference mainly in the degree of impairment rather than the shape of the profiles, as indicated by the smaller effect size for the interaction than for Group and Condition effects. Overall, children in both groups demonstrated similar patterns of strengths and weaknesses, with significantly lower scores in the ASD group across all measures. This poorer performance was slightly more pronounced for measures of social responding than for measures of social perception.

Problem-solving

-17.22*

-1.54

-16.72*

-0.55

Parent rating

-15.38*

-0.77

-16.52*

-0.77

Teacher rating

-14.23*

-0.56

-13.77*

-0.56

F

g2

6.64**

.01

97.34 ± 9.76

4.59*

.01

92.43 ± 12.58

91.29***

.14

76.97 ± 15.65

91.80 ± 10.24

154.65***

.21

Parent rating

81.46 ± 14.11

88.47 ± 14.57

20.46***

.04

Teacher rating

84.86 ± 12.45

91.53 ± 12.83

22.90***

.04

Task condition

ASD: M ± SD (N = 137)

ADHD: M ± SD (N = 436)

Facial affect

87.15 ± 17.60

94.67 ± 11.37

Vocal affect

90.72 ± 16.26

Pragmatic judgment

76.72 ± 18.85

Problem-solving

J Autism Dev Disord (2013) 43:1157–1170

Fig. 1 Standard scores on social cognitive tasks across diagnostic groups

examination of differences in abilities across measures in each diagnostic group, as well as differences in the overall pattern of the social cognitive skill profiles of each group. Both groups performed significantly below the normative mean on all social cognitive measures, further corroborating previous research indicating that children with ADHD, as well as those with an ASD, have deficits in social skills. Affect Recognition in ASD The ASD group performed significantly below the normative mean on the facial affect recognition task which is consistent with previous research identifying deficits in comprehension of facial affect in children with ASD (Bo¨lte and Poustka 2003; Braverman et al. 1989; Celani et al. 1999; Gross 2004; Hall et al. 2003; Mazefsky and Oswald, 2007; Ozonoff et al. 1990; Piggot et al. 2004; Welchew et al. 2005). The effect size for this difference was large (Cohen’s d = -0.86). Thus, the present results lend further support to the converging data that suggest that deficits in facial affect recognition are common in ASD (BaronCohen et al. 1997a, b; Castelli 2005; Gepner et al. 2001; Heerey et al. 2003; Wang et al. 2004). The present results are also consistent with previous studies showing a deficit in vocal affect recognition in children with an ASD (Golan et al. 2006; Ja¨rvinen-Palsey et al. 2008; Linder and Rosen 2006). That is, impairments were found relative to the normative mean on the vocal affect comprehension task, although the effect size was moderate. Affect Recognition in ADHD The ADHD group also performed below the normative mean on the facial and vocal affect comprehension tasks, corroborating the extant literature indicating that children

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with ADHD are less skilled than their normative peers on these aspects of social cognition (Corbett and Glidden 2000; Norvilitis et al. 2000; Pelc et al. 2006; Rapport et al. 2002; Singh et al. 1998). The effect sizes were small for both facial and vocal affect recognition, in contrast to the moderate to large effects found in the group with ASD. Thus, these data suggest that the ADHD group, although less skilled, still perform better than children with an ASD. Although both groups’ performances are in the average range, it is still possible that mildly inferior skills in affect perception may contribute to difficulties in social interaction. Alternatively, it is possible that a subset of children in each group demonstrated clinically significant impairments in affect recognition, while others within the group had intact affect recognition. Further investigations of the impact of minor difficulties in social perception as well as symptom-level examination of affect recognition and social outcome are warranted to inform recommendations for assessment and treatment.

Group Differences Results indicated that there were group differences in performance on all social cognitive tasks and ratings on both parent and teacher reports of social skills. Specifically, the ADHD group demonstrated better social skills in all task conditions when the effects of IQ were systematically removed. The effect sizes, however, were small in all conditions, indicating that performance on a variety of social skills is slightly worse in children with ASD than in children with ADHD, beyond that which would be expected by differences in intellectual ability. This finding is in line with the general diagnostic expectation that children with ASD generally display greater impairment in social interaction than children with ADHD. The small effect sizes contribute to the growing body of literature suggesting that children with ADHD also display traits of ASD (Clark et al. 1999; Reiersen et al. 2007; Santosh and Mijovic 2004) or also have difficulties on social cognitive tasks. For example, Dyck et al. (2001) reported extremely similar performance between groups with ADHD (M = 18.25, SD = 3.82) and Asperger’s Disorder (M = 18.92, SD = 4.12) on a facial cue recognition task; however, the performance was not similar to the group with Autism (M = 12.15, SD = 5.90). Further, Buitelaar et al. (1999) found that 78 % of children with ADHD were classified within the PDD-NOS cluster in a discriminant analysis involving emotion recognition and theory of mind tasks. The ADHD sample in that study was quite small, however, (N = 9), as they were part of a larger psychiatric control group. Also, in that study the children in the ASD group were described as ‘‘high functioning.’’

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The present results are also consistent with the extant literature indicating that, although children with both an ASD and ADHD have weaker social performance skills than a normative sample, performance of children with an ASD was significantly worse than the performance of those with ADHD. For example, Brieber et al. (2007) reported large discrepancies between groups on ASD symptoms but not symptoms of ADHD. The measure of ASD symptom presentation for this study, however, was a parent report questionnaire rather than a performance measure of social functioning. Other studies that have examined differences between ADHD and ASD on parent ratings of autism symptoms when symptoms are broken down by category have indicated significant group differences in the area of social interaction or social skills (Hattori et al. 2006; Jensen et al. 1997; Luteijn et al. 2000). Performance Profile Comparisons Results also indicated a small effect for an interaction between group and task, suggesting that the discrepancy in performance between groups was slightly, but significantly more pronounced on some tasks than others. Specifically, although the children with an ASD generally demonstrated weaker performance than the children with ADHD, this difference in performance was greater on tasks involving social responding (CASL and TOPS), than on tasks involving social perception without response. There are two possible explanations for the greater discrepancy between groups on this task. If these results were applied to Crick and Dodge’s (1994) model of social information processing, receptive social skills, such as the affect recognition tasks measured by the DANVA-2, would be necessary to inform and execute an appropriate response. Because the ASD group performed more poorly at this lower level of social information processing, the result of these low level deficits may have a greater impact on developing skills at the next level. A second possible explanation for the interaction effect is the methodological confound between measures of social perception versus response and outcome. The social perception measures were administered in a recognition format, requiring a single-word response from the participant to name the given emotional expression in a face or voice. The receptive language demands were also minimal, requiring comprehension of simple instructions and response choices. The vocal affect recognition task did involve a spoken sentence of neutral content, but because it was the same sentence for all stimuli, comprehension of the sentence was not necessary for accurate performance on this task. Thus, both receptive and expressive language demands for this task were very minimal. Measures of social responding, however, placed significantly greater language demands on

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participants. For example, both the CASL and the TOPS required participants to comprehend verbally-administered questions and then generate a verbal response for each item. Considering the high incidence of language disorder in ASD, it is possible that the greater performance gap between the ADHD and ASD groups on these two measures is an artifact of language abilities rather than social cognitive processes, specifically. Because language was not assessed in this study, it is not clear how language abilities impacted the differences in performance, especially with respect to the measures of social response, which had the greatest language demands. While verbal IQ may be a gross estimate of language function, it may not be sensitive to the specific aspects of language that may be differentially impaired in autism. These more subtle aspects of language require more formal assessment of language functioning. Withstanding the failure to control for language abilities, however, the interaction effect is small (partial g2 = .017), which is an unexpected finding with respect to our understanding of the nature of social deficits in ADHD and ASD. The fact that the social cognitive profiles of these two groups are nearly identical with respect to shape, and differ almost exclusively in terms of severity of impairment, is an unexpected finding. Historically, the social difficulties in children with ADHD have been considered to be secondary to symptoms of impulsivity or executive dysfunction. For example, Barkley (1997) hypothesized that poor social competence in ADHD is a problem of execution of social behavior, rather than being related to problems in social comprehension and knowledge base. Other researchers have also suggested that social skills deficits in ADHD are directly related to core symptoms of ADHD (Greene et al. 1996). Shapiro et al. (1993) tested a model of social information processing in a group of children with ADHD. Their results suggested that abilities were likely intact at the level of stimulus perception and encoding, and that social difficulties are likely arising at the level of behavior selection, performance, or regulation, thereby leading to the hypothesis that the social deficits in ADHD are secondary to executive dysfunction. These executive hypotheses of social dysfunction in ADHD are in direct contrast to the theories of social dysfunction in ASD, which are based upon the notion that social deficits are primary. Results of this study, however, suggest that the processes of social dysfunction in ADHD and ASD are more alike than once thought, as deficits in the early stages of social information processing (social perceptual deficits, as in affect recognition) were demonstrated in both groups, which would likely have an impact on functioning in later stages of the process (social responding and response evaluation). This raises the question of whether the underlying causes of the early stage social deficits are also brought about by the same pathological processes.

J Autism Dev Disord (2013) 43:1157–1170

A possible reason for the poor performance of children with ADHD on affect recognition tasks is that misidentifications are a result of impulsive responding, rather than impaired social perceptive knowledge. However, if the errors in facial affect recognition were an artifact of impulsivity, error patterns would be expected to be random, which is in contrast to results of several studies of ADHD (Marsh and Williams 2006). Further, in a study that examined reaction times on a static facial affect recognition task, children with ADHD demonstrated longer reaction times, a finding which is not suggestive of impulsive responding (Kats-Gold et al. 2007). In a preliminary study examining visual scanpaths of facial expressions of emotion in ADHD, it was found that individuals with ADHD demonstrate extensive patterns of scanning evidenced by longer scanpath lengths (Marsh et al. 2000). Thus, the evidence to date does not suggest that poor performance on facial affect recognition tasks in ADHD is not secondary to inattention or impulsivity; rather, it is likely to be associated with difficulty in perceptual processing or judgment of the social stimuli, again, suggesting that the underlying processes of social perceptual dysfunction may be similar in ASD and ADHD. Specifically, these findings suggest that the pattern of social skill deficits is extremely similar with regard to strengths and weaknesses, and that the difference between groups is mainly one of severity of skill deficit rather than type. This is not to suggest that the difference in severity is trivial. The ADHD group, while statistically different from the normative sample on all measures, still performed in what is considered the average range. Thus, there may be a qualitative difference in the impact on functional impairment produced by these minor deficits in social cognition. Alternatively, children with ADHD do demonstrate poor social outcomes, which could also indicate that even minor deficits in social cognition may impact social functioning. Yet another possibility is that group averages in this study obscured clinically significant deficits in some children and intact social functioning in others. Further investigation of the relationship between functional outcomes and different types and degrees of social cognitive impairment is warranted. Nevertheless, these data suggest that strategies designed to improve social perception skills may be a promising novel intervention approach that may be effective in improving the social performance of children not only with an ASD, but also those with ADHD. Limitations Several limitations of the current study must be considered. First, the homogeneity of the sample with respect to ethnic background limits the generalizability of these results to a more diverse population. A second limitation is that

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language ability was not systematically assessed in individuals with ADHD, which precluded the ability to control for differential effects of language difficulties in ASD. Because of the greater language demands of some tasks as opposed to others, it is unclear whether the interaction effect reported in these results would have reached significance if language ability had been controlled beyond that which was controlled in IQ. For example, the TOPS and CASL scores may have been more similar between groups if language had been systematically controlled, as these tasks had the greatest language demands and also the largest effect sizes for group differences. Further, greater performance differences may have been detected between different subtypes of diagnostic groups and this warrants investigation in future studies. A third limitation is the higher percentage of missing data in the ADHD group on the measures of social judgment and problem solving. Replication of this study in a research sample with a fixed battery is warranted. An additional limitation is the inclusion of some mildly impaired individuals with ASD, with low scores on the ADOS and/or SCQ despite their clinical presentation and developmental history being consistent with a diagnosis of ASD. While their inclusion may be representative of the very mildly impaired individuals on the Autism Spectrum, it may also have made it more difficult to detect differences between groups. Finally, because this study compared participants to the normative mean using one-sample t-tests, it was not possible to match groups on IQ for this analysis, which also may account for the difference in findings. Future Directions Despite these limitations, this study is one of the first to systematically compare social cognitive and social performance skills in children with ASD and ADHD. The present data show that both groups of children performed more poorly than a normative group on receptive and expressive aspects of social skills. Further, the present findings indicate that the differences in performance between these two groups appear to be quantitative rather than qualitative. Results of this study highlight the need for symptom-level investigations into the etiology of social dysfunction, as a categorical approach to this research may not be appropriate to the study of disorders with such diverse presentations characterized by equifinality. Future research aimed at modeling systems of social dysfunction should examine hierarchical models, such as that outlined by Crick and Dodge (1994). These models should incorporate nonsocial perceptual control measures as well as measures of behavioral regulation, language functioning, and general intellectual ability. These data also suggest that strategies designed to improve receptive social skills may

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be a promising new avenue for improving social skills in children with ADHD (as well as those with an ASD). Finally, incorporation of neurophysiological and functional imaging data will further add to understanding of the neurological processes underlying these deficits.

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