The Structure of Intelligence in Children and Adults With High Functioning Autism

Transcription

1 Neuropsychology Copyright 2008 by the American Psychological Association 2008, Vol. 22, No. 3, /08/$12.00 DOI: / The Structure of Intelligence in Children and Adults With High Functioning Autism Gerald Goldstein VA Pittsburgh Healthcare System Nancy J. Minshew University of Pittsburgh School of Medicine Fred Volkmar and Ami Klin Yale University Daniel N. Allen University of Nevada Las Vegas Diane L. Williams Duquesne University Robert T. Schultz Children s Hospital of Philadelphia Confirmatory factor analyses of the commonly used 11 subtests of the Wechsler child and adult intelligence scales were accomplished for 137 children and 117 adults with high functioning autism (HFA) and for comparable age groups from the standardization samples contained in the Wechsler manuals. The objectives were to determine whether the structure of intelligence in HFA groups was similar to that found in the normative samples, and whether a separate social context factor would emerge that was unique to HFA. Four-factor models incorporating a Social Context factor provided the best fit in both the autism and normative samples, but the subtest intercorrelations were generally lower in the autism samples. Findings suggest similar organization of cognitive abilities in HFA, but with the possibility of underconnectivity or reduced communication among brain regions in autism. Keywords: autism, intelligence testing, confirmatory factor analysis Gerald Goldstein, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; Daniel N. Allen, Department of Psychology, University of Nevada Las Vegas; Nancy J. Minshew, Departments of Psychiatry and Neurology, University of Pittsburgh School of Medicine; Diane L. Williams, Department of Speech-Language Pathology, Duquesne University; Fred Volkmar and Ami Klin, Department of Psychiatry, Yale University School of Medicine; Robert T. Schultz, Department of Pediatrics, Children s Hospital of Philadelphia. This research was supported by the University Of Pittsburgh Collaborative Programs Of Excellence in Autism, the Yale Collaborative Programs of Excellence in Autism and the Medical Research Service, Department of Veterans Affairs. Correspondence concerning this article should be addressed to Gerald Goldstein, Research Service (151R), VA Pittsburgh Health care System, 7180 Highland Drive, Pittsburgh, PA ggold@nb.net The Wechsler intelligence scales (Wechsler, 1974, 1981, 1991, 1997) have had widespread application in the assessment of individuals with autism. Aside from the role of these procedures in educational, vocational, and clinical diagnostic applications, they have been used in autism research to separate high functioning from low functioning autism, for differential diagnostic purposes within the autism spectrum and, perhaps most significantly, for delineating the pattern of cognitive function in autism. The Wechsler scales contain 11 or more subtests depending upon the version used, making it possible to obtain a cognitive profile of diverse abilities in addition to the Verbal, Performance, and Full-scale IQ scores. The Wechsler scales have been repeatedly factor analyzed in both normal and clinical populations. Exploratory factor analysis has generally demonstrated that when the 11 subtest versions of these scales are studied, a three-factor solution is consistently found. The factors are called Verbal Comprehension (VC), Perceptual Organization (PO), and Attention/Concentration or Freedom from Distractibility (FFD). The VC factor receives high loadings from Information, Comprehension, Similarities, and Vocabulary subtests. The PO factor receives high loadings from Picture Completion, Picture Arrangement, Block Design, and Object Assembly subtests. The Digit Span, Arithmetic and, at times, Digit Symbol or Coding subtests, constitute the FFD factor. More recent applications of factor analysis have employed confirmatory approaches, made possible by advances in structural equation modeling (Jöreskog, 1979). Confirmatory factor analysis (CFA) is designed to evaluate specific hypotheses about the number of factors that make up a particular test battery and the pattern of factor loadings. Thus, it is more useful than exploratory approaches when there are specific expectations about the factor structure of the test (Thompson, 2004). CFA studies of both the Wechsler Intelligence Scale for Children-Revised (WISC-R) and Wechsler Adult Intelligence Scale-Revised (WAIS-R) in normal and clinical samples have typically confirmed the VC, PO and FFD three-factor model. More recently, the addition of several subtests to the Wechsler Adult Intelligence Scale (3rd ed.) (WAIS-III) and Wechsler Intelligence Scale for Children (3rd ed.) (WISC-III) allowed for testing of a four-factor model that was more optimal than the previously reported three-factor model. In this four-factor solution, the VC, PO and FFD factors are retained. However, Letter-Number Sequencing is added to the FFD factor, forming a new factor called Working Memory. Also, the Digit Symbol subtest is paired with the Symbol Search subtest to form a Processing Speed factor (PS). Thus, even for these newer versions of the Wechsler scales, the factors identified in earlier versions continue to provide a meaningful organization of the subtests. Clinical groups may show a factor structure or loading pattern that differs from the pattern of VC, PO, and FFD factors found on 301

2 302 GOLDSTEIN ET AL the Wechsler scales for typically developing individuals (Allen et al., 1998; Burton, Ryan, Paolo, & Mittenberg, 1994; Burton et al., 2001; Lincoln, Courchesne, Kilman, Elmasian, & Allen, 1988; Russell, 1974; Waller & Waldman, 1990). The implication of different loading patterns is that they reflect a different organization of cognitive abilities than that found in the general population. Although the Wechsler factor structure has not been rigorously examined in high functioning autism (HFA), a profile of Wechsler scores has been identified that is characterized by relatively high scores on the Block Design subtest and relatively low scores on Comprehension (Rumsey, 1992; Siegel, Minshew & Goldstein, 1996; Yirmiya & Sigman, 1991). This unique subtest profile may cause the Wechsler subtests to have markedly different factor loadings from what is present in the normative samples. There is also the possibility that the factor structure itself in HFA may significantly vary from that present in typically developing individuals. A rigorous evaluation of the structure of intelligence in autism would provide considerable insight into these issues. In a study using principal components analysis with a small HFA sample (n 33), Lincoln et al. (1988) found a three-factor solution that differed from that of normal controls. While the expected Verbal Comprehension factor was identified, the second factor only received high loadings from Block Design, Object Assembly and Digit Symbol/Coding, and the third factor only received high loadings from Picture Arrangement and Picture Completion. Thus, while the small sample size precluded any definitive conclusions, the findings were suggestive of a dissociation of the performance tests between those involving visual analysis and integration and those that were social and context-relevant. Lincoln et al. described the pattern found in HFA as a mismatch between verbal reasoning and context recognition with better-developed perceptual-motor organization abilities. The identification of this factor may provide insight into the intellectual processes associated with appreciation of social context and inference making. The pattern of intact perceptual-motor ability demonstrated on tasks of the Block Design type with less well developed ability to use context may have a connection with theories of cognition in autism involving preference for local as opposed to global processing (Mottron, Burack, Iarocci, Belleville, & Emms, 2003) and weak central coherence (Happé & Frith, 2006). In this study, CFA was used to further investigate the structure of intelligence in autism. Since there has not been a rigorous investigation of the factor structure of the Wechsler scales in HFA the first goal was to examine the conventional one-, two-, and three-factor models in children and adults with HFA, to determine whether a similar structure was present as has been repeatedly identified in other clinical and nonclinical populations. A second goal was to examine Wechsler subtests that have social content in children and adults with HFA, particularly as they relate to the structure of intelligence. Thus, the 11 original subtests of the Wechsler scales were used to test a number of a priori models that included the conventional one-, two- and three-factor models, as well as two different four-factor models based on profiles commonly associated with autism as reviewed in Siegel et al. (1996). For comparison purposes, these models were also evaluated in the standardization samples. Attention was paid to verification of the Lincoln et al. (1988) report of a separate factor with high loadings obtained only from Picture Arrangement and Picture Completion. The hypothesis was that the autism groups would demonstrate a different factor structure than that of the national standardization samples, with one possible structure resembling a factor that represents the processing of social contextual information, assessed most clearly by the Picture Arrangement subtest; however, two factor analytic studies reviewed in Lezak, Howieson, and Loring (2004) reported that Picture Completion assesses reasoning about practical matters and can be viewed as a nonverbal equivalent of Comprehension. Participants Method The sample consisted of 137 children and 117 adults with autism, who were participants in Collaborative Programs of Excellence in Autism research at the University of Pittsburgh or Yale Child Study Center. See Table 1 for demographic and psychometric data. The sample was restricted to individuals with HFA (Full Scale and Verbal IQ scores of 70 or above) to assure that the participants could cooperate with psychological testing and were unlikely to have the numerous additional disorders commonly associated with low functioning autism. All participants met the cutoffs for autism (not autism spectrum disorder) according to the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2000; 1989) for the Communication, Reciprocal Social Interaction, and total algorithm scores. All participants also met cutoffs for autism on the Autism Diagnostic Interview-Revised (ADI-R; Le Couteur et al., 1989; Lord, Rutter, & Le Couteur, 1994) for Reciprocal Social Interaction, Communication, and Restricted, Repetitive, and Stereotyped Behaviors and had abnormal development before 3 years of age. The ADI-R assesses developmental history and reported current functioning based on caregiver report. The diagnosis of autism was verified by expert opinion. Individuals were excluded from the data set if they met the criteria for Asperger s disorder rather than autism. All participants with autism communicated in complete spoken sentences and had sufficient attention and cooperation to complete testing. Breaks were provided as needed to allow the participants to perform optimally. The participants did not have any associated or causative known genetic, metabolic, or infectious conditions, were in good medical health and had no history of seizures, birth injury, or head trauma. Data were collected over several years spanning the period during which new editions of both the WAIS and the WISC appeared. Thus, the adults received either the WAIS-R (n 76) or the WAIS-III (n 41), and the children received either the WISC-R (n 44) or the WISC-III (n 93). Table 1 Demographics of Child and Adult Autism Samples Children (n 137) Adults (n 117) M SD M SD Age Years of education Verbal IQ Performance IQ Full-Scale IQ

3 STRUCTURE OF INTELLIGENCE WITH AUTISM 303 Data Analyses For purposes of maximizing sample size in the adult and child autism groups, data from the two forms of the WAIS and the WISC (revised and third editions) were combined in each age group. This step was taken after determining that the intercorrelations among the subtests across the two editions were not significantly different from each other, with differences that would not be sufficient to alter factor structures. This determination was made using Mantel s Z (Mantel, 1967) to directly compare the correlation matrices of the WAIS-R to the WAIS-III, and the WISC-R to the WISC-III. Mantel s Z tests the hypothesis that there are no associations between the elements in two correlation matrices; therefore, significant results suggest that the matrices do not significantly differ from each other with regard to the pattern of correlations. For both the adult and child versions of the Wechsler scales, Mantel s Z was significant (adult versions: r.63, Z 16.18, g p.001; child versions: r.55, Z 9.83, g 2.87, p.007), indicating that the matrices did not differ significantly from each other. We also evaluated differences in demographic and IQ variables between the child and adult groups who received the Revised and III versions of the tests. For the children, no differences in Full- Scale IQ were present, F(1, 135) 0.34, p.56, partial 2.003, although the children that were administered the WISC-III were somewhat older (mean age 11.5 years, SD 2.6) than those who were administered the WISC-R (mean age 10.1 years, SD 3.5), and this difference was significant, F(1, 135) 6.86, p.01, partial Adults administered the WAIS-III were older (mean age 30.4 years, SD 10.4) than those taking the WAIS-R (mean age 26.0 years, SD 8.3), F(1, 115) 6.17, p.001, partial They also attained significantly higher Full Scale IQ scores (mean IQ 106.0, SD 14.2) than those who were administered the WAIS-R (mean IQ 94.7, SD 16.5), F(1, 115) 13.6, p.001), partial Given these differences, age-corrected scaled scores were used in the analyses, including the calculation of the correlation matrices that were subjected to confirmatory factor analysis. Data analyses were performed only for the 11 subtests of the WAIS-R and WISC-R, including Information, Comprehension, Similarities, Arithmetic, Vocabulary, Digit Span, Picture Completion, Picture Arrangement, Block Design, Object Assembly, and Digit Symbol/Coding. A consideration in using only these subtests was that they are the ones that have been used in most autism research. From a more practical standpoint, because data collection began before the WAIS-III and WISC-III were published, complete data were only available on the 11 subtests of the earlier R versions that were included in the III revisions of the tests. Theoretically, the consistency in factor structure across the various versions of the Wechsler scales in normal populations indicates that the associations among the subtests remain stable despite revisions of the instruments. The remarkable stability of Wechsler profiles for autism samples over the various versions of the tests provides additional assurance that the abilities measured by the subtests have remained largely the same across revisions. Therefore, models were tested that only included these 11 subtests. The conventional one-, two-, and three-factor models were examined in the current study, as had been done in previous CFA studies of the Wechsler Scales, although these models had not previously been examined in HFA (Burton et al., 1994, 2001; Brown, Hwang, Baron, & Yakimowski, 1991; Dickinson, Iannone, & Gold, 2002; O Grady, 1983; Plake, Gutkin, Wise, & Kroeten, 1987; Waller & Waldman, 1990). The one-factor model specified all subtests to load on a single factor, evaluating the hypothesis that intelligence involves a single latent trait, consistent with views concerning the existence of a g (general) factor. The two-factor model was based on Wechsler s original concept of intelligence divided along Verbal and Performance constructs, or possibly based on crystallized and fluid conceptualizations. Three-factor models specified VC, PO, and FFD factors. The FFD factor is primarily composed of the Arithmetic and Digit Span subtests, and is thought to reflect attention/concentration abilities. Slight variations of this three-factor model are accounted for by the tendency of Digit Symbol to load on both the PO and FFD factors in some populations, and on the FFD factor in others (Allen et al., 1998; Burton et al., 1994, 2001; Waller & Waldman, 1990). Based on these considerations, two three-factor models were evaluated that incorporated VC, PO, and FFD factors. In both models, the VC factor was composed of the Information, Vocabulary, Comprehension, and Similarities subtests, and the PO factor was composed of the Picture Completion, Block Design, Picture Arrangement, and Object Assembly subtests. However, in the first three-factor model (M 3 ), the FFD factor was composed of the Digit Span, Arithmetic, and Digit Symbol/Coding subtests. For the second three-factor model (M 3D ), Digit Symbol/Coding was specified as a doublet, loading on both the PO and FFD factors. An additional three-factor model was also examined based on the finding reported by Lincoln et al. (1988), and included a VC and PO factor, as well as a Social Context (SC) factor composed of the Picture Arrangement and Picture Completion subtests. As noted earlier, Lincoln and colleagues described this factor as reflecting context recognition. In addition, given the consistent findings of an FFD factor in both clinical and nonclinical populations, along with the unique Wechsler subtest profile and factor structure in autism, two different four-factor models were also evaluated to examine the possibility of a factor unique to autism. In both four-factor models, the VC, PO, and FFD factors were retained, but in the first model Picture Arrangement and Picture Completion were specified to load on a fourth factor, thereby evaluating the SC factor described by Lincoln and colleagues. Given that Comprehension and Picture Arrangement have been customarily viewed as measures of social reasoning, a second four-factor model was examined that specified Comprehension to load on the fourth factor with Picture Arrangement, producing a model with a SC factor assessed by both Verbal and Performance abilities. In summary we employed a one-factor model ( g ), a two-factor model (Verbal and Performance abilities), a number of three-factor models (VC, PO, and FFD or SC), and two four-factor models (VC, PO, FFD, and SC). It was predicted that the model that would provide the best overall fit for the autism samples was the four-factor model specifying that Picture Arrangement and Picture Completion would form a SC factor, while retaining the VC, PO, and FFD factors. All models were tested with CFA by using LISREL 8 (Jöreskog & Sörbom, 1993). CFA requires a priori specifications indicating that certain observed variables, in this case the Wechsler subtests, are related to specific unobserved variables called latent constructs or factors, that they are thought to measure, and are not related to unobserved variables they are not thought to measure. These a

4 304 GOLDSTEIN ET AL priori specifications are based on theoretical or empirical evidence, and form the basis of hypothesized statistical models that can then be tested against an actual set of data. To determine whether the hypothesized statistical model fits the actual data set, a number of goodness-of-fit statistics are used; the most common include the 2 statistic, the goodness-of-fit index (GFI), and the adjusted goodness-of-fit index (AGFI). The 2 statistic is used to evaluate the fit between the hypothesized statistical model and the actual data set. The GFI and AGFI provide estimates of the amount of variance and covariance that is explained by the model. Both the GFI and AGFI have ranges of 0.00 to 1.00, but the AGFI provides a correction for the complexity of the model, so that all else being equal, more parsimonious models attain higher AGFI s. For these indexes, an adequate fit is indicated by a GFI of.90 and above, and an AGFI of.80 or above (Cole, 1987; Ward, Ryan & Axelrod, 2000). Because the 2, GFI, and AGFI indexes are affected by sample size, a number of other fit indexes were examined, including the Bentler and Bonnett Normed Fit Index (NFI), the Tucker-Lewis Index (T-LI), the Root Mean Square Error of Approximation (RMSEA), and the Comparative Fit Index (CFI). The NFI and T-LI indexes provide estimations of improvement in model fit over a baseline model, typically the null model that specifies that there are no factors, that is, there are no correlations among the variables in the model. The T-LI has been shown to be relatively unaffected by sample size (Bollen, 1990), and unlike the NFI also adjusts for the complexity of the model so that more complex models attain a lower T-LI than less complex ones. NFI and T-LI at.90 and above indicate adequate fit, with values greater than.95 indicating good fit. The CFI and RMSEA provide additional estimates of fit based on the noncentrality parameter in which testing is done against an alternative rather than the null hypothesis. A good fit is indicated by a CFI of.95 or greater, and a RMSEA of less than.06. It should be noted that the cut-off values for all of these goodness of fit statistics are not universally agreed upon, and so decisions regarding the adequacy of a particular model are best based on considering all of the indexes together, rather than relying on only one or two. For the present study, comparisons with the Wechsler standardization samples were also important in selecting the optimal model, given the established factor structure for these scales in large, normal populations. In judging improvement in model fit, comparisons were made to both the null model and the one-factor model, which was included as an informed baseline model. Comparisons were made to the null model to demonstrate that each of the hypothesized models provided a better fit than the null. Comparisons to the one-factor baseline model provided a more rigorous test of relative model fit, because it is assumed that the Wechsler scale subtests are correlated with each other, an assumption that is consistent with numerous studies investigating them. Additionally, incremental improvement in model fit was evaluated by examining the difference in 2 between competing models. For nested models, magnitude of the differences in 2 s between the models provides one indication of the relative improvement in model fit, with a larger difference indicating a greater improvement in fit (Bentler & Bonnett, 1980; Mulaik et al., 1989). For comparison purposes, the same models were also evaluated for the normative samples reported in the test manuals for the various Wechsler scales. The CFA analyses were repeated for the age appropriate matrices contained in the manuals for the WISC-R, the WISC-III, the WAIS-R and the WAIS-III. The WISC-R and WISC-III correlations matrices from the 11-year-old age groups were used. This age group was chosen as the comparison sample because it most closely approximated the age of our sample of children with autism (mean age 11.1, SD 2.9, range 6 18 years). For adults, the correlation matrices for the WAIS-R 25- to 34-year-old age group and the WAIS-III 25- to 29-year-old age group were selected for analysis, again because these age groups most closely approximated that of our adult autism sample (mean age 27.6, SD 9.3, range years). Using these values from the national standardization samples as baselines, we went on to test various models for the children and adults with autism. The sensitivity of the SC factor to autism was evaluated by determining if the factor scores derived from this factor were significantly lower within IQ and age subgroups than the other factor scores in the autism samples. Since prior investigations have demonstrated that the VC and possibly PO factors are the least affected by neurocognitive dysfunction in HFA, we compared the SC factor to the VC and PO factors to determine whether it was uniquely sensitive to social context processing abnormalities. We also compared the performances of the HFA samples on the Wechsler factors to the performance of the normative samples, again anticipating that the hypothesized SC factor would be lower than the VC and PO factor scores. Finally, factor scores were examined across various IQ categories and across various age groups to determine the relative stability of deficits across different levels of intellectual ability and ages. Results GFI for Hypothesized Models Goodness of fit indexes for the seven hypothesized models are presented in Table 2 (Child) and Table 3 (Adult). For the child samples, the results of the one-, two- and three-factor models are highly consistent across the WISC-R, WISC-III, and Autism groups. In all cases, the one- and two-factor models provided relatively poor fit. The three-factor models incorporating VC, PO, and FFD factors provided relatively better fit, with little difference present between the model where Digit Symbol/Coding was specified to load on the FFD factor (M 3 ) and the model where it was specified to load on both PO and FFD factors (M 3D ). Because M 3 is the more parsimonious of the two models, it is considered the better model. The three-factor model that suggested a social context factor composed of the Picture Arrangement and Picture Completion subtests (M 3SC ) produced fit indexes that were comparable to the two-factor model, and much poorer than the other three-factor models. Results for the four-factor models were also consistent across groups, with the model specifying Picture Arrangement and Comprehension (M 4C ) as the fourth factor providing a poorer fit of the data when compared to the model with Picture Arrangement and Picture Completion (M 4PC ) loading on the fourth factor. For the WISC R, WISC III and Autism samples, Model M 4PC demonstrated improved fit over the three-factor models, whereas Model M 4C did not. As can be seen from Table 3, comparable results were obtained for the adult samples. The three-factor models specifying VC, PO, and FFD factors provided better fit for the WAIS-R, WAIS-III, and Autism groups than did the one-, two-, and alternate three-factor (M 3SC ) models. Again, because Model M 3 is more parsimonious

5 STRUCTURE OF INTELLIGENCE WITH AUTISM 305 Table 2 Goodness-of-Fit Indexes for all Models in Child Samples Fit indexes Models 2 df 2 /df GFI AGFI NFI T-LI RMSEA CFI WISC-R (1) M (2) M (3) M (4) M 3D (5) M 3SC (6) M 4PC (7) M 4C WISC-III (1) M (2) M (3) M (4) M 3D (5) M 3SC (6) M 4PC (7) M 4C Autism (1) M (2) M (3) M (4) M 3D (5) M 3SC (6) M 4PC (7) M 4C Note. M 1 one-factor model; M 2 two-factor model; M 3 three-factor model; M 3D three-factor model with Digit Symbol specified to load on Perceptual Organization and Freedom from Distractibility factors; M 3SC three-factor model with Picture Arrangement and Picture Completion specified to load on a Social Context factor; M 4PC four-factor model with Picture Arrangement and Picture Completion on factor 4; M 4C four-factor model with Picture Arrangement and Comprehension on factor 4. WISC-R Wechsler Intelligence Scale for Children Revised standardization sample 11.5 year old age group (n 200); WISC-III Wechsler Intelligence Scale for Children III standardization sample 11 year old age group (n 200); Autism Children with high-functioning autism (n 137). GFI Goodness-of-Fit Index; AGFI Adjusted GFI; RMSEA Root Mean Square Error of Approximation; NFI Normed Fit Index; T-LI Tucker-Lewis Index; CFI Comparative Fit Index. NFI and T-LI calculated using an independent null model: WISC-R ; df 55; n 200; WISC-III ; df 55; n 200; Autism , df 55, n 137. than M 3D, Model M 3 is considered the better model. Of the four-factor models, the model with Picture Arrangement and Picture Completion loading on the fourth factor (M 4PC ) was the superior model, and provided the best overall fit of the data when all seven models are considered. In the four-factor models for both children and adults, Digit Symbol/Coding was retained on the FFD factor, given that M 3 was the best three-factor model. In summary, no substantial difference in the pattern of the goodness-of-fit statistics was found across the seven models between the autism and national standardization samples in both children and adults, although the fit indices were generally lower in the autism groups, particularly the children. Incremental Improvement in Model Fit For the analyses examining incremental improvement in model fit, the two- and three-factor models were compared to the onefactor model, as was the optimal four-factor model (M 4C )tothe optimal three-factor model (M 3 ) to establish that the increase in model complexity was associated with significant increases in fit for the autism and national samples. Table 4 presents the 2 difference and NFIs for these models. The significant 2 difference statistics indicate that for all groups, M 2 and M 3 provided significantly better fit than M 1, while M 4PC provided significantly better fit than M 3. With regard to this latter result, although it was anticipated that M 4PC would provide statistically significant improvement in model fit over the other models in the autism groups, the finding for the Wechsler comparison samples was not anticipated. Examination of Subtest Intercorrelations Despite similarities noted across autism and control groups for goodness of fit and incremental fit indexes, some notable differences were also apparent. In almost all cases, the GFIs (GFI, AGFI, NFI, T-LI, and CFI) were lower for the autism samples. Comparing the correlation matrices for the autism samples to those of the standardization samples provides some insight into this matter. 1 For example, in the 11-year-old group of the national sample for the WISC-R, the VC tests correlate with each other in the range of.40 to mid.60. The PO subtests correlate with each other in the mid.40 to mid.50 range. For the FFD factor, Digit Span and Arithmetic have a.53 correlation but the correlations with Coding are low, with no subtest correlated with Coding higher than the mid.20 range. In the children with autism, the VC 1 Correlation matrices for the autism samples are available on request from Gerald Goldstein.

6 306 GOLDSTEIN ET AL Table 3 Goodness-of-Fit Indexes for all Models in Adult Samples Fit indexes Models 2 df 2 /df GFI AGFI NFI T-LI RMSEA CFI WAIS-R (1) M (2) M (3) M (4) M 3D (5) M 3SC (6) M 4PC (7) M 4C WAIS-III (1) M (2) M (3) M (4) M 3D (5) M 3SC (6) M 4PC (7) M 4C Autism (1) M (2) M (3) M (4) M 3D (5) M 3SC (6) M 4PC (7) M 4C Note. M 1 one-factor model; M 2 two-factor model; M 3 three-factor model; M 3D three-factor model with Digit Symbol specified to load on Perceptual Organization and Freedom from Distractibility factors; M 3SC three-factor model with Picture Arrangement and Picture Completion specified to load on a Social Context factor; M 4PC four-factor model with Picture Arrangement and Picture Completion on factor 4; M 4C four-factor model with Picture Arrangement and Comprehension on factor 4. WAIS-R Wechsler Adult Intelligence Scale Revised standardization sample 25- to 34-year-old age group (n 300); WAIS-III Wechsler Adult Intelligence Scale III standardization sample 25- to 29-year-old age group (n 200); Autism adults with high-functioning autism (n 117). GFI Goodness-of-Fit Index; AGFI Adjusted GFI; RMSEA Root Mean Square Error of Approximation; NFI Normed Fit Index; T-LI Tucker-Lewis Index; CFI Comparative Fit Index. NFI and T-LI calculated using an independent null model: WAIS-R ; df 55; n 300; WAIS-III ; df 55; n 200; Autism , df 55, n 117. tests correlate with each other in the.50 to.60 range, but the PO subtests show some differences. Intercorrelations among Picture Completion, Picture Arrangement, Block Design, and Object Assembly are lower, falling into the.20 to mid-.40 range. The most extreme case is the correlation between Block Design and Picture Completion. It is.54 in the national sample and.31 in the autism group. There were similar findings for the adults. In the national sample, the intercorrelations among the VC tests were quite high, ranging from approximately.50 to.80. There is a more coherent FFD factor in the national sample adults than in children with mid.40 range correlations with Digit Symbol and a.60 correlation between Digit Span and Arithmetic. In the autism group, there is also a coherent FFD factor with correlations in the mid.40 range. The PO factor is characterized by mid.30 to mid.60 range correlations in the national sample. These correlations are always lower in the autism group ranging from.34 to.55. To further examine this issue, the correlations matrices for the WISC and WAIS normative samples were combined and then compared to a comparable matrix based on the combined sample of children and adults with autism using Mantel s test. Mantel s test was significant (r.91, Z 39.71, g 4.59, p.0001) indicating that the pattern of correlations did not differ. However, out of 55 correlations, 54 were lower in the autism sample and Fisher s Z transformation indicated that 25 of these differences were significant ( p.05). This raises the possibility that the lower goodness of fit and incremental fit indexes for the autism samples is related primarily to a reduced association among cognitive abilities in autism. While this comparison suggests that children and adults with autism do not have remarkably different factor structures, there may be a difference in the coherence of those structures. Maximum Likelihood Factor Loadings The maximum likelihood factor loadings presented in Table 5 indicate little difference between the autism and national samples in the patterns of loadings both in the children and adults. However, reduced loadings relative to the national sample associated with reduced tendencies toward simple structure did occur in the autism samples. It seems that the autism samples, like the national samples, have a four-factor structure; however, because of the psychometric characteristics of the Wechsler scales, it only approached the level of simple structure but did not fully achieve it. As in the Lincoln et al. (1988) study, Picture Completion and Picture Arrangement loaded on a separate factor from Block Design and Object Assembly in the autism sample but the same loading pattern was found in the WISC-III and WAIS-III standardization samples. Thus, while the identification of a SC factor is a

7 STRUCTURE OF INTELLIGENCE WITH AUTISM 307 Table 4 Incremental Fit for Confirmatory Factor Analyses for Child and Adult Autism Samples and the Wechsler Scales Normative Samples Model Comparisons 2 difference df NFI WISC-R M 1 M *** 1.35 M 1 M *** 3.50 M 3 M 4PC 8.14 * 3.10 WISC-III M 1 M *** 1.39 M 1 M *** 3.57 M 3 M 4PC *** 3.18 Autism Children M 1 M *** 1.16 M 1 M *** 3.35 M 3 M 4PC *** 3.18 WAIS-R M 1 M *** 1.40 M 1 M *** 3.51 M 3 M 4PC *** 3.16 WAIS-III M 1 M *** 1.40 M 1 M *** 3.58 M 3 M 4PC *** 3.22 Autism Adults M 1 M *** 1.24 M 1 M *** 3.51 M 3 M 4PC ** 3.17 Note. M 1 one-factor model; M 2 two-factor model; M 3 threefactor model with Digit Span, Arithmetic, and Digit Symbol specified to load on the FFD factor. M 4PC four-factor model with Picture Arrangement and Picture Completion specified to load on factor 4. WISC-R Wechsler Intelligence Scale for Children Revised standardization sample 11.5 year old age group (n 200); WISC-III Wechsler Intelligence Scale for Children III standardization sample 11 year old age group (n 200); Autism - Children Children with high-functioning autism (n 137). WAIS-R Wechsler Adult Intelligence Scale Revised standardization sample 25- to 34-year-old age group (n 300); WAIS-III Wechsler Adult Intelligence Scale III standardization sample 25- to 29-year-old age group (n 200); Autism Adults Adults with high-functioning autism (n 117). * p.05. ** p.01. *** p.001. unique finding, it is not unique to children and adults with autism as a similar factor structure is evident in the standardization samples. Wechsler subtest descriptive statistics for the child and adult autism samples are also included in Table 5. Analyses of the SC Factor The SC factor as it relates to the unique structure of intelligence in autism was investigated in a number of ways. First, one-sample t-tests were used to compare the performance of the child and adult autism groups to the standardization samples for each factor score, with the expectation that the SC factor would be significantly lower than the other factor scores if it were indeed sensitive to the deficits in social functioning characteristic of autism. Comparisons to the PO factor were of particular interest given that tasks such as Block Design are well preserved relative to other tasks. Second, the factor scores were examined across the various IQ groups, with the prediction that consistently lower scores would be attained on the SC factor regardless of ability level. Finally, comparisons were made across various age groups, again anticipating that both children and adults with autism would exhibit relatively lower scores on the SC factor. Factor score comparisons. Performance of the child and adult autism groups on each factor was compared to the standardization samples using one sample t tests. The child group did not differ from the standardization sample for the VC factor, t(137) 0.30, p.05, d.02, or the PO factor, t(137) 1.65, p.05, d.15. They performed significantly worse on the FFD factor, t(137) 3.78, p.001, d.30, and the SC factor, t(137) 2.70, p.01, d 18. The adult group did not differ from the standardization sample on the VC factor, t(117) 0.55, p.05, d.06, or the FFD factor, t(117) 1.70, p.05, d.15, but performed significantly worse on the PO factor, t(117) 2.22, p.05, d.19, and on the SC factor, t(117) 6.29, p.001, d.44. A repeated measures analysis of variance (ANOVA) was then used to compare the child and adult autism groups performances across the various factor scores, to determine whether the SC factor differed significantly from the VC and PO factors. In this analysis, factors scores served as the repeated measure, and the child and adult autism groups were included as a between subject factor. There was no significant group effect, F(1, 252) 1.81, p.05, partial However, there was a significant multivariate effect for factor scores, F(3, 250) 16.01, p.001, partial 2.161, as well as a significant group by factor score interaction effect, F(3, 250) 5.00, p.01, partial Post hoc analyses indicated that the children with autism performed significantly worse on the SC factor than the VC factor, F(1, 136) 4.74, p.05, partial 2.034, and the PO factor, F(1, 136) 17.26, p.001, partial Correspondingly, for the adult autism group, the SC factor was significantly lower than the VC factor, F(1, 116) 19.71, p.001, partial 2.145, the PO factor, F(1, 116) 10.30, p.005, partial 2.082, and the FFD factor, F(1, 116) 10.07, p.005, partial Differences in general intelligence. Factor scores were examined across IQ categories by dividing the autism samples into groups based on increments of 10 Full Scale IQ points. This procedure formed six groups for the children and adults with IQs ranging from 70 to 120 in subgroups of 10 points. Repeated measures ANOVA s for the children and the adults were accomplished using the factor scores as the repeated measure and IQ range group as the between-subjects factor. For the children, there were significant multivariate effects for factor score, F(3, 129) 12.48, p.001, partial 2.225, and for IQ group, F(5, 131) , p.001, partial 2.914, as well as the factor score by IQ group interaction, F(15, 393) 1.71, p.05, partial Similarly, for the adults, ANOVA indicated significant multivariate effects for factor scores, F(3, 109) 11.98, p.001, partial 2.248, IQ group, F(5, 111) , p.001, partial 2.904, and a significant factor score by IQ range group interaction effect, F(15, 333) 2.82, p.001, partial Interaction effects for the children and adults are presented in Figure 1. As can be seen from the figure, for the children, the SC factor was lower than the VC and PO factors at all IQ range groups. For the adults, the SC factor was lower for the average to above-average IQ groups, although the VC factor was lower than the SC factor in the low average (80 89) and borderline (70 79) groups. Across child and adult samples, the SC factor was the

8 308 GOLDSTEIN ET AL Table 5 Optimal Maximum Likelihood Factor Solutions for Child and Adult Autism Samples in Comparison to the WISC-R and WAIS-R Standardization Samples for the Four Factor Model (M 4PC ) and Wechsler subtest descriptive statistics for the Autism Samples Wechsler III samples Autism samples Wechsler subtests Factor loadings Factor loadings VC PO FFD SC VC PO FFD SC M SD Children I V C S BD OA D A C/D PC PA Adults I V C S BD OA D A C/D PC PA Note. Model M 4PC four-factor model with Picture Arrangement and Picture Completion specified to load on factor 4. I Information, V Vocabulary, C Comprehension, S Similarities, BD Block Design, OA Object Assembly, D Digit Span, A Arithmetic, C/D Coding/Digit Symbol, PA Picture Arrangement, PC Picture Completion. Children Children with autism (n 137) or children in the WISC-III 11-year-old standardization sample age group (n 200) Adults Adults with autism (n 117) or adults in the WAIS-III 25- to 29-year-old standardization sample age group (n 200). VC Verbal Comprehension factor, PO Perceptual Organization factor, FFD Freedom From Distractibility factor, SC Social Context factor. lowest in the highest IQ groups, and poorer performance on this factor was particularly evident for adults with average to above average IQ, with the pattern being less clear for children. Age Differences The autism sample was then divided according to 5-year age brackets, ranging from 5 to 40 years. A repeated measures ANOVA was used to compared factor score performance across age groups, with the Wechsler factor scores serving as the repeated measure and age group as the between subjects factor. There were significant multivariate effects for factor score, F(3, 245) 14.99, p.001, partial 2.155, and the factor score by age group interaction effect, F(21, 741) 2.13, p.005, partial 2.057, although the age effect was not significant, F(7, 247) 1.30, p.25, partial The interaction effect is presented in Figure 2. While a number of changes in factor score performance across ages account for the interaction effect, the SC factor becomes relatively lower than the other factor scores beginning later in adolescence. The age pattern illustrated in Figure 2 shows the sharp decline in the SC factor score in the 15- to 19-year-old age group with some recovery during later years. However, after the 15- to 19-year-old group, it remains as the lowest factor score. Discussion The results of the study provide a number of findings with regard to the structure of intelligence in children and adults with autism. The hypothesis that the factor structure of the Wechsler scales would differ from the standardization sample in those with autism was not supported. Rather, across all of the models tested, similarities were present between the autism and standardization sample groups. For example, the conventional one-, two-, and three-factor models provided increasingly better fit for all of the groups investigated. For all of the groups, the three-factor model incorporating the SC factor provided a better fit than the conventional two-factor model, but a poorer fit than the conventional three-factor model consisting of the VC, PO and FFD factors. The four-factor model incorporating an SC factor composed of Picture Arrangement and Picture Completion provided the best fit for all groups, while a similar model with Comprehension and Picture Arrangement loading on the SC factor provided a relatively poorer fit. Additional work with the IV versions of the Wechsler scales will be necessary to confirm the present findings for those instruments, although given the consistency in subtest profiles across the various version of the tests (Siegel et al., 1996), it

9 STRUCTURE OF INTELLIGENCE WITH AUTISM 309 Children with Autism Adults with Autism VC PO FFD SC IQ Groups IQ Groups Figure 1. Factor scores for children and adults with HFA by IQ group. VC Verbal Comprehension factor, PO Perceptual Organization factor, FFD Freedom From Distractibility factor, SC Social Context factor. For the children, the descriptive statistics for the IQ s for each group were as follows: IQ group (n 10, M 75.90, SD 2.33), IQ group (n 34, M 83.76, SD 2.66), IQ group (n 31, M 93.81, SD 2.84), IQ group (n 36, M , SD 2.51), IQ group (n 11, M , SD 2.51), 120 or greater IQ group (n 16, M , SD 3.67). Comparable descriptive statistics for the adults were: IQ group (n 18, M 75.63, SD 2.75), IQ group (n 17, M 84.18, SD 2.83), IQ group (n 31, M 95.45, SD 2.68), IQ group (n 22, M , SD 2.60), IQ group (n 15, M , SD 2.94), 120 or greater (n 14, M , SD 7.13). would be surprising if the factors identified here were not also present for those more recent revisions. With that said, it is important to note that while similar in structure to the standardization sample, the results also indicate that in both children and adults with autism, goodness-of-fit with VC PO FFD SC Age Group Figure 2. Wechsler factor scores by age group. VC Verbal Comprehension factor, PO Perceptual Organization factor, FFD Freedom From Distractibility factor, SC Social Context Factor. the latent structure of the Wechsler scales is not as strong as it is when using subtest intercorrelations from age appropriate groups from the standardization samples. This finding is a general one involving all models tested. Even when an effort was made to evaluate a hypothetical SC factor involving the Comprehension, Picture Completion, and Picture Arrangement subtests, less robust goodness-of-fit was obtained in the autism samples than in the national samples. Despite the existence of a clearly prototypic subtest profile in individuals with autism, there is no indication of a particularly unique model that has a comparable goodness-of-fit to those obtained by typically developing individuals. In the case of the national samples, the goodness-of-fit-indices for the various models range around.90 or above. Thompson (2004) indicates that an NFI of.95 or above means there is an excellent fit. In the case of the WAIS-R several of the models are at or approach.95, but the autism group never exceeds.92. For the WISC R several of the models approach.95 for the national sample, but the NFIs for the autism group never exceed.85. In the case of the WISC III, which involves several additional subtests, the published AGFI s for four- and five-factor models exceed.95. Thus, while CFA of the Wechsler scales do not always achieve a fully adequate fit even for three or four-factor models, it is adequate in some cases. The fit in autism, particularly children, was always found to be somewhat less than adequate. The absence of a less than ideal goodness-of-fit for the Wechsler scales has been

10 310 GOLDSTEIN ET AL noted previously (Burton et al., 1994; O Grady, 1983; Waller & Waldman, 1990) and apparently reflects the fact that the Wechsler scales are not factorially pure instruments. The most direct interpretation of these findings is that, while the factorial structure of the Wechsler scales in autism is similar to structures found in the general population, cognitive abilities are less strongly associated among each other. As seen here, even individuals with autism who may have average or above general intelligence demonstrate this phenomenon. Put another way, if one knew the score of an individual on a test of one particular ability, one would be less accurate in estimating the score of another ability if that individual had autism. It is possible that the lower correlations in the autism group are not primarily because their intellectual function is not organized into three or four factors, but because individual abilities associated with the various subtests are not as highly associated with each other as they are in the national normative samples. A possibility is that their intellectual function is characterized by a reduced relative to normal g factor or general intelligence and their intellectual function is more modular (Gardner, 1999). This organization may have neurobiological significance. Recent functional magnetic resonance imaging (fmri) studies have extended the understanding of the altered pattern of information processing in autism. Across a range of social cognition (Kana, Keller, Williams, Minshew, & Just, 2005; Koshino et al., 2005), language (Just, Cherkassky, Keller, & Minshew, 2004; Kana, Keller, Cherkassky, Minshew, & Just, 2006), and problem solving tasks (Just, Cherkassky, Keller, Kana, & Minshew, 2007), reliably different reductions in the functional correlations (the degree of synchronization or correlation of the time series of the activation) occurred in the autism group. Thus, autism is thought to be characterized by a pattern of functional underconnectivity and reduced synchrony among the cortical regions supporting these higher order abilities. The reduced correlation among the tasks on the multiple subtests of the Wechsler scales and the relatively poorer fit attained in the CFA analyses may reflect a similar underlying principle. Indeed, direct comparisons indicated that while the overall patterns of correlations were similar between the autism and normative samples, those with autism obtained uniformly lower correlations many of which were significant. For the autism group, the cognitive skills measured by the individual scales are less correlated than those in the normative samples mirroring the lack of coordination among cognitive processing centers for higher order cognitive tasks. For example, while Block Design and Vocabulary measure clearly different abilities, the correlation in the national children s sample is.38 explaining 14% of the variance, while it is.23 for the children with autism, explaining only 5% of the variance. These small but consistent differences in the intercorrelation pattern are associated with the relatively reduced goodness-of-fit in the autism group. In general, the direction of the difference was toward lower correlations in the autism sample with some exceptions. Generalizing from underconnectivity theory, one could propose that the correlations among the intelligence test subtests reflect the underlying neurofunctional differences in autism. An alternate conceptualization is based on genetic research. Willerman and Bailey (1987) have indicated that, based on genetic research, correlations may exist because each test requires a common genetic matrix. Thus, correlations among subtests may not document neurophysiological connections between brain regions, but occur because the tasks considered involve what Willerman and Bailey describe as the same qualities of brain function. While underconnectivity theory still remains a possibility, it needs to be documented by direct evidence from functional MRI or related procedures demonstrating intact or impaired neurofunctioning associated with intelligence test correlations. The fact that the autism groups exhibited lower levels of performance than the standardization samples on both the PO and SC factors suggests that the SC factor accounts for additional variance separate from the more basic perceptual organizational abilities measured by the PO factor. Thus, while related to more basic cognitive and perceptual processes and sharing substantial variability with them, the perception and understanding of social information may be distinct from these more basic cognitive operations. This conclusion is consistent with that of Ruhl, Werner, and Poustka (1995) who reported that the lowest WAIS R and WISC R scores in a sample of individuals with autism were on those subtests that require understanding of social relations and concrete social actions. Picture Arrangement and Picture Completion require the perception and analysis of line drawings depicting various social situations and common objects, with identification of specific details crucial to successful performance. The social content of Picture Arrangement and some items from the Picture Completion subtest require knowledge of common social situations. Given these considerations, it appears that the SC factor assesses social perception and social knowledge, two areas that closely interface. It is not being suggested that performance on the SC factor is directly predictive of social functioning or that it is a social cognition factor, as additional validity studies will need to be performed to further assess this possibility. It is noteworthy, however, that consistent with our original predictions, the SC factor tended to be differentially impaired in our autism samples, which was particularly apparent when it was compared with the other Wechsler factors, as well as when it was examined across age and IQ groups. In this regard, the pattern of differential impairment was most apparent in HFA for the adults and for the higher IQ groups. It might be that the SC factor is a candidate mediator between formal cognitive ability and social behavior that might ultimately be demonstrated by studies in which a mediation model is tested with direct measures of social function, social cognition tasks as the mediator, and formal cognitive tasks (Brune, Abdel- Hamid, Lehmkamper, & Sonntag, 2007). With regard to autism specifically, there may be an association between difficulties on these tests and the widely studied weak central coherence theory of autism and the associated local as opposed to global processing preference (Happé & Frith, 2006; Mottron et al., 2003). A preference for local processing has been thought to be advantageous for performance on Block Design, perhaps because of heightened sensitivity to edge cues, but may be disadvantageous for Picture Arrangement in which grasp of the context in which the individual elements are contained must be appreciated. Picture Arrangement specifically may involve inference making, associated with Theory of Mind (the ability to attribute beliefs to oneself and others) that is thought to be impaired in autism (Baron-Cohen, 1989). Diminished performance on the SC factor was particularly evident for the highest IQ groups and for adult participants. Thus, impairment of the ability to utilize context may persist even at the