RESEARCH ARTICLE A Meta-Analysis of Imitation Abilities in Individuals With Autism Spectrum Disorders Laura A. Edwards Although imitation impairments are often reported in individuals with autism spectrum disorders (ASD), previous work has not yet determined whether these impairments are significant, specific to ASD, and present across the entire spectrum. This report of 53 studies on imitation in ASD seeks to determine whether individuals with ASD show significant imitation deficits, the magnitude of these deficits, and whether they are specific to ASD. Using standard meta-analytic techniques in a random-effects model, the data reviewed suggest that individuals with ASD show deficits in imitation, performing on average 0.81 SDs below individuals without ASD on imitation tasks. This deficit was specific to the condition of having ASD. Moderator analyses revealed that the average Autism Diagnostic Observation Schedule (ADOS) scores of groups of ASD participants were significantly and strongly negatively associated with the imitation abilities of these subjects, but average participant IQ was not associated with imitation abilities. Study setting, novelty of actions, format of imitation tasks (live vs. not), number of actions to imitate, or verbal prompts were not found to significantly affect the sizes of the imitation differences between individuals with and without ASD. The manner in which imitation was operationalized, however, had significant effects on whether imitation deficits were found between individuals with and without ASD. In tests that measured imitation of both form and end points, participants with ASD showed significant deficits compared with those without ASD; on tests of end point emulation only, individuals with ASD showed no deficits. Autism Res 2014, 7: 363–380. © 2014 International Society for Autism Research, Wiley Periodicals, Inc. Keywords: autism; imitation; review; meta-analysis; emulation

Introduction Imitation is a skill that emerges early in typical human development. It relies on and reflects social and cognitive processes, and it plays a pivotal role in learning throughout development. Developmental psychologists identify imitation as particularly foundational in four areas of child development: cognitively, imitation enables individuals to gain an understanding of fundamental aspects of the physical world, such as cause–effect relations; socially, imitation is a mechanism by which customs and behaviors are transmitted from one generation to the next, and it thus contributes to cultural differences between humans; imitation also plays social-emotional communicative functions, by signaling and generating feelings such as affiliation; finally, imitation is thought to be a key building block in the development of theory of mind—or the recognition that others have mental states distinct from one’s own [Chartrand & Bargh, 1999; Lakin & Chartrand, 2003; Meltzoff & Williamson, 2013; Uzgiris, 1981; Van Baaren, Janssen, Chartrand, & Dijksterhuis, 2009; Whiten et al., 2009].

Individuals with autism spectrum disorders (ASD) experience striking social and communicative deficits. Given the importance of imitation for social and cognitive functioning, the imitative abilities of individuals with ASD have long been under study. From a pedagogical standpoint, it is particularly important to understand the nature of imitation abilities in individuals with ASD, as imitation is a key mechanism in transmitting information and facilitating learning in individuals who have not yet developed language [Hanna & Meltzoff, 1993; Meltzoff, 1988a, 1988b]. Despite the abundance of research that exists on imitation abilities in autism, to date it is still not clear whether imitation deficits are ubiquitous and specific to the condition of having ASD. In 1991, for example, Rogers and Pennington found evidence for an imitation deficit in individuals with autism, and they hypothesized that this impairment might be central to the autistic disorder, preceding the cascade of social impairments observed in ASD. In a later study, Libby et al. [1997] found that children with autism actually performed better than typically developing (TD) children and children with Down

From the Harvard Graduate School of Education, Cambridge, Massachusetts and Boston Children’s Hospital, Boston, Massachusetts Received October 23, 2013; accepted for publication March 18, 2014 Address for correspondence and reprints: Laura A. Edwards, Harvard Graduate School of Education, 13 Appian Way, Cambridge, MA 02138. E-mail: [email protected] Published online 23 May 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/aur.1379 © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

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syndrome on imitation of certain pretend play tasks. D’Entremont & Yazbek [2007] showed that although individuals with ASD imitated the intentional actions of an experimenter as well as TD children, they showed atypicalities in that they also imitated an experimenter’s accidental actions. In a recent synthesis of the literature on imitation in autism, Williams, Whiten, and Singh [2004] again suggest a pattern of delayed behavioral imitation in individuals with ASD; this study focused only on imitation of the hands and body however, and the data reviewed did not permit calculation of effect sizes, which are essential to understanding how these deficits may affect daily functioning. In a later functional magnetic resonance imaging (fMRI) study of TD individuals and individuals with ASD, Dapretto et al. [2005] found that both groups imitated facial emotions equally well, although individuals with ASD did so while showing reductions in the BOLD response in the inferior frontal gyrus. Sevlever and Gillis [2010] argue that such mixed findings on the nature of imitation in individuals with ASD are in part due to an inconsistent operational definition of imitation across studies. Inconsistencies in the definition of imitation then give way to a diversity of research methods, imitation tests, and assessment conditions. These variations in study design likely lead to the measurement of distinct copying behaviors or components of imitation across studies purporting to measure the same underlying capacity. The authors argue that a standardized set of definitions, as well as the utilization of tasks derived from these definitions, may assist in producing a clearer understanding of the nature of imitative abilities and any delays or deficits therein in individuals with ASD. So that this meta-analysis may bring some clarity to the mixed findings within the ASD imitation literature, I will herein employ standardized definitions of imitation and related copying behaviors adapted from those utilized in past work [e.g. Sevlever & Gillis, 2010; Over & Carpenter, 2012]: that is, high fidelity imitation as a process by which organisms reproduce both the form and the end result of a modeled action. High fidelity imitation is, thus, differentiated from emulation, which is a process in which organisms attain the final goal of an act following the modeling of a behavior, but may use their own execution in order to obtain the goal [Tomasello, 1990, as cited by Sevlever & Gillis, 2010]. It should be noted that these definitions make no assumptions as to the novelty of the acts being reproduced, though in fact, Sevlever and Gillis argue that an action must be novel in order for “true imitation” to occur. As such, in this meta-analysis, I will also investigate the effects of action novelty on imitation performance. In addition to the definitional variations that Sevlever and Gillis identify, a further complicating factor in understanding imitation in ASD may be the behavioral and genetic heterogeneity within this population, and result-

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ing inconsistencies in the way that this population has been defined across studies. Individuals with ASD exhibit a spectrum of developmental difficulties and impairments, from only mild social impairments and intact or even superb language abilities, to profound deficits in both cognitive and social abilities. ASD is also four times more prevalent in males than in females, and may look qualitatively different between the two genders [Moriuchi, Klin, & Jones, 2013]. Being a developmental disorder, ASD also manifests differently in individuals of different ages. Finally, the measures utilized to diagnose ASD vary widely between clinical settings, and standardized diagnostic criteria used to define ASD have evolved over the past few decades. Here, I aim to disambiguate the mixed findings in this field by carrying out a meta-analysis that is sensitive to variations in operational definitions of imitation, study design, and ASD diagnostic criteria across the existing literature on imitation in ASD. Specifically, I first ask the question: Do individuals with ASD show overall impairments (as compared with individuals without ASD) on a range of tests of imitation? To assess whether any deficits found are practically significant, I also use this meta-analysis to determine the magnitude of any between-group differences in imitative abilities between individuals with and without ASD. Since the answers to these questions likely encompass (and conceal) significant variability and nuance in the definition of imitation, as well as the definition of ASD, my third and final goal in conducting this meta-analysis is to assess the extent to which between-group differences in imitation in individuals with and without ASD vary by the characteristics of the ASD population under study, the testing situation employed, or the operationalization of imitation.

Methods Literature Search and Inclusion Criteria Studies were retrieved by searching the contents pages of the last five issues of the Journal of Autism and Developmental Disorders, Autism Research, and Child Development for keywords, which were then used in an electronic database search of PsycINFO, ERIC, Education Abstracts, Academic Search Premier, Dissertation Abstracts, ProQuest, Web of Science, and Pubmed. The search was limited to articles published in English and produced a total of 1179 hits on December 31, 2012. Titles and abstracts of each of these articles were examined, and those studies judged as not meeting inclusion criteria were excluded. The remaining articles were read through to ensure that they met all inclusionary criteria. These methods generated a final list of 53 studies. Inclusion criteria were developed from a review of the past literature on imitation in autism, as well as from considerations specific to the meta-analytic methodology. Studies were only included if they directly assessed

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imitative abilities of individuals with ASD in experimental designs—in contrast to studies teaching imitation, or studies using parent report or other indirect measures, for example. This analysis also focuses only on studies or parts of studies in which imitation was assessed behaviorally. As this meta-analysis is intended to be an examination of whether children with ASD can imitate (with or without support), rather than whether children with ASD do spontaneously imitate, only studies assessing elicited imitation were included. For the purposes of this analysis, however, elicited imitation was conceptualized as a broad category, including those experiments in which children were given explicit prompts to copy a demonstrator, as well as those experiments in which children were not explicitly asked to imitate, but were placed in a structured interaction with a demonstrator and required to alternately watch a model and produce responses (with or without minimal verbal prompting). It follows from these definitions that the only experiments considered to be assessing true spontaneous imitation (and thus excluded from this analysis) were those in which children were placed in an unstructured situation, such as free play, and observed for any incidences in which they imitated another, regardless of whether they were already engaged in social interaction with the other. This analysis also excluded assessments of deferred imitation to prevent confounding of the imitative abilities of children with ASD with their memory abilities. In accordance with previous studies, which classify imitation tasks based on the particular modalities they employ [e.g., Williams et al., 2004, who differentiate actions on objects from actions using only the hands or body, and hypothesize that oral-facial imitation, particularly imitation of facial affect, may employ a broader range of neural mechanisms than those employed by object-oriented or hand-and-body action alone], this meta-analysis only includes studies that assessed facial and mouth actions (including affect), body and hand actions, or objectdirected actions; no studies of vocal imitation were included to avoid the confounding effect that language development may have on imitation in these cases. Studies included must also have assessed at least one non-ASD comparison group (usually a TD sample or a developmentally delayed non-autistic group (DD)) to allow for the computation of valid effect sizes. Studies had to include at least five participants in each test group; this sample size was determined via power analysis, to be the minimum required to provide sufficient power to detect very large effect sizes (SD = 2.0). Additionally, only studies that matched individuals with and without ASD on chronological or mental age were included, and all studies had to include enough information to enable calculation of effect sizes (either through the study report or by contacting an author)—studies that only reported on the significance or nonsignificance of findings

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without providing a specific statistical test value or P-value were not included. Finally, unpublished dissertations that met the criteria above were included in an attempt to minimize the influence of publication bias on the findings herein.1 Study Coding The final 53 studies were coded on characteristics that fell into three broad categories. Report characteristics included author surnames, year of publication, and whether the article was peer-reviewed or a dissertation. Participant characteristics included the number of participants in each study group, the percentage of males in the ASD group, DD group diagnoses if applicable, ages of the participants in each group, the tests on which comparison groups were matched, the methods used for diagnosing ASD, the average scores of ASD or DD groups on standardized autism diagnostic tests and tests of cognitive functioning, and whether participants were previously or currently enrolled in interventions. Imitation task characteristics included details of the imitation tests that were hypothesized to affect the underlying imitative mechanism being studied, or to differentially affect individuals with and without ASD: the operational definition(s) of imitation that were used in each study (high fidelity imitation or emulation); the domains of imitation tested (oral-facial, hand and body, or object-directed); whether tasks were demonstrated in live interactions or via digital/electronic means such as video; whether imitation was elicited using an explicit verbal prompt vs. a more open-ended verbal prompt or nonverbal cues inherent to the testing situation; whether subjects were to imitate familiar actions (defined for the purposes of this meta-analysis as actions that were known to be familiar to the participant, or high incidence in their occurrence in daily life) or unfamiliar actions (that is, actions known to be novel to subjects, or actions unlikely to be observed and utilized in daily life); whether individuals were required to imitate single actions or action sequences; and whether the study was carried out in a setting that was familiar or unfamiliar to the child. In the absence of information to the contrary and in order to carry out the meta-analytic procedures given the wide variety of outcome scales used across studies, it was assumed that all researcher-designed scales were normally

1 Three (3) studies were excluded for failing to provide enough data to compute effect sizes. Notably, all of the studies excluded for this reason reported nonsignificant effect sizes for the difference in imitation performance between ASD and non-ASD individuals. However, the presence of bias in the final sample of studies was not found to be statistically significant (see Footnote 3).

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distributed measures. The Hedge’s g effect size was an appropriate measure for comparing the performance of ASD and control participants across all studies, as this effect size represents the standardized estimate of the difference between the performance of the two groups, adjusted for small sample sizes. The guidelines for computing effect sizes outlined by Wilson and Lipsey [2001] and implemented in the Practical Meta-Analysis Effect Size Calculator at http://cebcp.org/practical-metaanalysis-effect-size-calculator/ were followed in order to utilize the various forms in which statistical data were reported across studies. Many of the studies included in this analysis contained multiple tests of imitation, outcome measures, and control groups (TD and DD). In order to use each study as a single unit of analysis, and in order to investigate deficits in imitation specific to ASD, the effect sizes within studies were combined as follows: where overall imitation scores were reported, these values were used so long as these only combined the results of tests meeting the inclusion criteria; where overall scores included tests outside the scope of this study, effect sizes were computed by averaging Hedge’s g for each subtest meeting inclusion criteria; for studies in which either a TD or a DD comparison group was present, a single effect size representing the difference in imitation performance between ASD and non-ASD groups was computed; in studies containing both TD and DD control groups, separate effect sizes were first computed for each comparison (ASD vs. TD and ASD vs. DD) and the mean of these two effect sizes was then calculated.2 Variances for combined effect sizes were calculated directly for these means, using pooled nonASD (TD + DD) sample sizes where applicable. Meta-Analytic Procedures I used standard meta-analytic procedures in a randomeffects model to investigate the relationship between diagnostic group and imitation ability. A random effects model—which assumes that variations in the results found across studies result from variations in the interventions and measures used, in addition to sampling error—extrapolates to the entire possible pool of studies that examine the imitation performance of individuals with ASD relative to individuals without ASD, and thus makes this meta-analysis generalizable to all studies.

2 Ideally, to calculate effect sizes for ASD/non-ASD comparisons, I would have collapsed TD and DD means and standard deviations into one non-ASD group statistic; from this, an overall study effect size could have been computed. However, as the available data for computing effect sizes varied across studies (e.g., some reported means and standard deviations for each test group, others reported only test statistics or P-values for specific comparisons), effect sizes were computed as reported to keep the method of calculation consistent across all studies.

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Moderator Analyses Given the assumed variability in effect sizes between studies, after computing the mean weighted Hedge’s g across all studies, I use mixed-effects models to conduct moderator analyses for the operationalization of imitation in the study, the modality of imitation tested, and the effect of the design of imitation tasks used. In order to determine the effects of participant characteristics (age, gender, IQ or cognitive functioning scores, and severity of ASD) on imitation abilities, I performed a series of metaregressions between these statistics and the effect size of the imitation deficit for each study in which these data were obtainable. All meta-analytic procedures and statistical tests were performed using Stata version 11.2 [StataCorp, 2009].

Results Study Characteristics The coded characteristics of each study are presented in Tables 1–3. Fifty of the studies analyzed were peerreviewed journal articles and three were dissertations; all were published between March 1, 1984 and November 16, 2012. The number of participants in any participant group ranged from 5 to 111, and the majority of participants in each group across all studies were male (M = 85.84%; SD = 10.52%). Participants with ASD ranged in age from 4.6 months to 37 years across studies (M = 9.28 years; SD = 6.65 years). Since participants were most often matched on mental age, individuals in the TD groups tended to be younger than individuals in either the DD or ASD groups within any study. Full-scale IQs of the ASD groups, where reported, were most commonly measured using the Wechsler Intelligence Scales (WPPSI, WISC, WISC-R, WASI or WAIS), and the average IQ scores of the ASD groups on these measures ranged from 68.8 to 119 (M = 96.9; SD = 15.0). In general, participants were diagnosed according to international diagnostic criteria (the current version of the Diagnostic and Statistical Manual of Mental Disorders or the International Classification of Diseases at the time the study report was written) by the expertise of a clinician or by the convergence of a clinician’s diagnosis with a standardized instrument. A variety of standardized instruments were used to verify autism diagnoses, including the Childhood Autism Rating Scale [Schopler, Reichler, & Renner, 1986], Gilliam Autism Rating Scale [Gilliam, 1995], Autism Behavior Checklist [Krug, Arick, & Almond, 1980], Autism Diagnostic Observation Schedule [ADOS; Lord et al., 1989], and Autism Diagnostic Interview, Revised [Rutter, Le Couteur, & Lord, 2003]. Average group scores on these instruments were most often reported for ASD groups on the ADOS; however, only

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Table 1.

Report and Participant Characteristics for Each of the Studies Included in This Analysis for ASD vs. Non-ASD Comparisons

Report characteristics

Authors Aldridge, Stone, Sweeney, & Bower [2000] Carpenter, Pennington, & Rogers [2001] Cossu et al. [2012] Bernier, Dawson, Webb, & Murias [2007] Carpenter, Tomasello, & Striano [2005] Charman & Baron-Cohen [1994] Charman et al. [1997] Chin-Chin, Chung-Hsin, & Yuh-Ming [2011] Bernabei, Fenton, Fabrizi, Camaioni, & Perucchini [2003] D’Entremont & Yazbek [2007] Colombi et al. [2009] Dapretto et al. [2005] Dawson, Meltzoff, Osterling, & Rinaldi [1998] Dewey, Cantell, & Crawford [2007] Freitag, Kleser, & von Gontard [2006] Ham et al. [2011] Hamilton, Brindley, & Frith [2007] Hertzig, Snow, & Sherman [1989] Hobson & Hobson [2008] Hobson & Lee [1999] Ingersoll, Schriebman, & Tran [2003] Gillis Mattson [2006] Lainé, Rauzy, Tardif, & Gepner [2011] Leighton, Bird, Charman, & Heyes [2008] Jones & Prior [1985] Libby, Powell, Messer, & Jordan [1997] McDonough, Stahmer, Schreibman, & Thompson [1997] McIntosh, Reichmann-Decker, Winkielman, & Wilbarger [2006] Meyer & Hobson [2004] Morgan, Cutrer, Coplin, & Rodrigue [1989] Nielsen & Hudry [2010] Nielsen, Slaughter, & Dissanayake [2012] Ohta [1987] Roeyers, Van Oost, & Bothuyne [1998] Rogers, Hepburn, Stackhouse, & Wehner [2003] Rogers, Young, Cook, Giolezetti, & Ozonoff [2010] Rogers, Bennetto, McEvoy, & Pennington [1996] Salowitz et al. [2012] Perra et al. [2008] Dziuk et al. [2007] MacNeil & Mostofsky [2012] Stone, Ousley, & Littleford [1997] Sigman & Ungerer [1984] Smith & Bryson [1998] Bernier [2007] Hornbeck [2001] Stone, Lemanek, Fishel, Fernandez, & Altemeier [1990] Tamura, Kitamura, Endo, Abe, & Someya [2012] Vanvuchelen, Roeyers, & De Weerdt [2007a] Vanvuchelen, Roeyers, & De Weerdt [2007b] Vivanti, Nadig, Ozonoff, & Rogers [2008] Warreyn, Roeyers, & de Groote [2005] Young et al. [2011]

Participant characteristics N N N Total (ASD) (TD) (DD) sample

ADOS score

Average age

% Male Developmental ASD age matched

35.6

26.2

10 0 21 15 0 0 19 19 0

0 11 0 0 8 23 9 18 39

20 22 36 0 16 43 38 55 79

49.4 97.32 163.2 96.2 140.3 20.7 40.44 44.4

17 14 5 20 49 15 19 25 18 16 11 15 14 19 16 10 10 6

14 0 5 20 78 29 23 31 18 0 0 14 12 18 16 10 10 6

6 15 0 19 111 0 0 0 14 16 11 0 0 17 0 0 10 0

37 29 10 59 238 44 42 56 50 32 22 29 26 54 32 20 30 12

53 42.1 150 64.6 122.4 198 145.2 97 222 137 165 37.4 89 140 444 103 126 55

14

14

0

28

324

78.57

Yes

16 10 22 16 16 18 24

0 10 0 17 16 0 15

16 10 12 0 0 18 20

32 30 34 34 32 36 59

137 100.8 63 74.44 122.3 57.94 34.17

87.5 80 81.25 93.75 88.89 83.33

Yes Yes Yes Yes Yes Yes Yes

23.67 16.58

41

22

22

85

90.24

Yes

27.74 20.04

17

0

15

32

88.24

Yes

13 14 47 24 18 16 20 14 22 22

14 16 47 24 18 16 20 15 20 20

0 13 0 24 18 16 20 0 19 49

27 43 94 72 54 0 60 29 61 91

84.62

Yes Yes No No Yes Yes Yes Yes Yes Yes

20

20

0

40

122.4

8 17 18 20 24

0 17 13 0 75

13 0 0 20 43

21 34 31 0 142

74 105 136 58.7 12

17.1

43.716 186 12

174 108 127 116.28 31.3 51.7 136 11.78571429 283.2 62.54 4.6

6.57

92.86 95 87.76 100 89.47 76 87.5 60 85.71 78.94 87.5 50 90

91.49 79.17 72.22 93.75 85 100 90.90

80 100 100 88.89 70 87.5

No Yes Yes Yes Yes Yes Yes Yes No

NVMA

10 11 15 14 8 20 10 18 40

8.4

70 90.9 86.67 100 87.5 80 100

VMA

Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes

PIQ

VIQ

95.8 107.6

86.8 117

91 114

53.4 85.1 46 17.1 30.42 22.11

27.7

21.5 92 60.45 91 106

108.3 102.5

100.1 106

116

116 49

119

51 56 73 60

82

47.63 54 52 26 101.1 72 36.36

64.06 55.63 101.44 85.3

96.19

84.56 64.9

93.44 72.1

84.75

89.38

94 99.4

48.1 8.74 107.6

114

114

46.23 44.23 54.1

Yes Yes No Yes Yes No

FSIQ

100.2 67.9 96.35 107 68.8 26.21 29.04

ADOS, Autism Diagnostic Observation Schedule; ASD, autism spectrum disorder; DD, developmentally delayed; FSIQ, Full Scale IQ; NVMA, Non-Verbal Mental Age; PIQ, Performance IQ; TD, typically developing; VIQ, Verbal IQ; VMA, Verbal Mental Age.

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368

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Aldridge et al. [2000] Carpenter et al. [2001] Cossu et al. [2012] Bernier et al. [2007] Carpenter et al. [2005] Charman & Baron-Cohen [1994] Charman et al. [1997] Chin-Chin et al. [2011] Bernabei et al. [2003] D’Entremont & Yazbek [2007] Colombi et al. [2009] Dapretto et al. [2005] Dawson et al. [1998] Dewey et al. [2007] Freitag et al. [2006] Ham et al. [2011] Hamilton et al. [2007] Hertzig et al. [1989] Hobson & Hobson [2008] Hobson & Lee [1999] Ingersoll et al. [2003] Gillis Mattson [2006] Lainé et al. [2011] Leighton et al. [2008] Jones & Prior [1985] Libby et al. [1997]

Authors

Familiar Familiar

Unfamiliar Familiar

Familiar Familiar

Familiar

Unfamiliar

Unfamiliar

Unfamiliar Familiar Familiar

Setting

Not Live Live Both Live Live Live Both Not Live Not Live Live Live

Live Live Not Live Live Live Live Live Live Live Live Live Not Live Live Live

Presentation format

Explicit Not explicit Explicit Explicit Explicit Explicit Explicit Not Explicit Not explicit Not explicit Explicit Explicit Explicit Explicit Explicit

Not explicit Not explicit Not explicit Not explicit

Not explicit Not explicit Explicit Explicit Not explicit

Verbal prompt Both Both Both Both Both Both Unfamiliar Both Both Both Both Familiar Both Both Both Familiar Unfamiliar Familiar Both Unfamiliar Unfamiliar Both Both Familiar Both Both

Novelty of actions Single Both Single Both Single Both Both Both Both Sequences Both Single Both Single Single Single Both Single Sequences Both Single Both Both Sequences Both Both

Single or sequential actions

–0.89 –1.778

–1.343

–0.183

–2.455 –0.246

–1.606

0.221 –2.073

–1.874 –1.388

g (True imitation)

0.243

–0.272

0.017

–0.544 –1.117 –0.733

0.116 0.163

–0.29

g (Emulation)

Imitation task characteristics

–1.326

–1.171

–0.733

0.361

–1.531 –1.994

g (Facial imitation)

–1.263 0.718

–0.721 –2.566

–0.99

0.009

0.163

–2.797 –1.051

g (Hand-body imitation)

Imitation Task Characteristics for Each of the Studies Included in This Analysis for Autism Spectrum Disorder (ASD) vs. Non-ASD Comparisons

Report characteristics

Table 2.

0.124

–1.778

–0.698 –0.238 –1.343 –0.82

–2.232

–0.544 –1.243

0.221 –2.073 –0.905

–0.29 –1.294

g (Object imitation)

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McDonough et al. [1997] McIntosh et al. [2006] Meyer & Hobson [2004] Morgan et al. [1989] Nielsen & Hudry [2010] Nielsen et al. [2012] Ohta [1987] Roeyers et al. [1998] Rogers et al. [2003] Rogers et al. [2010] Rogers et al. [1996] Salowitz et al. [2012] Perra et al. [2008] Dziuk et al. [2007] MacNeil & Mostofsky [2012] Stone et al. [1997] Sigman & Ungerer [1984] Smith & Bryson [1998] Bernier [2007] Hornbeck [2001] Stone et al. [1990] Tamura et al. [2012] Vanvuchelen et al. [2007a] Vanvuchelen et al. [2007b] Vivanti et al. [2008] Warreyn et al. [2005] Young et al. [2011] Unfamiliar Unfamiliar

Unfamiliar Unfamiliar

Unfamiliar

Familiar

Familiar Familiar Unfamiliar Familiar

Unfamiliar

Live Not Live Live Live Live Live Live Live Live Live Live Not Live Not Live Live Live Live Live Live Not Live Live Live Not Live Live Live Not Live Live Live Explicit Explicit Explicit Explicit Explicit Explicit Explicit Explicit Not explicit Explicit Explicit Explicit Explicit Explicit Not explicit Not explicit Explicit Not explicit Explicit

Not explicit Explicit Not explicit Not explicit Not explicit Not explicit Explicit

Both Both Both Both Both

Both Familiar Both Both Unfamiliar Unfamiliar Both Both Both Familiar Both Both Both Familiar Familiar Both Both Both Both Both Both

Single Single Both Both Single Both Single Both Both Single Both Both Both Both Both Both Both Both Both Both Both Both Both Both Both Sequences Sequences –0.069 –0.205

0.039 –0.25

–0.522 –0.12 –0.319

–2.685 0

–0.597 –0.639

–1.074 –0.911 0.057 –0.704 –0.915 0.005 –1.167 –1.108 –1.011

0

–0.378 –0.494

–1.878

–0.656

–0.85

–0.378

–0.484 –1.272

–0.892 –0.915 –0.293 –1.167 –1.108 –0.607 –0.235 –0.19 –1.051

–0.983 –1.219 –0.55

–0.447

–0.816 –0.239

–0.841 –1.241

–1.059

–0.93 –0.882 0.06 –0.147

–0.015 –0.235

–0.247

0.388

Table 3.

Overall Effect Sizes for Each of the Studies Included in This Analysis

Report characteristics Authors Aldridge et al. [2000] Carpenter et al. [2001] Cossu et al. [2012] Bernier et al. [2007] Carpenter et al. [2005] Charman & Baron-Cohen [1994] Charman et al. [1997] Chin-Chin et al. [2011] Bernabei et al. [2003] D’Entremont & Yazbek [2007] Colombi et al. [2009] Dapretto et al. [2005] Dawson et al. [1998] Dewey et al. [2007] Freitag et al. [2006] Ham et al. [2011] Hamilton et al. [2007] Hertzig et al. [1989] Hobson & Hobson [2008] Hobson & Lee [1999] Ingersoll et al. [2003] Gillis Mattson [2006] Lainé et al. [2011] Leighton et al. [2008] Jones & Prior [1985] Libby et al. [1997] McDonough et al. [1997] McIntosh et al. [2006] Meyer & Hobson [2004] Morgan et al. [1989] Nielsen & Hudry [2010] Nielsen et al. [2012] Ohta [1987] Roeyers et al. [1998] Rogers et al. [2003] Rogers et al. [2010] Rogers et al. [1996] Salowitz et al. [2012] Perra et al. [2008] Dziuk et al. [2007] MacNeil & Mostofsky [2012] Stone et al. [1997] Sigman & Ungerer [1984] Smith & Bryson [1998] Bernier [2007] Hornbeck [2001] Stone et al. [1990] Tamura et al. [2012] Vanvuchelen et al. [2007a] Vanvuchelen et al. [2007b] Vivanti et al. [2008] Warreyn et al. [2005] Young et al. [2011]

Overall effect sizes g (ASD vs. TD)

g (ASD vs. DD)

g (ASD vs. non-ASD)

–3.951 NA –1.874 –1.831 NA NA –2.996 –0.101 NA –0.927 NA –0.733 –0.756 –1.788 –0.901 –2.455 –0.092 –2.152 NA NA –1.343 –0.890 –1.101 –1.778 –1.263 0.251 0.388 -0.378 NA –0.447 NA –0.235 –0.983 NA –0.924 –0.416 NA –0.915 –0.289 –1.167 –1.117 –0.798 –1.418 –0.448 –1.831 –0.219 –2.456 0.000 NA –0.484 –1.044 NA –0.569

NA –0.290 NA NA 0.116 0.192 –1.150 –0.352 –0.212 –0.162 –1.120 NA –1.163 –1.424 NA NA NA –0.501 –0.698 –0.238 NA NA –0.678 NA NA 0.234 NA NA –0.247 –0.447 –0.027 NA NA –1.074 –0.899 0.536 –0.704 NA 0.299 NA –1.099 –1.224 0.948 –0.584 NA 0.005 –2.913 0.000 –2.133 NA NA –0.239 –0.068

–3.951 –0.290 –1.874 –1.831 0.116 0.192 –2.073 –0.227 –0.212 –0.544 –1.120 –0.733 –0.960 –1.606 –0.901 –2.455 –0.092 –1.326 –0.698 –0.238 –1.343 –0.890 –0.890 –1.778 –1.263 0.243 0.388 –0.378 –0.247 –0.447 –0.027 –0.235 –0.983 –1.074 –0.911 0.060 –0.704 –0.915 0.005 –1.167 –1.108 –1.011 –0.235 –0.516 –1.831 –0.107 –2.685 0.000 –2.133 –0.484 –1.044 –0.239 –0.319

ASD, autism spectrum disorder; DD, developmentally delayed; TD, typically developing.

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five of the studies analyzed reported the average ADOS scores of their ASD participant groups. The studies included in this report tended to pool some combination of facial, body, and object-directed imitation within one imitation battery. In determining whether studies operationalized imitation as (herein defined) high fidelity imitation or just emulation, particular attention was paid to the ways in which studies reported their scoring procedures. The scales used to score imitation varied widely across studies and were often designed by the researchers themselves, to be specific to the tasks that they employed. Across studies within this analysis then, studies were coded as measuring emulation when the scales used to rate imitation rewarded participants for effecting the demonstrated change on an object in the case of object-directed imitation, or achieving an approximation of the final posture or facial expression demonstrated in the case of hand and body, or oral-facial imitation. Studies were coded as measuring high fidelity imitation when their scoring scales took into account whether or not participants had reproduced the exact sequence of body movements or postures as the demonstrator in oral-facial, hand and body, and object-directed imitation tasks (with no errors such as reversal or sequencing errors, which were allowed in those studies measuring emulation). Scales for scoring the number or extent of errors that participants made while imitating were reverse-coded to contribute to that study’s high fidelity imitation scores. Depending on the number and type of scoring scales employed, each study may have been coded either as measuring emulation or high fidelity imitation only, or as containing both measures of emulation and high fidelity imitation. For these codes, 25% of the studies included in this meta-analysis were rated by two independent researchers naïve to the aims of this project. Inter-coder agreement was 0.93, producing a reliability (measured by Cohen’s kappa) of 0.86. Discrepancies were then resolved through discussion. Of the studies included in this analysis, 39 used live demonstrations, 11 used digital demonstrations, two utilized both live and digital demonstrations of the imitation tasks, and one did not provide this detail. In 43 of the studies, experimenters always gave individuals verbal prompts to imitate, while eight studies provided no prompts, one study used prompts inconsistently, and another study did not give this information. Seven studies tested only familiar (high-incidence) actions, six tested only novel (low-incidence) actions, and the remainder used a combination of high- and lowincidence actions in their imitation tasks. Fourteen studies engaged participants only in single-action imitation, five had individuals imitate action sequences, and the remaining 34 tested an assortment of both single actions and action sequences. Of the studies in which verbal prompts to imitate were given, 30 gave verbal

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prompts that explicitly asked individuals to imitate the experimenters actions (e.g., “Do what I do.”). Finally, of the studies in which settings were reported, 21 were conducted in environments presumably unfamiliar to the participants (including laboratories and clinic assessment rooms), while 15 were conducted in environments familiar to the participants (either at their homes or schools). Overall Effects The results of the meta-analysis of imitation in individuals with ASD vs. those without ASD are illustrated in Figure 1. The overall mean weighted effect size for the performance of individuals with ASD vs. non-ASD individuals on tests of imitation was −0.808 (z = 8.02, P < 0.001). Individuals with ASD, thus, performed 0.808 standard deviations below non-ASD individuals on the tests of imitation herein. As expected, a homogeneity analysis revealed significant variability between the magnitudes of the effect sizes across studies (Q[df = 52] = 245.09, P < 0.001). The amount of variability in excess of that attributable to sampling error alone for this set of studies was 78.8%. A moderator analysis indicated that there were no statistically significant differences in the effect sizes across studies that were published or unpublished (t = −0.192, P = 0.848).3 Moderator Analyses: The Effect of Participant Characteristics The gender composition of the group of participants with ASD was not found to have a statistically significant effect on their imitation performance (βGENDER = −0.0064, P = 0.59), although the variability in gender across studies in this analysis may have been too limited to detect such effects. ASD participants’ average age was also not significantly correlated with their imitation performance relative to matched participants without ASD (βAGE = −0.0015, P = 0.236). The average ADOS score of ASD participant groups was significantly, strongly, and negatively associated with subjects’ imitation performance relative to individuals without ASD (βADOS = −0.20, P = 0.017; Fig. 2). This association implies that participant groups with more severe ASD showed greater imitation deficits relative to participants without ASD.

3 Since only three studies comprised the unpublished group, a further test for publication bias was carried out by performing funnel plot asymmetry tests in Stata [StataCorp, 2009]. Funnel plots are scatterplots of each study’s effect size against a measure of its sample size; they are used as visual means for inspecting possible bias in meta-analyses. These plots revealed that the unpublished dissertations were distributed evenly throughout the entire population of studies included in this analysis. Finally, the Egger test for funnel plot asymmetry (which, if significant, would indicate bias in the sample due to a number of possible factors including publication bias) was also nonsignificant (P = 0.206).

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Figure 1. Forrest plot showing Hedge’s g effect sizes, 95% confidence intervals, and weights based on a random-effects model, for the comparison of autism spectrum disorder (ASD) and non-ASD individuals’ performance on imitation tests in the 53 studies analyzed. Negative effect sizes represent the number of standard deviations below the performance of typically developing individuals, scored by individuals with ASD. The overall mean weighted effect size for this comparison was −0.808 (95% CI = −1.006, −0.611); P < 0.001. Q-total = 245.09; I2 = 78.8%; P < 0.001.

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Figure 2. Meta-regression plot showing Hedge’s g effect sizes against the average ADOS severity scores of participants in ASD groups, for the comparison of ASD and non-ASD individuals’ performance on imitation tests in the studies analyzed. Each circle represents a single study, and circle sizes are proportional to studies’ sample sizes. A statistically significant strong negative effect of average ADOS score of the ASD sample on imitation effect size was found (βADOS = −0.199, P = 0.017). ADOS, Autism Diagnostic Observation Schedule; ASD, autism spectrum disorder.

Interestingly, however, the magnitude of the imitation deficit was not associated with any of the average cognitive measures reported for ASD groups (including verbal mental age, nonverbal mental age, overall mental age, performance IQ, verbal IQ, or full-scale IQ). The effects of these participant characteristics on imitation performance should be interpreted with caution, however, as each of these analyses utilizes an average group measure, which may conceal significant variability at the individual subject level.

than that expected by sampling error alone (Q[df = 26] = 115.36 P < 0.001 I2 = 77.5%), the variation between studies testing only emulation was not significantly different from that attributable to sampling error (Q[df = 6] = 1.57 P = 0.954 I2 = 0.0%). This suggests that the seven studies in which imitation was operationalized as emulation are measuring a common or fixed (and null) effect of ASD on individuals’ ability to emulate the actions of others. These findings suggest that individuals with ASD may emulate the actions of another comparably to individuals without ASD, but that individuals with ASD show significant deficits in reproducing the form of another’s actions in achieving a goal, or individuals with ASD may have specific trouble reproducing both form and end state in the same response. Forrest plots of the comparison between emulation alone and high fidelity imitation are presented in Figure 3.

Moderator Analyses: The Effect of Imitation Domain No statistically significant effects of the domain of the task to be imitated on the performance of individuals with ASD relative to those without ASD were observed (oral-facial: g = −0.991; hand-body: g = −0.795; objectdirected: g = −0.706). Additionally, tests for homogeneity revealed that the variability within each of these domains was significant (oral-facial: Q[df = 9] = 37.09, P < 0.001 I2 = 75.7%; hand-body: Q[df = 23] = 90.01, P < 0.001 I2 = 74.4%; object-directed: Q[df = 26] = 85.90, P < 0.001 I2 = 69.7%). Even within these imitation domains then, the studies analyzed were not measuring a common effect of ASD on individuals’ ability to imitate.

Moderator Analyses: The Effects of Imitation Task Design Moderator Analyses: Operationalizing Imitation Overall, effect sizes remained significant, large, and negative for studies employing assessment measures that captured high fidelity imitation (N = 27, g = −0.914, z = 7.12, P < 0.001). However, on tests of imitation that captured an individual’s ability to emulate only, individuals with and without ASD did not differ significantly in their performance (N = 7, g = −0.104, z = 0.77, P = 0.444). Subgroup comparisons indicated that the mean effect size for the differences between the performance of individuals with and without ASD on tests of imitation operationalized as high fidelity imitation was 0.81 standard deviations lower than that for the comparison of individuals with and without ASD on imitation tests in which imitation was operationalized as emulation only (z = 3.05, P < 0.01). Finally, tests for homogeneity revealed that although variation in the results of those studies measuring high fidelity imitation was significantly greater

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No statistically significant effects of the format of the demonstrated task (live vs. video t = 0.991, P = 0.327), the explicitness of the verbal prompt (explicit vs. not explicit t = 1.07, P = 0.291), the number of actions to be imitated (single vs. sequences t = 0.837, P = 0.414), the familiarity of the actions (high vs. low incidence t = 1.34, P = 0.332), or the study setting (familiar vs. unfamiliar t = 1.634, P = 0.117) on the performance of individuals with ASD relative to those without ASD were observed. However, tests for homogeneity revealed that although there was significant variation in the results of those studies conducted in unfamiliar settings (Q[df = 10] = 71.98, P < 0.001 I2 = 86.1%), the variation between studies conducted in familiar environments was not significant (Q[df = 11] = 15.20, P = 0.174 I2 = 27.6%). This suggests that the 12 studies conducted in familiar settings may be measuring a fixed effect of ASD on individuals’ ability to imitate.

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Figure 3. Forrest plot showing Hedge’s g effect sizes, 95% confidence intervals, and weights based on a mixed-effects model, for the subgroup analysis of ASD vs. non-ASD individuals’ performance on imitation tests based on whether imitation was operationalized as emulation only or high fidelity imitation. Negative effect sizes represent the number of standard deviations below the performance of non-ASD individuals, scored by individuals with ASD. Differences in performance between individuals with and without ASD were significantly different across tests of emulation only and tests of high fidelity imitation (z = 3.05, P < 0.01). The overall mean weighted effect size for studies testing emulation only is −0.104 (95% CI = −0.371, 0.162); Q[df = 6] = 1.57 P = 0.954 I2 = 0.0%. The overall mean weighted effect size for studies in which subjects in which high fidelity imitation was tested is −0.914 (95%CI = −1.166, −0.663); Q[df = 26] = 115.36 P < 0.001 I2 = 77.5%. ASD, autism spectrum disorder.

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Discussion The studies reviewed in this report confirm that individuals with ASD show deficits in overall imitation abilities compared with individuals without ASD. The magnitudes of these deficits were moderate to large and statistically significant, according to conventional criteria (overall g = −0.808; high fidelity imitation g = −0.914; [Cohen, 1988]). They indicate that the average individual with ASD performs between the 18th and 21st percentile of non-ASD individuals on imitation tasks. This finding is consistent with those of Williams, Whiten & Singh [2004], as well as a number of earlier reviews of imitation in autism [such as Rogers, 1999; Smith & Bryson, 1994], and they extend this literature by quantifying the magnitude of the observed deficit. It is worth noting that much larger effect sizes have been reported as treatment effects in past work on imitation interventions [e.g., Ingersoll & Gergans, 2007; Ingersoll & Schreibman, 2006]. Since the studies included in this analysis provided little or no information on whether participants were (at the time of testing or previously) enrolled in interventions, however, it is not possible to determine whether the imitation deficit found herein may be remediable through interventions such as those mentioned above, or if the moderate deficit found in individuals with ASD is that which remains, even after interventions. It is also worth noting that although the definitions of high fidelity imitation and emulation used herein were not strict in requiring that actions be novel in order for their reproduction to be considered “true imitation,” no differences were found in the magnitude of the imitation deficit between those studies that utilized familiar actions and those that tested novel actions only. As such, the deficit measured herein is one that pervades both true imitation in its strictest sense, as well as simpler copying behaviors that may be utilized to reproduce familiar actions. In this study, typically developing individuals and individuals with non-ASD developmental disabilities were combined into one control group to investigate the specific effect of ASD diagnosis on imitation abilities, beyond any effects that more general cognitive or developmental delays may have on the ability to imitate. The effects discussed above, therefore, represent the magnitude of the imitation deficit that is specific to the condition of having ASD, rather than a feature of more general developmental delay. Although past work has suggested that a portion of the imitation deficit in ASD may be explained by more general cognitive delays (such as Sigman & Ungerer [1984], who found an association between participants’ verbal mental age imitation abilities), this meta-analysis observed no associations between groups’ imitation deficits and their verbal mental ages, nonverbal mental ages, overall mental ages, performance IQs, verbal IQs or full scale IQs. Most studies on individuals

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with ASD exclude syndromic populations however; future studies examining imitation in individuals with a non-ASD primary diagnosis and a secondary diagnosis of ASD (such as those with fragile X syndrome) would be useful to provide definitive insight into the specificity of imitation deficits to ASD. In order to complement the findings of Williams, Whiten and Singh’s [2004] systematic review of action imitation in autism (in which imitated tasks were most broadly classified as actions upon objects or gestures), the studies included herein were classified according to the domains of imitation they tested (oral-facial, body, and object-oriented). Although the previous review found greater differences between ASD and control subjects on gestural imitation than actions on objects, this metaanalysis found similarly large and consistent ASD-specific imitation deficits across each domain. While Williams, Whiten and Singh also noted a larger deficit in imitated sequential actions, this study found no significant differences in the magnitude of the ASD imitation deficit associated with the presentation of single or sequential actions. Furthermore, imitation deficits were present regardless of other changes such as the presence of a verbal prompt, the form of the demonstration (live vs. video) or the setting in which the study was carried out. This report extends past reviews of imitation in ASD by investigating the association between ASD severity and the imitation deficit. Herein, groups of individuals with more severe ASD tended to show greater imitation deficits compared to those with less severe ASD. This result is based only on the small number of studies that included enough information to test this association however, and does not lend insight into whether an imitation deficit may lead to the development of ASD, or whether the imitation deficit is a symptom of ASD. It is plausible that a specific imitation deficit early in life may produce a cascade of learning delays that becomes an autism spectrum disorder, as well as that the social disability at the core of ASD may precede and lead to the impaired ability to imitate another. The finding that individuals with ASD show intact emulation accompanied by impaired high fidelity imitation abilities is not one that has been directly examined in past reviews, but arguably provides the most explanatory value in disambiguating the mixed findings on imitation in ASD, and offers insight into the possible nature of the imitation deficit. The deficit commonly reported in studies of individuals with ASD may be a deficit specific to copying the form of a demonstrated action, while the ability to emulate may remain intact in individuals with ASD. The tendency to copy a demonstrator’s exact form has been studied behaviorally, particularly in the literature on overimitation, or the reproduction of a model’s causally irrelevant actions [Marsh, Pearson, Ropar, & Hamilton, 2013; Over & Carpenter, 2012]. Overimitative

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behaviors in TD individuals are hypothesized to indicate the imitator’s social identification or affiliation with the demonstrator [Carpenter & Nielsen, 2008]. This hypothesis would suggest that individuals with ASD in the current study’s sample may have failed to overimitate, or copy the causally irrelevant aspects of a demonstrator’s form, due to a lack of social identification with the demonstrator. Such an explanation is appealing as a lack of social identification with others is potentially a core component of ASD. Furthermore, emulation of another’s actions may be achievable through an understanding of cause–effect relationships—a cognitive capacity, impairments in which are not characteristic of ASD. This hypothesis and the pattern of findings from which they flow are in keeping with the EP-M model [Hamilton, 2008], which proposed that neural correlates of goal emulation and planning (EP) are intact while substrates of mimicry (the precise understanding of the form of an act without implied understanding of the goal of that act; Sevlever & Gillis, 2010) may be dysfunctional in individuals with ASD. The ontogeny of overimitation, and its potential roots in social identification with others may also implicate another specific mechanism underlying imitative and other social deficits in ASD. In 1977, Meltzoff and Moore put forth the active intermodal mapping (AIM) hypothesis, to account for observations of facial imitation in neonates. According to this hypothesis, infants imitate because they register equivalences between the actions they observe others carrying out, and the actions that they feel themselves make. More recently it has been proposed that such a capacity for self-other correspondence in actions (or the ‘like-me’ framework) may give rise to the ability to interpret psychological states in others and thus constitute the foundation of social cognition in humans [Meltzoff, 2007]. If children with ASD emulate but do not imitate form due to a lack a fundamental social identification with others, the root of this imitation deficit may lie in a disruption of the ‘like-me’ system from early in life. In fact, this is the very hypothesis of social-communicative deficits in ASD put forward by Meltzoff & Gopnik [1993]. Recent neuroimaging work examining EEG mu rhythm desynchronization in infants has demonstrated that selfother correspondences in action observation and execution are sensitive not only to goals, outcomes or effects of these actions, but to how these actions have been carried out [Saby, Marshall, & Meltzoff, 2013; Marshall & Meltzoff, 2014]. Saby, Meltzoff & Marshall [2012] suggest that children with ASD may have specific alterations in the neural mapping of how actions are carried out, and that this may contribute to disruptions in basic self-other mapping. The neural mapping of how actions are carried out might thus be a correlate of the deficit in imitating form. Further neuroimaging research investigating the EP-M model and mu rhythm desynchronization in chil-

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dren with an ASD diagnosis as well as infant siblings of children with ASD would provide important insight into the biological substrates of imitation in ASD, as well as the ontogeny of social identification with others in this population. There was significant heterogeneity within the population of studies measuring high fidelity imitation in this sample, such that in a number of them, individuals with ASD did not actually perform worse than TD individuals. The findings of this significant minority of studies might indicate that under certain circumstances ASD individuals do—like TD individuals—copy both the form and the end point of a demonstrator’s actions; if this is the case, the previous hypotheses about a deficit in the ‘like-me’ framework may warrant revisin. Alternatively, perhaps in these studies neither TD nor ASD individuals imitated both the form and end point of a demonstrator’s actions. Both explanations find support in research literature. In a 2012 article, Nielsen et al. found to their surprise that high functioning children with ASD did overimitate. Much past work has also characterized TD children as flexible imitators, who overimitate in certain conditions, but emulate in others. Within this meta-analysis, I made an attempt to identify and test the effects of study conditions that might have had effects on the imitation fidelity of individuals with and without ASD, although none of the identified manipulations (study setting, explicit vs. non-explicit prompting, or the format of the demonstration) proved significantly explanatory. A recent study by Over and Carpenter [2012] has made headway in identifying the combination of social context factors that may explain whether and when TD individuals choose to overimitate or emulate. Similar studies with ASD populations have yet to be carried out and represent a ripe avenue for future research on their imitative behaviors. A potential threat to the validity of this study’s findings may have occurred if studies measuring emulation only tested participant groups that were of higher average functioning than those examining high fidelity imitation. Although attempts were made to investigate this, limited reporting (e.g., of average ADOS severity scores of the ASD groups) precludes an analysis of this question here. Further research into imitation should, thus, aim to conduct studies and report results on more homogeneous subsamples of the ASD population, as defined, for example, by ADOS severity scores or participant ages. The findings herein also point to the importance of adopting standardized definitions and explicitly operationalizing imitation in studies of this ability in any population, particularly in studies of individuals with ASD. Finally, although this study provides an in-depth analysis of whether individuals with ASD can imitate as well as individuals without ASD, it did not examine the practically significant question of whether individuals with

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ASD do spontaneously employ imitation skills. In a study investigating this contrast, Ingersoll [2008] finds that children with ASD are particularly impaired in their spontaneous imitation skills. A similar meta-analytic approach to studies on spontaneous imitation in ASD, paying particular attention to how imitation is operationalized across these conditions, would thus provide a useful complement to this one, in contributing to a fuller understanding of the nature of imitation in individuals with ASD.

Acknowledgments I am grateful to Dr. Charles A. Nelson for his mentorship and guidance throughout the process of completing this study, Dr. James Kim for his instruction in the techniques of meta-analysis, and Dr. Jenny Thomson for comments and revisions on an earlier draft of this manuscript. Thanks also to Barbara Basil, Deanna Palenzuela, Mary Burkhauser, and Beth Scheuler for reliability coding and comments on early work. This study was partially supported by the Harvard Graduate School of Education’s Dean’s Summer Fellowship.

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