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Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females

An Erratum to this article was published on 12 August 2015


The Autism-Spectrum Quotient (AQ) is a self-report measure of autistic traits. It is frequently cited in diverse fields and has been administered to adults of at least average intelligence with autism and to nonclinical controls, as well as to clinical control groups such as those with schizophrenia, prosopagnosia, anorexia, and depression. However, there has been no empirical systematic review of the AQ since its inception in 2001. The present study reports a comprehensive systematic review of the literature to estimate a reliable mean AQ score in individuals without a diagnosis of an autism spectrum condition (ASC), in order to establish a reference norm for future studies.

A systematic search of computerized databases was performed to identify studies that administered the AQ to nonclinical participant samples representing the adult male and female general population. Inclusion was based on a set of formalized criteria that evaluated the quality of the study, the usage of the AQ, and the population being assessed.

After selection, 73 articles, detailing 6,934 nonclinical participants, as well as 1,963 matched clinical cases of ASC (from available cohorts within each individual study), were analyzed. Mean AQ score for the nonclinical population was 16.94 (95% CI 11.6, 20.0), while mean AQ score for the clinical population with ASC was found to be 35.19 (95% CI 27.6, 41.1). In addition, in the nonclinical population, a sex difference in autistic traits was found, although no sex difference in AQ score was seen in the clinical ASC population.

These findings have implications for the study of autistic traits in the general population. Here, we confirm previous norms with more rigorous data and for the first time establish average AQ scores based on a systematic review, for populations of adult males and females with and without ASC. Finally, we advise future researchers to avoid risk of bias by carefully considering the recruitment strategy for both clinical and nonclinical groups and to demonstrate transparency by reporting recruitment methods for all participants.


Autism was traditionally considered as a clinical condition distinct from the general population, but recent evidence suggests autistic traits are continuously distributed across the population [13]. From observed data of measured autistic traits, people with a diagnosis of an autism spectrum condition (ASC) - at least those who have average IQ or above - score at the extreme end of this distribution [4]. It may be that ‘syndromic’ forms of autism, which often entail comorbid learning disability (or below average IQ) and a known genetic mutation, are discontinuous with autistic traits in the general population, but here the focus is on the general population without learning disability. The Autism-Spectrum Quotient (AQ) is widely used in research and clinical practice to quantify autistic traits. The AQ was first developed as a self-report measure for adults [5] and subsequently as a parent-report measure for adolescents (aged 12 to 15 years) [6] and for children (aged 4 to 11 years) [7]. A toddler version also exists (Q-CHAT (Quantitative Checklist for Autism in Toddlers) [8]). The AQ has 50 items, which are divided into five subscales consisting of 10 items each that assess domains of cognitive strengths and difficulties related to ASC: communication, social skills, imagination, attention to detail and attention switching. While the AQ is not the only research tool used to measure autistic traits (for example, see the SRS (Social Responsiveness Scale [9]), it has several advantages over other measures, including subscales for both social and nonsocial aspects of behavior and cognition and a format that is brief, self-administered, and forced-choice.

The AQ was designed for adults with average IQ or above [5], who comprise at least 50% of the autism spectrum [10]. Individuals are instructed to respond to each of the 50 items with one of four responses: ‘definitely agree’, ‘slightly agree’, ‘slightly disagree’, and ‘definitely disagree’. Responses are scored using a binary system, where an endorsement of the autistic trait (either mildly or strongly) is scored as a +1, while the opposite response is scored as a 0, leading to a maximum score on the AQ of 50. An alternative scoring system has also been employed that uses a 4-point Likert scale [11]. AQ items are counterbalanced to avoid a response bias, so that half of the ‘agree’ responses and half of the ‘disagree’ responses endorse the autistic trait. The AQ includes questions about both ability and preference. The questionnaire is not suitable for individuals with low IQ, low verbal ability, or language impairment, as it relies on receptive understanding of the 50 questions.

The AQ was originally validated in 2001 in adult males and females with Asperger Syndrome (AS) and high-functioning autism (HFA), in scientists versus nonscientists in Cambridge University students, in winners of the mathematical Olympiad (because of the finding that autism may be genetically linked to an aptitude for ‘systemizing’ [1214]), and nonstudent individuals drawn from the general population. This study found that the total AQ score and its five subscale scores are normally distributed and have demonstrated good test-retest reliability, good internal consistency [5], and that the measure has acceptably high sensitivity and specificity: at a cut-off score of 26, 83% of patients were correctly identified (sensitivity 0.95, specificity 0.52, positive predictive value 0.84, negative predictive value 0.78), while a cut-off score of 32 correctly identifies 76% of patients (sensitivity 0.77, specificity 0.74) [11, 15] when the AQ is used in a referred clinical sample.

These results indicate that the AQ is a sensitive measure of autistic traits in the general population, implying that traits reaching a clinical level in autism also exist to a lesser degree in nonclinical counterparts [5]. Within families, AQ score has shown heritability, which is in line with genetic evidence suggesting the heritability of autism [16]. Further, some (but not all) parents of children with autism show a subclinical set of characteristics or traits that index familiarity and/or genetic liability to autism [17, 18]. This is referred to as the ‘Broader Autism Phenotype’ (BAP). There is a consistent sex difference in mean AQ score, such that typical males score significantly higher than typical females, while people of both sexes with ASC score at the extreme high end of the scale, in line with the extreme male brain (EMB) theory of autism [19, 20].

The AQ is also widely referenced: a recent search of Google Scholar indicated that the original publication has been cited over 1,250 times. The present study reports the first large-scale systematic review of published AQ data over the last 13 years from adults with and without a diagnosis of ASC, in order to characterize the distribution of autistic traits in adult males and females and to contrast scores from clinical versus nonclinical samples. The specific goal is to establish a reliable mean AQ score in nonclinical controls, which can then be used as a guideline for researchers to define their control groups in future studies that compare people with and without a clinical diagnosis of ASC, as well as to other specially selected groups.



Identification of relevant literature

Citation indexing databases Scopus, PubMed (Medline), PsycINFO, and Web of Science were queried for articles utilizing the AQ. Titles, abstracts and keywords were searched for (“autism quotient”) OR (“autis* spectrum quotient”) OR (“AQ” AND “autism”). Exploded MeSH terms were not used because of the narrow target of interest; studies were only considered if they explicitly mentioned the AQ. However, an additional search of Scopus and Web of Science was performed by which all peer reviewed journal articles citing the 2001 Baron-Cohen paper introducing the measure were retrieved. The two searches were merged; the citation search delivered 837 hits and the keyword search delivered 321 hits, 287 of which were retrieved by both methods.

Titles and abstracts and then full text articles, were reviewed. Inclusion criteria specified that the study had to include peer-reviewed empirical research (excluding all meta-analyses, literature reviews, book chapters, conference proceedings, etcetera.), be published in English, that the AQ had to be the 50-item AQ adult self-report (and not the AQ-Child, AQ-Adolescent or any of the abbreviated versions of the AQ), and that there was evidence that the English-language version of the AQ had been administered rather than any translations. The nonclinical participant sample had to include both males and females recruited from the population, with a mean age of 18 years or older.

Exclusion criteria were applied that assessed the quality of the study, the usage of the AQ, and the population being assessed. See Figure 1 for the selection process. Articles were excluded if they were case reports, studies containing fewer than 10 participants, or if the study specifically recruited participants who were immediate family members of an individual with ASC or patients with a particular mental or physical disorder or condition. In addition, due to findings from within the original AQ publication indicating the potential for academic disciplines to score more highly on the AQ, and in an effort to remove confounding variables such as age and education level, articles were excluded if participants had been recruited exclusively from within a university (though partial university recruitment was acceptable if authors indicated that an effort was made to recruit from outside the academic community). Articles were also excluded if an AQ cut-off score was imposed when delineating the control or nonclinical group. Where it was unclear whether an article met eligibility criteria, the article was retained. A number of research groups frequently recruit participants from the same database, which may potentially lead to the same individuals’ AQ scores being duplicated in analyses across more than one publication; to guard against the risk of duplication, articles from the same research group were assessed. If authors used similar phrasing in describing the recruitment process or explicitly stated that participants were drawn from the same database, the publication with the largest population group was included in analysis while the rest were excluded. Finally, several articles published in the same year by the same authors contained identical AQ scores and numbers of participants; in these rare cases, the earliest instance was included while the later publications were conservatively excluded.

Figure 1

Selection process for systematic literature review: post-database searches for English-language peer-reviewed research.

In a number of instances, authors indicated that participants had completed the AQ but complete data sets were not reported. For 36 papers, authors were contacted for clarification or more information (11 articles were lost due to lack of a response). The deadline for data queries from authors and for literature searching was Monday, 14 July 2014. From the literature search and screening process 73 articles (reporting 78 independent studies) met the inclusion criteria.

Inter-rater agreement

The first author (ER) performed 100% of the literature search, quality assessment, and data extraction. In order to assess reliability of this process, approximately 10% of the results returned by the literature search were examined by authors CA, PS, and SBC. Each of the second reviewers received a random sample of 30 articles for evaluation (totaling 90). Where it was unclear whether an article met eligibility criteria, the article was discussed among the research team and if agreement was reached, it was retained for inclusion in the analysis. Initial percentage inter-rater agreement was respectively 97%, 90%, and 90%; after a resolution process, all disagreements between the lead author and the second raters were resolved in favor of the first author.

Extraction of data from included papers

The following information was recorded:

  1. 1.

    Number of participants, delineated by sex if reported

  2. 2.

    Mean and standard deviation of AQ score for males, females, and the sexes combined

  3. 3.

    Range of AQ score, if reported

  4. 4.

    Test for normality, if reported

  5. 5.

    Mean and standard deviation of participants’ age

  6. 6.

    Recruitment strategy, if reported

  7. 7.

    A comment on whether the study excluded individuals who were first-degree relatives of someone with a diagnosis of ASC

  8. 8.

    Margin of error and confidence intervals were calculated for each study by ER

  9. 9.

    Mean AQ score was recorded if the study included a matched sample of participants with ASC.

Data analysis

This systematic review aims to explore the distribution of a single variable - AQ score - in a large nonclinical population sample; therefore in this case, a meta-analysis (for effect sizes) is not possible. Data were imported into R [21] for systematic analysis. The mean of means was calculated by differentially weighting the reported values by sample size using weighted linear regression. In addition, the range of standard distributions, along with minimum and maximum values, was reported, and confidence intervals for reported average AQ scores were calculated. These values were also calculated for studies reporting separate male and female AQ scores, which were then compared using meta-analytic techniques. Finally, a small subset of studies (N = 9) reported that, in addition to taking a personal medical history, participants were only eligible to be considered a part of the nonclinical population group if they also had no first-degree relatives with ASC. For these studies, a separate mean of means for the AQ was also calculated. The focus of this study concerned average performance on the AQ, but standard deviation was also noted from eligible publications. From these scores, pooled variance was calculated.

While the primary focus of the review was to explore AQ scores in a nonclinical population sample, AQ score from the ASC sample for the selected papers was also noted where relevant. These scores were analyzed in the same method reported above. In addition to the quantitative approach described above, the papers that met criteria were subjected to a qualitative reading of the recruitment strategy for the nonclinical participant sample. This was in an effort to provide a description of the background for the participants included in analysis.


Quantitative characterization of the Autism-Spectrum Quotient in a nonclinical population sample

From a total of 73 articles reporting 78 studies that met eligibility criteria, data were recorded from 6,934 individual nonclinical participants. Table 1 describes the individual studies reviewed. See also inset plot for study AQ means and standard errors (Figure 2).

Table 1 Articles selected for review
Figure 2

Individual study overall Autism-Spectrum Quotient (AQ) means for nonclinical populations. Bars indicate confidence intervals, point size scaled to the number of individuals in each study. Studies are ordered chronologically. From 2001 to 2011, the unweighted mean AQ score = 15.27 (SD = 1.73); from 2012 to 2014, m = 15.37 (SD = 2.12). Overall mean is indicated by the dotted line.

Descriptive statistics (weighted mean AQ, range of standard deviation, total range, 95% confidence interval, number of studies, and number of participating individuals) are shown in Table 2. Overall SD reported from included studies ranged from 0.83-9.7. A pooled variance was calculated from the scores (σ2 = 31.26), leading to a pooled standard deviation of 5.59.

Table 2 Descriptive statistics for selected articles

To compare the weighted mean AQ scores between males and females in studies that reported this information, a continuous random-effects model was used to find standard difference in means, SMD. There was a significant difference in scores between males and females: Hedges’ g = 0.40, P <0.001, z = 3.36. This holds true even if simple unweighted means are compared, though individual mean values are slightly reduced (Figure 3). A suggestion of bimodality was observed for males and females. However, previous observations of AQ scores indicate that there is a normal distribution within the population; likely this observation stems from the comparatively small number of data points used in this calculation (10 studies per group) or from the internal differences in study recruitment paradigms.

Figure 3

AQ distributions for nonclinical populations. (A) Kernel density estimates for unweighted Autism-Spectrum Quotient (AQ) distributions for nonclinical populations. AQ score on the x-axis and density on the y-axis. Male scores in blue, female scores in red, and combined scores in black. (B) Box plot of mean AQ scores for all studies. Box width scaled to reflect the number of studies included.

After initial selection criteria were applied, N = 9; [35, 40, 48, 52, 59, 62, 70, 83, 91]) studies were identified that excluded any individuals who might have the BAP from the typical group [17]. Table 2 presents the descriptive statistics for this set.

Quantitative characterization of the Autism-Spectrum Quotient in a clinical sample

The 78 included studies were also examined for the presence of a matched clinical sample of individuals with a formal diagnosis of ASC. Of these, 43 studies contained data from 1,963 individuals with ASC (Table 1). Descriptive statistics for matched clinical cases are shown in Table 2. Overall SD reported from included studies ranged from 4.6 to 10.09. A pooled variance was calculated from the scores (σ2 = 39.27), leading to a pooled standard deviation of 6.27.

To compare the weighted mean AQ scores between clinical and nonclinical groups, a continuous random-effects model was used to find SMD. There was a significant difference in scores between these groups: Hedges’ g = 2.86, P <0.0001, z = 26.42, confirming that AQ scores are elevated in individuals with ASC. Contrasting with the findings reported for nonclinical controls, the SMD for males and females with ASC only reached a value of 0.33, which, while significant, indicates that males and females with ASC do not effectively differ in autistic traits as measured by the AQ; in fact, if anything, in this sample, the trend is reversed so that females self-report higher levels of traits.

Trends in use of the Autism-Spectrum Quotient

In addition to reviewing the reported AQ scores, an effort was made to qualitatively assess AQ usage for included studies. Several trends were noted in administration and reporting of the full-scale AQ for adults. The majority of studies included in this review had recruited via newspaper adverts, employment agencies, email, post, and flyers. In many cases, the participants were also partially drawn from continuously maintained participant databases and research pools. There was also evidence of partial recruitment through hospitals and universities (though, as stated, studies were excluded where recruitment was exclusively within an academic community). In a number of instances, participants were recruited using publicly available online survey tools such as Amazon Mechanical Turk (M-Turk) and Finally, several large studies were made possible through the use of birth cohorts, including the Raine Cohort (in Western Australia).

Few articles specified the precise inclusion and exclusion criteria for control participants, instead focusing primarily on the characterisation of the clinical group. While authors did routinely specify that control participants did not have a history of psychological or neurodevelopmental conditions, articles rarely reported having also excluded participants there was a family history of these. Studies also rarely reported testing the psychometric properties of the AQ or the normality of the distribution of AQ score. For instance, the mean was only reported alongside median in one instance [54], and there was also only one instance of a test for normality (Kolmogorov-Smirnov test [83]). There were occasional reports of other psychometric properties of the AQ, such as Cronbach’s alpha, establishing good internal consistency of the AQ.


This is the first systematic review of the AQ, with several findings emerging. First, the mean AQ score in a typical sample drawn from a nonclinical population is approximately 17 (CI 16.4 to 17.4) (for those explicitly excluding BAP, the mean is approximately 15 (CI 13.0-17.1)), with a narrow confidence interval of one to two points. In addition, the mean AQ score in individuals with ASC is approximately 35, nearly 20 points above that found in the general population. Second, control males and females have significantly different average AQ scores, with males scoring higher, confirming earlier reports. Third, from 2001 onwards, there is considerable fluctuation in reported mean AQ scores, but scores have not appreciably drifted in one direction or another within the general population.

Several rationales were employed by researchers for using the AQ. Many of the studies administered the AQ not as a central variable correlated with the outcome measure, but as an accessory measure for characterizing the population or defining the experimental groups. Further, a number of articles used the AQ as a proxy for diagnosis, using the cut-off scores of either 32 or 26 to exclude individuals either from the clinical or from the nonclinical control group. (These articles were not included in the final analysis). However, caution is recommended when using the AQ in this way, as the AQ was designed to be a descriptive, rather than a diagnostic, measure of autistic traits. While, perhaps due to it being freely available, easy to administer, and widely precedented in the literature, the AQ is used as a screening instrument (such as for patients referred to a diagnostic clinic for a detailed assessment for ASC [93]), it has been argued that the AQ does not have the sensitivity and specificity for population screening with an eye to diagnosis [9496]. This follows logically from the fact that the AQ is a brief self-report, reliant upon the individuals’ own self-awareness, and from the self-imposed limitations of age (16+) and IQ (85+). As discussed in the original publication, the AQ was developed from a theoretical understanding of autism, and therefore has not necessarily undergone the rigorous psychometric evaluation procedure that diagnostic screening tools must pass for inclusion in clinical practice. A more conservative use for the AQ is to segment the population into bands of autism phenotypes (broad, medium and narrow) as in the method of Wheelwright and colleagues [17], or as a descriptive quantitative measure of autistic traits.

Strengths of the current review include the exhaustive search criteria, especially the citation search for relevant papers, followed by the rigorous selection process. In addition, the total number of individuals (N = 8,897 clinical cases and nonclinical controls) examined by this review lends weight to the findings. The study was limited by a risk of bias, at the outcome level, the selection level and at the level of the review, though an effort was made to mitigate possible disproportionate effect of means from studies of varying samples through weighting by group size. Limitations also exist in the review procedure, in that each study included in the review was not judged for methodological rigor, rather a holistic evaluation was made of study methodology in an effort to report trends in items such as recruitment strategies, participant inclusion, and AQ data psychometric properties. Second, the number of participants from each study was relatively small (minimum N was set at 10); this is balanced by the large overall sample size derived from summing all studies together. More broadly, an ideal investigation of AQ score distribution would evaluate the raw data from each of the included studies in order to also measure data spread and subscale scores. However, this was not feasible for the current study. Finally, not every included article verified that the control group did not have ASC. Therefore there may have been incomplete information on how representative the demographic distribution of the nonclinical sample that make up this analysis may be.

We recommend that future researchers should think carefully when planning a recruitment strategy, both for nonclinical and clinical participants in order to be able to clearly define participants in each group. Furthermore, the field would greatly benefit if researchers better described the control participants, stating the method of recruitment in the methods. While healthy, typically-developing participants are often taken for granted, the considerable variability found in this review indicates that the method of recruitment of a ‘true’ representative sample - either of the general population or a specific patient group - may significantly impact results. This could have implications when examining group differences on dependent variables if the groups have not been carefully defined, potentially leading to attenuation of real groups differences.

We hope the current review holds value in the light of the considerable range of research types under which the AQ has been used. This dimensional approach to quantifying autistic traits has been found to correlate with a range of biological measures, including instances of brain activity [97], brain structure [98], social perception using gaze-tracking [42], prenatal testosterone [99], candidate genes and epigenetics [100]; clinical screening [44] and autism genetic risk [17]. Thus, although it is a self-report instrument, it correlates with a large number of more objective measures, suggesting that autistic traits are measurable aspect of personality, independent of the Big 5 [101].

Future research might consider a similar investigation of other versions of the AQ. Aside from the AQ-Adolescent and -Child, widely-used cross-cultural and foreign-language versions of the AQ exist, including translations into Chinese [102], Dutch [103], French [104], Italian [105], Japanese [106], Persian [107] and Polish [108], among others. On the whole, the results from studies that utilize these versions demonstrate analogous findings to those found using English-language versions of the AQ; however, validation by systematic review has not been done. In addition, a future study might attempt to undertake a whole population survey of autistic traits using the AQ, with more detailed information about the respondents collected in order to make stronger claims about generalizability. The underlying structure of taxa leading to AQ score distribution could be assessed using a number of modelling solutions, including latent class, taxometric, or factor mixture modelling. Perhaps, using these techniques in a population sample of individuals along the spectrum might help elucidate the apparent gap between clinical and nonclinical scores, despite the apparent continuity of autistic traits.


The AQ continues to be a useful brief assessment instrument for measuring autistic traits in adults of normal intelligence. By determining the distribution of the AQ in the nonclinical population, the AQ can now be used more definitively to assess the extent to which other specialist populations exhibit autistic traits.



Autism-spectrum quotient


Asperger syndrome


Autism spectrum condition


Broader autism phenotype


Extreme male brain


High-functioning autism


Quantitative checklist for autism in toddlers


Social responsiveness scale.


  1. 1.

    Wing L: The autistic continuum. In Aspects of autism: Biological Research. Edited by: Wing L. London: Gaskell/Royal College of Psychiatrists; 1988.

    Google Scholar 

  2. 2.

    Constantino J, Todd R: Autistic traits in the general population - A twin study. Arch Gen Psychiatry 2003, 60:524–30. 10.1001/archpsyc.60.5.524

    PubMed  Google Scholar 

  3. 3.

    Posserud MB, Lundervold AJ, Gillberg C: Autistic features in a total population of 7–9-year-old children assessed by the ASSQ (Autism Spectrum Screening Questionnaire). J Child Psychol Psychiatry 2006, 47:167–75. 10.1111/j.1469-7610.2005.01462.x

    PubMed  Google Scholar 

  4. 4.

    Baron-Cohen S: Empathizing, systemizing, and the extreme male brain theory of autism. In Sex differences in the human brain, their underpinnings and implications. Edited by: Savic I. Amsterdam, Netherlands: Elsevier science BV; 2010:167–75.

    Google Scholar 

  5. 5.

    Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E: The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord 2001, 31:5–17. 10.1023/A:1005653411471

    CAS  PubMed  Google Scholar 

  6. 6.

    Baron-Cohen S, Hoekstra RA, Knickmeyer R, Wheelwright S: The autism-spectrum quotient (AQ)–adolescent version. J Autism Dev Disord 2006, 36:343–50. 10.1007/s10803-006-0073-6

    PubMed  Google Scholar 

  7. 7.

    Auyeung B, Baron-Cohen S, Wheelwright S, Allison C: The autism spectrum quotient: children’s version (AQ-Child). J Autism Dev Disord 2008, 38:1230–40. 10.1007/s10803-007-0504-z

    PubMed  Google Scholar 

  8. 8.

    Allison C, Baron-Cohen S, Wheelwright S, Charman T, Richler J, Pasco G, Brayne C: The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): a normally distributed quantitative measure of autistic traits at 18–24 months of age: preliminary report. J Autism Dev Disord 2008, 38:1414–25. 10.1007/s10803-007-0509-7

    PubMed  Google Scholar 

  9. 9.

    Constantino JN, Cruber CP: Social Responsiveness Scale, second edition (SRS-2). Los Angeles, CA: Western Psychological Services; 2012.

    Google Scholar 

  10. 10.

    Baird G, Simonoff E, Pickles A, Chandler S, Loucas T, Meldrum D, Charman T: Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP). Lancet 2006, 368:210–5. 10.1016/S0140-6736(06)69041-7

    PubMed  Google Scholar 

  11. 11.

    Austin EJ: Personality correlates of the broader autism phenotype as assessed by the Autism Spectrum Quotient (AQ). Pers Individ Dif 2005, 38:451–60. 10.1016/j.paid.2004.04.022

    Google Scholar 

  12. 12.

    Baron-Cohen S, Wheelwright S, Burtenshaw A, Hobson E: Mathematical talent is linked to autism. Hum Nature-an Interdiscip Biosoc Perspect 2007, 18:125–31.

    Google Scholar 

  13. 13.

    Roelfsema MT, Hoekstra RA, Allison C, Wheelwright S, Brayne C, Matthews FE, Baron-Cohen S: Are autism spectrum conditions more prevalent in an information-technology region? A school-based study of three regions in the Netherlands. J Autism Dev Disord 2012, 42:734–9. 10.1007/s10803-011-1302-1

    PubMed  Google Scholar 

  14. 14.

    Baron-Cohen S, Wheelwright S, Stott C, Bolton P, Goodyer I: Is there a link between engineering and autism? Autism 1997, 1:101–9. 10.1177/1362361397011010

    Google Scholar 

  15. 15.

    Woodbury-Smith MR, Robinson J, Wheelwright S, Baron-Cohen S: Screening adults for asperger syndrome using the AQ: a preliminary study of its diagnostic validity in clinical practice. J Autism Dev Disord 2005, 35:331–5. 10.1007/s10803-005-3300-7

    CAS  PubMed  Google Scholar 

  16. 16.

    Hoekstra RA, Bartels M, Verweij CJH, Boomsma DI: Heritability of autistic traits in the general population. Arch Pediatr Adolesc Med 2007, 161:372–7. 10.1001/archpedi.161.4.372

    PubMed  Google Scholar 

  17. 17.

    Wheelwright S, Auyeung B, Allison C, Baron-Cohen S: Defining the broader, medium and narrow autism phenotype among parents using the Autism Spectrum Quotient (AQ). Mol Autism 2010, 1:10. 10.1186/2040-2392-1-10

    PubMed Central  PubMed  Google Scholar 

  18. 18.

    Bishop DVM, Maybery M, Maley A, Wong D, Hill W, Hallmayer J: Using self-report to identify the broad phenotype in parents of children with autistic spectrum disorders: a study using the Autism-Spectrum Quotient. J Child Psychol Psychiatry 2004, 45:1431–6. 10.1111/j.1469-7610.2004.00325.x

    PubMed  Google Scholar 

  19. 19.

    Baron-Cohen S: The extreme male brain theory of autism. Trends Cogn Sci 2002, 6:248–54. 10.1016/S1364-6613(02)01904-6

    PubMed  Google Scholar 

  20. 20.

    Baron-Cohen S, Cassidy S, Auyeung B, Allison C, Achoukhi M, Robertson S, Pohl A, Lai M-C: Attenuation of typical sex differences in 800 adults with autism vs. 3,900 controls. PLoS One 2014, 9:e102251. 10.1371/journal.pone.0102251

    PubMed Central  PubMed  Google Scholar 

  21. 21.

    R Core Team: R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.

    Google Scholar 

  22. 22.

    Bird G, Catmur C, Silani G, Frith C, Frith U: Attention does not modulate neural responses to social stimuli in autism spectrum disorders. Neuroimage 2006, 31:1614–24. 10.1016/j.neuroimage.2006.02.037

    PubMed  Google Scholar 

  23. 23.

    Dziobek I, Fleck S, Kalbe E, Rogers K, Hassenstab J, Brand M, Kessler J, Woike JK, Wolf OT, Convit A: Introducing MASC: a movie for the assessment of social cognition. J Autism Dev Disord 2006, 36:623–36. 10.1007/s10803-006-0107-0

    PubMed  Google Scholar 

  24. 24.

    Golan O, Baron-Cohen S, Hill JJ, Golan Y: The “Reading the Mind in Films” task: complex emotion recognition in adults with and without autism spectrum conditions. Soc Neurosci 2006, 1:111–23. 10.1080/17470910600980986

    PubMed  Google Scholar 

  25. 25.

    White S, Hill E, Winston J, Frith U: An islet of social ability in Asperger Syndrome: judging social attributes from faces. Brain Cogn 2006, 61:69–77. 10.1016/j.bandc.2005.12.007

    PubMed  Google Scholar 

  26. 26.

    Lombardo MV, Barnes JL, Wheelwright SJ, Baron-Cohen S: Self-referential cognition and empathy in austism. PLoS One 2007, 2:e883. 10.1371/journal.pone.0000883

    PubMed Central  PubMed  Google Scholar 

  27. 27.

    Corden B, Chilvers R, Skuse D: Emotional modulation of perception in Asperger’s syndrome. J Autism Dev Disord 2008, 38:1072–80. 10.1007/s10803-007-0485-y

    PubMed  Google Scholar 

  28. 28.

    Gowen E, Stanley J, Miall RC: Movement interference in autism-spectrum disorder. Neuropsychologia 2008, 46:1060–8. 10.1016/j.neuropsychologia.2007.11.004

    CAS  PubMed  Google Scholar 

  29. 29.

    Crane L, Goddard L, Pring L: Sensory processing in adults with autism spectrum disorders. AUTISM 2009, 13:215–28. 10.1177/1362361309103794

    PubMed  Google Scholar 

  30. 30.

    Crane L, Goddard L, Pring L: Specific and general autobiographical knowledge in adults with autism spectrum disorders: the role of personal goals. Memory 2009, 17:557–76. 10.1080/09658210902960211

    PubMed  Google Scholar 

  31. 31.

    Towgood KJ, Meuwese JDI, Gilbert SJ, Turner MS, Burgess PW: Advantages of the multiple case series approach to the study of cognitive deficits in autism spectrum disorder. Neuropsychologia 2009, 47:2981–8. 10.1016/j.neuropsychologia.2009.06.028

    PubMed Central  PubMed  Google Scholar 

  32. 32.

    Hall JK, Hutton SB, Morgan MJ: Sex differences in scanning faces: does attention to the eyes explain female superiority in facial expression recognition? Cogn Emot 2010, 24:629–37. 10.1080/02699930902906882

    Google Scholar 

  33. 33.

    Kuhn G, Benson V, Fletcher-Watson S, Kovshoff H, McCormick CA, Kirkby J, Leekam SR: Eye movements affirm: automatic overt gaze and arrow cueing for typical adults and adults with autism spectrum disorder. Exp Brain Res 2010, 201:155–65. 10.1007/s00221-009-2019-7

    PubMed  Google Scholar 

  34. 34.

    Marsh AA, Kozak MN, Wegner DM, Reid ME, Yu HH, Blair RJR: The neural substrates of action identification. Soc Cogn Affect Neurosci 2010, 5:392–403. 10.1093/scan/nsq004

    PubMed Central  PubMed  Google Scholar 

  35. 35.

    Philip RCM, Whalley HC, Stanfield AC, Sprengelmeyer R, Santos IM, Young AW, Atkinson AP, Calder AJ, Johnstone EC, Lawrie SM, Hall J: Deficits in facial, body movement and vocal emotional processing in autism spectrum disorders. Psychol Med 2010, 40:1919–29. 10.1017/S0033291709992364

    CAS  PubMed  Google Scholar 

  36. 36.

    Harris CD, Lindell AK: The influence of autism-like traits on cheek biases for the expression and perception of happiness. Brain Cogn 2011, 77:11–6. 10.1016/j.bandc.2011.07.005

    PubMed  Google Scholar 

  37. 37.

    Marsh LE, Hamilton AFDC: Dissociation of mirroring and mentalising systems in autism. Neuroimage 2011, 56:1511–9. 10.1016/j.neuroimage.2011.02.003

    PubMed  Google Scholar 

  38. 38.

    Palermo R, Rivolta D, Wilson CE, Jeffery L: Adaptive face space coding in congenital prosopagnosia: typical figural aftereffects but abnormal identity aftereffects. Neuropsychologia 2011, 49:3801–12. 10.1016/j.neuropsychologia.2011.09.039

    PubMed  Google Scholar 

  39. 39.

    Palermo R, Willis ML, Rivolta D, McKone E, Wilson CE, Calder AJ: Impaired holistic coding of facial expression and facial identity in congenital prosopagnosia. Neuropsychologia 2011, 49:1226–35. 10.1016/j.neuropsychologia.2011.02.021

    PubMed Central  PubMed  Google Scholar 

  40. 40.

    Rojas DC, Teale PD, Maharajh K, Kronberg E, Youngpeter K, Wilson LB, Wallace A, Hepburn S: Transient and steady-state auditory gamma-band responses in first-degree relatives of people with autism spectrum disorder. Mol Autism 2011, 2:11. 10.1186/2040-2392-2-11

    PubMed Central  PubMed  Google Scholar 

  41. 41.

    Von dem Hagen EAH, Nummenmaa L, Yu R, Engell AD, Ewbank MP, Calder AJ: Autism spectrum traits in the typical population predict structure and function in the posterior superior temporal sulcus. Cereb Cortex 2011, 21:493–500. 10.1093/cercor/bhq062

    PubMed Central  PubMed  Google Scholar 

  42. 42.

    White S, Coniston D, Rogers R, Frith U: Developing the Frith-Happé animations: a quick and objective test of theory of mind for adults with autism. Autism Res 2011, 4:149–54. 10.1002/aur.174

    PubMed  Google Scholar 

  43. 43.

    Zwickel J, White SJ, Coniston D, Senju A, Frith U: Exploring the building blocks of social cognition: spontaneous agency perception and visual perspective taking in autism. Soc Cogn Affect Neurosci 2011, 6:564–71. 10.1093/scan/nsq088

    PubMed Central  PubMed  Google Scholar 

  44. 44.

    Allison C, Auyeung B, Baron-Cohen S: Toward brief “Red Flags” for autism screening: the short Autism Spectrum Quotient and the Short Quantitative Checklist for Autism in toddlers in 1,000 cases and 3,000 controls [corrected]. J Am Acad Child Adolesc Psychiatry 2012, 51:202–212.e7. 10.1016/j.jaac.2011.11.003

    PubMed  Google Scholar 

  45. 45.

    Bakhtiari R, Zürcher NR, Rogier O, Russo B, Hippolyte L, Granziera C, Araabi BN, Nili Ahmadabadi M, Hadjikhani N: Differences in white matter reflect atypical developmental trajectory in autism: a tract-based spatial statistics study. Neuroimage Clin 2012, 1:48–56. 10.1016/j.nicl.2012.09.001

    PubMed Central  PubMed  Google Scholar 

  46. 46.

    Beacher FDCC, Radulescu E, Minati L, Baron-Cohen S, Lombardo MV, Lai M-C, Walker A, Howard D, Gray MA, Harrison NA, Critchley HD: Sex differences and autism: brain function during verbal fluency and mental rotation. PLoS One 2012, 7:e38355. 10.1371/journal.pone.0038355

    PubMed Central  CAS  PubMed  Google Scholar 

  47. 47.

    Beacher FDCC, Minati L, Baron-Cohen S, Lombardo MV, Lai M-C, Gray MA, Harrison NA, Critchley HD: Autism attenuates sex differences in brain structure: a combined voxel-based morphometry and diffusion tensor imaging study. Am J Neuroradiol 2012, 33:83–9. 10.3174/ajnr.A2880

    CAS  PubMed  Google Scholar 

  48. 48.

    Bejerot S, Eriksson JM, Bonde S, Carlström K, Humble MB, Eriksson E: The extreme male brain revisited: gender coherence in adults with autism spectrum disorder. Br J Psychiatry 2012, 201:116–23. 10.1192/bjp.bp.111.097899

    PubMed  Google Scholar 

  49. 49.

    Benson V, Castelhano MS, Au-Yeung SK, Rayner K: Eye movements reveal no immediate “WOW” (“which one’s weird”) effect in autism spectrum disorder. Q J Exp Psychol 2012, 65:1139–50. 10.1080/17470218.2011.644305

    Google Scholar 

  50. 50.

    Crane L, Pring L, Jukes K, Goddard L: Patterns of autobiographical memory in adults with autism spectrum disorder. J Autism Dev Disord 2012, 42:2100–12. 10.1007/s10803-012-1459-2

    PubMed  Google Scholar 

  51. 51.

    Damiano CR, Aloi J, Treadway M, Bodfish JW, Dichter GS: Adults with autism spectrum disorders exhibit decreased sensitivity to reward parameters when making effort-based decisions. J Neurodev Disord 2012, 4:13. 10.1186/1866-1955-4-13

    PubMed Central  PubMed  Google Scholar 

  52. 52.

    De Andres-Garcia S, Moya-Albiol L, Gonzalez-Bono E: Salivary cortisol and immunoglobulin A: responses to stress as predictors of health complaints reported by caregivers of offspring with autistic spectrum disorder. Horm Behav 2012, 62:464–74. 10.1016/j.yhbeh.2012.08.003

    CAS  PubMed  Google Scholar 

  53. 53.

    Dohn A, Garza-Villarreal EA, Heaton P, Vuust P: Do musicians with perfect pitch have more autism traits than musicians without perfect pitch? An empirical study. PLoS One 2012, 7:e37961. 10.1371/journal.pone.0037961

    PubMed Central  CAS  PubMed  Google Scholar 

  54. 54.

    Kessler K, Wang H: Spatial perspective taking is an embodied process, but not for everyone in the same way: differences predicted by sex and social skills score. Spat Cogn Comput 2012, 12:133–58.

    Google Scholar 

  55. 55.

    Lachat F, Hugueville L, Lemaréchal J-DJ-D, Conty L, George N: Oscillatory brain correlates of live joint attention: a dual-EEG study. Front Hum Neurosci 2012,6(JUNE 2012):156.

    PubMed Central  PubMed  Google Scholar 

  56. 56.

    Maras KL, Bowler DM: Brief report: suggestibility, compliance and psychological traits in high-functioning adults with autism spectrum disorder. Res Autism Spectr Disord 2012, 6:1168–75. 10.1016/j.rasd.2012.03.013

    Google Scholar 

  57. 57.

    Norenzayan A, Gervais WM, Trzesniewski KH: Mentalizing deficits constrain belief in a personal god. PLoS One 2012, 7:e36880. 10.1371/journal.pone.0036880

    PubMed Central  CAS  PubMed  Google Scholar 

  58. 58.

    Nummenmaa L, Engell AD, von dem Hagen E, Henson RNA, Calder AJ: Autism spectrum traits predict the neural response to eye gaze in typical individuals. Neuroimage 2012, 59:3356–63. 10.1016/j.neuroimage.2011.10.075

    PubMed Central  PubMed  Google Scholar 

  59. 59.

    Webb SJ, Merkle K, Murias M, Richards T, Aylward E, Dawson G: ERP responses differentiate inverted but not upright face processing in adults with ASD. Soc Cogn Affect Neurosci 2012, 7:578–87. 10.1093/scan/nsp002

    PubMed Central  PubMed  Google Scholar 

  60. 60.

    Berthoz S, Lalanne C, Crane L, Hill EL: Investigating emotional impairments in adults with autism spectrum disorders and the broader autism phenotype. Psychiatry Res 2013, 208:257–64. 10.1016/j.psychres.2013.05.014

    PubMed  Google Scholar 

  61. 61.

    Broadbent J, Galic I, Stokes MA: Validation of autism spectrum quotient adult version in an Australian sample. Autism Res Treat 2013, 2013:984205.

    PubMed Central  CAS  PubMed  Google Scholar 

  62. 62.

    Brown MS, Singel D, Hepburn S, Rojas DC: Increased glutamate concentration in the auditory cortex of persons with autism and first-degree relatives. Autism Res 2013, 6:1–10. 10.1002/aur.1260

    PubMed Central  PubMed  Google Scholar 

  63. 63.

    Cook R, Brewer R, Shah P, Bird G: Alexithymia, not autism, predicts poor recognition of emotional facial expressions. Psychol Sci 2013, 24:723–32. 10.1177/0956797612463582

    PubMed  Google Scholar 

  64. 64.

    Crane L, Lind SESE, Bowler DMDM: Remembering the past and imagining the future in autism spectrum disorder. Memory 2013, 21:157–66. 10.1080/09658211.2012.712976

    PubMed  Google Scholar 

  65. 65.

    Eriksson K: Autism-spectrum traits predict humor styles in the general population. Humor 2013, 26:461–75.

    Google Scholar 

  66. 66.

    Gibbard CR, Ren J, Seunarine KK, Clayden JD, Skuse DH, Clark CA: White matter microstructure correlates with autism trait severity in a combined clinical-control sample of high-functioning adults. Neuroimage Clin 2013, 3:106–14.

    PubMed Central  PubMed  Google Scholar 

  67. 67.

    Haffey A, Press C, O’Connell G, Chakrabarti B: Autistic traits modulate mimicry of social but not nonsocial rewards. Autism Res 2013, 6:614–20. 10.1002/aur.1323

    PubMed  Google Scholar 

  68. 68.

    Jackson BL, Blackwood EM, Blum J, Carruthers SP, Nemorin S, Pryor BA, Sceneay SD, Bevan S, Crewther DP: Magno - and parvocellular contrast responses in varying degrees of autistic trait. PLoS One 2013, 8:e66797. 10.1371/journal.pone.0066797

    PubMed Central  CAS  PubMed  Google Scholar 

  69. 69.

    Kapp SK, Gillespie-Lynch K, Sherman LE, Hutman T: Deficit, difference, or both? Autism and neurodiversity. Dev Psychol 2013, 49:59–71.

    PubMed  Google Scholar 

  70. 70.

    Lai M-C, Lombardo MV, Suckling J, Ruigrok ANV, Chakrabarti B, Ecker C, Deoni SCL, Craig MC, Murphy DGM, Bullmore ET, Consortium MA, Baron-Cohen S: Biological sex affects the neurobiology of autism. Brain 2013, 136:2799–815. 10.1093/brain/awt216

    PubMed Central  PubMed  Google Scholar 

  71. 71.

    Lind SE, Williams DM, Raber J, Peel A, Bowler DM: Spatial navigation impairments among intellectually high-functioning adults with autism spectrum disorder: exploring relations with theory of mind, episodic memory, and episodic future thinking. J Abnorm Psychol 2013, 122:1189–99.

    PubMed Central  PubMed  Google Scholar 

  72. 72.

    Miller LE, Saygin AP: Individual differences in the perception of biological motion: links to social cognition and motor imagery. Cognition 2013, 128:140–8. 10.1016/j.cognition.2013.03.013

    PubMed  Google Scholar 

  73. 73.

    Moseley RL, Mohr B, Lombardo M, Baron-Cohen S, Hauk O, Pulvermuller F: Brain and behavioral correlates of action semantic deficits in autism. Front Hum Neurosci 2013, 7:725.

    PubMed Central  PubMed  Google Scholar 

  74. 74.

    Nicolaidis C, Raymaker D, McDonald K, Dern S, Boisclair WC, Ashkenazy E, Baggs A: Comparison of healthcare experiences in autistic and non-autistic adults: a cross-sectional online survey facilitated by an academic-community partnership. J Gen Intern Med 2013, 28:761–9. 10.1007/s11606-012-2262-7

    PubMed Central  PubMed  Google Scholar 

  75. 75.

    Wilson LB, Tregellas JR, Slason E, Pasko BE, Hepburn S, Rojas DC: Phonological processing in first-degree relatives of individuals with autism: An fMRI study. Hum Brain Mapp 2013, 34:1447–63. 10.1002/hbm.22001

    PubMed Central  PubMed  Google Scholar 

  76. 76.

    Zürcher NR, Donnelly NN, Rogier OO, Russo BB, Hippolyte LL, Hadwin JJ, Lemonnier E, Hadjikhani NN: It’s all in the eyes: subcortical and cortical activation during grotesqueness perception in autism. PLoS One 2013, 8:e54313. 10.1371/journal.pone.0054313

    PubMed Central  PubMed  Google Scholar 

  77. 77.

    Bennett RH, Bolling DZ, Anderson LC, Pelphrey KA, Kaiser MD: fNIRS detects temporal lobe response to affective touch. Soc Cogn Affect Neurosci 2014, 9:470–6. 10.1093/scan/nst008

    PubMed Central  PubMed  Google Scholar 

  78. 78.

    Caldwell-Harris CL, Jordan CJ: Systemizing and special interests: characterizing the continuum from neurotypical to autism spectrum disorder. Learn Individ Differ 2014, 29:98–105.

    Google Scholar 

  79. 79.

    Damiano CR, Aloi J, Dunlap K, Burrus CJ, Mosner MG, Kozink RV, McLaurin RE, Mullette-Gillman OA, Carter RM, Huettel SA, McClernon FJ, Ashley-Koch A, Dichter GS: Association between the oxytocin receptor (OXTR) gene and mesolimbic responses to rewards. Mol Autism 2014, 5:7. 10.1186/2040-2392-5-7

    PubMed Central  PubMed  Google Scholar 

  80. 80.

    Egorova N, Pulvermuİİer F, Shtyrov Y: Neural dynamics of speech act comprehension: an MEG study of naming and requesting. Brain Topogr 2014, 27:375–92. 10.1007/s10548-013-0329-3

    PubMed Central  PubMed  Google Scholar 

  81. 81.

    Gökçen E, Petrides KV, Hudry K, Frederickson N, Smillie LD: Sub-threshold autism traits: the role of trait emotional intelligence and cognitive flexibility. Br J Psychol 2014, 105:187–99. 10.1111/bjop.12033

    PubMed Central  PubMed  Google Scholar 

  82. 82.

    Hadjikhani N, Zürcher NR, Rogier O, Hippolyte L, Lemonnier E, Ruest T, Ward N, Lassalle A, Gillberg N, Billstedt E, Helles A, Gillberg C, Solomon P, Prkachin KM: Emotional contagion for pain is intact in autism spectrum disorders. Transl Psychiatry 2014, 4:e343. 10.1038/tp.2013.113

    PubMed Central  CAS  PubMed  Google Scholar 

  83. 83.

    Kadak MT, Demirel OF, Yavuz M, Demir T: Recognition of emotional facial expressions and broad autism phenotype in parents of children diagnosed with autistic spectrum disorder. Compr Psychiatry 2014, 55:1146–51. 10.1016/j.comppsych.2014.03.004

    PubMed  Google Scholar 

  84. 84.

    Lind SE, Williams DM, Bowler DM, Peel A: Episodic memory and episodic future thinking impairments in high-functioning autism spectrum disorder: An underlying difficulty with scene construction or self-projection? Neuropsychology 2014, 28:55–67.

    PubMed Central  PubMed  Google Scholar 

  85. 85.

    Mayer JL, Heaton PF: Age and sensory processing abnormalities predict declines in encoding and recall of temporally manipulated speech in high-functioning adults with ASD. Autism Res 2014, 7:40–9. 10.1002/aur.1333

    PubMed  Google Scholar 

  86. 86.

    Moseley RL, Pulvermüller F, Mohr B, Lombardo MV, Baron-Cohen S, Shtyrov Y: Brain routes for reading in adults with and without autism: EMEG evidence. J Autism Dev Disord 2014, 44:137–53. 10.1007/s10803-013-1858-z

    PubMed Central  PubMed  Google Scholar 

  87. 87.

    Murray AL, Booth T, McKenzie K, Kuenssberg R, O’Donnell M: Are autistic traits measured equivalently in individuals with and without an autism spectrum disorder? An invariance analysis of the Autism Spectrum Quotient Short Form. J Autism Dev Disord 2014, 44:55–64. 10.1007/s10803-013-1851-6

    PubMed  Google Scholar 

  88. 88.

    Rogers RD, Bayliss AP, Szepietowska A, Dale L, Reeder L, Pizzamiglio G, Czarna K, Wakeley J, Cowen PJ, Tipper SP: I want to help you, but I am not sure why: gaze-cuing induces altruistic giving. J Exp Psychol Gen 2014, 143:763–77.

    PubMed Central  PubMed  Google Scholar 

  89. 89.

    Schwarzkopf DS, Anderson EJ, de Haas B, White SJ, Rees G: Larger extrastriate population receptive fields in autism spectrum disorders. J Neurosci 2014, 34:2713–24. 10.1523/JNEUROSCI.4416-13.2014

    PubMed Central  CAS  PubMed  Google Scholar 

  90. 90.

    Steeb H, Ramsey JM, Guest PC, Stocki P, Cooper JD, Rahmoune H, Ingudomnukul E, Auyeung B, Ruta L, Baron-Cohen S, Bahn S: Serum proteomic analysis identifies sex-specific differences in lipid metabolism and inflammation profiles in adults diagnosed with Asperger syndrome. Mol Autism 2014, 5:4. 10.1186/2040-2392-5-4

    PubMed Central  PubMed  Google Scholar 

  91. 91.

    Tottenham N, Hertzig ME, Gillespie-Lynch K, Gilhooly T, Millner AJ, Casey BJ: Elevated amygdala response to faces and gaze aversion in autism spectrum disorder. Soc Cogn Affect Neurosci 2014, 9:106–17. 10.1093/scan/nst050

    PubMed Central  PubMed  Google Scholar 

  92. 92.

    Williams DM, Jarrold C, Grainger C, Lind SE: Diminished time-based, but undiminished event-based, prospective memory among intellectually high-functioning adults with autism spectrum disorder: relation to working memory ability. Neuropsychology 2014, 28:30–42.

    PubMed Central  PubMed  Google Scholar 

  93. 93.

    Baron-Cohen S, Wheelwright S, Robinson J, Woodbury-Smith M: The Adult Asperger Assessment (AAA): a diagnostic method. J Autism Dev Disord 2005, 35:807–19. 10.1007/s10803-005-0026-5

    PubMed  Google Scholar 

  94. 94.

    Brugha TS, McManus S, Smith J, Scott FJ, Meltzer H, Purdon S, Berney T, Tantam D, Robinson J, Radley J, Bankart J: Validating two survey methods for identifying cases of autism spectrum disorder among adults in the community. Psychol Med 2012, 42:647–56. 10.1017/S0033291711001292

    CAS  PubMed  Google Scholar 

  95. 95.

    Ingersoll B, Hopwood CJ, Wainer A, Brent Donnellan M: A comparison of three self-report measures of the broader autism phenotype in a non-clinical sample. J Autism Dev Disord 2011, 41:1646–57. 10.1007/s10803-011-1192-2

    PubMed  Google Scholar 

  96. 96.

    Nishiyama T, Suzuki M, Adachi K, Sumi S, Okada K, Kishino H, Sakai S, Kamio Y, Kojima M, Suzuki S, Kanne SM: Comprehensive comparison of self-administered questionnaires for measuring quantitative autistic traits in adults. J Autism Dev Disord 2013, 44:993–1007.

    Google Scholar 

  97. 97.

    Belmonte MK, Gomot M, Baron-Cohen S: Visual attention in autism families: ‘unaffected’ sibs share atypical frontal activation. J Child Psychol Psychiatry 2010, 51:259–76. 10.1111/j.1469-7610.2009.02153.x

    PubMed  Google Scholar 

  98. 98.

    Ecker C, Suckling J, Deoni SC, Lombardo MV, Bullmore ET, Baron-Cohen S, Catani M, Jezzard P, Barnes A, Bailey AJ, Williams SC, Murphy DGM, Consortium MRCA: Brain anatomy and its relationship to behavior in adults with autism spectrum disorder. Arch Gen Psychiatry 2012, 69:195–209. 10.1001/archgenpsychiatry.2011.1251

    PubMed  Google Scholar 

  99. 99.

    Auyeung B, Baron-Cohen S, Ashwin E, Knickmeyer R, Taylor K, Hackett G: Fetal testosterone and autistic traits. Br J Psychol 2009,100(Part 1):1–22.

    PubMed  Google Scholar 

  100. 100.

    Chakrabarti B, Dudbridge F, Kent L, Wheelwright S, Hill-Cawthorne G, Allison C, Banerjee-Basu S, Baron-Cohen S: Genes related to sex steroids, neural growth, and social-emotional behavior are associated with autistic traits, empathy, and Asperger syndrome. Autism Res 2009, 2:157–77. 10.1002/aur.80

    CAS  PubMed  Google Scholar 

  101. 101.

    Wakabayashi A, Baron-Cohen S, Wheelwright S: Are autistic traits an independent personality dimension? A study of the Autism-Spectrum Quotient (AQ) and the NEO-PI-R. Pers Individ Dif 2006, 41:873–83. 10.1016/j.paid.2006.04.003

    Google Scholar 

  102. 102.

    Lau WY-P, Gau SS-F, Chiu Y-N, Wu Y-Y, Chou W-J, Liu S-K, Chou M-C: Psychometric properties of the Chinese version of the Autism Spectrum Quotient (AQ). Res Dev Disabil 2013, 34:294–305. 10.1016/j.ridd.2012.08.005

    PubMed  Google Scholar 

  103. 103.

    Hoekstra RA, Bartels M, Cath DC, Boomsma DI: Factor structure, reliability and criterion validity of the Autism-Spectrum Quotient (AQ): a study in Dutch population and patient groups. J Autism Dev Disord 2008, 38:1555–66. 10.1007/s10803-008-0538-x

    PubMed Central  PubMed  Google Scholar 

  104. 104.

    Sonié S, Kassai B, Pirat E, Masson S, Bain P, Robinson J, Reboul A, Wicker B, Chevallier C, Beaude-Chervet V, Deleage M-H, Charvet D, Barthelemy C, Rochet T, Tatou M, Arnaud V, Manificat S, Sonié S, Barthélémy C: French version of screening questionnaire for high-functioning autism or Asperger syndrome in adolescent: Autism Spectrum Quotient, Empathy Quotient and Systemizing Quotient. Protocol and questionnaire translation [Version française des questionnaires de. Press Medicale 2011, 40:e181–8. 10.1016/j.lpm.2010.07.016

    Google Scholar 

  105. 105.

    Ruta L, Mazzone D, Mazzone L, Wheelwright S, Baron-Cohen S: The Autism-Spectrum Quotient–Italian version: a cross-cultural confirmation of the broader autism phenotype. J Autism Dev Disord 2012, 42:625–33. 10.1007/s10803-011-1290-1

    PubMed  Google Scholar 

  106. 106.

    Wakabayashi A, Tojo Y, Baron-Cohen S, Wheelwright S: [The Autism-Spectrum Quotient (AQ) Japanese version: evidence from high-functioning clinical group and normal adults]. Shinrigaku Kenkyu 2004, 75:78–84. 10.4992/jjpsy.75.78

    PubMed  Google Scholar 

  107. 107.

    Mohammadi MR, Zarafshan H, Ghasempour S: Broader autism phenotype in Iranian parents of children with autism spectrum disorders vs. normal children. Iran J Psychiatry 2012, 7:157–63.

    PubMed Central  PubMed  Google Scholar 

  108. 108.

    Pisula E, Kawa R, Szostakiewicz L, Lucka I, Kawa M, Rynkiewicz A: Autistic traits in male and female students and individuals with high functioning autism spectrum disorders measured by the Polish version of the Autism-Spectrum Quotient. PLoS One 2013, 8:e75236. 10.1371/journal.pone.0075236

    PubMed Central  CAS  PubMed  Google Scholar 

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This research was supported by the Medical Research Council UK, the Wellcome Trust, and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East of England at Cambridgeshire and Peterborough NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

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Correspondence to Emily Ruzich.

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The authors declare that they have no competing interests.

Authors’ contributions

ER developed the study; performed the data search, assessment, extraction, and analysis; and wrote the manuscript under the supervision of CA, PS, HR, and SBC. PW provided advice on statistical analysis approaches. BA provided additional supervisory support. All authors have read and approved the final version of this manuscript.

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Ruzich, E., Allison, C., Smith, P. et al. Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females. Molecular Autism 6, 2 (2015).

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  • Autism
  • Autism-spectrum quotient
  • Autistic traits
  • Mean score
  • Sex differences
  • Systematic review