Skip to main content

Autism traits outweigh alexithymia traits in the explanation of mentalising performance in adults with autism but not in adults with rejected autism diagnosis



Pronounced alexithymia traits have been found in autism spectrum disorder (ASD) and recent research has been carving out the impact alexithymia traits might have on mentalising deficits associated with ASD.


In this cross-sectional study, a large representative referral population for diagnostic examination for possible ASD (n = 400) was screened for clinical alexithymia with a German version of the Reading the Mind in the Eyes test (RME). In contrast to previous attempts to carve out the impact of alexithymia traits on mentalising deficits though, we employed dominance analysis to account for the correlation between predictors. The relative relationship between alexithymia traits and autism traits with RME performance was investigated in the group of individuals with confirmed ASD diagnosis (N = 281) and compared to the clinical referral sample in which ASD was ruled out (N = 119).


Dominance analysis revealed autism traits to be the strongest predictor for reduced mentalising skills in the ASD sample, whereas alexithymia contributed significantly less. In the sample of individuals with ruled out diagnosis, autism traits were the strongest predictor, but alexithymia traits were in sum equally associated to mentalising, with the External-Oriented Thinking subscale as an important predictor of this association.


It needs to be considered that the cross-sectional study design does not allow for causal inference. Furthermore, mentalising is a highly facetted capacity and measurements need to reduce this complexity into simple quantities which limits the generalizability of results.


While alexithymia traits should be considered for their mental health importance, they do not dominate the explanation of reduced mentalising skills in individuals with ASD, but they might do to a larger degree in individuals with ruled out ASD.


Autism spectrum disorder (ASD) is characterised by pervasive difficulties in communication and social interaction, as well as restricted and repetitive behaviour [1]. During the past decade, a psychopathological construct that oftentimes co-occurs with ASD caught scientific attention, referred to as alexithymia, which was first described by Sifneos [2] and shows a general population prevalence of 10–20% [3,4,5,6,7]. Within samples of individuals with ASD, the prevalence has been meta-analytically shown to be significantly increased to 50% [8]. Measures of alexithymia include three components: Difficulties Identifying Feelings (DIF), Difficulties Describing Feelings (DDF), and Externally-Oriented Thinking (EOT) [9, 10]. Alexithymia is relevant for its high prevalence in ASD and its role in comorbidities such as depression [11,12,13,14,15], anxiety [16], as well as autism symptomatology related to emotional processing [4, 17,18,19].

Yet, an important question beyond the clinical relevance of alexithymia traits for comorbidities is the relationship between alexithymia and mentalising. A recent review of the relationship between alexithymia and mentalising reported mixed results overall, due to sampling and differences in the measurement of mentalising, which may have affected the extent to which mentalising tests co-measured socio-affective skills [20]. In this context, one line of research supposes that socio-affective deficits observed in ASD might be due to co-occurring alexithymia, referred to as the so-called ‘alexithymia hypothesis’ [4]. Indeed, two recent studies suggested an association of alexithymia and attenuated performance on a popular mentalising task, the Reading the Mind in the Eyes (RME) test [21] in adults with ASD and an alexithymia-matched comparison group. Performance depended on the degree of alexithymia, but not on diagnostic group affiliation, further, alexithymia scores outweighed autism traits in the prediction of RME scores employing hierarchical regression [22]. The same dataset showed a negative correlation between alexithymia and RME scores without group interaction [23], which was not the case for a different measure of mentalising (i.e., emotional scale of the Movie for Assessment of Social Cognition [24]). The authors therefore suggested that lower RME performance may not be indicative for ASD diagnosis, but rather be explained by co-occurring alexithymia in ASD samples. So besides the fact that the RME has been frequently used as an index of mentalising abilities in autism research [25], RME performance may in part display emotion recognition abilities, as postulated by Oakley et al. [22] and Pisani et al. introduced a taxonomy of mentalising measures and suggested to refer to RME as a measure of mentalising abilities with emotional demand that may be substantially influenced by emotion recognition abilities [20. However, the authors further report that studies with clinical populations did not always report negative associations of alexithymia with RME, indicating possibly distinct strategies that impacted the extent to which emotion recognition abilities affected RME performance and a need for further investigations in large datasets.

Apart from the important contribution of previous studies for our understanding of the relationship of alexithymia with mentalising, the relative effects of autism traits and alexithymia traits are not necessarily assessable by regression weights due to the strong covariation of alexithymia and autism traits in individuals with ASD [26,27,28,29]. Thus, an adequate analysis of continuous effects of alexithymia and autism traits on mentalising that can account for the strong covariation is required. Moreover, Demers and Koven [30] showed that only EOT was negatively associated with RME scores in a control sample, which was also found in Lyvers et al. [31]. This indicates that splitting alexithymia into subfactors can be informative when investigating differential effects of alexithymia on mentalising performance, as a characteristic autism trait. In light of their extensive systematic review in this field, Pisani et al. further highlight the importance to investigate the relationship of alexithymia and mentalising while controlling for influencing factors such as intelligence, executive functions, and verbal abilities [20].

Consequently, the first aim of the current study was to investigate whether either autism traits or alexithymia traits might predict mentalising skills more strongly by means of dominance analysis while taking important covariates into account.

Moreover, if the previously reported association between alexithymia and mentalising [22, 23] is confirmed in the current representative referral population of individuals who presented with social interaction difficulties at an autism outpatient clinic without final ASD diagnosis, this would imply that individuals without ASD, but with alexithymia, may share some of the autistic clinical picture. This would potentially add to the challenge of differential diagnostics of ASD, which is particularly difficult in adulthood [32]. Thus, the second aim of the current study was to investigate whether the pattern of association between alexithymia traits and autism traits with mentalising skills might differ between individuals with confirmed ASD and a clinical comparison sample with ruled out ASD diagnosis, both drawn from a representative referral population for ASD diagnostic clarification.



Data from the referral population of the outpatient clinic for autism in adulthood at the University Hospital Cologne were sampled post hoc from referrals in the period 2006–2019 (see Table 1). Ethical approval was granted by the ethics committee of the medical faculty, University of Cologne (20-1432). Patients were referred to the outpatient clinic by medical consultants for the purpose of diagnostic clarification due to self-reported social-emotional symptoms. Diagnostic procedures were in accordance to German guidelines on ASD [33], including neuropsychological testing and consensus diagnostics based on the independent assessment by a minimum of two experienced clinicians.

Table 1 Characteristics of participants with (ASD+) and without (ASD−) ASD

Individuals with confirmed ASD (ASD+; N = 281) received a diagnosis of F84.5 (n = 242; Asperger Syndrome), F84.1 (n = 18; Atypical Autism), or F84.0 (n = 21; Childhood Autism) according to ICD-10 [28]. This group was compared to individuals from the referral population for who any diagnosis of F84 was ruled out (ASD-; N = 119).

The standard clinical neuropsychological assessment included, amongst others, the Autism Spectrum Quotient (AQ) [34], the 20-Item Toronto Alexithymia Scale (TAS-20) [10], and the Wechsler Adult Intelligence Scale WIE-III [35]. Inclusion criteria for sampling were cases with total IQ scores > 70 as well as < 4 missing items on the relevant questionnaires. For the AQ, one to three missing item were recorded for n = 26 in the ASD+ sample and n = 17 in the ASD− sample. For the TAS-20, one to three missing items were recorded for n = 11 in the ASD+ sample and n = 4 in the ASD− sample. Missing items were filled by item means. In addition, one case (ASD+) was an extreme outlier on the AQ score (< 3 SD from M) and was therefore not included in the analysis.Footnote 1

Neuropsychological assessment

The revised version of the RME test measures mentalising abilities in adults and was introduced as a tool for ASD screening [36]. A modified German version of the test was used in this study [24, 37,38,39,40], consisting of 24 pictures of the eye region with different affective and cognitive expressions. Participants were instructed to choose the most appropriate label for the mental state of the presented person out of four possible suggestions. Correct answers were summed to a total score with a maximum of 24.

The AQ is a 50-item self-rating questionnaire of autism traits [34]. Responses are given on four-point Likert-scales (1 = strongly agree to 4 = strongly disagree). Based on the ratings, each item was recoded to values 1 or 0 and summed up to a maximum score of 50 [34]. Higher values indicate more pronounced autism traits. AQ Scores > 32 depict pronounced autism traits [34]. In the ASD+ 83.63% and in the ASD− group, 78.15% of cases reported autism traits above this threshold. The AQ showed good discriminant validity and retest-reliability [41]. Cronbach’s \(\alpha\) revealed good internal consistency (\(\alpha\) = .86).

The TAS-20 is a self-rating questionnaire of alexithymia traits comprising three factors: Difficulties Identifying Feelings (DIF), Difficulties Describing Feelings (DDF), and Externally-Oriented Thinking (EOT) [10]. Responses are given on five-point Likert-scales, ranging from 5 = strongly agree to 1 = strongly disagree. Five inverse items need to be recoded. All items are then summed up to a maximum score of 100 (max DIF: 35, max DDF: 25, max EOT: 40). The recommended cut-off for clinically relevant alexithymia is 61 [42]. The factor structure was confirmed in different languages [43] and showed good internal validity in a large German sample [7]. Cronbach’s \(\alpha\) showed good internal consistencies for DIF (\(\alpha\) = .85) and DDF (\(\alpha\) = .74) and acceptable internal consistency for EOT (\(\alpha\) = .60). Most importantly in this research context is a recent study showing that despite their frequently observed covariation, autism and alexithymia traits measured with AQ and TAS can be considered as distinct constructs [44].

Statistical procedures

Data preprocessing and analysis were performed using RStudio [45] with a similar analysis approach as in our previous study [11].

For bivariate relationships, Pearson zero-order correlations of measures of interest were calculated (Table 2). For multivariate analysis, regression models were calculated with control variables, autism traits (AQ), and alexithymia traits (DIF, DDF, EOT) as predictors of mentalising scores (RME), separately for the ASD+ and ASD− samples (Table 3). The model controlled for variables age, sex, PIQ, and VIQ (Table 1). The model fit was adequate (< 6% of standardized residuals > 2), independence of errors was confirmed (Durbin-Watson statistic ≈ 2) and there was no multicollinearity (variance inflation factor < 2 for all predictors in all models). Heteroscedasticity was implied by residual plots and a significant result in the Breusch Pagan Test for both models. Robust regressions with heteroscedasticity-consistent variance covariance matrix (HCCM) were conducted, using the sandwich and lmtest packages in R [46, 47]. HCCMs were retrieved by the vcovHC() function, using HC3 method [48, 49].

Table 2 Descriptive statistics and correlations for variables of interest
Table 3 Models with RME as dependent variable and results of dominance analysis 

Correlations of AQ, DIF, DDF, and EOT could have resulted in problems when interpreting their relative effects by means of beta coefficients, whereas beta coefficients only represent total effects,Ggeneral Dominance Weights (GDW) constitute an index of importance for each predictor that was suggested as a new comparison standard in multiple regression because it considers direct, total and partial effects [50]. GDWs and bootstrapped 95% confidence intervals (100 resamples) were calculated with yhat package in R [51]. We applied dominance analysis to calculate GDWs. Unique variance explained by each predictor was calculated by the squared semipartial correlation averaged across all models in all possible subsets that included that predictor [50]. The total determination factor R2 of the model represents the sum of GDWs [28].

Additional tests of statistical significance at 0.05 level in form of deviance tests of nested multivariate models were conducted. Nullmodels that included covariates only were first compared to models including AQ (first comparison), and this model was compared to an additional model containing TAS full scores (second comparison). To account for the effect by the order of intake of predictor, in a third step, nullmodels were compared to models including TAS (first comparison) and these models were then compared to a model that additionally contained AQ (second comparison).

As an additional exploration that was suggested by a reviewer, dominance analysis was calculated with AQ subscales instead of AQ full-scale. Results of this analysis can be found in the Additional file 1.


The groups did not significantly differ with respect to our predictor variables (AQ, DIF, DDF, EOT; largest t = 1.698). Descriptive statistics and correlations of variables of interest are to be found in Table 2. DIF was significantly correlated with AQ in the ASD+ sample (r = .52, p < .001, 95% CI [.43, .60]) and in the ASD− sample (r = .44, p < .001, 95% CI [.28, .57]). The same was found for DDF, which significantly correlated with AQ in the ASD+ sample (r = .47, p < .001, 95% CI [.38, .56]) and in the ASD− sample (r = .47, p < .001, 95% CI [.32, .60]).

In the ASD+ sample, dominance analysis showed that the greatest proportion of variance in RME scores was explained by AQ (GDW = .088, CI [.035, .149]), followed by DIF, that had a noticeably smaller effect size (GDW = .014, CI [.003, .045]). DDF was ranked third (GDW = .007, CI [.003, .026]) and EOT fourth (GDW = .004, CI [.000, .029]) with respect to their explanatory power (Table 3).

Likewise, model estimates in the ASD− sample revealed AQ as the strongest predictor of variance in RME scores (GDW = .059, CI [.015, .152]), but in contrast to the ASD+ sample, EOT had the second largest weight (GDW = .032, CI [.006, .089]). DIF was ranked third (GDW = .021, CI [.005, .090]) and DDF weakest predictor (GDW = .015, CI [.005, .063]).

Deviance tests in the ASD+ sample showed that the inclusion of AQ significantly improved model fit (F(1) = 3.05, p < .001) and the additional inclusion of TAS did not further improve model fit beyond AQ (F(1) = 0.03, p = .853). With the order of inclusion changed, the inclusion of TAS did significantly improve model fit (F(1) = 5.62, p = .018), however, the additional inclusion of AQ still significantly improved the variance explanation (F(1) = 23.90, p < .001). In the ASD− sample, the first comparison showed that the AQ significantly improved the model fit (F(1) = 8.57, p = .004) and there was a marginal improvement by further inclusion of TAS (F(1) = 3.23, p = .075). Likewise, first including TAS scores significantly improved the variance explanation (F(1) = 8.81, p = .004) and the further inclusion of AQ marginally improved the model fit (F(1) = 3.00, p = .086).

Inclusion of the five subscales of the AQ instead of the total scale showed in the ASD+ group that compared to all subscales (i.e., AQ and TAS subscales), the AQ subscales Attention to Detail, Communication, and Imagination achieved the strongest weights, whereas in the ASD- group, it was the AQ subscale Imagination and the TAS subscale EOT, see Additional file 1.


While increasing evidence points to an important role of alexithymia traits for comorbidities in ASD [11, 16, 52], the focus in the current study was on whether alexithymia traits might also be responsible for mentalising as one characteristic autism trait. For this purpose, we targeted mentalising abilities and performed dominance analysis on the relative explanatory power of alexithymia traits (divided into three subdomains DIF, DDF, EOT) and autism traits in a large representative sample of adults referred to an autism outpatient clinic. We (i) replicated the result of a negative correlation between alexithymia and RME scores by Rødgaard et al. [23], yet furthermore found (ii) that autism traits outweighed alexithymia traits in explaining mentalising abilities in ASD deploying methodology that controls for the intersection of autism and alexithymia trait measures and (iii) that this was the case in both individuals with confirmed and ruled out ASD diagnosis (iv) with alexithymia traits showing a stronger association with mentalising in the diagnostic exclusion group, for which alexithymic traits may be considered, in the aggregate, to be a similarly good predictor for attenuated RME performance as autistic traits. The significance tests provided further evidence for a larger relevance of autism traits in the explanation of mentalising abilities in ASD, as the AQ significantly improved the models, irrespective of the order of inclusion, whereas TAS scores did not. However, in the ASD− sample, AQ and TAS scores showed similar significance patterns in the deviance tests.

Both groups showed similarly high autism and alexithymia traits in line with a recent meta-analysis [8]. The point prevalence of alexithymia was 66.19% in ASD+ and 68.91% in the ASD− group, compared to 10–20% for community samples [3,4,5,6,7].

Our results have direct relevance to the recently formulated ‘alexithymia hypothesis’ [4]. In their article, Bird and Cook argue that many tests of mentalising using emotional stimuli, such as the RME task [36], might yield attenuated performance in individuals with ASD due to confounding high traits of alexithymia in this population. While we agree that alexithymia should be carefully monitored in studies of ASD, our results indicate that, compared to autism traits, alexithymia contributes less to mentalising performance in ASD employing dominance analysis in a large representative sample of individuals with ASD. This is actually consistent with the alexithymia hypothesis, which refers exclusively to affective autism symptomatology, under the assumption that the RME actually measures mentalising in ASD. However, it contradicts with the assumption that RME measures predominantly emotion recognition in individuals with ASD that should have resulted in a stronger weighting of TAS.

Focusing on the different alexithymia components, DDF was a significant predictor of mentalising abilities in the ASD+ sample, but dominance analysis revealed a different weighting with a larger GDW for DIF and a clear dominance of AQ in comparison to both. Considering that dominance analysis controls for the correlations of predictors (which is not the case for regression coefficients), we built our inference on the predictor weighting by means of GDWs, aligning to previous approaches that deployed dominance analysis for predictor weighting [26, 53]. Thus, while alexithymia is to be taken into account due to its corroborated importance for anxiety [16] and depression in ASD [11], our results indicate that alexithymia’s influence does not explain mentalising when autism traits are additionally considered.

Our results are in accordance with previous studies showing only weak associations between alexithymia and mentalising skills [54, 55]. Furthermore, our results relate to previous findings of an association of alexithymia and reduced RME performance in ASD [22, 23] by showing in a large sample that autism traits outweighed alexithymia traits in the prediction of mentalising. As explained before, the large covariation of alexithymia and autism traits within ASD populations [26, 27] calls for a thoughtful choice of analysis methods. We chose dominance analysis as it is able to account for the relative effects of strongly covarying predictors. In particular, our finding of marked dominance of autism traits predicting mentalising skills was especially pronounced in the ASD+ sample.

Given that the large ASD and clinical comparison samples tested in this study did likewise not differ in the quantity of their autism traits and in the pattern of dominant prediction of RME performance by those autism traits indicates that differential diagnostics faces an equal challenge by both the high alexithymia traits and the high autism traits found in other clinical samples of individuals with socio-emotional difficulties but without ASD (see discussion in [56]).

However, regarding the results of deviance tests and dominance analysis in the ASD- sample, it should be highlighted that alexithymia and autism traits both contributed to RME performance which contrasts the pronounced dominance of autism traits that we found in the ASD sample. Moreover, the findings by Demers and Koven [30] and Lyvers et al. [31], showing that the subscale of the TAS-20 (Externally-Oriented Thinking, EOT) was negatively associated with RME scores in control samples, called for investigating the subscale dimensionality of the alexithymia scale and probably also the alexithymia construct per se. In the current study, we therefore considered the three TAS-20 subscales and found similar results. In relation to the other scales, EOT had the second largest effect on RME scores in the clinical comparison group tested in the current study. Demers and Koven [30] had argued for a conceptual differentiation of EOT from DIF and DDF, because of its stability over time [57] and its cognitive instead of affective characterization [30, 58]. In our ASD+ sample, however, it was DIF that was the second best predictor of mentalising skills after autism traits. While autism traits were the strongest predictor in both, some aspects of alexithymia contributed to mentalising performance in the RME task in both groups, and especially in the non-ASD sample. Arguably, the contribution of alexithymia aspects on mentalising abilities might be found even lower in tasks not drawing heavily on facial emotion recognition such as the RME task (as discussed by [22, 23]).


There were no control participants included in this study. Instead, individuals with confirmed ASD diagnosis were compared to a clinical group of individuals who were referred for a possible ASD diagnosis.

Furthermore, it has to be stated clearly that a cross-sectional study design as used in the current study does not allow for causal inference. Longitudinal studies are required to pin down causal directionality of effects and developmental relationships between alexithymia and autism. This implies though that alexithymia traits might be acquired along developmental pathways, another issue to be determined by future investigations.

Our aim was to investigate the relative explanatory contribution of alexithymia and autism traits to mentalising abilities, which was effectively measured by a modified proven German version [24, 37,38,39,40] of the RME test [21]. As the current RME version differed from the version in Oakley et al. [22], we note that a direct comparison of results is limited. Furthermore, Rødgaard et al. [23] reported no correlation of TAS scores with a different mentalising measure (i.e. emotional MASC scale [24]) that comprises video stimuli which might have facilitated mentalising due to additional contextual information. In this context, it is worth mentioning that mentalising is a high-level, multi-facetted capacity that incorporates various cognitive mechanisms and its neural correlates are distributed in multiple brain regions [59,60,61,62,63]. Assumingly, there is no measure that spans all facets of mentalising and that controls for all external factors on performance (e.g., verbal abilities, empathetic abilities). Thus, the generalisability and comparibilty of mentalising tests is always limited to the specific stimulus material and specificities of the test. However, it must be emphasized that there are innovative measurement tools for mentalisation that were recently developed and should be considered as an alternative to the RME in future studies [64,65,66].

Another limitation of the current study is the inclusion of a heterogeneous clinical controlcomparison sample due to naturalistic sampling from representative patient populations from outpatient clinic. The generalisability of the findings in the clinical comparison sample is therefore limited. Nonetheless, the heterogeneity of the clinical comparison sample does not impact the findings from dominance analysis within the ASD sample. And furthermore, the advantage of our clinical comparison sample is that it stems from a representative referral population at an outpatient clinic for autism and therefore is exactly the type of clinical group that needs to be differentiated in clinical reality. This type of comparison group shows various social interaction problems and is therefore phenomenologically very close to our ASD sample and hence our approach can be regarded as conservative.


In conclusion, by deploying dominance analysis to infer the relative predictive power of alexithymia and autism traits in a large representative and homogeneous ASD sample and comparison to individuals with ruled out ASD from the referral population for autism diagnosis, the current study contributes to the differentiation between autism and alexithymia. By far the strongest relative predictor of mentalising skills were autism traits for individuals with ASD. To a far lesser extent, differential alexithymia subscales contributed to mentalising skills in ASD but in sum to a similar extent in a group of individuals with ruled out ASD diagnosis. Our results contribute to further differentiation of the alexithymia hypothesis of autism [4] in that alexithymia should be assessed for its high relevance for mental health issues in ASD [11, 16], but at the same time, our findings suggest autism traits to be the dominant predictor of mentalising as one characteristic autism trait.

Availability of data and materials

The dataset analysed in this study is not publicly available because it contains clinical data from patient that are subject to privacy protections.


  1. Inclusion of this case in ASD + group did neither change significance of predictors in multiple regression nor did it change the pattern of results of dominance analysis.



Reading the Mind in the Eyes Test


Autism Spectrum Quotient


20-Item Toronto Alexithymia Scale


Difficulties Identifying Feelings


Difficulties Describing Feelings


Externally-Oriented Thinking


Verbal Intelligence Quotient


Performance-based Intelligence Quotient


  1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub; 2013.

  2. Sifneos PE. The prevalence of “Alexithymic” characteristics in psychosomatic patients. Psychother Psychosom. 1973;22(2–6):255–62.

    Article  CAS  PubMed  Google Scholar 

  3. Gunzelmann T, Kupfer J, Brähler E. Alexithymia in the elderly general population. Compr Psychiatry. 2002;43(1):74–80.

    Article  PubMed  Google Scholar 

  4. Bird G, Cook R. Mixed emotions: the contribution of alexithymia to the emotional symptoms of autism. Transl Psychiatry. 2013;3(May):1–8.

    Google Scholar 

  5. Salminen JK, Saarijärvi S, Äärelä E, Toikka T, Kauhanen J. Prevalence of alexithymia and its association with sociodemographic variables in the general population of Finland. J Psychosom Res. 1999;46(1):75–82.

    Article  CAS  PubMed  Google Scholar 

  6. Mattila AK, Salminen JK, Nummi T, Joukamaa M. Age is strongly associated with alexithymia in the general population. J Psychosom Res. 2006;61(5):629–35.

    Article  PubMed  Google Scholar 

  7. Franz M, Popp K, Schaefer R, Sitte W, Schneider C, Hardt J, et al. Alexithymia in the German general population. Soc Psychiatry Psychiatr Epidemiol. 2008;43(1):54–62.

    Article  PubMed  Google Scholar 

  8. Kinnaird E, Stewart C, Tchanturia K. Investigating alexithymia in autism: A systematic review and meta-analysis. Eur Psychiatry. 2019;55:80–9.

    Article  PubMed  Google Scholar 

  9. Bagby RM, Parker JDA, Taylor GJ. The twenty-item Toronto Alexithymia scale-I. Item selection and cross-validation of the factor structure. J Psychosom Res. 1994;38(1):23–32.

    Article  CAS  PubMed  Google Scholar 

  10. Bagby RM, Taylor GJ, Parker JDA. The twenty-item Toronto Alexithymia scale-II. Convergent, discriminant, and concurrent validity. J Psychosom Res. 1994;38(1):33–40.

    Article  CAS  PubMed  Google Scholar 

  11. Bloch C, Burghof L, Lehnhardt F-G, Vogeley K, Falter-Wagner CM. Alexithymia traits outweigh autism traits in the explanation of depression in adults with autism. Sci Rep. 2021;11(1):2258.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fietz J, Valencia N, Silani G. Alexithymia and autistic traits as possible predictors for traits related to depression, anxiety, and stress: a multivariate statistical approach. J Eval Clin Pract. 2018;24(4):901–8.

    Article  PubMed  Google Scholar 

  13. Hollocks MJ, Lerh JW, Magiati I, Meiser-Stedman R, Brugha TS. Anxiety and depression in adults with autism spectrum disorder: a systematic review and meta-analysis. Psychol Med. 2019;49(4):559–72.

    Article  PubMed  Google Scholar 

  14. Morie KP, Jackson S, Zhai ZW, Potenza MN, Dritschel B. Mood disorders in high-functioning autism: the importance of alexithymia and emotional regulation. J Autism Dev Disord. 2019;49(7):2935–45.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hemming L, Haddock G, Shaw J, Pratt D. Alexithymia and its associations with depression, suicidality, and aggression: an overview of the literature. Front Psychiatry. 2019;10(APR).

  16. Oakley BFM, Jones EJH, Crawley D, Charman T, Buitelaar J, Tillmann J, et al. Alexithymia in autism: Cross-sectional and longitudinal associations with social-communication difficulties, anxiety and depression symptoms. Psychol Med. 2020;1–13.

  17. Cook R, Brewer R, Shah P, Bird G. Alexithymia, not autism, predicts poor recognition of emotional facial expressions. Psychol Sci. 2013;24(5):723–32.

    Article  PubMed  Google Scholar 

  18. Trevisan DA, Bowering M, Birmingham E. Alexithymia, but not autism spectrum disorder, may be related to the production of emotional facial expressions. Mol Autism. 2016;7:46.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Heaton P, Reichenbacher L, Sauter D, Allen R, Scott S, Hill E. Measuring the effects of alexithymia on perception of emotional vocalizations in autistic spectrum disorder and typical development. Psychol Med. 2012;42(11):2453–9.

    Article  CAS  PubMed  Google Scholar 

  20. Pisania S, Murphy J, Conway J, Millgate E, Catmure C, Bird G. The relationship between alexithymia and theory of mind: a systematic review. Neurosci Biobehav Rev. 2021;131:497–524.

    Article  Google Scholar 

  21. Baron-Cohen S, Wheelwright S, Hill J, Raste Y, Plumb I. The, “Reading the Mind in the Eyes” test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. J child Psychol psychiatry. 2001;42(2):241–51.

    Article  CAS  PubMed  Google Scholar 

  22. Oakley BFM, Brewer R, Bird G, Catmur C. Theory of mind is not theory of emotion: a cautionary note on the reading the mind in the eyes test. J Abnorm Psychol. 2016;125(6):818–23.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Rødgaard EM, Jensen K, Mottron L. An opposite pattern of cognitive performance in autistic individuals with and without alexithymia. J Abnorm Psychol. 2019;128(7):735–7.

    Article  PubMed  Google Scholar 

  24. Dziobek I, Fleck S, Kalbe E, Rogers K, Hassenstab J, Brand M, et al. Introducing MASC: a movie for the assessment of social cognition. J Autism Dev Disord. 2006;36(5):623–36.

    Article  PubMed  Google Scholar 

  25. Peñuelas-Calvo I, Sareen A, Sevilla-Llewellyn-Jones J, Fernández-Berrocal P. The, “reading the mind in the eyes” test in autism-spectrum disorders comparison with healthy controls: a systematic review and meta-analysis. J Autism Dev Disord. 2019;49(3):1048–61.

    Article  PubMed  Google Scholar 

  26. Shah P, Livingston LA, Callan MJ, Player L. Trait autism is a better predictor of empathy than alexithymia. J Autism Dev Disord. 2019;49(10):3956–64.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Poquérusse J, Pastore L, Dellantonio S, Esposito G. Alexithymia and autism spectrum disorder: a complex relationship. Front Psychol. 2018;9(JUL):1–10.

    Google Scholar 

  28. Nimon KF, Oswald FL. Understanding the results of multiple linear regression: beyond standardized regression coefficients. Organ Res Methods. 2013;16(4):650–74.

    Article  Google Scholar 

  29. Budescu DV. Dominance analysis: a new approach to the problem of relative importance of predictors in multiple regression. Psychol Bull. 1993;114(3):542–51.

    Article  Google Scholar 

  30. Demers LA, Koven NS. The relation of alexithymic traits to affective theory of mind. Am J Psychol. 2015;128(1):31–42.

    Article  PubMed  Google Scholar 

  31. Lyvers M, McCann K, Coundouris S, Edwards MS, Thorberg FA. Alexithymia in relation to alcohol use, emotion recognition, and empathy: the role of externally oriented thinking. Am J Psychol. 2018;131(1):41–51.

    Article  Google Scholar 

  32. Lehnhardt FG, Gawronski A, Pfeiffer K, Kockler H, Schilbach L, Vogeley K. Diagnostik und Differenzialdiagnose des Asperger-Syndroms im Erwachsenenalter. Dtsch Arztebl Int. 2013;110(45):755–63.

    PubMed  PubMed Central  Google Scholar 

  33. Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften. Autismus-Spektrum-Störungen im Kindes-, Jugend- und Erwachsenenalter - Teil 1: Diagnostik - Interdisziplinäre S3-Leitlinie der DGKJP und der DGPPN sowie der beteiligten Fachgesellschaften, Berufsverbände und Patientenorganisationen Langversion. AWMF online. 2016.

  34. 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(1):5–17.

    Article  CAS  PubMed  Google Scholar 

  35. von Aster M, Neubauer A, Horn R. Wechsler Intelligenztest für Erwachsene WIE. Deutschsprachige Bearbeitung und Adaptation des WAIS-III von David Wechsler (2., korrigierte Auflage). 2006.

  36. Baron-Cohen S, Wheelwright S, Hill J, Raste Y, Plumb I. The, “Reading the Mind in the Eyes” Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol Psychiatry Allied Discip. 2001;42(2):241–51.

    Article  CAS  Google Scholar 

  37. Schroeter ML, Pawelke S, Bisenius S, Kynast J, Schuemberg K, Polyakova M, et al. A modified reading the mind in the eyes test predicts behavioral variant frontotemporal dementia better than executive function tests. Front Aging Neurosci. 2018;10(JAN):1–11.

    Google Scholar 

  38. Schwartz C, Bente G, Gawronski A, Schilbach L, Vogeley K. Responses to nonverbal behaviour of dynamic virtual characters in high-functioning autism. J Autism Dev Disord. 2010;40(1):100–11.

    Article  PubMed  Google Scholar 

  39. Lehnhardt FG, Falter CM, Gawronski A, Pfeiffer K, Tepest R, Franklin J, et al. Sex-related cognitive profile in autism spectrum disorders diagnosed late in life: implications for the female autistic phenotype. J Autism Dev Disord. 2016;46(1):139–54.

    Article  PubMed  Google Scholar 

  40. Dziobek I, Rogers K, Fleck S, Hassenstab J, Gold S, Wolf OT, et al. In search of “master mindreaders”: are psychics superior in reading the language of the eyes? Brain Cogn. 2005;58(2):240–4.

    Article  PubMed  Google Scholar 

  41. 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(3):331–5.

    Article  CAS  PubMed  Google Scholar 

  42. Parker JDA, Taylor GJ, Bagby RM. The 20-Item Toronto Alexithymia Scale: III. Reliability and factorial validity in a community population. J Psychosom Res. 2003;55(3):269–75.

    Article  PubMed  Google Scholar 

  43. Taylor GJ, Bagby RM, Parker JDA. The 20-Item Toronto Alexithymia Scale: IV. Reliability and factorial validity in different languages and cultures. J Psychosom Res. 2003;55(3):277–83.

  44. Cuve HC, Murphy J, Hobson H, Ichijo E, Catmur C, Bird G. Are Autistic and alexithymic traits distinct? A factor-analytic and network approach. J Autism Dev Disord. 2022;52(5):2019–34.

    Article  PubMed  Google Scholar 

  45. RStudioTeam. RStudio: Integrated Development for R [Internet]. Boston, MA; 2020. Available from:

  46. Zeileis A. Econometric computing with HC and HAC covariance matrix estimators. J Stat Softw. 2004;11(October):1–17.

    Google Scholar 

  47. Zeileis A, Hothorn T. Diagnostic checking in regression relationships. R News. 2002;2(3):7–10.

    Google Scholar 

  48. Long JS, Ervin LH. Using heteroscedasticity consistent standard errors in the linear regression model. Am Stat. 2000;54(3):217–24.

    Google Scholar 

  49. Rosopa PJ, Schaffer MM, Schroeder AN. Managing heteroscedasticity in general linear models. Psychol Methods. 2013;18(3):335–51.

    Article  PubMed  Google Scholar 

  50. LeBreton JM, Ployhart RE, Ladd RT. A Monte Carlo comparison of relative importance methodologies. Organ Res Methods. 2004;7(3):258–82.

    Article  Google Scholar 

  51. Nimon K, Oswald F, Roberts J, Nimon M. Package “yhat” - Interpreting Regression Effects. 2013;1–25. Available from:

  52. Vellante M, Baron-Cohen S, Melis M, Marrone M, Petretto DR, Masala C, et al. The “reading the Mind in the Eyes” test: systematic review of psychometric properties and a validation study in Italy. Cogn Neuropsychiatry. 2013;18(4):326–54.

    Article  PubMed  Google Scholar 

  53. Rösch S, Puhlmann L, Preckel K. A cross-modal component of alexithymia and its relationship with performance in a social cognition task battery. J Affect Disord. 2022;298(2):625–33.

    Article  PubMed  Google Scholar 

  54. Milosavljevic B, Carter Leno V, Simonoff E, Baird G, Pickles A, Jones CRG, et al. Alexithymia in adolescents with autism spectrum disorder: its relationship to internalising difficulties, sensory modulation and social cognition. J Autism Dev Disord. 2016;46(4):1354–67.

    Article  PubMed  Google Scholar 

  55. Lane RD, Hsu CH, Locke DEC, Ritenbaugh C, Stonnington CM. Role of theory of mind in emotional awareness and alexithymia: implications for conceptualization and measurement. Conscious Cogn. 2015;33:398–405.

    Article  PubMed  Google Scholar 

  56. Koehler J, Alexandra K, Georgescu L, Weiske J, Spangemacher M, Burghof L, et al. Brief report: specificity of interpersonal synchrony deficits to autism spectrum disorder and its potential for digitally assisted diagnostics. J Autism Dev Disord. 2021.

    Article  PubMed  Google Scholar 

  57. Saarijärvi S, Salminen JK, Toikka T. Temporal stability of alexithymia over a five-year period in outpatients with major depression. Psychother Psychosom. 2006;75(2):107–12.

    Article  PubMed  Google Scholar 

  58. Li S, Zhang B, Guo Y, Zhang J. The association between alexithymia as assessed by the 20-item Toronto Alexithymia Scale and depression: a meta-analysis. Psychiatry Res. 2015;227(1):1–9.

    Article  PubMed  Google Scholar 

  59. Geiger A, Bente G, Lammers S, Tepest R, Roth D, Bzdok D, et al. Distinct functional roles of the mirror neuron system and the mentalizing system. Neuroimage. 2019;202: 116102.

    Article  PubMed  Google Scholar 

  60. Vogeley K. Two social brains: Neural mechanisms of intersubjectivity. Philos Trans R Soc B Biol Sci. 2017;372(1727):1–11.

    Article  Google Scholar 

  61. Frith U, Frith CD. Development and neurophysiology of mentalizing. Philos Trans R Soc B Biol Sci. 2003;358(1431):459–73.

    Article  Google Scholar 

  62. Vogeley K, Bussfeld P, Newen A, Herrmann S, Happé F, Falkai P, et al. Mind reading: Neural mechanisms of theory of mind and self-perspective. Neuroimage. 2001;14(1):170–81.

    Article  CAS  PubMed  Google Scholar 

  63. Alcalá-López D, Smallwood J, Jefferies E, Van Overwalle F, Vogeley K, Mars RB, et al. Computing the social brain connectome across systems and states. Cereb Cortex. 2018;28(7):2207–32.

    Article  PubMed  Google Scholar 

  64. Livingston LA, Shah P, White SJ, Happé F. Further developing the Frith-Happé animations: a quicker, more objective, and web-based test of theory of mind for autistic and neurotypical adults. Autism Res. 2021;14(9):1905–12.

    Article  PubMed  Google Scholar 

  65. Clutterbuck RA, Callan MJ, Taylor EC, Livingston LA, Shah P. Development and validation of the four-item mentalising index. Psychol Assess. 2021;33(7):629–36.

    Article  PubMed  Google Scholar 

  66. Murray K, Johnston K, Cunnane H, Kerr C, Spain D, Gillan N, et al. A new test of advanced theory of mind: the “Strange Stories Film Task” captures social processing differences in adults with autism spectrum disorders. Autism Res. 2017;10(6):1120–32.

    Article  PubMed  Google Scholar 

Download references


Open Access funding enabled and organized by Projekt DEAL. CB and CFW were supported by the DFG (Deutsche Forschungsgemeinschaft) [Grant Numbers FA 876/3-1, FA 876/5-1]. KV was supported by the EC, Horizon 2020 Framework Programme, FET Proactive [Project VIRTUALTIMES; Grant Agreement ID: 824128].

Author information

Authors and Affiliations



LB collected the sample. CB expanded and analysed the sample. CB and CFW designed the analysis plan and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Christine M. Falter-Wagner.

Ethics declarations

Ethics approval and consent to participate

Ethical approval was granted by the ethics committee of the medical faculty, University of Cologne (20-1432).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

. Supplementary Analysis 1 and Supplementary Figure 1.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Falter-Wagner, C.M., Bloch, C., Burghof, L. et al. Autism traits outweigh alexithymia traits in the explanation of mentalising performance in adults with autism but not in adults with rejected autism diagnosis. Molecular Autism 13, 32 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: