Social Cognitive Performance in Schizophrenia Spectrum Disorders Compared With Autism Spectrum Disorder: A Systematic Review, Meta-analysis, and Meta-regression

Lindsay D Oliver; Iska Moxon-Emre; Meng-Chuan Lai; Laura Grennan; Aristotle N Voineskos; Stephanie H Ameis

doi:10.1001/jamapsychiatry.2020.3908

. 2020 Dec 8;78(3):1–13. doi: 10.1001/jamapsychiatry.2020.3908

Social Cognitive Performance in Schizophrenia Spectrum Disorders Compared With Autism Spectrum Disorder

A Systematic Review, Meta-analysis, and Meta-regression

Lindsay D Oliver ¹, Iska Moxon-Emre ¹, Meng-Chuan Lai ^1,^2,^3,⁴, Laura Grennan ¹, Aristotle N Voineskos ^1,², Stephanie H Ameis ^1,^2,^✉

¹Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

²Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada

³Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

⁴Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan

Accepted for Publication: September 27, 2020.

Published Online: December 8, 2020. doi:10.1001/jamapsychiatry.2020.3908

^✉

Corresponding Author: Stephanie H. Ameis, MD, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, No. 5224, Toronto, ON M6J 1H4, Canada (stephanie.ameis@camh.ca).

Author Contributions: Drs Oliver and Ameis had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Oliver, Moxon-Emre, Lai, Grennan, Ameis.

Drafting of the manuscript: Oliver.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Oliver.

Obtained funding: Voineskos, Ameis.

Administrative, technical, or material support: Moxon-Emre, Grennan, Voineskos, Ameis.

Supervision: Lai, Voineskos, Ameis.

Conflict of Interest Disclosures: Drs Oliver and Moxon-Emre reported receiving funding from the Canadian Institutes of Health Research. Dr Lai reported receiving funding from the Ontario Brain Institute via the Province of Ontario Neurodevelopmental Disorders Network, the Canadian Institutes of Health Research, the Academic Scholars Award from the Department of Psychiatry of the University of Toronto, and the Centre for Addiction and Mental Health (CAMH) Foundation. Dr Voineskos reported receiving funding from the National Institute of Mental Health, the Canadian Institutes of Health Research, the Canada Foundation for Innovation, the CAMH Foundation, and the University of Toronto. Dr Ameis reported receiving funding from the National Institute of Mental Health, the Canadian Institutes of Health Research, the Academic Scholars Award from the Department of Psychiatry of the University of Toronto, and the CAMH Foundation. No other disclosures were reported.

Funding/Support: This work was supported by funding from the National Institute of Mental Health (grant R01MH114879; Dr Ameis).

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Meeting Presentation: This paper was presented at the 59th Annual Meeting of the American College of Neuropsychopharmacology; December 8, 2020; virtual meeting.

Additional Contributions: We thank Sarah Bonato, MIS (reference and research librarian, CAMH Library), for assistance with the development of the search strategy. She was not compensated for her contributions.

^✉

Corresponding author.

PMCID: PMC7724568 PMID: 33291141

This systematic review and meta-analysis investigates how deficits in social cognitive domains diverge or overlap between schizophrenia spectrum disorders and autism spectrum disorder based on the extant literature.

Key Points

Question

How do deficits in social cognition diverge or overlap between individuals with schizophrenia spectrum disorders (SSDs) and autism spectrum disorder (ASD)?

Findings

In this systematic review and meta-analysis of 36 studies directly comparing social cognitive performance in individuals with SSDs vs ASD, there were no statistically significant differences in emotion processing or theory of mind.

Meaning

Similar levels of social cognitive impairment may be present in individuals with SSDs and ASD, but cross-disorder studies probing social cognitive domains with larger samples, consistent reporting of clinical measures, and neuroimaging are needed to substantiate these findings, clarify underlying mechanisms, and parse heterogeneity.

Abstract

Importance

Schizophrenia spectrum disorders (SSDs) and autism spectrum disorder (ASD) both feature social cognitive deficits; however, these disorders historically have been examined separately using a range of tests and subdomain focus and at different time points in the life span. Moving beyond diagnostic categories and characterizing social cognitive deficits can enhance understanding of shared pathways across these disorders.

Objective

To investigate how deficits in social cognitive domains diverge or overlap between SSDs and ASD based on the extant literature.

Data Sources

Literature searches were conducted in MEDLINE, PsycInfo, Embase, and Web of Science from database inception until July 26, 2020.

Study Selection

Original research articles were selected that reported performance-based measures of social cognition in both SSDs and ASD samples. Selected articles also had to be published in English and use International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, DSM-IV, or more recent diagnostic criteria.

Data Extraction and Synthesis

This systematic review and meta-analysis was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology reporting guidelines, including data extraction and quality assessment using a modified version of the Newcastle-Ottawa Scale. Data were pooled using a random-effects model.

Main Outcomes and Measures

Effect sizes were calculated as Hedges g (SSDs vs ASD). The primary outcomes were performance on emotion processing tasks, theory of mind (ToM) tasks, and the Reading the Mind in the Eyes Test (RMET) in SSDs compared with ASD. Meta-regressions were performed for age difference, publication year, quality assessment scores, and antipsychotic medication use.

Results

Of the 4175 screened articles, 36 studies directly comparing social cognitive performance in individuals with SSDs vs ASD were included in the qualitative analysis (n = 1212 for SSDs groups and n = 1109 for ASD groups), and 33 studies were included in the quantitative analyses (n = 1113 for SSDs groups and n = 1015 for ASD groups). Most study participants were male (number of studies [k] = 36, 72% [878 of 1212] in SSDs groups and 82% [907 of 1109] in ASD groups), and age (k = 35) was older in SSDs groups (mean [SD], 28.4 [9.5] years) than in ASD groups (mean [SD], 23.3 [7.6] years). Included studies highlighted the prevalence of small, male-predominant samples and a paucity of cross-disorder clinical measures. The meta-analyses revealed no statistically significant differences between SSDs and ASD on emotion processing measures (k = 15; g = 0.12 [95% CI, –0.07 to 0.30]; P = .21; I² = 51.0%; 1 outlier excluded), ToM measures (k = 17; g = –0.01 [95% CI, –0.21 to 0.19]; P = .92; I² = 56.5%; 1 outlier excluded), or the RMET (k = 13; g = 0.25 [95% CI, –0.04 to 0.53]; P = .10; I² = 75.3%). However, SSDs vs ASD performance differences between studies were statistically significantly heterogeneous, which was only minimally explained by potential moderators.

Conclusions and Relevance

In this analysis, similar levels of social cognitive impairment were present, on average, in individuals with SSDs and ASD. Cross-disorder studies of social cognition, including larger samples, consensus batteries, and consistent reporting of measures, as well as data across multiple levels of analysis, are needed to help identify subgroups within and across disorders that may be more homogeneous in etiology and treatment response.

Introduction

Schizophrenia spectrum disorders (SSDs) and autism spectrum disorder (ASD) both feature social cognitive deficits,^1,2 which contribute to disability and poor functional outcome,^3,4 and have limited treatment options. Overlapping clinical symptoms in SSDs and ASD, and social impairments in particular, have long been recognized. Highly heterogeneous clinical presentation is also characteristic within and across these disorders.^5,6,7 Although historically few studies have included both diagnostic groups, cross-disorder research on social cognition in people with SSDs and ASD is increasing because of a shift toward transdiagnostic research,⁸ use of the Research Domain Criteria framework,⁹ and the prioritization of improving functional outcome.^10,11

Social cognition can be divided into subprocesses, including emotion processing and theory of mind (ToM [also known as mentalizing]), subserved by partially dissociable neural networks.^12,13 Meta-analyses have provided evidence for impaired emotion processing and ToM in people with SSDs^14,15 and those with ASD^16,17 vs typically developing individuals. The onset of ASD begins within the early years of life, during the development of lower-level social cognitive processing (eg, emotion processing), whereas the onset of SSDs begins during late adolescence, when higher-level social cognitive processing (eg, ToM) is still developing.⁶ Despite these developmental onset differences, similar levels of ToM impairment have been shown in both clinical groups in 2 meta-analyses^18,19 of studies that included people with SSDs compared with studies that included people with ASD. However, methods and sample characteristics across studies have been highly variable, making cross-disorder comparisons difficult. Investigations directly comparing social cognition in both conditions are less common, with mixed results regarding relative levels of impairment.

Only 1 meta-analysis²⁰ has examined social cognition across studies directly comparing SSDs and ASD; it found similar ToM performance but greater emotion processing impairment in ASD vs SSDs. Since then, more than 10 studies directly comparing social cognitive performance in SSDs and ASD have been published. Including these new studies more than doubles the sample size and allows for the examination of additional moderators, such as differences in age and antipsychotic treatment, to examine their contribution to the heterogeneity of findings. Formal assessment of study quality, detection of outliers, and sensitivity analyses to evaluate robustness are also needed, which were not included in the previous meta-analysis.²⁰

Herein, we aimed to investigate how deficits in emotion processing and ToM diverge or overlap between individuals with SSDs and ASD by conducting a comprehensive, updated systematic review and meta-analysis of studies directly comparing social cognitive performance in SSDs and ASD. Given differences in age at onset, variation in the use of antipsychotic medication, and changes in diagnostic criteria and research practices over time, we evaluated the implications of age difference, publication year, study quality assessment scores, and antipsychotic medication use as potential moderators for group differences in social cognition using meta-regressions. We hypothesized that effect sizes would be heterogeneous, with a large degree of overlap in emotion processing and ToM performance across SSDs and ASD. Based on meta-analyses to date and evidence of early-onset lower-level social cognitive challenges in ASD,¹⁷ we also hypothesized that people with ASD would be more impaired overall on emotion processing than those with SSDs but that similar levels of ToM impairment would be observed.

Methods

This systematic review and meta-analysis was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)²¹ and Meta-analysis of Observational Studies in Epidemiology (MOOSE)²² guidelines, including data extraction and quality assessment using a modified version of the Newcastle-Ottawa Scale (NOS). Data were pooled using a random-effects model.

Search Strategy

After consultation with a reference librarian, literature searches were conducted in MEDLINE, PsycInfo, Embase, and Web of Science (eMethods in the Supplement) to identify original research articles published from database inception until July 26, 2020, that reported performance-based measures of social cognition in both SSDs and ASD samples. Backward and forward citation searches were also conducted for all studies that met inclusion criteria, as well as relevant reviews and meta-analyses.^5,6,18,23,24

Eligibility Criteria

Eligibility criteria for study inclusion were as follows: (1) a group with SSDs (that could include first-episode psychosis) and (2) a group with ASD according to International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10),²⁵ DSM-IV,²⁶ or DSM-5¹ definitions (details are provided in the eMethods in the Supplement), (3) reported data from a performance-based measure of social cognition, and (4) original research article. Additional eligibility criteria implemented during the full-text review stage were (5) articles published in English and (6) diagnoses based on ICD-10, DSM-IV, or more recent criteria. Only studies that used performance-based measures (rather than questionnaire or self-report measures) were included because of their greater validity as an index of social cognitive abilities.

References were managed using EndNote Online (Thomson Reuters). Titles and abstracts of nonduplicate publications were screened twice independently (by L.D.O., I.M.-E., and L.G.). Full texts of the potentially eligible studies were then assessed for inclusion by 2 reviewers (L.D.O. and I.M.-E.). Any uncertainties regarding eligibility (n = 135) were reconciled among the reviewers (eMethods in the Supplement).

Data Extraction

Data including social cognitive outcomes and participant demographics (eMethods in the Supplement) were extracted from included articles by 1 of 2 reviewers (L.D.O. and I.M.-E.) and subsequently cross-checked by the other. When SSDs and ASD group means and SDs were not provided, they were calculated^{27,28,29,30,31} or extracted from plots^32,33,34 where possible (eMethods in the Supplement). Calculations and plot extractions were performed twice independently.

Quality Assessment

A modified version of the NOS³⁵ was used to assess the quality and risk of bias of included articles (eg, group comparability) by either L.D.O. or I.M-E. (score range, 1-10 from low to high quality) (eTable 1 in the Supplement). Uncertainties were discussed between reviewers (L.D.O. and I.M-E.) until consensus was reached.

Statistical Analysis

Data were analyzed in RStudio, version 1.1.447 (R Foundation for Statistical Computing) using the metafor package.³⁶ Effect sizes were calculated as standardized mean difference (SSDs vs ASD Hedges g).³⁷ The primary outcomes were performance on emotion processing tasks, ToM tasks, and the RMET (Reading the Mind in the Eyes Test) in SSDs compared with ASD. Random-effects meta-analyses were conducted using inverse-variance weighting and the restricted maximum likelihood estimator.³⁸ For each meta-analysis (emotion processing, ToM, and RMET), heterogeneity of the pooled effect sizes was assessed using the Cochran Q test. Tau² (τ²) was calculated to estimate between-study variance. The I² statistic was used to quantify the percentage of total variation across studies (ie, variation in effect sizes for differences between SSDs vs ASD) that was because of true between-study heterogeneity vs chance; 25%, 50%, and 75% reflect low, moderate, and high heterogeneity, respectively.³⁹ Outliers and impactful studies were detected using studentized residuals greater than 3 (in absolute value) and leave-one-out and combinatorial meta-analyses (eMethods in the Supplement).^40,41 Normality was checked for the final models using Q-Q plots. Publication bias was also assessed for each meta-analysis via visual inspection of funnel plots and Egger regression tests for funnel plot asymmetry.⁴² Statistical tests were performed with a significance level of .05 (2-sided).

Meta-analyses

Separate meta-analyses were conducted for emotion processing and ToM performance. A third meta-analysis was performed for the RMET⁴³ because it was the most commonly used measure (number of studies [k] = 13) and the literature is mixed regarding whether it assesses emotion processing or ToM abilities or both.^43,44,45 We defined emotion processing tasks as those assessing emotion labeling or matching (eg, emotion recognition), largely thought to involve less complex, lower-level processing, whereas ToM (or mentalizing) is thought to involve more complex, higher-level processing, including perspective taking and mental or emotional state inference.^12,13,45 Social cognitive outcomes were categorized by L.D.O. and verified by coauthors (Table 1 and eMethods in the Supplement).

Table 1. Overview of 36 Studies^{27,28,29,30,31,32,33,34,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73} Included in the Qualitative Analysis.

Source	No. of SSDs	No. of ASD	Male, No./total No. (%)		Age, mean (SD), y		QA score^a	Meta-analysis	Social cognitive measure^b
Source	No. of SSDs	No. of ASD	SSDs	ASD	SSDs	ASD	QA score^a	Meta-analysis	Social cognitive measure^b
Boada et al,⁶⁴ 2020	35	42	20/35 (57.1)	37/42 (88.1)	27.1 (5.8)	21.4 (5.3)	5	ToM	Frith Happé Animations (modified Triangles Task): ToM Questions (17 SSDs and 16 ASD)
Bölte and Poustka,³¹ 2003	21	35	15/21 (71.4)	29/35 (82.9)	18.1 (3.1)	13.6 (6.3)	7	Emotion processing	Facial Affect Recognition Test
Booules-Katri et al,⁴⁹ 2019	30	35	22/30 (73.3)	34/35 (97.1)	28.8 (11.5)	18.6 (6.4)	5	ToM	Strange Stories Test: ToM
Booules-Katri et al,⁴⁹ 2019	30	35	22/30 (73.3)	34/35 (97.1)	28.8 (11.5)	18.6 (6.4)	5	RMET	RMET
Ciaramidaro et al,⁵⁰ 2015	18	23	14/18 (77.8)	21/23 (91.3)	25.7 (4.5)	18.7 (4.8)	8	ToM	Picture Sequencing Task: Communicative Intention (21 ASD)
Ciaramidaro et al,⁵⁴ 2018	20	33	14/20 (70.0)	31/33 (93.9)	24.7 (5.0)	18.8 (5.0)	8	Emotion processing	Explicit Facial Affect Recognition (30 ASD)
Couture et al,⁶⁵ 2010	44	36	39/44 (88.6)	29/36 (80.6)	27.5 (6.3)	20.9 (5.7)	5	RMET	RMET
Craig et al,⁶⁶ 2004	16	17	11/16 (68.8)	15/17 (88.2)	31.7 (9.9)	24.1 (6.7)	3	ToM	Hinting Task
Craig et al,⁶⁶ 2004	16	17	11/16 (68.8)	15/17 (88.2)	31.7 (9.9)	24.1 (6.7)	3	RMET	RMET
Eack et al,⁶⁷ 2013	47	43	34/47 (72.3)	38/43 (88.4)	35.0 (12.5)	24.9 (5.8)	8	Emotion processing	Penn Emotion Recognition Test (ER-40)
Graux et al,⁶⁸ 2019	47	18	34/47 (72.3)	16/18 (88.9)	33.9 (9.5)	26.5 (5.8)	1	Emotion processing	Facial Emotion Recognition Test (TREF)
Graux et al,⁶⁸ 2019	47	18	34/47 (72.3)	16/18 (88.9)	33.9 (9.5)	26.5 (5.8)	1	ToM	Movie for the Assessment of Social Cognition: ToM
Hirata et al,²⁸ 2018	15	13	12/15 (80.0)	12/13 (92.3)	40.1 (15.7)	31.7 (13.5)	4	Emotion processing	Emotional Face Recognition Task: Facial Emotional Matching
Hyatt et al,⁶² 2020	30	30	19/30 (63.3)	26/30 (86.7)	26.0 (3.5)	21.7 (3.4)	8	RMET	RMET
Kandalaft et al,⁶⁹ 2012	19	20	12/19 (63.2)	16/20 (80.0)	31.1 (5.4)	22.9 (4.9)	6	Emotion processing	Ekman 60 Faces Test (Ekman 60) (17 SSDs)
								ToM	Triangles Task: ToM Intentionality (16 SSDs and 18 ASD)
								RMET	RMET (16 SSDs and 19 ASD)
Krawczyk et al,⁵⁹ 2014	13	15	7/13 (53.8)	11/15 (73.3)	30.0 (5.7)	21.7 (4.4)	5	RMET	RMET
Kuo et al,⁶³ 2019	103	113	74/103 (72.0)	92/113 (81.0)	24.8 (5.4)	24.8 (6.7)	6	Emotion processing	Mayer-Salovey-Caruso Emotional Intelligence Test: Perceiving Emotion, Faces
Lugnegård et al,⁷⁰ 2013	36	53	22/36 (61.1)	26/53 (49.1)	28.8 (4.1)	27.3 (4.1)	7	ToM	Triangles Task: ToM Intentionality
Lugnegård et al,⁷⁰ 2013	36	53	22/36 (61.1)	26/53 (49.1)	28.8 (4.1)	27.3 (4.1)	7	RMET	RMET
Martinez et al,⁶¹ 2017	36	19	30/36 (83.3)	15/19 (78.9)	23.4 (3.5)	22.7 (4.1)	5	ToM	Movie for the Assessment of Social Cognition: ToM
Martinez et al,⁷¹ 2019	51	32	42/51 (82.4)	25/32 (78.1)	23.4 (3.6)	22.6 (3.5)	8	ToM	Animated Shapes Task (modified Triangles Task): ToM Intentionality (28 ASD)
Martínez et al,⁵⁷ 2019	19	20	15/19 (78.9)	16/20 (80.0)	37.2 (10.2)	28.9 (7.7)	8	Emotion processing	Penn Emotion Recognition Test (ER-40) (19 ASD)
Morrison et al,⁵⁸ 2017	54	54	47/54 (87.0)	47/54 (87.0)	28.7 (10.1)	25.7 (7.2)	7	NA	Social Skills Performance Assessment
Murphy,³² 2006	13	13	13/13 (100)	13/13 (100)	29.7 (6.2)	35.0 (7.5)	4	ToM	Modified Advanced ToM Test: Second-Order
Murphy,³² 2006	13	13	13/13 (100)	13/13 (100)	29.7 (6.2)	35.0 (7.5)	4	RMET	RMET
Ozguven et al,²⁷ 2010	20	14	20/20 (100)	14/14 (100)	27.0 (4.8)	24.4 (7.1)	9	ToM	Second-Order ToM Stories Task
Pepper et al,⁴⁸ 2018	51	53	36/51 (70.6)	35/53 (66.0)	21.8 (4.4)	23.9 (7.4)	8	Emotion processing	Ekman 60 Faces Test (Ekman 60)
								ToM	Picture Sequencing Task: False Belief
								RMET	RMET
Pilowsky et al,³³ 2000	12	12	9/12 (75.0)	11/12 (91.7)	12.2 (1.7)	13.0 (3.9)	6	ToM	False Belief Task
Pinkham et al,³⁰ 2008	24	12	24/24 (100)	12/12 (100)	27.2 (4.6)	24.1 (5.7)	6	NA	Trustworthiness Task
Pinkham et al,⁷² 2019	92	101	65/92 (70.7)	90/101 (89.1)	27.8 (7.3)	24.2 (6.2)	10	Emotion processing	Penn Emotion Recognition Test (ER-40)
								ToM	Hinting Task
								RMET	RMET (98 ASD)
Radeloff et al,⁵⁵ 2014	21	34	16/21 (76.2)	31/34 (91.2)	24.7 (5.2)	19.1 (5.1)	9	RMET	RMET
Sachse et al,⁵¹ 2014	19	22	14/19 (73.7)	18/22 (81.8)	25.5 (4.9)	20.9 (5.6)	9	Emotion processing	Frankfurt Test for the Recognition of Facial Affects: Face
Sachse et al,⁵¹ 2014	19	22	14/19 (73.7)	18/22 (81.8)	25.5 (4.9)	20.9 (5.6)	9	RMET	RMET
Sasson et al,⁵² 2007	10	10	9/10 (90.0)	10/10 (100)	28.1 (5.07)	23 (5.3)	6	Emotion processing	Modified Social Scenes Task: Face-Present
Sasson et al,⁷³ 2016	44	21	27/44 (61.4)	18/21 (85.7)	35.3 (10.6)	23.4 (4.4)	6	Emotion processing	Emotions in Context Task: Face in Congruent Context
Schwarz et al,⁵⁶ 2020	27	25	18/27 (66.7)	14/25 (56.0)	32.4 (10.4)	32.1 (9.6)	9	RMET	RMET (24 ASD)
Stanfield et al,⁵³ 2017	21	28	14/21 (66.7)	22/28 (78.6)	NA	NA	5	NA	Ekman 60 Faces Test (Ekman 60; usable values not reported)
Stefanik et al,⁶⁰ 2018	51	38	32/51 (62.7)	26/38 (68.4)	26.5 (4.8)	22.6 (4.6)	7	Emotion processing	TASIT-R I: Emotion Evaluation Task
Stefanik et al,⁶⁰ 2018	51	38	32/51 (62.7)	26/38 (68.4)	26.5 (4.8)	22.6 (4.6)	7	ToM	TASIT-R III: Social Inference–Enriched
Tin et al,⁴⁷ 2018	30	30	19/30 (63.3)	23/30 (76.7)	17.5 (1.2)	17.0 (0.9)	9	ToM	Faux Pas Task: Inference of Intention
Tobe et al,³⁴ 2016	92	19	79/92 (85.9)	17/19 (89.5)	37.8 (10.4)	39.4 (12.5)	4	Emotion processing	Penn Emotion Recognition Test (ER-40)
Veddum et al,²⁹ 2019	21	11	10/21 (47.6)	4/11 (36.4)	40.5 (12.4)	25.1 (6.4)	6	ToM	Triangles Task: ToM Intentionality (19 SSDs and 9 ASD)
Waris et al,⁴⁶ 2016	10	15	2/10 (20.0)	7/15 (46.7)	16.2 (1.6)	16.1 (1.6)	6	Emotion processing	NEPSY-II: Emotion Recognition Matching (9 SSDs)
Waris et al,⁴⁶ 2016	10	15	2/10 (20.0)	7/15 (46.7)	16.2 (1.6)	16.1 (1.6)	6	ToM	NEPSY-II: ToM Verbal (9 SSDs)

Open in a new tab

Abbreviations: ASD, autism spectrum disorder; NA, not applicable; NEPSY, A Developmental Neuropsychological Assessment; QA, quality assessment; RMET, Reading the Mind in the Eyes Test; SSDs, schizophrenia spectrum disorders; TASIT-R, The Awareness of Social Inference Test–Revised; ToM, theory of mind.

^{^a}

The maximum QA score was 10 points, with lower scores reflecting greater risk of bias.

^{^b}

Where numbers differed by social cognitive measure, this number is indicated beside the task description.

Moderator Analyses

Meta-regressions were performed for age difference, publication year, quality assessment scores, and antipsychotic medication use. The associations of age difference (SSDs vs ASD Hedges g) and publication year were examined using multivariable meta-regressions for each meta-analysis (eMethods in the Supplement). Exploratory univariable meta-regressions were also conducted to examine the associations of quality assessment scores and medication use (SSDs vs ASD mean proportion of participants taking antipsychotic medication) for emotion processing and ToM, as well as stimulus type (verbal and visual) for ToM (emotion processing was all visual).

Sensitivity Analyses

The main meta-analyses were rerun after excluding studies that required calculations or plot extraction for the quantitative analyses^{27,28,29,31,32,33,34} or those with only youth participants (age range, 10-20 years)^33,46,47 to ensure that these samples were not unduly altering results. Meta-analyses were also rerun after excluding articles with SSDs groups composed only of individuals with first-episode psychosis,⁴⁸ because this designation does not constitute a formal SSDs diagnosis,¹ and articles with SSDs groups composed only of those with schizotypal or schizoid personality disorder,⁴⁹ because this classification can be considered both part of the schizophrenia spectrum and a personality disorder.

Results

Study Selection and Characteristics

Of the 4175 screened articles, the literature search (Figure 1) resulted in 36 studies directly comparing social cognitive performance in individuals with SSDs vs ASD being included in the qualitative analysis (n = 1212 for SSDs groups and n = 1109 for ASD groups) and 33 studies being included in the quantitative analyses (n = 1113 for SSDs groups and n = 1015 for ASD groups). Eleven authors were contacted for further information (eTable 2 in the Supplement). Across included studies (Table 1),^{27,28,29,30,31,32,33,34,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73} sample sizes of the SSDs and ASD groups tended to be small (a group with <25 participants in 22 studies; range, 10-113). Most study participants were male (k = 36, 72% [878 of 1212] in SSDs groups and 82% [907 of 1109] in ASD groups), and age (k = 35) was older in SSDs groups (mean [SD], 28.4 [9.5] years) than in ASD groups (mean [SD], 23.3 [7.6] years). IQ data reported (k = 31) were inconsistent (eg, full-scale, verbal, nonverbal, and unspecified), precluding data pooling. Antipsychotic medication use status was reported for both groups in 22 articles,^{27,28,30,32,34,47,48,50,51,52,53,54,55,56,57,58,60,62,67,70,72,73} 10 of which reported chlorpromazine equivalents for both.^{28,30,50,51,52,53,54,55,56,57} All studies used ICD-10, DSM-IV, or DSM-5 criteria for SSDs and ASD diagnoses.

Social Cognitive and Additional Measures

Among the studies included in the meta-analyses, 16 studies^{28,31,34,46,48,51,52,54,57,60,63,67,68,69,72,73} used performance-based emotion processing measures, 18 studies^{27,29,32,33,46,47,48,49,50,60,61,64,66,68,69,70,71,72} included ToM measures, and 13 studies^{32,48,49,51,55,56,59,62,65,66,69,70,72} used the RMET (Table 1). Three articles^30,53,58 were excluded from the quantitative analyses (details are provided in the eMethods in the Supplement).

Reporting of measures of nonsocial cognition, clinical symptoms, everyday or adaptive functioning, and neuroimaging was uncommon and inconsistent within and between studies (eMethods in the Supplement). Nine studies^{27,28,29,34,46,56,59,60,74} included a measure of nonsocial cognition, but these varied widely. Clinical scores or subscores provided from each study varied; only 2 studies^61,62 reported measures of schizophrenia and autistic symptoms in both groups. Measures of everyday functioning were also rare (k = 5).^{29,60,62,63,64} Ten articles^{28,30,50,53,54,55,56,57,60,62} reported neuroimaging data in conjunction with performance-based social cognition.

Quality Assessment

Five studies^{28,32,34,66,68} received low (<5 points), 18 studies^{29,30,31,33,46,49,52,53,58,59,60,61,63,64,65,69,70,73} received moderate (5-7 points), and 13 studies^{27,47,48,50,51,54,55,56,57,62,67,71,72} received high (8-10 points) scores on the modified NOS (Table 1). Independent validation of diagnoses was most often unreported, and additional points were lost fairly uniformly among the remaining criteria.

Quantitative Findings

Quantitative findings included emotion processing, ToM, and RMET analyses. Detailed results from the main meta-analyses and sensitivity analyses are listed in Table 2.

Table 2. Main Meta-analyses and Sensitivity Analyses.

Variable	k	No. of SSDs	No. of ASD	Hedges g (95% CI)	P value	Q	Q P value	τ²	I² statistic, %	Bias coefficient	Egger P value
Emotion processing
Main meta-analysis	16	657	570	0.21 (−0.03 to 0.44)	.08	48.1	<.001	0.152	71.8	1.22	.22
Main meta-analysis (outlier removed)	15	636	535	0.12 (−0.07 to 0.30)	.21	28.9	.01	0.061	51.0	1.15	.25
Sensitivity analyses^a
Data available	13	529	503	0.15 (−0.05 to 0.36)	.14	27.6	.006	0.071	56.1	1.30	.19
SSDs diagnosis	14	585	482	0.11 (−0.09 to 0.31)	.28	28.3	.008	0.072	54.2	1.19	.23
Adults only	14	627	520	0.09 (−0.09 to 0.27)	.33	25.5	.02	0.053	49.0	0.57	.57
Theory of mind
Main meta-analysis	18	564	510	−0.13 (−0.45 to 0.18)	.42	66.9	<.001	0.360	83.0	−3.18	.002
Main meta-analysis (outlier removed)	17	555	495	−0.01 (−0.21 to 0.19)	.92	35.1	.004	0.091	56.5	−0.08	.94
Sensitivity analyses^a
Data available	12	474	431	0.05 (−0.18 to 0.28)	.68	27.6	.004	0.099	63.1	1.32	.19
SSDs diagnosis	15	474	407	−0.06 (−0.27 to 0.16)	.62	30.8	.006	0.099	56.7	0.13	.89
Adults only	15	513	453	−0.02 (−0.25 to 0.20)	.85	34.6	.002	0.113	62.4	−0.31	.76
Reading the Mind in the Eyes Test
Main meta-analysis	13	408	449	0.25 (−0.04 to 0.53)	.10	42.0	<.001	0.200	75.3	0.85	.39
Sensitivity analyses^a
Data available	12	395	436	0.29 (0.00 to 0.58)	.05	39.3	<.001	0.191	75.4	1.71	.08
SSDs diagnosis	11	327	361	0.23 (−0.12 to 0.57)	.20	39.4	<.001	0.253	78.1	0.81	.42

Open in a new tab

Abbreviations: ASD, autism spectrum disorder; k, number of studies; SSDs, schizophrenia spectrum disorders.

^{^a}

For sensitivity analyses, “Data available” excluded studies that required calculations or plot extraction for the quantitative analyses, “SSDs diagnosis” excluded studies with SSDs groups composed entirely of individuals with schizotypal or schizoid personality disorder or only individuals with first-episode psychosis, and “Adults only” excluded studies with only youth participants (age range, 10-20 years). Heterogeneity of the pooled effect sizes are calculated using the Cochran Q test. Tau² (τ²) provides an estimate of between-study variance, and the I² statistic quantifies the percentage of total variation across studies because of between-study heterogeneity vs chance.

Emotion Processing

There was no statistically significant between-group difference in emotion processing, although a suggestion toward better performance in the SSDs vs ASD groups was found (k = 16; g = 0.21 [95% CI, −0.03 to 0.44]; P = .08) (eFigure 1A in the Supplement). Effect sizes were statistically significantly heterogeneous (I² = 71.8%). One impactful outlier was detected using studentized residuals and the leave-one-out (eTable 3 in the Supplement) and combinatorial (Figure 2A) meta-analyses.^31,41 After removal of 1 outlier, the between-group difference remained non–statistically significant and decreased in size (k = 15; g = 0.12 [95% CI, −0.07 to 0.30]; P = .21) (Figure 3A),^{28,34,46,48,51,52,54,57,60,63,67,68,69,72,73} and heterogeneity was reduced (I² = 51.0%). Funnel plot visualization (eFigure 2A in the Supplement) and Egger regression test revealed no evidence of publication bias (Table 2).

Figure 2. — GOSH plots were generated by performing separate meta-analyses on all possible subsets of included studies (combinatorial meta-analyses) for emotion processing (A), theory of mind (B), and the Reading the Mind in the Eyes Test (C). Each plot shows overall standardized mean difference (Hedges g) and between-study heterogeneity (I² statistic) for each of the meta-analyses performed to enable visualization of patterns between effect sizes and heterogeneity. The data points for meta-analyses that included studies detected as outliers are shown in red (with larger effect sizes and higher heterogeneity), and all others are shown in blue. Hedges g values exceeding 0 indicate that SSDs groups outperformed ASD groups. Greater I² statistics represent higher between-study heterogeneity.

Figure 3. — After outlier removal (where detected), forest plots for meta-analyses show standardized mean difference (Hedges g) and 95% CIs for emotion processing (A),^{28,34,46,48,51,52,54,57,60,63,67,68,69,72,73} theory of mind (B),^{28,34,46,48,51,52,54,57,60,63,67,68,69,72,73} and the Reading the Mind in the Eyes Test (C).^{32,48,49,51,55,56,59,62,65,66,69,70,72} Hedges g values exceeding 0 indicate that SSDs groups outperformed ASD groups. Square size is proportional to study weight in the model. The diamond signifies the overall effect size and its 95% CI.

Moderator analyses demonstrated no statistically significant associations of age difference (z = −0.27; P = .79) or publication year (z = −1.58; P = .11) with emotion processing effect sizes (k = 15; P = .26; R² = 8.96%). Exploratory moderator analyses also revealed no statistically significant association of quality assessment scores (k = 15; z = 0.05; P = .96; R² = 0%) or antipsychotic medication use (k = 11; z = 0.50; P = .61; R² = 0%). The overall emotion processing effect size remained non–statistically significant in sensitivity analyses (Table 2 and eResults in the Supplement).

Theory of Mind

There was no statistically significant between-group difference in ToM performance (k = 18; g = −0.13 [95% CI, −0.45 to 0.18]; P = .42) (eFigure 1B in the Supplement). Effect sizes were highly heterogeneous (I² = 83.0%), and 1 impactful outlier was identified (Figure 2B and eTable 4 in the Supplement).⁴⁶ The between-group difference remained non–statistically significant after removal of 1 outlier (k = 17; g = −0.01 [95% CI, −0.21 to 0.19]; P = .92) (Figure 3B),^{28,34,46,48,51,52,54,57,60,63,67,68,69,72,73} and heterogeneity decreased but remained statistically significant (I² = 56.5%). There was no evidence of publication bias after outlier removal based on funnel plot inspection (eFigure 2B in the Supplement) and Egger regression test (Table 2).

There were no statistically significant associations of age difference (z = 0.59; P = .55) or publication year (z = −0.16; P = .88) with ToM effect sizes (k = 17; P = .84; R² = 0%). Exploratory meta-regressions also revealed no statistically significant association of quality assessment scores (k = 17; z = 0.22; P = .83; R² = 0%) or stimulus type (k = 14; z = −0.54; P = .59; R² = 0%). The between-group difference in the proportion of participants taking antipsychotic medication was statistically significantly associated with ToM effect sizes, accounting for a substantial amount of heterogeneity (k = 8; z = −1.99; P = .047; R² = 46.40%) (eFigure 3 in the Supplement). As the proportion of individuals with SSDs vs ASD taking antipsychotic medication increased, ToM performance of the SSDs vs ASD groups worsened. The overall ToM effect size remained non–statistically significant in sensitivity analyses (Table 2 and eResults in the Supplement).

Reading the Mind in the Eyes Test

The RMET meta-analysis revealed no statistically significant difference in performance between groups, although SSDs groups performed slightly better than ASD groups (k = 13; g = 0.25 [95% CI, −0.04 to 0.53]; P = .10) (Figure 3C).^{32,48,49,51,55,56,59,62,65,66,69,70,72} Effect sizes were statistically significantly and highly heterogeneous (I² = 75.3%), although no outliers were identified (Figure 2C and eTable 5 in the Supplement). Funnel plot visualization (eFigure 2C in the Supplement) and Egger regression test revealed no evidence of publication bias (Table 2).

Moderator analyses yielded no statistically significant associations of age difference (z = 0.93; P = .35) or publication year (z = 1.36; P = .17) with RMET performance (k = 13; P = .25; R² = 3.60%). Exploratory meta-regressions also revealed no statistically significant association of quality assessment scores (k = 13; z = 1.60; P = .11; R² = 8.55%) or antipsychotic medication use (k = 7; z = 1.11; P = .27; R² = 0.33%). The overall RMET effect size remained non–statistically significant in sensitivity analyses (Table 2 and eResults in the Supplement).

Discussion

This systematic review and meta-analysis included studies that directly compared individuals with SSDs vs ASD on performance-based social cognitive measures to identify overlapping and divergent deficits. Findings herein suggest that similar levels of social cognitive impairment may be present in SSDs and ASD across emotion processing and ToM domains. However, heterogeneity of effect sizes was apparent and was only minimally explained by the moderators explored. Included studies highlighted the prevalence of small, male-predominant samples and a paucity of cross-disorder clinical measures.

Across the main meta-analyses comparing emotion processing, ToM, and RMET performance in individuals with SSDs vs ASD, no statistically significant group differences were identified. This finding builds on previous meta-analyses showing similar impairment levels in those with SSDs and ASD vs typically developing individuals on ToM tasks^18,19 and the RMET,¹⁸ providing further evidence based on direct SSDs vs ASD comparisons. The ToM and RMET findings herein align with results from the only other meta-analysis²⁰ to date examining direct SSDs vs ASD comparisons; the present study provides additional support for minimal differences among individuals with SSDs vs ASD and includes 9 and 5 additional studies, respectively. The previous meta-analysis²⁰ found that participants with SSDs outperformed those with ASD on emotion processing tasks; however, heterogeneity was high, only 8 studies were included, and assessment of impactful studies was not reported. With twice the number of studies, we found no statistically significant group differences in emotion processing performance. Results herein suggest that a consistent pattern of differences in social cognitive performance between SSDs and ASD is not apparent across studies to date. However, many sample sizes were small, and the moderate to high levels of effect size heterogeneity indicate that differences in social cognitive performance between SSDs and ASD varied among studies, warranting further investigation. This heterogeneity represents variability in SSDs vs ASD performance differences rather than within-disorder performance variability.

Age difference between SSDs vs ASD and publication year (partially reflecting changes in clinical practices and diagnostic criteria over time) did not explain between-study variability observed herein in emotion processing, ToM, or RMET effect sizes. The previous meta-analysis²⁰ found better emotion processing and RMET performance in SSDs vs ASD among studies with younger participants across groups. It may be that age difference is less relevant than whether participants are generally younger or older across diagnoses because of relative differences in neurodevelopment and pathoplastic progression (eg, earlier emerging challenges in ASD).⁶ The exploratory moderator analyses herein revealed that a greater proportion of people with SSDs vs ASD taking antipsychotic medication was associated with poorer ToM performance in SSDs vs ASD, accounting for a moderate amount of between-study heterogeneity. This finding may suggest that antipsychotic medication use negatively impacts ToM performance or could reflect worse performance in those receiving antipsychotic treatment potentially because of greater illness severity. Although there is some meta-analytic evidence for moderating associations of antipsychotic medication use with emotion processing performance in SSDs,¹⁵ other meta-regressions have found no association between antipsychotic medication use in SSDs and emotion processing, ToM, or RMET performance.^18,75 Additional work that includes complete reporting of medication use status is needed to investigate how antipsychotic treatment and other medication use impact social cognition across SSDs and ASD.

Both SSDs and ASD are highly heterogeneous disorders, and differences in nonsocial cognition, clinical symptoms, and everyday functioning may also have contributed to between-study variability observed herein. However, reporting of such metrics was uncommon across included studies, and measures used to assess these domains were inconsistent both within and between groups. Social cognition has been consistently positively associated with nonsocial cognition and functional outcome in SSDs³ and ASD⁴ and inversely associated with negative symptoms in SSDs.^45,76 Meta-regressions^15,75 have demonstrated a negative association between emotion processing performance and negative symptoms in SSDs. Illness duration may also be an important factor. Studies that concurrently include assessments tapping these variables in both SSDs and ASD are necessary to elucidate their association and facilitate transdiagnostic comparisons.

Of the studies reviewed, those with neuroimaging alongside social cognitive measures had mixed findings and tended to include small samples and a single imaging modality. Neurobiological investigations into shared or distinct mechanisms underlying the common social cognitive deficits across SSDs and ASD are warranted. For example, hypermentalizing or hypomentalizing at a cognitive level or opposing patterns of brain activation or connectivity could both lead to impaired ToM performance,⁵⁰ aligning with a diametrical hypothesis of SSDs and ASD.⁵ However, the degree of between-study heterogeneity may also align with evidence suggesting that subgroups with overlapping brain-behavior associations may exist transdiagnostically,⁶⁰ reflecting multiple etiological pathways. Future work is needed to directly test these and alternative theories.⁷ Integrating data across multiple levels of analysis through approaches like network theory⁷⁷ will provide additional insight into factors contributing to the emergence of psychiatric disorders, those associated with social cognition, and potential treatment targets.⁷⁸

Limitations

This study has some limitations. Few of the reviewed studies focused on adolescents, limiting generalizability of these findings. This limitation is particularly important given the different developmental trajectories of SSDs and ASD and of lower-level vs higher-level social cognition.⁶ Future work that includes people with co-occurring diagnoses of SSDs and ASD could be informative from a transdiagnostic perspective given the high prevalence of shared traits⁷⁹ and co-occurrence of these conditions.^80,81 Indeed, dimensional analyses of shared symptoms are becoming more common in investigations of the overlap between SSDs and ASD.^82,83,84 Many existing social cognitive tasks may also lack discriminatory power between SSDs and ASD. Finally, although most studies reviewed herein implemented at least some practices to limit bias, a standardized approach would have improved opportunities for comparisons across studies.

Conclusions

Based on meta-analyses of the extant literature, similar levels of social cognitive impairment may be present in SSDs and ASD across emotion processing and ToM. These results highlight the need for cross-disorder studies of social cognition with larger samples, including adolescents, and consistent reporting of measures that may impact outcome. Integrating data spanning multiple levels of analysis across SSDs and ASD is a critical next step to identify associations that may delineate more homogeneous subgroups with similar etiology, treatment response, and phenotypic characteristics.

Supplement.

eMethods. Supplemental Methods

eTable 1. Modified Newcastle-Ottawa Scale for Quality Assessment Legend

eTable 2. Additional Information Provided by Contacted Authors

eTable 3. Emotion Processing Leave-One-Out Meta-analyses

eTable 4. Theory of Mind Leave-One-Out Meta-analyses

eTable 5. Reading the Mind in the Eyes Test Leave-One-Out Meta-analyses

eFigure 1. Forest Plots of Effect Sizes for Meta-analyses Prior to Outlier Removal

eFigure 2. Funnel Plots for Each Meta-analysis

eFigure 3. Theory of Mind Meta-Regression Scatterplot With Difference in Proportion of Participants on Antipsychotics as Moderator

eResults. Supplemental Results

eReferences.

Click here for additional data file.^{(857.2KB, pdf)}

References

1.American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 5th ed. American Psychiatric Association; 2013. [Google Scholar]
2.Green MF, Horan WP, Lee J. Social cognition in schizophrenia. Nat Rev Neurosci. 2015;16(10):620-631. doi: 10.1038/nrn4005 [DOI] [PubMed] [Google Scholar]
3.Halverson TF, Orleans-Pobee M, Merritt C, Sheeran P, Fett AK, Penn DL. Pathways to functional outcomes in schizophrenia spectrum disorders: meta-analysis of social cognitive and neurocognitive predictors. Neurosci Biobehav Rev. 2019;105:212-219. doi: 10.1016/j.neubiorev.2019.07.020 [DOI] [PubMed] [Google Scholar]
4.Sasson NJ, Morrison KE, Kelsven S, Pinkham AE. Social cognition as a predictor of functional and social skills in autistic adults without intellectual disability. Autism Res. 2020;13(2):259-270. doi: 10.1002/aur.2195 [DOI] [PubMed] [Google Scholar]
5.Crespi B, Badcock C. Psychosis and autism as diametrical disorders of the social brain. Behav Brain Sci. 2008;31(3):241-261. doi: 10.1017/S0140525X08004214 [DOI] [PubMed] [Google Scholar]
6.Sasson NJ, Pinkham AE, Carpenter KL, Belger A. The benefit of directly comparing autism and schizophrenia for revealing mechanisms of social cognitive impairment. J Neurodev Disord. 2011;3(2):87-100. doi: 10.1007/s11689-010-9068-x [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Chisholm K, Lin A, Abu-Akel A, Wood SJ. The association between autism and schizophrenia spectrum disorders: a review of eight alternate models of co-occurrence. Neurosci Biobehav Rev. 2015;55:173-183. doi: 10.1016/j.neubiorev.2015.04.012 [DOI] [PubMed] [Google Scholar]
8.Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13(1):28-35. doi: 10.1002/wps.20087 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Insel T, Cuthbert B, Garvey M, et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167(7):748-751. doi: 10.1176/appi.ajp.2010.09091379 [DOI] [PubMed] [Google Scholar]
10.Fleischhacker WW, Arango C, Arteel P, et al. Schizophrenia: time to commit to policy change. Schizophr Bull. 2014;40(suppl 3):S165-S194. doi: 10.1093/schbul/sbu006 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Warren Z, Taylor JL, McPheeters ML, et al. Future research needs: interventions for adolescents and young adults with autism spectrum disorders: identification of future research needs from comparative effectiveness review No. 65. Agency for Healthcare Research and Quality; September 2012. Future Research Needs Papers No. 20. Accessed May 13, 2020. https://www.ncbi.nlm.nih.gov/books/NBK121979/ [PubMed]
12.Ochsner KN. The social-emotional processing stream: five core constructs and their translational potential for schizophrenia and beyond. Biol Psychiatry. 2008;64(1):48-61. doi: 10.1016/j.biopsych.2008.04.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Shamay-Tsoory SG. The neural bases for empathy. Neuroscientist. 2011;17(1):18-24. doi: 10.1177/1073858410379268 [DOI] [PubMed] [Google Scholar]
14.Savla GN, Vella L, Armstrong CC, Penn DL, Twamley EW. Deficits in domains of social cognition in schizophrenia: a meta-analysis of the empirical evidence. Schizophr Bull. 2013;39(5):979-992. doi: 10.1093/schbul/sbs080 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Kohler CG, Walker JB, Martin EA, Healey KM, Moberg PJ. Facial emotion perception in schizophrenia: a meta-analytic review. Schizophr Bull. 2010;36(5):1009-1019. doi: 10.1093/schbul/sbn192 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Velikonja T, Fett AK, Velthorst E. Patterns of nonsocial and social cognitive functioning in adults with autism spectrum disorder: a systematic review and meta-analysis. JAMA Psychiatry. 2019;76(2):135-151. doi: 10.1001/jamapsychiatry.2018.3645 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Uljarevic M, Hamilton A. Recognition of emotions in autism: a formal meta-analysis. J Autism Dev Disord. 2013;43(7):1517-1526. doi: 10.1007/s10803-012-1695-5 [DOI] [PubMed] [Google Scholar]
18.Chung YS, Barch D, Strube M. A meta-analysis of mentalizing impairments in adults with schizophrenia and autism spectrum disorder. Schizophr Bull. 2014;40(3):602-616. doi: 10.1093/schbul/sbt048 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Bliksted V, Ubukata S, Koelkebeck K. Discriminating autism spectrum disorders from schizophrenia by investigation of mental state attribution on an on-line mentalizing task: a review and meta-analysis. Schizophr Res. 2016;171(1-3):16-26. doi: 10.1016/j.schres.2016.01.037 [DOI] [PubMed] [Google Scholar]
20.Fernandes JM, Cajão R, Lopes R, Jerónimo R, Barahona-Corrêa JB. Social cognition in schizophrenia and autism spectrum disorders: a systematic review and meta-analysis of direct comparisons. Front Psychiatry. 2018;9:504. doi: 10.3389/fpsyt.2018.00504 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group . Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting: Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group. JAMA. 2000;283(15):2008-2012. doi: 10.1001/jama.283.15.2008 [DOI] [PubMed] [Google Scholar]
23.Cotter J, Granger K, Backx R, Hobbs M, Looi CY, Barnett JH. Social cognitive dysfunction as a clinical marker: a systematic review of meta-analyses across 30 clinical conditions. Neurosci Biobehav Rev. 2018;84:92-99. doi: 10.1016/j.neubiorev.2017.11.014 [DOI] [PubMed] [Google Scholar]
24.Sugranyes G, Kyriakopoulos M, Corrigall R, Taylor E, Frangou S. Autism spectrum disorders and schizophrenia: meta-analysis of the neural correlates of social cognition. PLoS One. 2011;6(10):e25322. doi: 10.1371/journal.pone.0025322 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.World Health Organization . International Statistical Classification of Diseases, Tenth Revision (ICD-10). World Health Organization; 1992. [Google Scholar]
26.American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 4th ed. American Psychiatric Association; 1994. [Google Scholar]
27.Ozguven HD, Oner O, Baskak B, Oktem F, Olmez S, Munir K. Theory of mind in schizophrenia and Asperger’s syndrome: relationship with negative symptoms. Klinik Psikofarmakol Bulteni. 2010;20(1):5-13. doi: 10.1080/10177833.2010.11790628 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Hirata K, Egashira K, Harada K, et al. Differences in frontotemporal dysfunction during social and non-social cognition tasks between patients with autism spectrum disorder and schizophrenia. Sci Rep. 2018;8(1):3014. doi: 10.1038/s41598-018-21379-w [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Veddum L, Pedersen HL, Landert AL, Bliksted V. Do patients with high-functioning autism have similar social cognitive deficits as patients with a chronic cause of schizophrenia? Nord J Psychiatry. 2019;73(1):44-50. doi: 10.1080/08039488.2018.1554697 [DOI] [PubMed] [Google Scholar]
30.Pinkham AE, Hopfinger JB, Pelphrey KA, Piven J, Penn DL. Neural bases for impaired social cognition in schizophrenia and autism spectrum disorders. Schizophr Res. 2008;99(1-3):164-175. doi: 10.1016/j.schres.2007.10.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Bölte S, Poustka F. The recognition of facial affect in autistic and schizophrenic subjects and their first-degree relatives. Psychol Med. 2003;33(5):907-915. doi: 10.1017/S0033291703007438 [DOI] [PubMed] [Google Scholar]
32.Murphy D. Theory of mind in Asperger’s syndrome, schizophrenia and personality disordered forensic patients. Cogn Neuropsychiatry. 2006;11(2):99-111. doi: 10.1080/13546800444000182 [DOI] [PubMed] [Google Scholar]
33.Pilowsky T, Yirmiya N, Arbelle S, Mozes T. Theory of mind abilities of children with schizophrenia, children with autism, and normally developing children. Schizophr Res. 2000;42(2):145-155. doi: 10.1016/S0920-9964(99)00101-2 [DOI] [PubMed] [Google Scholar]
34.Tobe RH, Corcoran CM, Breland M, et al. Differential profiles in auditory social cognition deficits between adults with autism and schizophrenia spectrum disorders: a preliminary analysis. J Psychiatr Res. 2016;79:21-27. doi: 10.1016/j.jpsychires.2016.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Wells G, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Accessed April 2019. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
36.Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Software. 2010;36(3):1-48. doi: 10.18637/jss.v036.i03 [DOI] [Google Scholar]
37.Hedges LV, Olkin I. Statistical Methods for Meta-Analysis. Academic Press; 1985. [Google Scholar]
38.Viechtbauer W. Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Stat. 2005;30(3):261-293. doi: 10.3102/10769986030003261 [DOI] [Google Scholar]
39.Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557-560. doi: 10.1136/bmj.327.7414.557 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Viechtbauer W, Cheung MW. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112-125. doi: 10.1002/jrsm.11 [DOI] [PubMed] [Google Scholar]
41.Olkin I, Dahabreh IJ, Trikalinos TA. GOSH: a graphical display of study heterogeneity. Res Synth Methods. 2012;3(3):214-223. doi: 10.1002/jrsm.1053 [DOI] [PubMed] [Google Scholar]
42.Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-634. doi: 10.1136/bmj.315.7109.629 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.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):241-251. doi: 10.1111/1469-7610.00715 [DOI] [PubMed] [Google Scholar]
44.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):818-823. doi: 10.1037/abn0000182 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Oliver LD, Haltigan JD, Gold JM, et al. Lower- and higher-level social cognitive factors across individuals with schizophrenia spectrum disorders and healthy controls: relationship with neurocognition and functional outcome. Schizophr Bull. 2019;45(3):629-638. doi: 10.1093/schbul/sby114 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Waris P, Tani P, Lindberg N, et al. Are there differences in neurocognition and social cognition among adolescents with schizophrenia, a pervasive developmental disorder, and both disorders? Appl Neuropsychol Child. 2016;5(4):303-310. doi: 10.1080/21622965.2015.1064001 [DOI] [PubMed] [Google Scholar]
47.Tin LNW, Lui SSY, Ho KKY, et al. High-functioning autism patients share similar but more severe impairments in verbal theory of mind than schizophrenia patients. Psychol Med. 2018;48(8):1264-1273. doi: 10.1017/S0033291717002690 [DOI] [PubMed] [Google Scholar]
48.Pepper KL, Demetriou EA, Park SH, et al. Autism, early psychosis, and social anxiety disorder: understanding the role of social cognition and its relationship to disability in young adults with disorders characterized by social impairments. Transl Psychiatry. 2018;8(1):233. doi: 10.1038/s41398-018-0282-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Booules-Katri TM, Pedreño C, Navarro JB, Pamias M, Obiols JE. Theory of mind (ToM) performance in high functioning autism (HFA) and schizotypal-schizoid personality disorders (SSPD) patients. J Autism Dev Disord. 2019;49(8):3376-3386. doi: 10.1007/s10803-019-04058-1 [DOI] [PubMed] [Google Scholar]
50.Ciaramidaro A, Bölte S, Schlitt S, et al. Schizophrenia and autism as contrasting minds: neural evidence for the hypo-hyper-intentionality hypothesis. Schizophr Bull. 2015;41(1):171-179. doi: 10.1093/schbul/sbu124 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Sachse M, Schlitt S, Hainz D, et al. Facial emotion recognition in paranoid schizophrenia and autism spectrum disorder. Schizophr Res. 2014;159(2-3):509-514. doi: 10.1016/j.schres.2014.08.030 [DOI] [PubMed] [Google Scholar]
52.Sasson N, Tsuchiya N, Hurley R, et al. Orienting to social stimuli differentiates social cognitive impairment in autism and schizophrenia. Neuropsychologia. 2007;45(11):2580-2588. doi: 10.1016/j.neuropsychologia.2007.03.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Stanfield AC, Philip RCM, Whalley H, et al. Dissociation of brain activation in autism and schizotypal personality disorder during social judgments. Schizophr Bull. 2017;43(6):1220-1228. doi: 10.1093/schbul/sbx083 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Ciaramidaro A, Bölte S, Schlitt S, et al. Transdiagnostic deviant facial recognition for implicit negative emotion in autism and schizophrenia. Eur Neuropsychopharmacol. 2018;28(2):264-275. doi: 10.1016/j.euroneuro.2017.12.005 [DOI] [PubMed] [Google Scholar]
55.Radeloff D, Ciaramidaro A, Siniatchkin M, et al. Structural alterations of the social brain: a comparison between schizophrenia and autism. PLoS One. 2014;9(9):e106539. doi: 10.1371/journal.pone.0106539 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Schwarz K, Moessnang C, Schweiger JI, et al. Transdiagnostic prediction of affective, cognitive, and social function through brain reward anticipation in schizophrenia, bipolar disorder, major depression, and autism spectrum diagnoses. Schizophr Bull. 2020;46(3):592-602. doi: 10.1093/schbul/sbz075 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Martínez A, Tobe R, Dias EC, et al. Differential patterns of visual sensory alteration underlying face emotion recognition impairment and motion perception deficits in schizophrenia and autism spectrum disorder. Biol Psychiatry. 2019;86(7):557-567. doi: 10.1016/j.biopsych.2019.05.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Morrison KE, Pinkham AE, Penn DL, Kelsven S, Ludwig K, Sasson NJ. Distinct profiles of social skill in adults with autism spectrum disorder and schizophrenia. Autism Res. 2017;10(5):878-887. doi: 10.1002/aur.1734 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Krawczyk DC, Kandalaft MR, Didehbani N, et al. An investigation of reasoning by analogy in schizophrenia and autism spectrum disorder. Front Hum Neurosci. 2014;8:517. doi: 10.3389/fnhum.2014.00517 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Stefanik L, Erdman L, Ameis SH, et al. Brain-behavior participant similarity networks among youth and emerging adults with schizophrenia spectrum, autism spectrum, or bipolar disorder and matched controls. Neuropsychopharmacology. 2018;43(5):1180-1188. doi: 10.1038/npp.2017.274 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Martinez G, Alexandre C, Mam-Lam-Fook C, et al. Phenotypic continuum between autism and schizophrenia: evidence from the Movie for the Assessment of Social Cognition (MASC). Schizophr Res. 2017;185:161-166. doi: 10.1016/j.schres.2017.01.012 [DOI] [PubMed] [Google Scholar]
62.Hyatt CJ, Calhoun VD, Pittman B, et al. Default mode network modulation by mentalizing in young adults with autism spectrum disorder or schizophrenia. Neuroimage Clin. 2020;27:102343. doi: 10.1016/j.nicl.2020.102343 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Kuo SS, Wojtalik JA, Mesholam-Gately RI, Keshavan MS, Eack SM. Establishing a standard emotion processing battery for treatment evaluation in adults with autism spectrum disorder: evidence supporting the Mayer-Salovey-Caruso Emotion Intelligence Test (MSCEIT). Psychiatry Res. 2019;278:116-124. doi: 10.1016/j.psychres.2019.05.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Boada L, Lahera G, Pina-Camacho L, et al. Social cognition in autism and schizophrenia spectrum disorders: the same but different? J Autism Dev Disord. 2020;50(8):3046-3059. doi: 10.1007/s10803-020-04408-4 [DOI] [PubMed] [Google Scholar]
65.Couture SM, Penn DL, Losh M, Adolphs R, Hurley R, Piven J. Comparison of social cognitive functioning in schizophrenia and high functioning autism: more convergence than divergence. Psychol Med. 2010;40(4):569-579. doi: 10.1017/S003329170999078X [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Craig JS, Hatton C, Craig FB, Bentall RP. Persecutory beliefs, attributions and theory of mind: comparison of patients with paranoid delusions, Asperger’s syndrome and healthy controls. Schizophr Res. 2004;69(1):29-33. doi: 10.1016/S0920-9964(03)00154-3 [DOI] [PubMed] [Google Scholar]
67.Eack SM, Bahorik AL, McKnight SA, et al. Commonalities in social and non-social cognitive impairments in adults with autism spectrum disorder and schizophrenia. Schizophr Res. 2013;148(1-3):24-28. doi: 10.1016/j.schres.2013.05.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Graux J, Thillay A, Morlec V, et al. A transnosographic Self-Assessment of Social Cognitive Impairments (ACSo): first data. Front Psychiatry. 2019;10:847. doi: 10.3389/fpsyt.2019.00847 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Kandalaft MR, Didehbani N, Cullum CM, et al. The Wechsler ACS social perception subtest: a preliminary comparison with other measures of social cognition. J Psychoeduc Assess. 2012;30(5):455-465. doi: 10.1177/0734282912436411 [DOI] [Google Scholar]
70.Lugnegård T, Unenge Hallerbäck M, Hjärthag F, Gillberg C. Social cognition impairments in Asperger syndrome and schizophrenia. Schizophr Res. 2013;143(2-3):277-284. doi: 10.1016/j.schres.2012.12.001 [DOI] [PubMed] [Google Scholar]
71.Martinez G, Mosconi E, Daban-Huard C, et al. “A circle and a triangle dancing together”: alteration of social cognition in schizophrenia compared to autism spectrum disorders. Schizophr Res. 2019;210:94-100. doi: 10.1016/j.schres.2019.05.043 [DOI] [PubMed] [Google Scholar]
72.Pinkham AE, Morrison KE, Penn DL, et al. Comprehensive comparison of social cognitive performance in autism spectrum disorder and schizophrenia. Psychol Med. 2019;1-9. doi: 10.1017/S0033291719002708 [DOI] [PubMed] [Google Scholar]
73.Sasson NJ, Pinkham AE, Weittenhiller LP, Faso DJ, Simpson C. Context effects on facial affect recognition in schizophrenia and autism: behavioral and eye-tracking evidence. Schizophr Bull. 2016;42(3):675-683. doi: 10.1093/schbul/sbv176 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Mehta UM, Thirthalli J, Subbakrishna DK, Gangadhar BN, Eack SM, Keshavan MS. Social and neuro-cognition as distinct cognitive factors in schizophrenia: a systematic review. Schizophr Res. 2013;148(1-3):3-11. doi: 10.1016/j.schres.2013.05.009 [DOI] [PubMed] [Google Scholar]
75.Chan RC, Li H, Cheung EF, Gong QY. Impaired facial emotion perception in schizophrenia: a meta-analysis. Psychiatry Res. 2010;178(2):381-390. doi: 10.1016/j.psychres.2009.03.035 [DOI] [PubMed] [Google Scholar]
76.Pelletier-Baldelli A, Holt DJ. Are negative symptoms merely the “real world” consequences of deficits in social cognition? Schizophr Bull. 2020;46(2):236-241. doi: 10.1093/schbul/sbz095 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Borsboom D. A network theory of mental disorders. World Psychiatry. 2017;16(1):5-13. doi: 10.1002/wps.20375 [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Galderisi S, Rucci P, Mucci A, et al. ; Italian Network for Research on Psychoses . The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients. World Psychiatry. 2020;19(1):81-91. doi: 10.1002/wps.20700 [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Zhou HY, Yang HX, Gong JB, et al. Revisiting the overlap between autistic and schizotypal traits in the non-clinical population using meta-analysis and network analysis. Schizophr Res. 2019;212:6-14. doi: 10.1016/j.schres.2019.07.050 [DOI] [PubMed] [Google Scholar]
80.Zheng Z, Zheng P, Zou X. Association between schizophrenia and autism spectrum disorder: a systematic review and meta-analysis. Autism Res. 2018;11(8):1110-1119. doi: 10.1002/aur.1977 [DOI] [PubMed] [Google Scholar]
81.Lai MC, Kassee C, Besney R, et al. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. Lancet Psychiatry. 2019;6(10):819-829. doi: 10.1016/S2215-0366(19)30289-5 [DOI] [PubMed] [Google Scholar]
82.Trevisan DA, Foss-Feig JH, Naples AJ, Srihari V, Anticevic A, McPartland JC. Autism spectrum disorder and schizophrenia are better differentiated by positive symptoms than negative symptoms. Front Psychiatry. 2020;11:548. doi: 10.3389/fpsyt.2020.00548 [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Ziermans TB, Schirmbeck F, Oosterwijk F, Geurts HM, de Haan L; Genetic Risk and Outcome of Psychosis (GROUP) Investigators . Autistic traits in psychotic disorders: prevalence, familial risk, and impact on social functioning. Psychol Med. 2020;1-10. doi: 10.1017/S0033291720000458 [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Larson FV, Wagner AP, Chisholm K, Reniers RLEP, Wood SJ. Adding a dimension to the dichotomy: affective processes are implicated in the relationship between autistic and schizotypal traits. Front Psychiatry. 2020;11:712. doi: 10.3389/fpsyt.2020.00712 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eMethods. Supplemental Methods

eTable 1. Modified Newcastle-Ottawa Scale for Quality Assessment Legend

eTable 2. Additional Information Provided by Contacted Authors

eTable 3. Emotion Processing Leave-One-Out Meta-analyses

eTable 4. Theory of Mind Leave-One-Out Meta-analyses

eTable 5. Reading the Mind in the Eyes Test Leave-One-Out Meta-analyses

eFigure 1. Forest Plots of Effect Sizes for Meta-analyses Prior to Outlier Removal

eFigure 2. Funnel Plots for Each Meta-analysis

eFigure 3. Theory of Mind Meta-Regression Scatterplot With Difference in Proportion of Participants on Antipsychotics as Moderator

eResults. Supplemental Results

eReferences.

Click here for additional data file.^{(857.2KB, pdf)}

[yoi200072r1] 1.American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 5th ed. American Psychiatric Association; 2013. [Google Scholar]

[yoi200072r2] 2.Green MF, Horan WP, Lee J. Social cognition in schizophrenia. Nat Rev Neurosci. 2015;16(10):620-631. doi: 10.1038/nrn4005 [DOI] [PubMed] [Google Scholar]

[yoi200072r3] 3.Halverson TF, Orleans-Pobee M, Merritt C, Sheeran P, Fett AK, Penn DL. Pathways to functional outcomes in schizophrenia spectrum disorders: meta-analysis of social cognitive and neurocognitive predictors. Neurosci Biobehav Rev. 2019;105:212-219. doi: 10.1016/j.neubiorev.2019.07.020 [DOI] [PubMed] [Google Scholar]

[yoi200072r4] 4.Sasson NJ, Morrison KE, Kelsven S, Pinkham AE. Social cognition as a predictor of functional and social skills in autistic adults without intellectual disability. Autism Res. 2020;13(2):259-270. doi: 10.1002/aur.2195 [DOI] [PubMed] [Google Scholar]

[yoi200072r5] 5.Crespi B, Badcock C. Psychosis and autism as diametrical disorders of the social brain. Behav Brain Sci. 2008;31(3):241-261. doi: 10.1017/S0140525X08004214 [DOI] [PubMed] [Google Scholar]

[yoi200072r6] 6.Sasson NJ, Pinkham AE, Carpenter KL, Belger A. The benefit of directly comparing autism and schizophrenia for revealing mechanisms of social cognitive impairment. J Neurodev Disord. 2011;3(2):87-100. doi: 10.1007/s11689-010-9068-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r7] 7.Chisholm K, Lin A, Abu-Akel A, Wood SJ. The association between autism and schizophrenia spectrum disorders: a review of eight alternate models of co-occurrence. Neurosci Biobehav Rev. 2015;55:173-183. doi: 10.1016/j.neubiorev.2015.04.012 [DOI] [PubMed] [Google Scholar]

[yoi200072r8] 8.Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13(1):28-35. doi: 10.1002/wps.20087 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r9] 9.Insel T, Cuthbert B, Garvey M, et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167(7):748-751. doi: 10.1176/appi.ajp.2010.09091379 [DOI] [PubMed] [Google Scholar]

[yoi200072r10] 10.Fleischhacker WW, Arango C, Arteel P, et al. Schizophrenia: time to commit to policy change. Schizophr Bull. 2014;40(suppl 3):S165-S194. doi: 10.1093/schbul/sbu006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r11] 11.Warren Z, Taylor JL, McPheeters ML, et al. Future research needs: interventions for adolescents and young adults with autism spectrum disorders: identification of future research needs from comparative effectiveness review No. 65. Agency for Healthcare Research and Quality; September 2012. Future Research Needs Papers No. 20. Accessed May 13, 2020. https://www.ncbi.nlm.nih.gov/books/NBK121979/ [PubMed]

[yoi200072r12] 12.Ochsner KN. The social-emotional processing stream: five core constructs and their translational potential for schizophrenia and beyond. Biol Psychiatry. 2008;64(1):48-61. doi: 10.1016/j.biopsych.2008.04.024 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r13] 13.Shamay-Tsoory SG. The neural bases for empathy. Neuroscientist. 2011;17(1):18-24. doi: 10.1177/1073858410379268 [DOI] [PubMed] [Google Scholar]

[yoi200072r14] 14.Savla GN, Vella L, Armstrong CC, Penn DL, Twamley EW. Deficits in domains of social cognition in schizophrenia: a meta-analysis of the empirical evidence. Schizophr Bull. 2013;39(5):979-992. doi: 10.1093/schbul/sbs080 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r15] 15.Kohler CG, Walker JB, Martin EA, Healey KM, Moberg PJ. Facial emotion perception in schizophrenia: a meta-analytic review. Schizophr Bull. 2010;36(5):1009-1019. doi: 10.1093/schbul/sbn192 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r16] 16.Velikonja T, Fett AK, Velthorst E. Patterns of nonsocial and social cognitive functioning in adults with autism spectrum disorder: a systematic review and meta-analysis. JAMA Psychiatry. 2019;76(2):135-151. doi: 10.1001/jamapsychiatry.2018.3645 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r17] 17.Uljarevic M, Hamilton A. Recognition of emotions in autism: a formal meta-analysis. J Autism Dev Disord. 2013;43(7):1517-1526. doi: 10.1007/s10803-012-1695-5 [DOI] [PubMed] [Google Scholar]

[yoi200072r18] 18.Chung YS, Barch D, Strube M. A meta-analysis of mentalizing impairments in adults with schizophrenia and autism spectrum disorder. Schizophr Bull. 2014;40(3):602-616. doi: 10.1093/schbul/sbt048 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r19] 19.Bliksted V, Ubukata S, Koelkebeck K. Discriminating autism spectrum disorders from schizophrenia by investigation of mental state attribution on an on-line mentalizing task: a review and meta-analysis. Schizophr Res. 2016;171(1-3):16-26. doi: 10.1016/j.schres.2016.01.037 [DOI] [PubMed] [Google Scholar]

[yoi200072r20] 20.Fernandes JM, Cajão R, Lopes R, Jerónimo R, Barahona-Corrêa JB. Social cognition in schizophrenia and autism spectrum disorders: a systematic review and meta-analysis of direct comparisons. Front Psychiatry. 2018;9:504. doi: 10.3389/fpsyt.2018.00504 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r21] 21.Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group . Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r22] 22.Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting: Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group. JAMA. 2000;283(15):2008-2012. doi: 10.1001/jama.283.15.2008 [DOI] [PubMed] [Google Scholar]

[yoi200072r23] 23.Cotter J, Granger K, Backx R, Hobbs M, Looi CY, Barnett JH. Social cognitive dysfunction as a clinical marker: a systematic review of meta-analyses across 30 clinical conditions. Neurosci Biobehav Rev. 2018;84:92-99. doi: 10.1016/j.neubiorev.2017.11.014 [DOI] [PubMed] [Google Scholar]

[yoi200072r24] 24.Sugranyes G, Kyriakopoulos M, Corrigall R, Taylor E, Frangou S. Autism spectrum disorders and schizophrenia: meta-analysis of the neural correlates of social cognition. PLoS One. 2011;6(10):e25322. doi: 10.1371/journal.pone.0025322 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r25] 25.World Health Organization . International Statistical Classification of Diseases, Tenth Revision (ICD-10). World Health Organization; 1992. [Google Scholar]

[yoi200072r26] 26.American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 4th ed. American Psychiatric Association; 1994. [Google Scholar]

[yoi200072r27] 27.Ozguven HD, Oner O, Baskak B, Oktem F, Olmez S, Munir K. Theory of mind in schizophrenia and Asperger’s syndrome: relationship with negative symptoms. Klinik Psikofarmakol Bulteni. 2010;20(1):5-13. doi: 10.1080/10177833.2010.11790628 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r28] 28.Hirata K, Egashira K, Harada K, et al. Differences in frontotemporal dysfunction during social and non-social cognition tasks between patients with autism spectrum disorder and schizophrenia. Sci Rep. 2018;8(1):3014. doi: 10.1038/s41598-018-21379-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r29] 29.Veddum L, Pedersen HL, Landert AL, Bliksted V. Do patients with high-functioning autism have similar social cognitive deficits as patients with a chronic cause of schizophrenia? Nord J Psychiatry. 2019;73(1):44-50. doi: 10.1080/08039488.2018.1554697 [DOI] [PubMed] [Google Scholar]

[yoi200072r30] 30.Pinkham AE, Hopfinger JB, Pelphrey KA, Piven J, Penn DL. Neural bases for impaired social cognition in schizophrenia and autism spectrum disorders. Schizophr Res. 2008;99(1-3):164-175. doi: 10.1016/j.schres.2007.10.024 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r31] 31.Bölte S, Poustka F. The recognition of facial affect in autistic and schizophrenic subjects and their first-degree relatives. Psychol Med. 2003;33(5):907-915. doi: 10.1017/S0033291703007438 [DOI] [PubMed] [Google Scholar]

[yoi200072r32] 32.Murphy D. Theory of mind in Asperger’s syndrome, schizophrenia and personality disordered forensic patients. Cogn Neuropsychiatry. 2006;11(2):99-111. doi: 10.1080/13546800444000182 [DOI] [PubMed] [Google Scholar]

[yoi200072r33] 33.Pilowsky T, Yirmiya N, Arbelle S, Mozes T. Theory of mind abilities of children with schizophrenia, children with autism, and normally developing children. Schizophr Res. 2000;42(2):145-155. doi: 10.1016/S0920-9964(99)00101-2 [DOI] [PubMed] [Google Scholar]

[yoi200072r34] 34.Tobe RH, Corcoran CM, Breland M, et al. Differential profiles in auditory social cognition deficits between adults with autism and schizophrenia spectrum disorders: a preliminary analysis. J Psychiatr Res. 2016;79:21-27. doi: 10.1016/j.jpsychires.2016.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r35] 35.Wells G, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Accessed April 2019. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp

[yoi200072r36] 36.Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Software. 2010;36(3):1-48. doi: 10.18637/jss.v036.i03 [DOI] [Google Scholar]

[yoi200072r37] 37.Hedges LV, Olkin I. Statistical Methods for Meta-Analysis. Academic Press; 1985. [Google Scholar]

[yoi200072r38] 38.Viechtbauer W. Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Stat. 2005;30(3):261-293. doi: 10.3102/10769986030003261 [DOI] [Google Scholar]

[yoi200072r39] 39.Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557-560. doi: 10.1136/bmj.327.7414.557 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r40] 40.Viechtbauer W, Cheung MW. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112-125. doi: 10.1002/jrsm.11 [DOI] [PubMed] [Google Scholar]

[yoi200072r41] 41.Olkin I, Dahabreh IJ, Trikalinos TA. GOSH: a graphical display of study heterogeneity. Res Synth Methods. 2012;3(3):214-223. doi: 10.1002/jrsm.1053 [DOI] [PubMed] [Google Scholar]

[yoi200072r42] 42.Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-634. doi: 10.1136/bmj.315.7109.629 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r43] 43.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):241-251. doi: 10.1111/1469-7610.00715 [DOI] [PubMed] [Google Scholar]

[yoi200072r44] 44.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):818-823. doi: 10.1037/abn0000182 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r45] 45.Oliver LD, Haltigan JD, Gold JM, et al. Lower- and higher-level social cognitive factors across individuals with schizophrenia spectrum disorders and healthy controls: relationship with neurocognition and functional outcome. Schizophr Bull. 2019;45(3):629-638. doi: 10.1093/schbul/sby114 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r46] 46.Waris P, Tani P, Lindberg N, et al. Are there differences in neurocognition and social cognition among adolescents with schizophrenia, a pervasive developmental disorder, and both disorders? Appl Neuropsychol Child. 2016;5(4):303-310. doi: 10.1080/21622965.2015.1064001 [DOI] [PubMed] [Google Scholar]

[yoi200072r47] 47.Tin LNW, Lui SSY, Ho KKY, et al. High-functioning autism patients share similar but more severe impairments in verbal theory of mind than schizophrenia patients. Psychol Med. 2018;48(8):1264-1273. doi: 10.1017/S0033291717002690 [DOI] [PubMed] [Google Scholar]

[yoi200072r48] 48.Pepper KL, Demetriou EA, Park SH, et al. Autism, early psychosis, and social anxiety disorder: understanding the role of social cognition and its relationship to disability in young adults with disorders characterized by social impairments. Transl Psychiatry. 2018;8(1):233. doi: 10.1038/s41398-018-0282-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r49] 49.Booules-Katri TM, Pedreño C, Navarro JB, Pamias M, Obiols JE. Theory of mind (ToM) performance in high functioning autism (HFA) and schizotypal-schizoid personality disorders (SSPD) patients. J Autism Dev Disord. 2019;49(8):3376-3386. doi: 10.1007/s10803-019-04058-1 [DOI] [PubMed] [Google Scholar]

[yoi200072r50] 50.Ciaramidaro A, Bölte S, Schlitt S, et al. Schizophrenia and autism as contrasting minds: neural evidence for the hypo-hyper-intentionality hypothesis. Schizophr Bull. 2015;41(1):171-179. doi: 10.1093/schbul/sbu124 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r51] 51.Sachse M, Schlitt S, Hainz D, et al. Facial emotion recognition in paranoid schizophrenia and autism spectrum disorder. Schizophr Res. 2014;159(2-3):509-514. doi: 10.1016/j.schres.2014.08.030 [DOI] [PubMed] [Google Scholar]

[yoi200072r52] 52.Sasson N, Tsuchiya N, Hurley R, et al. Orienting to social stimuli differentiates social cognitive impairment in autism and schizophrenia. Neuropsychologia. 2007;45(11):2580-2588. doi: 10.1016/j.neuropsychologia.2007.03.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r53] 53.Stanfield AC, Philip RCM, Whalley H, et al. Dissociation of brain activation in autism and schizotypal personality disorder during social judgments. Schizophr Bull. 2017;43(6):1220-1228. doi: 10.1093/schbul/sbx083 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r54] 54.Ciaramidaro A, Bölte S, Schlitt S, et al. Transdiagnostic deviant facial recognition for implicit negative emotion in autism and schizophrenia. Eur Neuropsychopharmacol. 2018;28(2):264-275. doi: 10.1016/j.euroneuro.2017.12.005 [DOI] [PubMed] [Google Scholar]

[yoi200072r55] 55.Radeloff D, Ciaramidaro A, Siniatchkin M, et al. Structural alterations of the social brain: a comparison between schizophrenia and autism. PLoS One. 2014;9(9):e106539. doi: 10.1371/journal.pone.0106539 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r56] 56.Schwarz K, Moessnang C, Schweiger JI, et al. Transdiagnostic prediction of affective, cognitive, and social function through brain reward anticipation in schizophrenia, bipolar disorder, major depression, and autism spectrum diagnoses. Schizophr Bull. 2020;46(3):592-602. doi: 10.1093/schbul/sbz075 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r57] 57.Martínez A, Tobe R, Dias EC, et al. Differential patterns of visual sensory alteration underlying face emotion recognition impairment and motion perception deficits in schizophrenia and autism spectrum disorder. Biol Psychiatry. 2019;86(7):557-567. doi: 10.1016/j.biopsych.2019.05.016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r58] 58.Morrison KE, Pinkham AE, Penn DL, Kelsven S, Ludwig K, Sasson NJ. Distinct profiles of social skill in adults with autism spectrum disorder and schizophrenia. Autism Res. 2017;10(5):878-887. doi: 10.1002/aur.1734 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r59] 59.Krawczyk DC, Kandalaft MR, Didehbani N, et al. An investigation of reasoning by analogy in schizophrenia and autism spectrum disorder. Front Hum Neurosci. 2014;8:517. doi: 10.3389/fnhum.2014.00517 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r60] 60.Stefanik L, Erdman L, Ameis SH, et al. Brain-behavior participant similarity networks among youth and emerging adults with schizophrenia spectrum, autism spectrum, or bipolar disorder and matched controls. Neuropsychopharmacology. 2018;43(5):1180-1188. doi: 10.1038/npp.2017.274 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r61] 61.Martinez G, Alexandre C, Mam-Lam-Fook C, et al. Phenotypic continuum between autism and schizophrenia: evidence from the Movie for the Assessment of Social Cognition (MASC). Schizophr Res. 2017;185:161-166. doi: 10.1016/j.schres.2017.01.012 [DOI] [PubMed] [Google Scholar]

[yoi200072r62] 62.Hyatt CJ, Calhoun VD, Pittman B, et al. Default mode network modulation by mentalizing in young adults with autism spectrum disorder or schizophrenia. Neuroimage Clin. 2020;27:102343. doi: 10.1016/j.nicl.2020.102343 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r63] 63.Kuo SS, Wojtalik JA, Mesholam-Gately RI, Keshavan MS, Eack SM. Establishing a standard emotion processing battery for treatment evaluation in adults with autism spectrum disorder: evidence supporting the Mayer-Salovey-Caruso Emotion Intelligence Test (MSCEIT). Psychiatry Res. 2019;278:116-124. doi: 10.1016/j.psychres.2019.05.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r64] 64.Boada L, Lahera G, Pina-Camacho L, et al. Social cognition in autism and schizophrenia spectrum disorders: the same but different? J Autism Dev Disord. 2020;50(8):3046-3059. doi: 10.1007/s10803-020-04408-4 [DOI] [PubMed] [Google Scholar]

[yoi200072r65] 65.Couture SM, Penn DL, Losh M, Adolphs R, Hurley R, Piven J. Comparison of social cognitive functioning in schizophrenia and high functioning autism: more convergence than divergence. Psychol Med. 2010;40(4):569-579. doi: 10.1017/S003329170999078X [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r66] 66.Craig JS, Hatton C, Craig FB, Bentall RP. Persecutory beliefs, attributions and theory of mind: comparison of patients with paranoid delusions, Asperger’s syndrome and healthy controls. Schizophr Res. 2004;69(1):29-33. doi: 10.1016/S0920-9964(03)00154-3 [DOI] [PubMed] [Google Scholar]

[yoi200072r67] 67.Eack SM, Bahorik AL, McKnight SA, et al. Commonalities in social and non-social cognitive impairments in adults with autism spectrum disorder and schizophrenia. Schizophr Res. 2013;148(1-3):24-28. doi: 10.1016/j.schres.2013.05.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r68] 68.Graux J, Thillay A, Morlec V, et al. A transnosographic Self-Assessment of Social Cognitive Impairments (ACSo): first data. Front Psychiatry. 2019;10:847. doi: 10.3389/fpsyt.2019.00847 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r69] 69.Kandalaft MR, Didehbani N, Cullum CM, et al. The Wechsler ACS social perception subtest: a preliminary comparison with other measures of social cognition. J Psychoeduc Assess. 2012;30(5):455-465. doi: 10.1177/0734282912436411 [DOI] [Google Scholar]

[yoi200072r70] 70.Lugnegård T, Unenge Hallerbäck M, Hjärthag F, Gillberg C. Social cognition impairments in Asperger syndrome and schizophrenia. Schizophr Res. 2013;143(2-3):277-284. doi: 10.1016/j.schres.2012.12.001 [DOI] [PubMed] [Google Scholar]

[yoi200072r71] 71.Martinez G, Mosconi E, Daban-Huard C, et al. “A circle and a triangle dancing together”: alteration of social cognition in schizophrenia compared to autism spectrum disorders. Schizophr Res. 2019;210:94-100. doi: 10.1016/j.schres.2019.05.043 [DOI] [PubMed] [Google Scholar]

[yoi200072r72] 72.Pinkham AE, Morrison KE, Penn DL, et al. Comprehensive comparison of social cognitive performance in autism spectrum disorder and schizophrenia. Psychol Med. 2019;1-9. doi: 10.1017/S0033291719002708 [DOI] [PubMed] [Google Scholar]

[yoi200072r73] 73.Sasson NJ, Pinkham AE, Weittenhiller LP, Faso DJ, Simpson C. Context effects on facial affect recognition in schizophrenia and autism: behavioral and eye-tracking evidence. Schizophr Bull. 2016;42(3):675-683. doi: 10.1093/schbul/sbv176 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r74] 74.Mehta UM, Thirthalli J, Subbakrishna DK, Gangadhar BN, Eack SM, Keshavan MS. Social and neuro-cognition as distinct cognitive factors in schizophrenia: a systematic review. Schizophr Res. 2013;148(1-3):3-11. doi: 10.1016/j.schres.2013.05.009 [DOI] [PubMed] [Google Scholar]

[yoi200072r75] 75.Chan RC, Li H, Cheung EF, Gong QY. Impaired facial emotion perception in schizophrenia: a meta-analysis. Psychiatry Res. 2010;178(2):381-390. doi: 10.1016/j.psychres.2009.03.035 [DOI] [PubMed] [Google Scholar]

[yoi200072r76] 76.Pelletier-Baldelli A, Holt DJ. Are negative symptoms merely the “real world” consequences of deficits in social cognition? Schizophr Bull. 2020;46(2):236-241. doi: 10.1093/schbul/sbz095 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r77] 77.Borsboom D. A network theory of mental disorders. World Psychiatry. 2017;16(1):5-13. doi: 10.1002/wps.20375 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r78] 78.Galderisi S, Rucci P, Mucci A, et al. ; Italian Network for Research on Psychoses . The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients. World Psychiatry. 2020;19(1):81-91. doi: 10.1002/wps.20700 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r79] 79.Zhou HY, Yang HX, Gong JB, et al. Revisiting the overlap between autistic and schizotypal traits in the non-clinical population using meta-analysis and network analysis. Schizophr Res. 2019;212:6-14. doi: 10.1016/j.schres.2019.07.050 [DOI] [PubMed] [Google Scholar]

[yoi200072r80] 80.Zheng Z, Zheng P, Zou X. Association between schizophrenia and autism spectrum disorder: a systematic review and meta-analysis. Autism Res. 2018;11(8):1110-1119. doi: 10.1002/aur.1977 [DOI] [PubMed] [Google Scholar]

[yoi200072r81] 81.Lai MC, Kassee C, Besney R, et al. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. Lancet Psychiatry. 2019;6(10):819-829. doi: 10.1016/S2215-0366(19)30289-5 [DOI] [PubMed] [Google Scholar]

[yoi200072r82] 82.Trevisan DA, Foss-Feig JH, Naples AJ, Srihari V, Anticevic A, McPartland JC. Autism spectrum disorder and schizophrenia are better differentiated by positive symptoms than negative symptoms. Front Psychiatry. 2020;11:548. doi: 10.3389/fpsyt.2020.00548 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r83] 83.Ziermans TB, Schirmbeck F, Oosterwijk F, Geurts HM, de Haan L; Genetic Risk and Outcome of Psychosis (GROUP) Investigators . Autistic traits in psychotic disorders: prevalence, familial risk, and impact on social functioning. Psychol Med. 2020;1-10. doi: 10.1017/S0033291720000458 [DOI] [PMC free article] [PubMed] [Google Scholar]

[yoi200072r84] 84.Larson FV, Wagner AP, Chisholm K, Reniers RLEP, Wood SJ. Adding a dimension to the dichotomy: affective processes are implicated in the relationship between autistic and schizotypal traits. Front Psychiatry. 2020;11:712. doi: 10.3389/fpsyt.2020.00712 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Social Cognitive Performance in Schizophrenia Spectrum Disorders Compared With Autism Spectrum Disorder

Lindsay D Oliver, PhD

Iska Moxon-Emre, PhD

Meng-Chuan Lai, MD, PhD

Laura Grennan, BSc

Aristotle N Voineskos, MD, PhD

Stephanie H Ameis, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Data Sources

Study Selection

Data Extraction and Synthesis

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Search Strategy

Eligibility Criteria

Data Extraction

Quality Assessment

Statistical Analysis

Meta-analyses

Table 1. Overview of 36 Studies27,28,29,30,31,32,33,34,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73 Included in the Qualitative Analysis.

Moderator Analyses

Sensitivity Analyses

Results

Study Selection and Characteristics

Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Flow Diagram Showing Study Selection.

Social Cognitive and Additional Measures

Quality Assessment

Quantitative Findings

Table 2. Main Meta-analyses and Sensitivity Analyses.

Emotion Processing

Figure 2. Graphical Display of Study Heterogeneity (GOSH)41 Plots for Visualizing Between-Study Heterogeneity.

Figure 3. Forest Plots of Effect Sizes for Each Meta-analysis.

Theory of Mind

Reading the Mind in the Eyes Test

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Overview of 36 Studies^{27,28,29,30,31,32,33,34,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73} Included in the Qualitative Analysis.

Figure 2. Graphical Display of Study Heterogeneity (GOSH)⁴¹ Plots for Visualizing Between-Study Heterogeneity.