This study investigated the differences between clinically-ascertained individuals with ASD and individuals recruited online based on self-reported autistic traits. Key findings include a lack of significant relationship between self-reported and clinician-rated autistic traits in the ASD group (b = 0.025, P = 0.251), and differences in social behavior, with the ASD group showing reduced ability to exert social influence in the social controllability task and less affiliative behavior in the social navigation task. The online high-trait group reported significantly higher levels of social anxiety (F(2,163) = 59.80, P = 3.33 x 10^-20) and AVPD symptoms (F(2,163) = 107.84, P = 1.46 x 10^-30) compared to both the ASD and low-trait groups.
The study provides valuable insights into the differences between clinically-ascertained individuals with ASD and individuals recruited online based on self-reported autistic traits. It clearly demonstrates that while self-reported traits can be informative, they do not always align with clinician-administered assessments and may not accurately reflect observable social behavior. The study makes a strong case for caution when using online self-report measures for autism research, particularly when drawing conclusions about the ASD population as a whole.
The study's findings have practical implications for both research and clinical practice. For researchers, it highlights the need to carefully consider the limitations of online recruitment and self-report measures, particularly in the context of ASD. It suggests that online studies should be used in conjunction with, rather than as a replacement for, traditional lab-based research. For clinicians, the study reinforces the importance of using a multi-faceted approach to assessment, incorporating both self-report and observational measures, and being aware that high self-reported autistic traits may be indicative of other conditions, such as social anxiety or AVPD.
While the study provides valuable guidance, it also acknowledges key uncertainties. The reliance on a single self-report measure (BAPQ) and the lack of a direct measure of insight are limitations that need to be addressed in future research. The study also suggests that future research should explore the use of additional trait measures and self-reported diagnoses in online studies, as well as investigate potential platform differences in social profiles.
Critical unanswered questions remain, such as the extent to which the findings generalize to other populations and the specific mechanisms underlying the observed discrepancies between self-report and behavior. The methodological limitations, particularly the reliance on a single self-report measure and the lack of a direct measure of insight, do not fundamentally undermine the study's conclusions, but they do highlight the need for further research to replicate and extend these findings. The high range of IQ scores in the in-person sample is another limitation that affects the generalizability of the conclusions.
The abstract clearly states the research question, comparing in-person recruited, clinically-assessed individuals with autism to online-recruited individuals with high and low autistic traits.
The abstract concisely summarizes the key findings, highlighting the differences between the groups in social anxiety, avoidant symptoms, and social tendencies during decision-making tasks.
The abstract succinctly states the main conclusion and its implication for autism research, emphasizing the need for differentiation between clinically ascertained and trait-defined samples.
High impact. This would enhance the reader's understanding of the study's scope and the specific context of the findings. It is appropriate for the abstract as it provides a concise overview of the methods used.
Implementation: Include a brief phrase indicating the types of self-report surveys used (e.g., '...evaluated via self-reported surveys on autistic traits, social anxiety, and avoidant personality.').
Medium impact. This would improve the abstract's completeness by providing a more comprehensive overview of the study's findings, impacting reader understanding. It is appropriate for the abstract as it gives a quick overview of the findings.
Implementation: Add a sentence briefly summarizing the finding regarding the lack of relationship between self-rated and clinician-rated autistic traits within the in-person sample. For example: 'Notably, within the clinically-assessed ASD group, self-reported and clinician-rated autistic traits were not correlated.'
The introduction clearly establishes the motivation for the study by highlighting the increasing use of online platforms for data collection in human-participant research and the associated concerns about data quality and validity, particularly in the context of neuropsychiatric disorders.
The introduction effectively introduces the core problem of relying on self-report measures for autism spectrum disorder (ASD) research in online settings, where clinical characterizations are typically absent. It points out the potential limitations of self-report due to altered insight and diagnostic specificity issues.
The introduction logically connects the general problem of online research validity to the specific context of ASD, highlighting the known discrepancies between self- and caregiver-reported traits and the potential impact of altered theory of mind (ToM) on self-report accuracy in individuals with ASD.
The introduction clearly states the study's objective, which is to systematically examine the differences between adults with high autistic traits recruited online via self-report and adults with ASD defined via in-person clinical characterization.
The introduction provides a concise overview of the study's approach, mentioning the use of dynamic social interaction tasks to compare the online and in-person samples. It also states the hypothesis.
Medium impact. This would strengthen the rationale for the study by providing context. This belongs in the introduction to set the stage for the research.
Implementation: Include a sentence or two summarizing the prevalence of ASD and the importance of accurate diagnosis and research in this area. For example: 'Given the increasing prevalence of ASD and the significant impact it has on individuals' lives, rigorous research is crucial for developing effective interventions and supports. However, the challenges of traditional, in-person research have led to a growing reliance on online data collection methods.'
Low impact. This would help readers understand the specific aspects of social interaction being investigated. This belongs in the introduction to provide a more complete picture of the research focus.
Implementation: Expand slightly on the description of the dynamic social interaction tasks, providing a brief, non-technical explanation of what they involve. For example: 'These tasks involved simulated social scenarios where participants made decisions that influenced their relationships with virtual characters, allowing us to assess their social navigation and influence strategies.'
High Impact. This would enhance the flow of the introduction. It is important to establish this connection early in the paper.
Implementation: Add transition sentence. For example: 'To address the limitations of online self-report in ASD research, this study directly compares...'
The Results section clearly presents the demographic and trait characteristics of the three groups (ASD, high-trait, and low-trait), demonstrating the expected differences in self-reported autistic traits (BAPQ scores) based on the group definitions. It also effectively uses Table 1 to summarize demographic information.
The section effectively presents the findings on social anxiety and avoidant personality disorder (AVPD) symptoms, highlighting the differences between the groups and demonstrating that the high-trait group reported higher levels of these symptoms compared to both the low-trait and ASD groups.
The section clearly reports the lack of a significant relationship between self-reported ASD traits (BAPQ) and clinician-rated traits (ADOS) within the in-person ASD group, highlighting a key finding that challenges the assumption of agreement between these measures.
The section effectively presents the results of the social controllability task, showing that the ASD group rejected a smaller percentage of high offers in the controllable condition and perceived less control, indicating a reduced ability to exert social influence.
The section clearly presents the findings of the social navigation task, showing that the ASD group acted less affiliative with the characters, despite similar subjective ratings of character liking, highlighting a difference in social behavior.
The results are presented with appropriate statistical details, including F-statistics, p-values, effect sizes, and confidence intervals, allowing for a thorough evaluation of the findings.
High impact. This would improve the clarity and organization of the Results section. Currently, the section jumps between different types of results (trait comparisons, social behavior tasks) without clear subheadings to guide the reader. This makes it harder to follow the flow of the findings and understand the overall structure of the results.
Implementation: Introduce clear subheadings within the Results section to organize the findings. For example: 'Autistic and Other Social Traits', 'Social Controllability Task', 'Social Navigation Task', 'Relationship Between Self-Reported Traits and Behavior'.
Medium impact. While the Results section reports the statistical findings, it could benefit from a more explicit interpretation of these findings in the context of the study's hypotheses and research questions. This would help the reader understand the significance of the results and how they relate to the broader aims of the study.
Implementation: After presenting the statistical results for each analysis, add a sentence or two briefly interpreting the findings in plain language and relating them back to the study's hypotheses. For example, after reporting the BAPQ differences, state: 'This confirms our initial expectation that the groups would differ in self-reported autistic traits based on their recruitment criteria.'
Low impact. This would provide additional context for interpreting the results. The current presentation assumes reader familiarity with the tasks.
Implementation: Briefly describe the core concepts being measured by each task. For example, before presenting the social controllability results, add a sentence like: 'The social controllability task measures participants' ability to influence others' offers in a monetary exchange game, reflecting their capacity to exert social influence.'
Low impact. This would help readers better understand the specific measures used. It is appropriate for the Results section to briefly introduce the measures before presenting the findings.
Implementation: When first mentioning each measure (BAPQ, ADOS, etc.), briefly state what it measures. For example: '...self-reported autistic traits, as measured by the Broad Autism Phenotype Questionnaire (BAPQ), a self-report instrument assessing autistic traits...' and '...clinician-rated autistic traits measured via the Autism Diagnostic Observation Schedule (ADOS-2), a standardized diagnostic assessment...'
Medium impact. This would help readers visualize the magnitude of the differences between groups. While the statistical values are reported, adding a visual representation would make the results more accessible.
Implementation: Consider adding bar graphs or box plots to visually represent the group differences in key measures (e.g., BAPQ scores, social anxiety scores, rejection rates, affiliation tendencies). Ensure the figures are clearly labeled and referenced in the text.
Fig. 1 | Trait comparisons. a, The ASD (n = 56 participants) and high-trait (HT; n = 56 participants) groups had comparable levels of self-reported autistic traits (measured via BAPQ; two-sided pairwise comparisons using estimated marginal means, with confidence intervals and P values adjusted for multiple comparisons using the Tukey method: t(111) = -0.28, P=0.957, estimated difference = -0.026, 95% CI [-0.25, 0.19], Cohen's d = -0.05; mean ASD: 3.82, mean HT: 3.85, mean low-trait (LT): 2.11).
Fig. 1 | Trait comparisons. b, c, Investigation into traits of other disorders characterized by social impairment revealed that, compared with both other groups (n = 56 participants each), the high-trait group (n = 56 participants) self-reported a higher level of social anxiety (two-sided mixed-effects model with random intercept for matched pair ID: F(2,163) = 59.80, P = 3.33 × 10-20, Npartial2 = 0.42; mean ASD: 35.39, mean HT: 46.43, mean LT: 19.21 (b)) and avoidant personality disorder (AVPD) symptoms (two-sided mixed-effects model with random intercept for
Fig. 1 | Trait comparisons. matched pair ID: F(2,163) = 107.84, P=1.46×10-30, Npartial² = 0.57; mean ASD: 20.09, mean HT: 23.80, mean LT: 11.36 (c)).
Fig. 1 | Trait comparisons. d, In the in-person ASD group (n = 56 participants), there was no relationship between clinician-rated autistic traits measured via ADOS (mean = 13.85) and self-reported autistic traits measured via BAPQ (two-sided general linear model: b = 0.025, s.e.m. = 0.02, t(51) = 1.16, P = 0.251, 95% CI [-0.018, 0.067], Npartial² = 0.01).
Fig. 1 | Trait comparisons. e,f, Broken down by subscales, there was no agreement in the restricted and repetitive behavior domain (RRB; general linear model: b = 0.12, s.e.m. = 0.06, t(51) = 1.95, P = 0.057, 95% CI [0.0, 1.0], Npartial² = 0.05 (e)) or the social domain (general linear model: b = 0.05, s.e.m. = 0.04, t(51) = 1.17, P = 0.249, 95% CI [0.0, 1.0], Npartial² = 0.03 (f)).
Fig. 2 | Social controllability. a, As shown in the representative task screen, the social control task involved participants accepting or rejecting splits of $20 proposed by members of two virtual teams. b, Participants played the game with two different teams sequentially, the order of which was counterbalanced.
Fig. 2 | Social controllability. c, All groups (n = 56 participants each) showed comparable overall rejection rates for both conditions (two-sided mixed-effects model with random intercept for matched pair ID: F(2,281) = 0.77, P = 0.46, Npartial² = 0.006; mean ASD controllable: 52.4%, mean HT controllable: 55.5%, mean LT controllable: 54.7%, mean ASD uncontrollable: 49.6%, mean HT uncontrollable: 51.9%, mean LT uncontrollable: 48.2%).
Fig. 2 | Social controllability. d, When rejection rate is broken down by offer size, we see that the ASD group (n = 56 participants) rejected a lower percentage of high offers than the two online groups (n = 56 participants each) during the controllable condition (two-sided mixed-effects model with random intercept for matched pair ID, P values false discovery rate (FDR)-corrected for multiple
Fig. 2 | Social controllability. e, Unlike the online groups (n = 56 participants each), the ASD group (n = 56 participants) did not detect a difference in controllability between the conditions (two-sided mixed-effects models with random intercepts for matched pair IDs: F(2,269) = 18.52, P = 2.91×10-8, Npartial² = 0.12; mean ASD controllable: 45.47, mean HT controllable: 67.45, mean LT controllable: 61.07, mean ASD uncontrollable: 41.79, mean HT uncontrollable: 19.66, mean LT uncontrollable: 24.70).
Fig. 3 | Social navigation. a, The social navigation task involved participants interacting with different characters with the goal of finding a job and a home. At each interaction, participants could choose between two options that affected either the affiliation or power dynamics of the relationship.
Fig. 3 | Social navigation. b, Compared with the low-trait group, the high-trait and ASD groups (n = 56 participants each) both reported a reduced liking of the characters in the social navigation task (two-sided mixed-effects models with random intercepts for matched pair IDs: F(2,111) = 8.11, P = 0.0005, npartial² = 0.13; mean ASD: 51.09, mean HT: 51.98, mean LT: 59.10).
Fig. 3 | Social navigation. c, Despite having comparable feelings toward characters, the ASD group (n = 56 participants) acted less affiliative than the high-trait group (n = 56 participants; two-sided mixed-effects models with random intercepts for matched pair IDs: F(2,111) = 17.21, P = 3.098×10-7, Npartial2 = 0.24; mean ASD: 0.16, mean HT: 0.30, mean LT: 0.46).
Fig. 3 | Social navigation. d, The groups (n = 56 participants each) did not differ in their power tendencies (two-sided mixed-effects models with random intercepts for matched pair IDs: F(2,163) = 1.89, P = 0.15, Npartial2 = 0.02; mean ASD: 0.13, mean HT: 0.19, mean LT: 0.09).
Fig. 3 | Social navigation. e, No group-by-trait interaction on character liking was detected (two-sided mixed-effects models with random intercepts for matched pair IDs: F(2,155) = 1.76, P= = 0.18, npartial² = 12 = 0.02).
Fig. 3 | Social navigation. f, However, the relationship between affiliative behavior and self-reported traits differed by group (n = 56 participants each; two-sided mixed-effects models with random intercepts for matched pair IDs: F(2,160) = 3.42, P = 0.035, npartial² = 0.04).
The Discussion clearly summarizes the main findings of the study, highlighting the lack of agreement between self-rated and clinician-assessed autistic traits and the differences in social behavior between the clinically ascertained ASD group and the online high-trait group.
The section effectively connects the findings back to the broader issue of using online self-report measures in autism research, cautioning against over-reliance on self-report for identifying diagnostic groups and extrapolating about ASD as a whole.
The Discussion acknowledges the importance of self-report questionnaires for understanding subjective experiences and internal distress in individuals with ASD, emphasizing their role in shaping the narrative and challenging assumptions.
The section provides a plausible explanation for the observed discrepancies between self- and clinician-rated traits, linking them to altered introspection and reduced social self-awareness, which are known characteristics of ASD and other conditions.
The Discussion offers potential explanations for the observed differences in social behavior on the tasks, relating them to theory of mind (ToM) impairments, reduced understanding of social intentions, and impaired affordance perception in the ASD group.
The section discusses the implications of the findings for understanding the relationship between subjective beliefs and social behavior, highlighting the importance of measuring behavior for a comprehensive understanding of trait presentation.
The Discussion acknowledges the potential role of social anxiety and avoidant personality disorder (AVPD) in the online high-trait group, suggesting that self-reported autistic traits in the general population may reflect generalized social avoidance rather than autism-specific difficulties.
The section discusses the implications of the findings for intervention and support, emphasizing the need to tailor interventions based on individual needs rather than solely on diagnostic labels or self-reported traits.
The Discussion appropriately acknowledges the limitations of the study, including the reliance on a single self-report measure (BAPQ), the lack of a direct measure of insight, and the high range of IQ scores in the in-person sample.
The section concludes by suggesting future research directions, including investigating the use of additional trait measures and self-reported diagnoses in online studies, exploring platform differences in social profiles, and conducting large-scale replications of lab-based studies.
Medium impact. This would improve the flow and coherence of the Discussion section. The current structure jumps between different topics (e.g., interpretation of task results, implications for self-report, limitations) without clear transitions or subheadings. This makes it harder for the reader to follow the main arguments and understand the overall narrative of the discussion.
Implementation: Introduce subheadings within the Discussion section to organize the different topics being addressed. For example: 'Discrepancies Between Self-Report and Clinician Assessment', 'Interpretation of Social Behavior Findings', 'Implications for Online Autism Research', 'Limitations and Future Directions'.
Medium impact. This would provide a more balanced and nuanced perspective on the findings. The current Discussion focuses primarily on the limitations of self-report, but it could also more explicitly acknowledge the potential benefits of online research and how it can complement lab-based studies.
Implementation: Include a paragraph or section discussing the potential advantages of online research, such as increased sample size, diversity, and accessibility. Emphasize that the study's findings do not invalidate online research but rather highlight the need for careful consideration of its limitations and the importance of combining it with other approaches.
Low impact. This would strengthen the connection between the study's findings and the broader literature. The current Discussion mentions some relevant studies, but it could more explicitly relate the findings to specific theories or models of ASD, such as the theory of mind deficit, the social motivation theory, or the enhanced perceptual functioning account.
Implementation: Incorporate more specific references to relevant theories and models of ASD when discussing the findings. For example, when discussing the social controllability results, explicitly link them to the theory of mind deficit and how it might explain the observed differences in social influence. When discussing the social navigation results, relate them to the social motivation theory and how it might explain the reduced affiliation in the ASD group.
Low impact. This would provide a more complete picture of the potential implications of the findings. The current Discussion focuses primarily on research implications, but it could also briefly touch upon potential clinical implications, such as the need for clinicians to consider both self-report and observational measures when assessing individuals with ASD.
Implementation: Add a brief paragraph discussing potential clinical implications of the findings. For example: 'These findings suggest that clinicians should consider both self-report and observational measures when assessing individuals for ASD, as relying solely on self-report may not capture the full range of social and behavioral difficulties. Furthermore, clinicians should be aware that high self-reported autistic traits in individuals without an ASD diagnosis may be indicative of other conditions, such as social anxiety or AVPD, and should tailor their assessments and interventions accordingly.'
The Methods section clearly describes the participant recruitment process for both online and in-person samples, providing detailed eligibility criteria and the platforms used (Prolific for online, local advertisements and listservs for in-person).
The section provides a comprehensive description of the clinical assessment procedures used for the in-person ASD group, including the use of the ADOS-2, developmental and clinical history, self- and informant-report, cognitive functioning assessment, and clinical judgment based on DSM-5 criteria.
The section clearly outlines the measures used to assess autistic traits (BAPQ), other psychiatric traits (Liebowitz Social Anxiety Scale, Avoidant Personality Disorder Impairment Scale), self-reported clinical diagnoses, and cognitive ability (Wechsler scales for in-person, ICAR for online). The rationale for selecting the BAPQ is also provided.
The section describes the grouping strategy, explaining how the online sample was subdivided into high-trait and low-trait groups based on BAPQ scores and how age- and sex/gender-matched participants were selected to match the in-person ASD group. The matching function is also mentioned, with a reference to the code availability.
The section provides detailed descriptions of the two experimental paradigms: the social controllability task and the social navigation task. The descriptions include the task goals, procedures, conditions, and how participant choices were measured and analyzed. The use of virtual partners and the manipulation of controllability in the social controllability task are clearly explained.
The section clearly specifies the statistical analyses used, including general linear models for continuous variables and mixed-effects regression models for group comparisons and interactions. The use of appropriate statistical packages (lme4, emmeans) and methods for handling multiple comparisons (Tukey's adjustment, FDR correction) is also mentioned.
Medium impact. This would improve the reproducibility of the study and allow other researchers to fully understand and replicate the experimental procedures. The Methods section is the appropriate place for this level of detail, as it is where readers expect to find comprehensive information about the study's design and procedures.
Implementation: Include more specific details about the social navigation task, such as examples of the narrative trials and decision trials. Provide examples of the pro-affiliative and anti-affiliative options, as well as the pro-power and anti-power options. This could be included in the main text or as supplementary material.
Low impact. This would provide additional context for the study and help readers understand the rationale behind the chosen tasks. While the tasks are described, a brief explanation of *why* these specific tasks were chosen to assess social behavior in ASD would be beneficial. The Methods section is appropriate for this because it sets the stage for the experimental design.
Implementation: Add a sentence or two at the beginning of the 'Experimental paradigms' subsection explaining why the social controllability and social navigation tasks were selected. For example: 'To assess social behavior in a controlled and quantifiable manner, we utilized two established experimental paradigms: the social controllability task, which measures participants' ability to exert and perceive social influence, and the social navigation task, which assesses social decision-making in a dynamic, narrative-based context.'
Low impact. This would enhance the clarity and transparency of the Methods section. While the section mentions that clinicians were 'licensed' and 'research-reliable,' it does not specify their professional backgrounds or the specific training they received for administering the ADOS-2 and making clinical judgments about ASD diagnoses. The Methods section is the correct location for this information, as it is essential for evaluating the rigor of the clinical assessment procedures.
Implementation: Specify the professional backgrounds of the clinicians who screened participants for ASD (e.g., clinical psychologists, psychiatrists) and the specific training they received in administering the ADOS-2 and making clinical judgments about ASD diagnoses (e.g., ADOS-2 research reliability training).
High impact. This would improve the clarity and flow of the Methods section. Currently, the section jumps between different topics (participants, measures, grouping, experimental paradigms, statistics) without clear subheadings to guide the reader. This makes it harder to follow the different aspects of the methodology.
Implementation: Introduce clear subheadings within the Methods section to organize the different components. For example: 'Participants', 'Measures', 'Grouping Procedure', 'Social Controllability Task', 'Social Navigation Task', 'Statistical Analysis'.
Medium impact. This would provide a more complete picture of the study's ethical considerations. While the section mentions IRB approval and informed consent, it does not explicitly address the ethical considerations related to diagnosing participants with ASD during the study, particularly those who were not previously diagnosed. The Methods section is the appropriate place for this information, as it pertains to the ethical conduct of the research.
Implementation: Add a sentence or two addressing the ethical considerations related to diagnosing participants with ASD during the study. For example: 'Participants who received a first-time ASD diagnosis through this study were provided with information about the diagnosis and resources for support and further evaluation. The potential benefits and risks of receiving a diagnosis were discussed with participants as part of the informed consent process.'
Low impact. This would provide additional context for interpreting the results. The current section mentions the versions of the software used but does not explain why these specific versions were used.
Implementation: Add a brief justification for the versions of Psychopy and JavaScript used. For example, you could say that these were the most up-to-date stable versions at the time of the study, or that they offered specific features necessary for the experimental design.