A novel context-discrepancy assessment of impression management facets in autistic and non-autistic adults: an initial validation - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Mol Autism . 2026 Apr 10;17:20. doi: 10.1186/s13229-026-00715-2 Search in PMC Search in PubMed View in NLM Catalog Add to search A novel context-discrepancy assessment of impression management facets in autistic and non-autistic adults: an initial validation Wei Ai Wei Ai 1 Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario Canada 2 Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, Ontario Canada Find articles by Wei Ai 1, 2 , Jennifer Xiaofan Yu Jennifer Xiaofan Yu 1 Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario Canada 3 Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada Find articles by Jennifer Xiaofan Yu 1, 3 , Benjamin Koshy Jacob Benjamin Koshy Jacob 1 Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario Canada 2 Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, Ontario Canada Find articles by Benjamin Koshy Jacob 1, 2 , Meng-Chuan Lai Meng-Chuan Lai 1 Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario Canada 2 Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, Ontario Canada 3 Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada 4 Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada 5 Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK 6 Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan Find articles by Meng-Chuan Lai 1, 2, 3, 4, 5, 6, ✉ Author information Article notes Copyright and License information 1 Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario Canada 2 Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, Ontario Canada 3 Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada 4 Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada 5 Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK 6 Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan ✉ Corresponding author. Received 2025 Nov 12; Accepted 2026 Mar 28; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13081252 PMID: 41964080 Abstract Background Some autistic people “camouflage” by modifying autistic characteristics in social situations. Operationalizations of camouflaging remain limited and inconsistent. Quantitative studies primarily use retrospective self-report questionnaires, constraining ecological precision and conceptual scope. Methodological limitations obscure knowledge about (1) how multi-faceted camouflaging behaviors and experiences, as part of general impression management (IM), differ between autistic and non-autistic people; and (2) how episodic IM vary by social demands and individual traits. Methods We developed a context-discrepancy assessment of camouflaging, conceptualized as a form of IM, in autistic and non-autistic adults. Forty-eight adults (23 autistic, 25 non-autistic) recorded video responses to two hypothetical scenarios with discrepant social evaluative pressure (i.e., job interview vs. video call with trusted other). Immediately after filming, participants rated facets of their IM experiences, including felt inauthenticity, behavioral monitoring extent, effort, and anxiety, and identified behavioral aspects that they particularly monitored. We analyzed IM experiential facet ratings using a 2 (diagnosis) by 2 (social demand) mixed-factorial ANOVA and open-ended data using summative content analysis. Pooled elastic-net regression explored associations between self-reported individual traits (including social coping, cognitive skills, expressivity, gender identity, and neurodivergent traits) and context-dependent discrepancies in IM experiential facet ratings. Results IM felt more effortful for all participants when social demands increased, but autistic adults experienced greater inauthenticity and behavioral monitoring extent when social demands elevated. Increased social demands also disproportionately heightened anxiety for autistic compared to non-autistic adults. Both groups monitored more behavioral aspects under higher social demands, but autistic adults especially increased their monitoring of nonverbal and idiosyncratic behaviors. Context-discrepancies in IM experiential facet ratings showed distinct associations with trait-level camouflaging use versus subjective self-monitoring and executive functioning abilities, as well as with autistic versus ADHD traits. Limitations The video-mediated design may not capture the spontaneity of real-life IM. Findings warrant replication with larger, more diverse cohorts, with counter-balanced or randomized designs. Future research is needed for the current findings to generalize to autistic individuals across sexes and genders, communication and intellectual abilities, as well as ethnicities and cultures. Conclusions This novel context-discrepancy assessment suggests that increased social-evaluative demands exacerbate the psychological tolls of IM for autistic compared to non-autistic adults. Autistic relative to non-autistic adults may particularly monitor nonverbal behaviors in such circumstances. Individual trait associations further delineate IM facets and implicate cognitive and neurodivergent characteristics as potential sources of heterogeneous IM experiences. These insights shed new light into how autistic and non-autistic adults similarly and differently coordinate and experience social coping in context-dependent ways. Supplementary Information The online version contains supplementary material available at 10.1186/s13229-026-00715-2. Keywords: Camouflaging, Impression management, Discrepancy, Autism, Adults, Social behavior Background In social settings, some autistic people “camouflage” to fit in by using strategies that conceal, compensate for, or modulate autistic traits [ 1 , 2 ]. Research on camouflaging initially emerged from autistic people’s lived accounts [ 3 ] and has proliferated given its high relevance to autistic people’s mental and social wellbeing [ 4 , 5 ]. Despite this momentum, current camouflaging measurement tools are limited in scope, tapping into partial and inconsistent conceptualizations of the construct [ 6 , 7 ]. This measurement limitation constrains what we currently know about camouflaging. These gaps include whether camouflaging experiences and behaviors differ from the social coping strategies generally practiced across human groups (i.e., impression management [IM]) [ 8 , 9 ], and why individual differences emerge in how these behaviors are used and experienced. Quantitative research about camouflaging mostly relies on the Camouflaging Autistic Traits Questionnaire (CAT-Q) [ 6 , 10 , 11 ]. The Compensation Checklist is another self-report measure that similarly quantifies people’s use of specific strategies to cope with social difficulties and demands [ 12 ]. Because existing research predominantly relies on retrospective questionnaires, the data on the socio-motivational and cognitive enablers as well as mental health consequences of camouflaging are largely cross-sectional and confined to the set of behaviors captured by these measures. Alternatively, the “discrepancy” approach indexes camouflaging as the standardized difference between observable autistic behaviors (e.g., as detected by diagnostic clinical tools) and internal autistic states (e.g., mentalizing ability) [ 13 – 15 ]. Yet, its reliance on clinical tools (e.g., the Autism Diagnostic Observation Schedule [ 16 ]) makes it ill-suited for clarifying how camouflaging compares with social coping strategies used by non-autistic people. Film-based reflective methods such as interpersonal recall is an innovative way to capture near real-time camouflaging in naturalistic social scenes [ 17 ]. Nonetheless, these current approaches are all restricted to a partial understanding of camouflaging as the intentional use of specific behaviors, and do not assess alternative experiential facets and their contextual contingencies. The transactional IM framework [ 8 ] conceptualizes camouflaging as a part of broader IM experiences. Importantly, IM (inclusive of camouflaging) is multi-faceted ; it extends beyond the deliberate use of strategies to also encompasses other related but separable constituent facets, such as the extent of behavioral monitoring and its effortfulness, how successful these strategies are (as perceived by oneself or by others), and immediate psychological strains. These facets are also consistently described in lived accounts of autistic camouflaging [ 5 , 18 ]. In both general population and autistic samples, psychometric work shows that CAT-Q scores converge with other general IM measures (e.g., Self-Presentation Tactics scale [ 19 ], Self-Concealment Scale [ 20 ], Concern for Appropriateness [ 21 ]) on a latent “intentional use” facet, indicating that deliberate IM strategy use to cope with social demands is continuously distributed across human groups [ 22 , 23 ]. By contrast, the Revised Self-Monitoring Scale (RSMS) [ 24 ], another established IM measure, shows only weak associations with the CAT-Q and other IM measures in both the general population and autistic adults [ 22 , 23 ]. The RSMS may instead index a “self-efficacy” facet capturing one’s self-perceived ability to adapt IM behaviors, which exhibits divergent links with cognitive abilities and mental health compared to the “intentional use” of IM [ 23 ]. Together, these findings underscore the need for conceptual and methodological updates that differentiate facets implicated in camouflaging (as a form of IM), that are missed by current approaches [ 10 , 11 ]. Notwithstanding these conceptual overlaps, camouflaging as a kind of multi-faceted IM likely exhibits autism-distinctive features as autistic people traverse neurotypical social structures [ 8 ]. Lived accounts describe the particular intensity and difficulties in sustained camouflaging in some autistic people, as well as unique tactics aimed at suppressing stimming or managing sensory discomfort [ 2 , 4 – 6 , 25 ]. It is also more difficult for autistic individuals to gauge how they are socially evaluated by others compared to non-autistic peers, adding further uncertainty that may exacerbate the effortfulness and anxiety of IM [ 26 ]. Still, little is known about which IM facets carry autism-distinctive features, and whether such differences occur in degree (i.e., quantitatively) or in kind (i.e., qualitatively). Even within the autistic population, IM experiences can vary greatly. Some autistic people describe camouflaging as exhausting, inauthentic, and compelled, whereas others experience it as ingrained and instrumental for meeting social demands, and still others report no intention to camouflage [ 4 , 5 , 27 – 30 ]. IM outcomes may also be shaped by socio-demographic and neurodivergent features. Greater IM use is linked to elevated mental health strains in both autistic and non-autistic people, including anxiety, identity problems, and exhaustion [ 1 , 4 , 5 , 31 , 32 ], but effect sizes vary and appear exacerbated in women compared to men in the general population [ 13 , 33 – 37 ]. As well, increased IM use is linked to greater mental health burden in those with elevated autistic traits, but to reduced burden in those with elevated ADHD traits [ 33 ]. These observations indicate that “how”, “to what extent”, and “at what costs” individuals manage impressions are consequential facets to explore. Anchoring measurement developments on this multi-faceted foundation of IM will help clarify the heterogeneity in IM experiences within and beyond the autistic community, as well as how they are linked to individual trait profiles. There is thus a pressing need for novel assessments of IM that can (a) yield indices tapping into IM facets missed by current measurements, (b) compare the quantitative and qualitative IM profiles between autistic and non-autistic individuals, and (c) assess how individual traits relate to IM facets. To this end, we build upon the limitations of current measurements [ 7 , 10 , 38 , 39 ] and cultivate insights from new psychometric perspectives [ 11 ] to introduce a novel task that captures the “context-discrepancy” in IM behaviors and experiences across experimental social scenarios. In this task, the term “context-discrepancy” refers to the variation in social demands across contexts, which elicits corresponding shifts in IM experiences and strategies. This formulation is grounded in two premises. First, human cognition operates from a “social baseline” that reflexively anticipates the presence of others [ 40 ]. Second, IM pressures stem from social-evaluative threat, where individuals monitor their relational value via a “sociometer” and engage in IM to enhance specific dimensions of social favorability (e.g., likability, competence) for securing pragmatic gains, alleviating discrimination, among other reasons dependent on particular contexts and stakes [ 8 , 9 , 41 ]. Accordingly, when the sociometer reads “safe” in familiar and supportive settings (e.g., with trusted friends or family), the gap between one’s current and desired social image is minimal, lowering social-evaluative pressure and favoring low-effort, authentic, or default self-presentation. Conversely, in unreliable or high-stakes contexts where the risk of negative evaluation is salient (e.g., an interview), social-evaluative threat amplifies IM behaviors and experiences. In this novel “context-discrepancy assessment,” autistic and non-autistic adults engaged in naturalistic social tasks with varied social demands (i.e., high-stakes professional setting vs. low-stakes informal setting) while video-recorded. The assessment integrates reflective self-report and performance-based components to capture contextual shifts in facets of IM experiences and behaviors. Moreover, by implementing the context-discrepancy assessment alongside established self-report camouflaging/IM measures (i.e., the CAT-Q and RSMS), we test how one’s general tendencies to use IM strategies and one’s subjectively perceived ability to deploy them are both linked to episodic, context-dependent IM experiences. In this initial validation, we demonstrate how insights derived from this task expand our understanding of camouflaging as a part of IM. Specifically, we show how these insights begin to fill existing knowledge gaps regarding different behavioral and experiential IM facets and their individual trait correlates in autistic and non-autistic people. Methods Participants The study was approved by the Department of Psychology Ethics Review Committee at the University of Toronto and conducted in accordance with the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2). In total, 48 autistic and non-autistic adults were recruited using community-based sampling and local networks in the Greater Toronto Area. Participants were included if they were at least 18 years of age, had normal or corrected-to-normal vision and hearing, and were capable of providing informed consent. Participants were excluded if they were experiencing acute distress, a psychotic episode, or a mental health crisis. The current study was part of a larger research session that included heart rate variability data collection; therefore, we also excluded individuals with existing heart conditions or those taking heart-rate-altering medications. All autistic participants provided official letters of autism diagnoses confirmed by healthcare professionals. The final sample included 23 autistic and 25 non-autistic adults ( M age 25.8 years, 31 birth-assigned females; see Table 1 for demographics information). Table 1. Sample demographics Autistic ( N = 23) Non-Autistic ( N = 25) Total Sample ( N = 48) N % N % N % Gender (missing N = 3, prefer not to answer N = 1) Men (including transgender men) 6 26.1 7 28.0 13 27.1 Women (including transgender women) 9 39.1 10 40.0 19 39.6 Gender-diverse a 7 30.4 5 20.0 12 25.0 Sex assigned at birth (missing N = 5) Male 5 21.7 7 28.0 12 25.0 Female 17 73.9 14 56.0 31 64.6 Ethnicity (missing N = 3) White 14 60.9 5 20.0 19 39.6 Black or African American 0 0.00 0 0.00 0 0.00 American or Alaskan Native 0 0.00 0 0.00 0 0.00 Asian 7 30.4 11 44.0 18 37.5 Latin/a/o/e 0 0.00 0 0.00 0 0.00 Middle Eastern 0 0.00 2 8.00 2 4.20 Native Hawaiian or Pacific Islander 0 0.00 0 0.00 0 0.00 Multiracial 1 4.40 4 16.0 5 10.4 Prefer not to answer or not listed 1 4.40 0 0.00 1 2.10 Sexual orientation (missing N = 3) Lesbian or gay 0 0.00 1 4.00 1 2.10 Two-Spirit 0 0.00 0 0.00 0 0.00 Pansexual 2 8.70 2 8.00 4 8.30 Bisexual 3 12.0 1 4.00 4 8.30 Queer 8 32.0 2 8.00 10 20.8 Not sure or questioning 0 0.00 0 0.00 0 0.00 Straight or heterosexual 8 32.0 15 60.0 23 47.9 Asexual 0 0.00 1 4.00 1 2.10 Prefer not to say or not listed 2 8.70 0 0.00 2 4.20 Education level (missing N = 3) Primary school diploma 0 0.00 0 0.00 0 0.00 Middle school diploma 0 0.00 0 0.00 0 0.00 High school diploma 9 39.1 1 4.00 10 20.8 University/college diploma 11 47.8 19 82.6 30 62.5 Master’s degree 2 8.70 1 4.00 3 6.25 Doctoral degree (e.g., PhD) or equivalent 0 0.00 0 0.00 0 0.00 Other (e.g., trade, associate degree) 1 4.40 1 4.00 2 4.20 M SD M SD M SD Age (years) 27.0 9.40 24.7 5.20 25.8 7.60 Education years 17.6 2.90 17.5 1.80 17.6 2.40 Autistic traits (as measured by SATQ) 34.4 21.9 26.7 25.9 30.6 26.2 Open in a new tab Note. a Gender-diverse individuals include two-spirit, genderqueer, genderfluid, androgynous, gender-expansive, and nonbinary. Due to missing data and data rounding, percentage may not sum to 100% Abbreviations: number ( N ), mean ( M ), standard deviation ( SD ) Procedure Participants were invited into a quiet testing room and were seated in a stationary chair. Participants were seated with their back to a gray-colored filming screen curtain and facing a 23” Dell computer monitor (1920 × 1080 pixels, 60 Hz). Attached to the top of the monitor was a DJI Osmo Action 4 camera (1080p), which was chosen for its small size to minimize discomfort and distraction. A LED video light panel was attached next to the camera to optimize visibility and control for lighting conditions. The experimenter provided all study instructions and left the testing room, but was available in the next room to assist the participant if needed. Afterwards, the participants independently began the study via Qualtrics ( www.qualtrics.com/ ). The Qualtrics survey page was pre-loaded on the computer, which guided participants through each step of the procedure, including the survey questions and filming process. They sequentially completed the digital consent form and then the demographics questionnaire. As part of the demographics questionnaire, participants were asked to indicate their birth-assigned sex using the following options: (1) Female, (2) Male, (3) Intersex, (4) Prefer not to say. Participants also indicated their self-identified gender identity using the following options: (1) Girl/Woman, (2) Boy/Man, (3) Two-spirit, (4) Genderqueer, (5) Gender fluid, (6) Androgynous, (7) Non-binary, (8) Gender-expansive (existing on a spectrum of genders not otherwise represented), and (9) Prefer not to say. Participants who chose options other than “Girl/Woman” or “Boy/Man” (with exception of one participant who preferred not to say) were grouped together as “Gender-diverse.” Afterwards, participants began the context-discrepancy assessment, which involved a filming phase and a reporting phase. Participants were first presented with instructions explaining that they will be presented with two scenario prompts and will record a brief (1.5 to 2 min each) video for each prompt. They were also instructed that they will have 3 min after reading the instructions to prepare their video response on a piece of blank paper. They were presented first with the “high social demand” (HSD) condition. Participants were asked to imagine they were in a remote job interview for their ideal job position. Video-recorded mock-interview for an ideal job position has been used to induce naturalistic social demands in previous investigations of impression formation in autistic and non-autistic adults [ 42 ]. Participants first named what the job position would be in an open-text box and then responded to the following: “ You want to make a good impression. The interviewer has asked you to tell them about yourself. They’re looking to learn more about your interests in the position , personality , strengths & weaknesses , and other relevant experiences. You can speak about any or all the above. How would you respond?” After drafting their response for 3 min, each participant then independently recorded their video response for 1.5 to 2 min. The video was framed to capture the frontal view of the participants’ face and upper torso. While filming, a timer was presented on the Qualtrics page to inform participants of their time left. Immediately after filming, participants were asked to report using a 7-point Likert scale (1 = Not at all, 7 = Extremely) on facets of their IM experience, including “ to what extent did you feel like you were able to ‘be yourself’ ?” (authenticity), “ to what extent did you monitor your appearance (e.g. , speech , gestures , facial expressions)? ” (extent of monitoring), “ how much effort did you have to put into recording the video? ” (effort), and “ how anxious did you feel when recording the video? ” (anxiety). Finally, participants answered an open-text question: “ Which aspects of yourself (e.g. , facial expressions , speech tone or content , gestures) were you particularly attending to or monitoring during the recording? ” Scores on the authenticity facet were reverse-coded to create an “inauthenticity” facet score used in subsequent analyses, which carries the same polarity as the other IM experiential facets for ease of interpretability. After participants recorded their video response to the HSD condition, they were presented with the scenario prompt for the “low social demand” (LSD) condition. In the LSD condition, participants were asked the following: “ Imagine you are on a video call with someone you feel very comfortable being around and whom you trust deeply (e.g. , a family member , close friend , or an imaginary other). You can “be yourself” with this person by speaking or behaving authentically. They have asked you to tell them about your day. You may speak about any particular day you wish. How would you respond? ” The procedure for the LSD condition is identical to the HSD condition, where participants first prepared their response on a piece of blank paper, recorded their video response, and answered the 7-point Likert scale questions on facets of their IM experience (with verbatim wording). The discrepancy between the HSD and LSD contexts gauges shifts in IM experiences and behaviors where one socially copes with heightened social-evaluative pressures versus baseline self-presentational tendencies in the assumed presence of trusted social others. We opted for a fixed-order design (i.e., all participants completed the HSD before the LSD condition) so that every participant experienced the same contrast in evaluative pressure; this design sidesteps order-effect analyses that our sample size was not adequately powered for, and enabled us to end the study session with as little participant discomfort as possible. Measures Following the context-discrepancy assessment, participants completed a battery of self-report measures on individual traits. These measures enable us to examine whether and how facet scores from the context-discrepancy assessment relate to individual traits that have previously been studied in camouflaging and IM research [ 1 , 43 , 44 ]. In particular, we used the CAT-Q and RSMS as initial tests of task validity. Given that these two scales tap into distinct IM facets with divergent links to socio-motivational and cognitive drivers as well as mental health [ 23 ], we expect the two to be differently linked to context-discrepancy patterns across IM experiential facet scores in the current study. Specifically, greater trait-level camouflaging use (indexed by higher CAT-Q scores) would be associated with more inauthentic, extensive, effortful, and anxious IM under the HSD compared to LSD conditions, whereas greater self-efficacy at enacting IM (indexed by higher RSMS scores) would be associated with the opposite patterns. Interpersonal Reactivity Index–Perspective Taking Subscale (IRI-PT) The IRI-PT [ 45 ] assesses one’s tendency to adopt others’ psychological viewpoints in daily life. The subscale contains 7 items rated on a 5-point Likert scale. The IRI-PT shows good internal consistency, test-retest reliability, and converges with measures of interpersonal functioning [ 45 ]. We used only the PT subscale to isolate the perspective taking component, which has been theorized to support social decoding and IM decisions [ 8 ]. Amsterdam Executive Function Inventory (AEFI) The AEFI is a self-report questionnaire scored on a 3-point Likert scale that assesses executive aspects of daily behaviors, including key components of attention, self-control and self-monitoring, and planning and initiative [ 46 ]. The scale shows adequate construct validity and reliability [ 46 ]. A revised 10-item version validated with young adults shows high internal consistency and robust factor loadings and was thus used [ 47 ]. Subthreshold Autism Trait Questionnaire (SATQ) The SATQ includes 24 items scored on a 4-point Likert scale that measure a broad range of autistic traits along the five subscales of Social Interaction and Enjoyment, Oddness, Reading Facial Expressions, Expressive Language, and Rigidity [ 48 ]. The scale demonstrates sound psychometric properties and convergent validity with other autistic trait measures [ 48 ]. The brevity of the SATQ relative to other autism trait measures assists in reducing participant burden. Adult ADHD Self-Report Scale (ASRS) The ASRS is a widely used measurement for dimensional ADHD traits in adults [ 49 ]. The full scale includes 18 items; all items were rated on a 5-point Likert scale reflecting symptom frequency along the Inattentiveness and Hyperactivity-Impulsivity subscales. Validation studies report high internal consistency and good test–retest reliability [ 50 , 51 ]. Construct and convergent validity are supported by strong associations with student-reported school functioning and other scales measuring ADHD symptoms [ 51 ]. Camouflaging Autistic Traits Questionnaire (CAT-Q) The CAT-Q measures one’s intentional use of camouflaging behaviors in daily life [ 52 ]. It contains 25 items across three factors (i.e., Compensation, Masking, and Assimilation) rated on a 7-point Likert scale. The measure was validated in both autistic and non-autistic samples, demonstrating excellent internal and good test–retest reliability [ 52 ]. The scale exhibits good content validity through autistic lived-experience-inspired item generation and shows convergent validity through associations with social anxiety, depression, and autistic traits [ 2 , 52 ]. Revised Self-Monitoring Scale (RSMS) The Revised Self-Monitoring Scale (RSMS) includes 13 items rated on a 6-point Likert scale measuring one’s self-perceived ability to monitor and modify self-presentational behaviors [ 24 ]. The RSMS includes two subscales for Self-Presentation Modification and Sensitivity to Expressions of Others. The RSMS has acceptable internal consistency [ 24 ] and sound test–retest reliability [ 53 ]. Berkeley Expressivity Questionnaire (BEQ) The BEQ assesses how readily an individual expresses emotions outwardly across three domains: Impulse Strength, Negative Expressivity, and Positive Expressivity [ 54 , 55 ]. It contains 16 items rated on a 7-point Likert scale, demonstrating both high internal consistency and good test–retest reliability. Convergent validity has been shown with expected gender and cultural differences as well as associations to personality dimensions [ 55 ]. Data analyses All analyses were conducted in R (Version 4.4.0). A Mann-Whitney U test indicated that the proportion of missing data did not significantly differ between autistic and non-autistic participants, U = 283.5, p = 0.936. The data were treated as missing at random. Analysis 1: Mixed-factorial analysis of variance (ANOVA) of differences in IM experiential facets We performed separate 2 (within-subjects, social demand: HSD vs. LSD) by 2 (between-subjects, diagnosis: autistic vs. non-autistic) mixed-factorial ANOVA models with each of the four self-reported IM experiential facet as the outcome variable (i.e., inauthenticity, extent of monitoring, effort, and anxiety). We first assessed the main effects of social demands and diagnosis separately to determine task validity concerning whether the social demand discrepancy was salient and elicited meaningful IM differences. Then, we examined the interactions between the two factors to assess if the context-discrepancy effects on facets of IM experience were different between the two groups. Significance was set at α = 0.05 and follow‑up simple‑effects t ‑tests were Benjamini–Hochberg adjusted [ 56 ]. Only complete item-level data were used for this analysis so sample sizes slightly varied across the four mixed-factorial ANOVA models. Analysis 2: Summative content analysis of self-reflected IM behaviors Open-ended responses describing what each participant particularly attended to during filming were analyzed with summative content analysis [ 57 , 58 ]. We chose this approach because responses were mainly succinct manifest phrases; hence, we analyzed how often specific behavioral domains were mentioned to explore usage rather than their latent narrative structure [ 58 ]. Summative content analysis also allows us to quantify code frequencies for group comparisons in IM behaviors. Two non-autistic experimenters independently read all responses, highlighted recurring words or phrases (e.g., “tone,” “hand movement”), and clustered them into content categories representing behavioral domains. The preliminary code frame was discussed and refined for clarity and face validity. After reaching consensus, the two coders applied the final code frame to the entire dataset, which included the following behavioral domains: voice delivery, speech content, facial expressions, gestures, eye contact, and miscellaneous others. The “miscellaneous others” category included idiosyncratic behavioral aspects that could not be coded into any of the other recurring behavioral domains. This included attention to the timer or eye gaze location, “staying in camera frame,” “appearing engaging” or “appearing relaxed,” and “to avoid embarrassment.” Participants’ responses were binarily coded for each domain (i.e., 0 for not reported, 1 for reported). Discrepancies were resolved through discussion. The counts for each domain were tallied to characterize the overall IM behavioral profiles of autistic and non-autistic participants in each social demand condition. We performed a Wilcoxon signed-ranks test to compare counts of overall monitored behavioral aspects between the HSD and LSD conditions. Then, we computed an “IM behavioral discrepancy score” for each participant by subtracting their total count of monitored behavioral aspects in the LSD condition from their total count in the HSD condition. Larger scores thus represent a greater magnitude of increased discrete behavioral monitoring in HSD relative to LSD condition. Afterwards, we utilized a Mann-Whitney U test to compare IM behavioral discrepancy scores between autistic and non-autistic participants. Because participants generally reported monitoring only one or two particular behavioral aspects, the count data for each behavioral domain were binary and low-prevalence (i.e., most observations were 0). Due to this distribution, we did not perform binary logistic regressions to analyze behavioral domain-level data, but instead examined their descriptive patterns across social demand conditions between autistic and non-autistic participants. Analysis 3: Multiple imputation and variable selection of individual traits related to IM experiential facets We computed four “IM experiential discrepancy scores” for each participant by subtracting each of their IM experiential facet scores in the LSD condition from the corresponding scores in the HSD condition. These scores hence represent a relative difference in IM experiences of inauthenticity, extent of monitoring, effort, and anxiety when the level of social demands shifted. For example, a large IM experiential discrepancy score for the anxiety facet indicates that the participant reported being much more anxious in the HSD compared to LSD condition. The computation used raw facet scores because they indexed the same facet constructs with verbatim question items and were measured on the same 7-point Likert scale within the same person, preserving directly interpretable differences in scale points. We then fitted elastic-net regression models for each IM experiential discrepancy score to identify reliable leads on which individual traits accounted for the variability in IM experiences across social demand conditions, as well as how these discrepancies are linked to existing IM measures (i.e., the CAT-Q and RSMS). Elastic-net regression is a data-driven, exploratory approach that combines penalties from ridge and lasso regressions to perform variable regularization and selection [ 59 ]. This technique is suited for mitigating multicollinearity and over-fitting in high dimensional datasets with modest samples [ 59 , 60 ]. We performed the elastic-net regressions over multiply-imputed samples then harmonized the selection stability of predictors [ 61 – 64 ]. Instead of using the “stacked” approach that estimates a single model on combined imputed datasets [ 65 ], we casted elastic-net regression on each imputed dataset to evaluate if the same set of predictors are reliably selected for across imputations [ 66 – 68 ]. First, multiple imputation was conducted for 20 iterations, aligned with recommendations for datasets with < 30% missing data [ 69 ], using multiple imputation by chained equations with predictive mean matching in the mice R package [ 70 ]. Item-level missingness was 12% in the current dataset. Then, for each imputed dataset, an elastic-net regression model was estimated using the glmnet R package [ 60 ] to predict each IM experiential discrepancy score from the full set of cognitive (i.e., executive functioning and perspective taking), neurodivergent (i.e., autistic and ADHD traits), IM (i.e., camouflaging, self-monitoring), categorical gender identity (i.e., men, women, gender-diverse), and emotional expressivity variables. In particular, emotional expressivity was included as a covariate to determine whether other theoretical correlates would be saliently linked to IM facets over and above individual differences in trait-level expressiveness. Each model was tuned with repeated 10-fold cross-validation. Lastly, the elastic-net regression results across multiply-imputed datasets were pooled by calculating the mean standardized coefficient and inclusion frequency (i.e., proportion of times a predictor survives penalization with non-zero coefficients) of each predictor. Predictors were retained and interpreted as meaningful if (a) their mean standardized coefficient was ≥ 0.20 (i.e., at least a “minimal practically significant” effect) [ 71 – 73 ] and (b) they survived regularization in all of the imputed datasets, which is more stringent than existing guidelines [ 63 , 74 ]. This ad-hoc dual criterion weighs both the effect magnitude and stability of each predictor, guarding against spurious findings driven by imputation noise while discarding trivially small effects [ 75 ]. Results Results 1: Do facets of IM experience differ by social demands and diagnosis? Four 2 (social demand: HSD vs. LSD) by 2 (diagnosis: autistic vs. non-autistic) mixed-factorial ANOVAs were conducted for each self-reported IM experiential facet (i.e., inauthenticity, extent of monitoring, effort, anxiety; Fig. 1 ; Table 2 ). For inauthenticity, although a significant main effect of social demand was observed, simple-effects test of the significant interaction between diagnosis and social demand revealed that autistic adults reported greater inauthenticity in the HSD relative to the LSD condition, t (46) = 4.47, adjusted-p < 0.001, whereas non‑autistic adults did not exhibit differences between social demand conditions, t (46) = 1.19, adjusted-p = 0.240. Likewise, for extent of monitoring, a significant main effect of social demand was observed. Yet, simple-effects test of the significant interaction between diagnosis and social demand suggested that autistic adults reported markedly greater extent of behavioral monitoring in the HSD relative to the LSD condition, t (42) = 5.07, adjusted-p < 0.001, whereas non‑autistic adults did not exhibit differences between social demand conditions, t (42) = 1.86, adjusted-p = 0.071. For effort, a significant main effect of social demand was found, without a significant diagnosis-by-social demand interaction, such that participants in general found IM more effortful in the HSD compared to the LSD condition, regardless of diagnosis, t (42) = 7.04, adjusted-p < 0.001. Lastly, for anxiety, a significant main effect of social demand was found, such that IM during the HSD relative to the LSD condition provoked greater anxiety across participants, t (40) = 9.46, adjusted-p < 0.001. Here, a significant ordinal interaction effect showed that elevated social demands disproportionately heightened anxiety for autistic, t (40) = 8.36, adjusted-p < 0.001, compared to non-autistic adults, t (40) = 5.02, adjusted-p < 0.001. Fig. 1. Open in a new tab Violin boxplots of IM experiential facet ratings by social demand condition and participant diagnosis. Note . Solid-colored (purple and pink) circles represent mean of facet rating for each diagnosis (autistic vs. non-autistic) by social demand (HSD vs. LSD) condition. Low opacity gray-colored dots represent individual data points. Table 2. Mixed factorial ANOVA results for each IM facet rating Outcome Variable Effect df (between, within) F p -value Partial η² Inauthenticity Diagnosis 1, 46 1.39 0.245 0.029 Social demand 1, 46 16.4 < 0.001 0.263 Diagnosis: Social demand 1, 46 5.77 0.020 0.111 Extent of monitoring Diagnosis 1, 42 0.16 0.688 0.004 Social demand 1, 42 24.5 < 0.001 0.368 Diagnosis: Social demand 1, 42 5.69 0.022 0.119 Effort Diagnosis 1, 42 0.94 0.339 0.022 Social demand 1, 42 49.6 < 0.001 0.541 Diagnosis: Social demand 1, 42 0.04 0.850 < 0.001 Anxiety Diagnosis 1, 40 0.32 0.575 0.008 Social demand 1, 40 89.5 < 0.001 0.691 Diagnosis: Social demand 1, 40 5.59 0.023 0.123 Open in a new tab Note. Diagnosis effect refers to the contrast between autistic versus non-autistic participants; Social demand effect refers to the contrast between high versus low social demand conditions Results 2: Do reported IM behavioral aspects differ by social demand and diagnosis? Because qualitative responses were coded into tallied count data and positively skewed, we opted to report the median ( Mdn ) and interquartile range (IQR) from Q1 (25th percentile) to Q3 (75th percentile) in describing the number of monitored behavioral aspects in each social demand condition. Participants reported monitoring fewer behavioral aspects in the LSD condition ( Mdn = 1, [Q1–Q3: 1–1]) than in the HSD condition ( Mdn = 2, [Q1–Q3: 1–2]). The Wilcoxon signed-ranks test showed a statistically significant difference in the number of monitored behavioral aspects between the two social demand conditions (Z = 4.04, p < 0.001). The Mann-Whitney U test revealed a significant difference in IM behavioral discrepancy scores between autistic and non-autistic participants ( U = 105.5, p = 0.041). Specifically, IM behavioral discrepancy scores were higher in autistic ( Mdn = 1, [Q1–Q3: 0–2]) than in non-autistic participants ( Mdn = 0, [Q1–Q3: 0–1]). This indicates that autistic adults, relative to non-autistic peers, reported a greater magnitude of increased discrete monitored behaviors in the HSD compared to LSD condition. Descriptively, more diagnosis-specific patterns were uncovered when delving into each behavioral domain (Fig. 2 ), suggesting that autistic and non-autistic adults coordinated IM behaviors differently in response to shifting social demands. Non-autistic adults mentioned monitoring nonverbal behaviors such as gestures and eye contact equally, or even less so, in the HSD compared to the LSD condition. By contrast, autistic adults reported monitoring gestures (e.g., “ hand movements ,” “ body language ”), eye contact, and facial expressions at least 26% more often in the HSD than the LSD condition. Autistic adults also reported monitoring idiosyncratic behavioral aspects, including stimming-related behaviors such as fidgeting or playing with their hair. They described both the efforts required to suppress these actions (“ I was trying not to fidget…balancing those things can be tricky for me ”) and the relief felt when suppression was unnecessary in the LSD condition (“ I was happy to be able to stim with my hair like I usually do ”). Fig. 2. Open in a new tab The count and percentage of open-ended responses endorsing each monitored behavioral domain in autistic and non-autistic adults for each social demand condition. Note . Percentage (%) reflects the proportion of open-ended responses endorsing each monitored behavioral domain, as reported in each diagnosis (autistic vs. non-autistic) by social demand (HSD vs. LSD) condition. The relative pie chart sizes between social demand conditions reflect the percentage increase in total counts of monitored behavioral aspects from the LSD to HSD condition. Results 3: Which individual traits are most associated with context discrepancies in facets of IM experience? The pooled elastic-net regressions (Fig. 3 ) suggested several individual traits as related to the IM experiential discrepancy scores based on our stringent dual criterion (i.e., of 100% inclusion rate and average standardized coefficient of 0.20). Individuals who reported feeling much more inauthentic in the HSD than the LSD condition also reported greater expressivity, greater ADHD traits, and greater CAT-Q scores. Individuals who reported much more extensive behavioral monitoring in the HSD than the LSD condition also reported lower subjectively perceived self-monitoring ability, greater CAT-Q scores, and lower executive functioning. The IM experiential discrepancy score for the effort facet was not associated with any of the individual traits. Finally, individuals who felt much more anxious in the HSD than the LSD condition also reported greater CAT-Q scores, lower autistic traits, greater expressivity, and lower executive functioning. Fig. 3. Open in a new tab Variable importance plots depicting the coefficient values of each individual trait across imputations in relation to each IM experiential discrepancy (HSD − LSD) score. Note. Blue dots represent the coefficient value ( β ) of each individual trait predictor estimated at each imputation, and white diamonds represent the mean coefficient value across imputations; Inclusion frequency (IF); Gray bands capture | β | < 0.2 to represent region of coefficient sizes that do not meet our criteria for minimal practical significance; Individual trait predictors are ordered by mean coefficient sizes in each panel; Metrics are provided only for predictors that met our dual criterion. Discussions This proof-of-concept study outlines a novel assessment to deliver the first context-dependent characterization of IM experiences and behaviors in autistic and non-autistic adults, specifically by capturing the discrepancies in IM facets across systematically varied social demand contexts. Using this context-discrepancy assessment, we showed that higher social demands made IM feel more effortful, irrespective of being autistic or non-autistic. However, autistic adults reported greater inauthenticity and behavioral monitoring extent when social-evaluative demands were higher. Furthermore, provoked anxiety was greater in autistic compared to non-autistic adults when social-evaluative demands were higher. Although greater social demands prompted both groups to monitor more of their own behavioral aspects, autistic adults descriptively increased their monitoring particularly for nonverbal IM channels such as gestures, facial expressions, and eye contact. Autistic adults also described suppressing idiosyncratic behaviors like stimming. Finally, IM experiential discrepancy scores (except the effort facet) were associated with specific individual traits, pointing to possible sources of the heterogeneity in IM experiences. The general findings across IM facets demonstrate the successful experimental manipulation to induce situational shifts in IM experiences. It also supports the view that heightened social evaluation is a universal trigger for IM, whose expression varies with immediate contextual affordances rather than simply reflecting a stable dispositional trait [ 8 , 9 , 76 , 77 ]. Yet, the extent of behavioral monitoring and felt inauthenticity remained largely unchanged for non-autistic adults but clearly increased for autistic adults when social demands were high compared to low. Under the same circumstances, autistic adults also reported a greater increase in provoked anxiety than non-autistic adults. These patterns align with the idea that autistic people start social interactions from a lower baseline of social favorability and are thus compelled to engage in more extensive and hypervigilant IM—often having to “change” presentations of the self more fundamentally and at greater emotional costs [ 78 – 80 ]—to close that wider interpersonal gap [ 8 , 81 – 84 ]. The absence of a diagnostic group difference for effort was unexpected, given qualitative reports of the significant cognitive tolls of camouflaging for autistic people [ 1 , 4 , 5 ]. This raises the question of whether differences in IM effort emerge primarily in spontaneous or cognitively taxing social situations. It is also possible that our structured task provides sufficient preparation time to offset such group differences in IM effort. In future work, the context-discrepancy assessment can be expanded to incorporate a more diverse set of social settings to enhance its sensitivity. One’s experience and social coping may differ, for example, in scenarios involving dating someone new or meeting new colleagues or classmates, where social evaluation pressures persist but the financial or practical stakes of IM are lower compared with a job interview. In such scenarios, it is possible that non-autistic individuals would show a decrease in IM effortfulness due to the ubiquity of such social exchanges, whereas autistic individuals may exert comparable efforts as in the job interview scenario due to their innate social communication differences and pervasively experienced autism-related prejudice. Overall, our results underscore the need to model IM as a transactional phenomenon, involving both contextual and individual factors, when studying social coping across human groups. Behaviorally, autistic relative to non-autistic adults reported a greater number of monitored behavioral aspects when social demands were high compared to low, mirroring the interaction effect observed for the extent of behavioral monitoring facet in the ANOVA (Results 1). This new evidence from episodic IM experiences corroborates previous questionnaire-based findings (e.g., using the CAT-Q [ 52 ]) indicating heightened camouflaging use in autistic people and individuals with elevated autistic traits [ 1 , 39 , 85 , 86 ]. Behavioral domain-specific patterns added important nuances. Under high social demand, both groups descriptively up-regulated their monitoring of voice delivery, suggesting that speech modulation is a ubiquitous IM response to social evaluation. Only autistic adults, however, showed sharp increases in the proportion of monitored nonverbal behaviors, including facial expressions, gestures, and eye contact. These autism-specific patterns echo qualitative reports that autistic individuals track nonverbal expressions to avoid negative judgments [ 2 , 80 , 87 , 88 ]. Moreover, when perceived by non-autistic peers, facial expressivity in autistic individuals often oscillates between “flat in affect” and “exaggerated,” and tends to be judged as “odd” or “unnatural” [ 89 – 91 ]. During mock-interviews, the level of emotional expressivity differs between autistic and non-autistic adults, and negative expressivity in autistic adults is particularly linked to their worse impressions made than non-autistic adults [ 42 ]. It is thus possible that autistic individuals feel a need to particularly up-regulate non-verbal cues (such as eye contact, facial expressions, and gestures) to meet non-autistic display rules and improve impressions when social stakes are high. Meanwhile, expectations on gestures and eye contact may be less salient and more intuitive for non-autistic than autistic adults across social scenarios and thus less sensitive to situational shifts [ 8 ]. The elastic-net regressions revealed preliminary links between individual traits and context-dependent IM experiences. Higher CAT-Q scores, indexing more frequent camouflaging intention and use during daily life, were linked to increased IM experiential discrepancies (HSD–LSD) in felt inauthenticity, behavioral monitoring extent, and provoked anxiety. This is consistent with existing research linking camouflaging with poorer mental health and greater cognitive burden [ 4 , 5 , 34 ]; such an alignment between findings of the context-discrepancy assessment and of prior CAT-Q-based research suggests construct convergence and lends support for task validity. In contrast, individuals with higher subjectively perceived self-monitoring ability reported smaller IM experiential discrepancies in behavioral monitoring extent. This distinction between CAT-Q and RSMS scores in relation to IM experiential facets underscores a key conceptual difference between how often one uses IM and one’s self-efficacy in doing so [ 23 ]. Likewise, greater self-rated executive functioning was linked to smaller IM experiential discrepancies in both behavioral monitoring extent and provoked anxiety, suggesting that ample cognitive resources for planning, inhibition, and working memory may cushion the strain of social cue tracking and behavioral adjustments [ 8 , 32 , 92 , 93 ]. Overall, people who view themselves as skilled self-monitors with robust executive functioning seem to require less additional monitoring when social demands rise—possibly because IM is more intuitive or adaptive, or because of their confidence in strategy use [ 94 , 95 ]. Future work could further assess IM facets and outcomes by using the recorded videos in the context-discrepancy assessment as study stimuli (informed consent already obtained) and incorporating third-party evaluations on, for instance, the observers’ judgements of the narrator’s diagnosis/neurotype, social favorability, or IM efforts and efficiency. Individuals reporting higher compared to lower ADHD traits felt much more inauthentic during IM in the HSD relative to LSD condition. Meanwhile, individuals with higher compared to lower autistic traits reported smaller IM experiential discrepancies in provoked anxiety. These preliminary observations highlight the need for research to unpack how and why autistic and ADHD traits show diverging links with the immediate psychological strains of IM. Notably, although the negative link between autistic trait levels and the IM experiential discrepancy in provoked anxiety appears contradictory to the ANOVA-based results, one plausible interpretation is that it may not be person-level autistic traits (e.g., autism-associated cognitive characteristics) that primarily contribute to greater felt anxiety when social-evaluative demands are higher, but instead the binary group designation and minoritized status of autism that accounts for elevated anxiety. Future well-powered investigations are needed to untangle the likely differential roles of dimensional autistic traits and categorical autism diagnosis in shaping facets of IM experiences. Limitations This study has several limitations. First, although video-mediated social interactions are increasingly prevalent (e.g., online interviews, video conferencing and messaging) [ 96 – 98 ], the laboratory-based setting cannot fully capture the spontaneity of IM during real-life encounters. Alternative designs such as ecological momentary assessments will be helpful for mapping how individual characteristics interact with dynamic social affordances to shape real-world IM experiences. Second, the exploratory elastic-net regression analyses, although appropriate for modest samples, may have missed weaker but meaningful trait–facet links, underscoring the need for replication with larger and more diverse cohorts. The links uncovered by the elastic-net regressions can only serve as preliminary signposts rather than definitive conclusions. Nonetheless, these results guide future work on IM heterogeneity across autistic and non-autistic individuals, expanding beyond questionnaire-based studies on the individual trait correlates with the CAT-Q [ 52 ]. We administered the social demand conditions in a fixed order to create the same discrepancy in social-evaluative pressure for each participant, circumvent order-effect analyses that our sample size precludes, and close the study session with minimal participant discomfort. However, using a fixed-order design means we could not rule out order-related confounds. For instance, participants may have been more fatigued in the LSD condition after completing the more stress-inducing HSD condition, which could have reduced their IM. Meanwhile, practice effects are also possible; as participants became more familiar with the equipment and procedures, they might have felt more at ease, potentially altering how they deployed IM across conditions. Future work using the context-discrepancy assessment should examine whether the findings can be replicated using counter-balanced or fully randomized designs. We were unable to perform well-powered sex- and gender-moderated analyses in this initial validation study due to the modest sample size. Nevertheless, given our female-majority sample, we repeated Analyses 1 and 2 in an assigned-female-only subsample to assess robustness of the finding patterns (Supplementary Materials). Patterns of context-discrepancies in IM experiences and behavioral profiles between diagnosis groups were broadly consistent in the full sample and the assigned-female-only subsample (Tables S1 , S2 , S3 , and Figure S1 ). Although some interaction effects in Analysis 1 were no longer significant, the directions of descriptive differences between autistic and non-autistic assigned-females were comparable to those of the full sample (see Table S1 for details). Because we cannot determine whether these changes reflect reduced power or true sex-based differences, future targeted work is needed to clarify how sex- and gender-related factors shape the processes, goals, and outcomes of IM. For example, recent work has often reported on average higher camouflaging among autistic assigned-females than autistic assigned-males [ 1 , 39 ], but it is unclear which specific facets of camouflaging/IM are elevated. Furthermore, first impressions of autistic girls tend to be more positive than those of autistic boys, but autistic girls are perceived to be less feminine [ 99 , 100 ]. Autistic girls’ IM may stem from and portray better social communication skills, but their IM may involve an additional burden of meeting gendered expectations surrounding feminine expressions and roles [ 101 ], which generally involve qualities of being sensitive to others, caring, and interpersonal warmth [ 102 ]. Future research powered by larger, more representative, and sex- and gender-balanced samples is needed to elucidate these differences in IM experiences and properly unpack how they are shaped by sex- and gender-related mechanisms. We were also unable to account for participants’ racial and ethnic identities, which may shape how individuals engage in IM. Impression formation research suggests that Black and White autistic individuals differ in first impression social favorability ratings (e.g., likeability, trustworthiness [ 103 ]), which may in turn foster race-contingent IM goals and strategies to meet racialized expectations. Recent findings suggest that camouflaging may elicit complex and potentially distinct experiential burdens in Black and Latino autistic young people as they contend with intersectional societal pressures [ 104 , 105 ]. More broadly, much more research in non-English-speaking and non-WEIRD countries is needed to clarify how culture shapes autistic people’s IM [ 106 ]. IM is likely embedded in one’s surrounding socio-cultural niche, and emerging evidence of cross-cultural variations in IM among autistic individuals underscores the importance of studying these processes across cultural settings [ 107 – 109 ]. A more complete understanding of how IM facets emerge from the transactions between individuals (e.g., racial/ethnic and neurodivergent identities) and their environments (e.g., intersectional socio-cultural norms and expectations) is necessary to delineate the unique challenges, strategies, and consequences of IM—and to move toward more personalized social coping supports for autistic people across intersectional identities and cultures. Finally, our findings apply only to verbal adults without intellectual disability; IM strategies such as modulating voice delivery or speech content are unlikely to apply to minimally speaking individuals. Conclusions This study addresses the limited measurement scope of IM in autistic individuals by introducing a novel context-discrepancy assessment. This task advances the field by capturing the discrepancies in multiple facets of episodic IM across naturalistic social scenarios. Insights derived from this task shed light into how autistic and non-autistic adults differently coordinate and experience IM in response to varying social demands, and the individual traits linked to its heterogeneity. Across autistic and non-autistic adults, heightened social demands raised IM effort. Yet, autistic adults reported greater inauthenticity and extent of behavioral monitoring when social demands were high compared to low, as well as disproportionately increased anxiety relative to non-autistic adults in these circumstances. Autistic adults also particularly coped with higher social demands through increased monitoring of nonverbal behaviors. Facets of IM experiences were differentially associated with self-reported individual traits. These multi-faceted insights extend prior qualitative, questionnaire-based, and theoretical work in autistic IM. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (141.8KB, docx) Acknowledgements The authors would like to thank Madison Gibbs for assistance in data collection as well as everyone who participated in the study. Abbreviations CAT-Q Camouflaging Autistic Traits Questionnaire IM Impression Management HSD High Social Demand LSD Low Social Demand IRI-PT Interpersonal Reactivity Index–Perspective Taking Subscale AEFI Amsterdam Executive Function Inventory SATQ Subthreshold Autism Trait Questionnaire ASRS Adult ADHD Self-Report Scale RSMS Revised Self-Monitoring Scale BEQ Berkeley Expressivity Questionnaire Mdn Median IQR Interquartile Range Q1 25th percentile of IQR Q3 75th percentile of IQR Author contributions W.A.—Conceptualization, Data curation, Methodology, Formal analysis, Writing–original draft preparation. J.X.Y.—Methodology, Data curation, Formal analysis, Writing–Reviewing and editing. B.K.J.—Data curation, Writing–Reviewing and editing. M-C.L.—Supervision, Conceptualization, Methodology, Resources, Writing–original draft preparation, Writing–Reviewing and editing. All authors read and approved the final manuscript. Funding This work was supported by the Canadian Institutes of Health Research (Sex and Gender Science Chair, GSB 171373) and the Centre for Addiction and Mental Health Foundation. The funding source had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to submit the manuscript for publication. Data availability The anonymized dataset could be available upon reasonable request reviewed by the research team, from M-C.L. at: [email protected]. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the University of Toronto Research Ethics Board in the Department of Psychology. All participants provided consent to participate before they took part in the study. Consent for publication Not applicable. Competing interests M-C.L. is an Associate Editor of Molecular Autism. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Cook J, Hull L, Crane L, Mandy W. Camouflaging in autism: A systematic review. Clin Psychol Rev. 2021;89:102080. 10.1016/j.cpr.2021.102080. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Hull L, Petrides KV, Allison C, Smith P, Baron-Cohen S, Lai M-C, et al. Putting on my best normal: Social camouflaging in adults with autism spectrum conditions. J Autism Dev Disord. 2017;47:2519–34. 10.1007/s10803-017-3166-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Attwood T. The complete guide to Asperger’s syndrome. London, England: Jessica Kingsley; 2007. p. 397. [ Google Scholar ] 4. Zhuang S, Tan DW, Reddrop S, Dean L, Maybery M, Magiati I. Psychosocial factors associated with camouflaging in autistic people and its relationship with mental health and well-being: A mixed methods systematic review. Clin Psychol Rev. 2023;105:102335. 10.1016/j.cpr.2023.102335. [ DOI ] [ PubMed ] [ Google Scholar ] 5. Field SL, Williams MO, Jones CRG, Fox JRE. A meta-ethnography of autistic people’s experiences of social camouflaging and its relationship with mental health. Autism. 2024;28:1328–43. 10.1177/13623613231223036. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Khudiakova V, Alexandrovsky M, Ai W, Lai M-C. What we know and do not know about camouflaging, impression management, and mental health and wellbeing in autistic people. Autism Res. 2025;18:273–80. 10.1002/aur.3299. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Arnold WM, Bitsika V, Sharpley CF. Camouflaging and autism: Conceptualisation and methodological issues. Autism Int J Res Pract. 2026;13623613261420085. 10.1177/13623613261420085. [ DOI ] [ PMC free article ] [ PubMed ] 8. Ai W, Cunningham WA, Lai M-C. Reconsidering autistic ‘camouflaging’ as transactional impression management. Trends Cogn Sci. 2022;26:631–45. 10.1016/j.tics.2022.05.002. [ DOI ] [ PubMed ] [ Google Scholar ] 9. Leary MR, Kowalski RM. Impression management: A literature review and two-component model. Psychol Bull. 1990;107:34–47. 10.1037/0033-2909.107.1.34. [ Google Scholar ] 10. Hannon B, Mandy W, Hull L. A comparison of methods for measuring camouflaging in autism. Autism Res. 2023;16:12–29. 10.1002/aur.2850. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Williams ZJ, Commentary et al. The construct validity of ‘camouflaging’ in autism: psychometric considerations and recommendations for future research - reflection on Lai. (2020). J Child Psychol Psychiatry. 2022;63:118–21. 10.1111/jcpp.13468 [ DOI ] [ PMC free article ] [ PubMed ] 12. Livingston LA, Shah P, Milner V, Happé F. Quantifying compensatory strategies in adults with and without diagnosed autism. Mol Autism. 2020;11:15. 10.1186/s13229-019-0308-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Lai M-C, Lombardo MV, Ruigrok AN, Chakrabarti B, Auyeung B, Szatmari P, et al. Quantifying and exploring camouflaging in men and women with autism. Autism. 2017;21:690–702. 10.1177/1362361316671012. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Milner V, Colvert E, Mandy W, Happé F. A comparison of self-report and discrepancy measures of camouflaging: Exploring sex differences in diagnosed autistic versus high autistic trait young adults. Autism Res. 2023;16:580–90. 10.1002/aur.2873. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Corbett BA, Schwartzman JM, Libsack EJ, Muscatello RA, Lerner MD, Simmons GL, et al. Camouflaging in autism: Examining sex-based and compensatory models in social cognition and communication. Autism Res. 2021;14:127–42. 10.1002/aur.2440. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, et al. The Autism Diagnostic Observation Schedule—Generic: A standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000;30:205–23. 10.1023/A:1005592401947. [ PubMed ] [ Google Scholar ] 17. Cook J, Crane L, Bourne L, Hull L, Mandy W. Camouflaging in an everyday social context: An interpersonal recall study. Autism. 2021;25:1444–56. 10.1177/1362361321992641. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Livingston LA, Shah P, Happé F. Compensatory strategies below the behavioural surface in autism: a qualitative study. Lancet Psychiatry. 2019;6:766–77. 10.1016/S2215-0366(19)30224-X. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Lee S-J, Quigley BM, Nesler MS, Corbett AB, Tedeschi JT. Development of a self-presentation tactics scale. Personal Individ Differ. 1999;26:701–22. 10.1016/S0191-8869(98)00178-0. [ Google Scholar ] 20. Larson DG, Chastain RL. Self-concealment: Conceptualization, measurement, and health implications. J Soc Clin Psychol. 1990;9:439–55. 10.1521/jscp.1990.9.4.439. [ Google Scholar ] 21. Cutler BL, Wolfe RN. Construct validity of the Concern for Appropriateness scale. J Pers Assess. 1985;49:318–23. 10.1207/s15327752jpa4903_19. [ DOI ] [ PubMed ] [ Google Scholar ] 22. AiW, Anchordoqui A, Bogdanova O, Pomies V, Coutelle R, Atzori P et al. Untangling sex and gender differences in impression management and associated autism features in French autistic adults. Preprint at 10.31234/osf.io/ervmj_v1. 23. Ai W, Wang YA, Lai MC. Intentional use and self-efficacy as distinct facets of impression management and their relationships with socio-motivational, cognitive, and mental health factors. Sci Rep. 2025;15:41050. 10.1038/s41598-025-24899-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Lennox RD, Wolfe RN. Revision of the Self-Monitoring Scale. J Pers Soc Psychol. 1984;46:1349–64. 10.1037/0022-3514.46.6.1349. [ DOI ] [ PubMed ] [ Google Scholar ] 25. Miller D, Rees J, Pearson A. Masking is life: Experiences of masking in autistic and nonautistic adults. Autism Adulthood. 2021;3:330–8. 10.1089/aut.2020.0083. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Sasson NJ, Morrison KE, Pinkham AE, Faso DJ, Chmielewski M. Brief Report: Adults with autism are less accurate at predicting how their personality traits are evaluated by unfamiliar observers. J Autism Dev Disord. 2018;48:2243–8. 10.1007/s10803-018-3487-z. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Cage E, Troxell-Whitman Z. Understanding the reasons, contexts and costs of camouflaging for autistic adults. J Autism Dev Disord. 2019;49:1899–911. 10.1007/s10803-018-03878-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Cage E, Troxell-Whitman Z. Understanding the relationships between autistic identity, disclosure, and camouflaging. Autism Adulthood. 2020;2:334–8. 10.1089/aut.2020.0016. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Lawson W. Adaptive morphing and coping with social threat in autism: An autistic perspective. J Intellect Disabil. 2020;8:519–26. 10.6000/2292-2598.2020.08.03.29. [ Google Scholar ] 30. Pearson A, Rose K. A Conceptual analysis of autistic masking: Understanding the narrative of stigma and the illusion of choice. Autism Adulthood. 2021;3:52–60. 10.1089/aut.2020.0043. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Pachankis JE, Mahon CP, Jackson SD, Fetzner BK, Bränström R. Sexual orientation concealment and mental health: A conceptual and meta-analytic review. Psychol Bull. 2020;146:831–71. 10.1037/bul0000271. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Critcher CR, Ferguson MJ. The cost of keeping it hidden: Decomposing concealment reveals what makes it depleting. J Exp Psychol Gen. 2014;143:721–35. 10.1037/a0033468. [ DOI ] [ PubMed ] [ Google Scholar ] 33. Ai W, Cunningham WA, Lai M-C. Camouflaging, internalized stigma, and mental health in the general population. Int J Soc Psychiatry. 2024;70:1239–53. 10.1177/00207640241260020. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Khudiakova V, Russell E, Sowden-Carvalho S, Surtees ADR. A systematic review and meta-analysis of mental health outcomes associated with camouflaging in autistic people. Res Autism Spectr Disord. 2024;118:102492. 10.1016/j.rasd.2024.102492. [ Google Scholar ] 35. Evans JA, Krumrei-Mancuso EJ, Rouse SV. What you are hiding could be hurting you: Autistic masking in relation to mental health, interpersonal trauma, authenticity, and self-esteem. Autism Adulthood. 2024;6:229–40. 10.1089/aut.2022.0115. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Hull L, Levy L, Lai M-C, Petrides KV, Baron-Cohen S, Allison C, et al. Is social camouflaging associated with anxiety and depression in autistic adults? Mol Autism. 2021;12:13. 10.1186/s13229-021-00421-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Schuck RK, Flores RE, Fung LK. Brief Report: Sex/gender differences in symptomology and camouflaging in adults with autism spectrum disorder. J Autism Dev Disord. 2019;49:2597–604. 10.1007/s10803-019-03998-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Nel J, Spedding M, Malcolm-Smith S. Consolidating a framework of autistic camouflaging strategies: An integrative systematic review. Autism Int J Res Pract. 2025;29:2379–94. 10.1177/13623613251335472. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Cancino-Barros I, Villacura-Herrera C, Castillo RD. A meta-analytic review of quantification methods for camouflaging behaviors in autistic and neurotypical individuals. Sci Rep. 2025;15:22885. 10.1038/s41598-025-06137-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Coan JA, Sbarra DA. Social Baseline Theory: The social regulation of risk and effort. Curr Opin Psychol. 2015;1:87–91. 10.1016/j.copsyc.2014.12.021. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Leary MR, Baumeister RF. The nature and function of self-esteem: Sociometer theory. Adv Exp Soc Psychol. 2000;1–62. 10.1016/S0065-2601(00)80003-9. 42. Foster SJ, Jones DR, Pinkham AE, Sasson NJ. Facial affect differences in autistic and non-autistic adults across contexts and their relationship to first-impression formation. Autism Adulthood. 2025;7:581–93. 10.1089/aut.2023.0199. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Ai W, Cunningham WA, Lai M-C. The dimensional structure of the Camouflaging Autistic Traits Questionnaire (CAT-Q) and predictors of camouflaging in a representative general population sample. Compr Psychiatry. 2024;128:152434. 10.1016/j.comppsych.2023.152434. [ DOI ] [ PubMed ] [ Google Scholar ] 44. Riggio RE, Friedman HS. Impression formation: The role of expressive behavior. J Pers Soc Psychol. 1986;50:421–7. 10.1037/0022-3514.50.2.421. [ DOI ] [ PubMed ] [ Google Scholar ] 45. Davis MH. Measuring individual differences in empathy: Evidence for a multidimensional approach. J Pers Soc Psychol. 1983;44:113–26. 10.1037/0022-3514.44.1.113. [ Google Scholar ] 46. Van der Elst W, Ouwehand C, van der Werf G, Kuyper H, Lee N, Jolles J. The Amsterdam Executive Function Inventory (AEFI): Psychometric properties and demographically corrected normative data for adolescents aged between 15 and 18 years. J Clin Exp Neuropsychol. 2012;34:160–71. 10.1080/13803395.2011.625353. [ DOI ] [ PubMed ] [ Google Scholar ] 47. Baars MAE, Nije Bijvank M, Tonnaer GH, Jolles J. Self-report measures of executive functioning are a determinant of academic performance in first-year students at a university of applied sciences. Front Psychol. 2015;6. 10.3389/fpsyg.2015.01131. [ DOI ] [ PMC free article ] [ PubMed ] 48. Kanne SM, Wang J, Christ SE. The Subthreshold Autism Trait Questionnaire (SATQ): Development of a brief self-report measure of subthreshold autism traits. J Autism Dev Disord. 2012;42:769–80. 10.1007/s10803-011-1308-8. [ DOI ] [ PubMed ] [ Google Scholar ] 49. Kessler RC, Adler LA, Gruber MJ, Sarawate CA, Spencer T, Van Brunt DL. Validity of the World Health Organization Adult ADHD Self-Report Scale (ASRS) Screener in a representative sample of health plan members. Int J Methods Psychiatr Res. 2007;16:52–65. 10.1002/mpr.208. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. Adler LA, Spencer T, Faraone SV, Kessler RC, Howes MJ, Biederman J, et al. Validity of pilot adult ADHD Self- Report Scale (ASRS) to rate adult ADHD symptoms. Ann Clin Psychiatry. 2006;18:145–8. 10.1080/10401230600801077. [ DOI ] [ PubMed ] [ Google Scholar ] 51. Green JG, DeYoung G, Wogan ME, Wolf EJ, Lane KL, Adler LA. Evidence for the reliability and preliminary validity of the Adult ADHD Self-Report Scale v1.1 (ASRS v1.1) Screener in an adolescent community sample. Int J Methods Psychiatr Res. 2019;28:e1751. 10.1002/mpr.1751. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Hull L, Mandy W, Lai M-C, Baron-Cohen S, Allison C, Smith P, et al. Development and validation of the Camouflaging Autistic Traits Questionnaire (CAT-Q). J Autism Dev Disord. 2019;49:819–33. 10.1007/s10803-018-3792-6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Anderson LR. Test-retest reliability of the Revised Self-monitoring Scale over a two-year period. Psychol Rep. 1991;68:1057–8. 10.2466/pr0.1991.68.3.1057. [ DOI ] [ PubMed ] [ Google Scholar ] 54. Gross JJ, John OP. Revealing feelings: facets of emotional expressivity in self-reports, peer ratings, and behavior. J Pers Soc Psychol. 1997;72:435–48. 10.1037//0022-3514.72.2.435. [ DOI ] [ PubMed ] 55. Gross JJ, John OP. Facets of emotional expressivity: Three self-report factors and their correlates. Personal Individ Differ. 1995;19:555–68. 10.1016/0191-8869(95)00055-B. [ Google Scholar ] 56. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300. 10.1111/j.2517-6161.1995.tb02031.x. [ Google Scholar ] 57. Krippendorff K. Content analysis: An introduction to its methodology. Sage. 1980. 10.4135/9781071878781. [ Google Scholar ] 58. Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15:1277–88. 10.1177/1049732305276687. [ DOI ] [ PubMed ] [ Google Scholar ] 59. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67:301–20. 10.1111/j.1467-9868.2005.00503.x. [ Google Scholar ] 60. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22. [ PMC free article ] [ PubMed ] [ Google Scholar ] 61. Du J, Boss J, Han P, Beesley LJ, Kleinsasser M, Goutman SA, et al. Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods. J Comput Graph Stat. 2022;31:1063–75. 10.1080/10618600.2022.2035739. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Geronimi J, Saporta G. Variable selection for multiply-imputed data with penalized generalized estimating equations. Comput Stat Data Anal. 2017;110:103–14. 10.1016/j.csda.2017.01.001. [ Google Scholar ] 63. Wang Q, Hall GJ, Zhang Q, Comella S. Predicting implementation of response to intervention in math using elastic net logistic regression. Front Psychol. 2024;15. 10.3389/fpsyg.2024.1410396. [ DOI ] [ PMC free article ] [ PubMed ] 64. Wood AM, White IR, Royston P. How should variable selection be performed with multiply imputed data? Stat Med. 2008;27:3227–46. 10.1002/sim.3177. [ DOI ] [ PubMed ] [ Google Scholar ] 65. Wan Y, Datta S, Conklin DJ, Kong M. Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect. J Stat Comput Simul. 2015;85:1902–16. 10.1080/00949655.2014.907801. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Heymans MW, van Buuren S, Knol DL, van Mechelen W, de Vet HCW. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Methodol. 2007;7:33. 10.1186/1471-2288-7-33. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Liu Y, Wang Y, Feng Y, Wall MM. Variable selection and prediction with incomplete high-dimensional data. Ann Appl Stat. 2016;10:418–50. 10.1214/15-AOAS899. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. Long Q, Johnson BA. Variable selection in the presence of missing data: Resampling and imputation. Biostat Oxf Engl. 2015;16:596–610. 10.1093/biostatistics/kxv003. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Graham JW. Missing data analysis: Making it work in the real world. Annu Rev Psychol. 2009;60:549–76. 10.1146/annurev.psych.58.110405.085530. [ DOI ] [ PubMed ] [ Google Scholar ] 70. van Burren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. J Stat Softw 2011; 45. https://www.jstatsoft.org/article/view/v045i03 71. Ferguson CJ. An effect size primer: A guide for clinicians and researchers. Prof Psychol Res Pract. 2009;40:532–8. 10.1037/a0015808. [ Google Scholar ] 72. Gignac GE, Szodorai ET. Effect size guidelines for individual differences researchers. Personal Individ Differ. 2016;102:74–8. 10.1016/j.paid.2016.06.069. [ Google Scholar ] 73. Zahid FM, Faisal S, Heumann C. Variable selection techniques after multiple imputation in high-dimensional data. Stat Methods Appl. 2020;29:553–80. 10.1007/s10260-019-00493-7. [ Google Scholar ] 74. Gunn HJ, Hayati Rezvan P, Fernández MI, Comulada WS. How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion. Psychol Methods. 2023;28:452–71. 10.1037/met0000478. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Mizuno A, Karim HT, Newmark J, Khan F, Rosenblatt MJ, Neppach AM, et al. Thinking of me or thinking of you? Behavioral correlates of self vs. other centered worry and reappraisal in late-life. Front Psychiatry. 2022;13:780745. 10.3389/fpsyt.2022.780745. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. Goffman E. The presentation of self in everyday life. Soc Theory Re-Wired. 3rd ed. Routledge; 2023. 77. Bolino MC, Kacmar KM, Turnley WH, Gilstrap JB. A multi-level review of impression management motives and behaviors. J Manag. 2008;34:1080–109. 10.1177/0149206308324325. [ Google Scholar ] 78. Crane L, Adams F, Harper G, Welch J, Pellicano E. Something needs to change: Mental health experiences of young autistic adults in England. Autism Int J Res Pract. 2019;23:477–93. 10.1177/1362361318757048. [ DOI ] [ PubMed ] [ Google Scholar ] 79. Trembath D, Germano C, Johanson G, Dissanayake C. The experience of anxiety in young adults with autism spectrum disorders. Focus Autism Dev Disabil. 2012;27:213–24. 10.1177/1088357612454916. [ Google Scholar ] 80. Chapman L, Rose K, Hull L, Mandy W. I want to fit in… but I don’t want to change myself fundamentally: A qualitative exploration of the relationship between masking and mental health for autistic teenagers. Res Autism Spectr Disord. 2022;99:102069. 10.1016/j.rasd.2022.102069. [ Google Scholar ] 81. Leary MR, Baumeister RF. The nature and function of self-esteem: Sociometer theory. Adv Exp Soc Psychol. 2000;32:1–62. 10.1016/S0065-2601(00)80003-9. [ Google Scholar ] 82. DeBrabander KM, Morrison KE, Jones DR, Faso DJ, Chmielewski M, Sasson NJ. Do first impressions of autistic adults differ between autistic and nonautistic observers? Autism Adulthood. 2019;1:250–7. 10.1089/aut.2019.0018. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 83. Sasson NJ, Faso DJ, Nugent J, Lovell S, Kennedy DP, Grossman RB. Neurotypical peers are less willing to interact with those with autism based on thin slice judgments. Sci Rep. 2017;7:40700. 10.1038/srep40700. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 84. Sutherland HEA, Fletcher-Watson S, Long J, Crompton CJ. A difference in typical values: Autistic perspectives on autistic social communication. Scand J Disabil Res. 2025;27:313–29. 10.16993/sjdr.1184. [ Google Scholar ] 85. Lei J, Leigh E, Charman T, Russell A, Hollocks MJ. Exploring the association between social camouflaging and self- versus caregiver-report discrepancies in anxiety and depressive symptoms in autistic and non-autistic socially anxious adolescents. Autism. 2024;28:2657–74. 10.1177/13623613241238251. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 86. van der Putten WJ, Mol AJJ, Groenman AP, Radhoe TA, Torenvliet C, van Rentergem JAA, et al. Is camouflaging unique for autism? A comparison of camouflaging between adults with autism and ADHD. Autism Res. 2024;17:812–23. 10.1002/aur.3099. [ DOI ] [ PubMed ] [ Google Scholar ] 87. Cook J, Crane L, Hull L, Bourne L, Mandy W. Self-reported camouflaging behaviours used by autistic adults during everyday social interactions. Autism. 2022;26:406–21. 10.1177/13623613211026754. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 88. Perry E, Mandy W, Hull L, Cage E. Understanding camouflaging as a response to autism-related stigma: A social identity theory approach. J Autism Dev Disord. 2022;52:800–10. 10.1007/s10803-021-04987-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 89. Faso DJ, Sasson NJ, Pinkham AE. Evaluating posed and evoked facial expressions of emotion from adults with autism spectrum disorder. J Autism Dev Disord. 2015;45:75–89. 10.1007/s10803-014-2194-7. [ DOI ] [ PubMed ] [ Google Scholar ] 90. Grossman RB, Edelson LR, Tager-Flusberg H. Emotional facial and vocal expressions during story retelling by children and adolescents with high-functioning autism. J Speech Lang Hear Res. 2013;56:1035–44. 10.1044/1092-4388(2012/12-0067). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 91. Volker MA, Lopata C, Smith DA, Thomeer ML. Facial encoding of children with high-functioning autism spectrum disorders. Focus Autism Dev Disabil. 2009;24:195–204. 10.1177/1088357609347325. [ Google Scholar ] 92. Baumeister RF, Vohs KD. Self-regulation and the executive function of the self. Handb Self Identity. 2nd ed. New York, NY, US: The Guilford Press; 2012. pp. 180–97. [ Google Scholar ] 93. White SW, Mazefsky CA, Dichter GS, Chiu PH, Richey JA, Ollendick TH. Social-cognitive, physiological, and neural mechanisms underlying emotion regulation impairments: understanding anxiety in autism spectrum disorder. Int J Dev Neurosci. 2014;39:22–36. 10.1016/j.ijdevneu.2014.05.012. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 94. Pillow DR, Hale WJ, Crabtree MA, Hinojosa TL. Exploring the relations between self-monitoring, authenticity, and well-being. Personal Individ Differ. 2017;116:393–8. 10.1016/j.paid.2017.04.060. [ Google Scholar ] 95. Snyder M. Self-monitoring of expressive behavior. J Pers Soc Psychol. 1974;30:526–37. 10.1037/h0037039. [ Google Scholar ] 96. Țarcă V, Luca F-A, Țarcă E. The digital edge: Skills that matter in the European labour market after COVID-19. Economies. 2024;12:273. 10.3390/economies12100273. [ Google Scholar ] 97. Government of Canada SC. The daily — Canadian internet use survey, 2022 [Internet]. 2023 [cited 2025 Nov 8]. https://www150.statcan.gc.ca/n1/daily-quotidien/230720/dq230720b-eng.htm 98. Pfund GN, Hill PL, Harriger J. Video chatting and appearance satisfaction during COVID-19: Appearance comparisons and self-objectification as moderators. Int J Eat Disord. 2020;53:2038–43. 10.1002/eat.23393. [ DOI ] [ PubMed ] [ Google Scholar ] 99. Baer M, Cola M, Knox A, Lyons M, Schillinger S, Lee A, et al. Social first impressions and perceived gender in autistic and non-autistic youth. Sci Rep. 2025;15:5240. 10.1038/s41598-025-89083-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 100. Cola ML, Plate S, Yankowitz L, Petrulla V, Bateman L, Zampella CJ, et al. Sex differences in the first impressions made by girls and boys with autism. Mol Autism. 2020;11:49. 10.1186/s13229-020-00336-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 101. Bargiela S, Steward R, Mandy W. The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. J Autism Dev Disord. 2016;46:3281–94. 10.1007/s10803-016-2872-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 102. Bem SL. The measurement of psychological androgyny. J Consult Clin Psychol. 1974;42:155–62. 10.1037/h0036215. [ PubMed ] [ Google Scholar ] 103. Jones DR, Sasson NJ. Intersectional effects of race and gender on first impressions of Black and White autistic adults. Autism Int J Res Pract. 2026;30:452–65. . 10.1177/13623613251389291 [ DOI ] [ PubMed ] [ Google Scholar ] 104. Nelson T, Lichwa H. The lived experiences of masking black Autistic girls in UK education: Before people see the autism, they see my race. Educ Psychol Pract. 2025;41:417–38. 10.1080/02667363.2025.2541211. [ Google Scholar ] 105. Pagán AF, Loveland KA, Acierno R. Cultural influences on camouflaging and autistic burnout: Examining the experiences of Latino autistic young adults. Autism. 2025;30:346–61. 10.1177/13623613251380340. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 106. Keating CT, Hickman L, Geelhand P, Takahashi T, Leung J, Monk R, et al. Cross-cultural variation in experiences of acceptance, camouflaging and mental health difficulties in autism: A registered report. PLoS ONE. 2024;19:e0299824. 10.1371/journal.pone.0299824. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 107. Hongo M, Oshima F, Guan S, Takahashi T, Nitta Y, Seto M, et al. Reliability and validity of the Japanese version of the camouflaging autistic traits questionnaire. Autism Res. 2024;17:1205–17. 10.1002/aur.3137. [ DOI ] [ PubMed ] [ Google Scholar ] 108. Liu C-H, Chen Y-L, Chen P-J, Ni H-C, Lai M-C. Exploring camouflaging by the Chinese version Camouflaging Autistic Traits Questionnaire in Taiwanese autistic and non-autistic adolescents: An initial development. Autism. 2024;28:690–704. 10.1177/13623613231181732. [ DOI ] [ PubMed ] [ Google Scholar ] 109. Oshima F, Takahashi T, Tamura M, Guan S, Seto M, Hull L, et al. The association between social camouflage and mental health among autistic people in Japan and the UK: a cross-cultural study. Mol Autism. 2024;15:1. 10.1186/s13229-023-00579-w. [ 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 Supplementary Material 1 (141.8KB, docx) Data Availability Statement The anonymized dataset could be available upon reasonable request reviewed by the research team, from M-C.L. at: [email protected]. Articles from Molecular Autism are provided here courtesy of BMC ACTIONS View on publisher site PDF (3.2 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top