Do Autistic Adolescents Talk Differently in Same-Sex Versus Mixed-Sex Conversations?

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Cho, S., Fulop, L., Mulqueen, A., Faulk, C., Bordofsky, A., Williams, Z. J., Vernon, T., & Parish-Morris, J. (2026). Do Autistic Adolescents Talk Differently in Same-Sex Versus Mixed-Sex Conversations? Autism. https://doi.org/10.1177/13623613261485603

Publication date: 18 Sep 2026 Added to AutiHub: 19 Sep 2026 Type: Article Article language: English
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Authors

Publication authors
8
Publication authors identified as autistic
1 / 8 (12.5%)
  1. Sunghye Cho
  2. Lizzy Fulop
  3. Alexandria Mulqueen
  4. Calliana Faulk
  1. Aniela Bordofsky
  2. Zachary J. Williams *
  3. Ty Vernon
  4. Julia Parish-Morris

Abstract

Autistic individuals often struggle with social communication, but the nature of each person’s difficulties varies substantially by context. Recent conversation-based research suggests that males and females (autistic and nonautistic) produce subtle but consistent differences in language during spontaneous conversation. However, the majority of prior studies of conversation in autism include female conversation partners only rather than systematically manipulating whether conversation dyads were same-sex or mixed-sex, thus limiting the generalizability of the findings to everyday life. In this first-of-its-kind study, we examine speech-language behavior in 35 autistic adolescents engaged in two 5-min conversations with nonautistic partners who were sex-matched or -mismatched to the participant. Data were collected at a public research university in the United States as part of a baseline assessment for a randomized control trial that concluded in 2015. Linear mixed effects regression revealed significant effects of sex-match or -mismatch on language, such that participants in same-sex conversations were more talkative, took longer turns, and paused differently than participants in mixed-sex conversations. Results highlight the importance of considering a partner’s demographic characteristics when designing studies involving natural language in autism and suggest that prior research with single-sex conversation partners should be interpreted cautiously as results may not generalize to real-world contexts. Improved understanding of everyday conversational language in autism can be used to inform neurodiversity-affirming supportive interventions while also identifying robust linguistic markers of social phenotype that can be used as outcome measures in future clinical trials. Lay Abstract Autistic individuals often speak and interact differently than nonautistic people, which can lead to challenges when these groups engage in conversation together. Communication can look very different according to the situation, however, including who is in the conversation. For example, prior research found sex differences in autistic language during natural, get-to-know-you conversations, but most existing studies did not consider the potential influence of who a person is talking to. In other words, does it matter whether you are talking to someone that is similar to you versus different from you? This gap in prior research limits our understanding of how natural conversation plays out in real life, when individuals talk to a wide variety of people. In this first-of-its-kind study, we examine whether autistic participants’ language changes depending on whether they talk to a male or a female conversation partner. Thirty-five female and male autistic adolescents participated in two 5-min conversations with nonautistic partners who were sex-matched or -mismatched to the participant. Analyses showed that participants in same-sex conversations were more talkative, took longer turns, and paused differently than participants in mixed-sex conversations. Results highlight the importance of considering partner characteristics when analyzing natural language in autism and suggest that prior research should be interpreted cautiously as results may not generalize to real-world contexts. This study contributes to the goal of better understanding conversational language in autism.

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Cited bibliography overview

These indicators describe the bibliography cited by this publication. An author name is counted each time it appears in one cited reference, so the same person can be counted more than once. Names not yet linked to an author already present in AutiHub are treated as unknown, not as non-autistic. Last computed: 20 Sep 2026 00:21.

Cited references
46
With a DOI
42
Without a DOI, from raw bibliography text
4
2 / 46 (4.3%) cited references include at least one author identified as autistic.
References with data to complete
1 / 46 (2.2%)
References with detected author names
46 / 46 (100.0%)
Without detected author names
0
Without a structured title
1
Without a stable identifier
4
References with raw author names still to review
0
References with external metadata lookup issues
0
These indicators apply to cited references displayed on this page, after technical duplicates have been merged. A reference without a DOI can still support author statistics when a title and author names are available.
Author names detected in the cited bibliography
212
From DOI or external metadata
212
From validated raw bibliography text
0
Raw names already validated
0
Raw names still to review
0
51 / 212 (24.1%) names are linked to an author already present in AutiHub. 161 / 212 (75.9%) names are not yet linked.
Names linked to a person identified as autistic
3 / 212 (1.4%)
Calculated across all author names detected in the cited bibliography. Among names linked to an author already present in AutiHub: 3 / 51 (5.9%). Distinct people identified as autistic: 3 / 168 (1.8%).
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