5 recherche sur le mot-clé 'computer vision'

Computer Vision Analysis of Reduced Interpersonal Affect Coordination in Youth With Autism Spectrum Disorder / Casey J. ZAMPELLA in Autism Research, 13-12 (December 2020)  

Public ISBD [article]  inAutism Research > 13-12 (December 2020) . - p.2133-2142 Titre : Computer Vision Analysis of Reduced Interpersonal Affect Coordination in Youth With Autism Spectrum Disorder Type de document : texte imprimé Auteurs : Casey J. ZAMPELLA, Auteur ; Loisa BENNETTO, Auteur ; John D. HERRINGTON, Auteur Article en page(s) : p.2133-2142 Langues : Anglais (eng) Mots-clés : affect/emotion  computer vision  facial expression  interpersonal coordination  social-emotional reciprocity  synchrony Index. décimale : PER Périodiques Résumé : Atypical social-emotional reciprocity is a core feature of autism spectrum disorder (ASD) but can be difficult to operationalize. One measurable manifestation of reciprocity may be interpersonal coordination, the tendency to align the form and timing of one's behaviors (including facial affect) with others. Interpersonal affect coordination facilitates sharing and understanding of emotional cues, and there is evidence that it is reduced in ASD. However, most research has not measured this process in true social contexts, due in part to a lack of tools for measuring dynamic facial expressions over the course of an interaction. Automated facial analysis via computer vision provides an efficient, granular, objective method for measuring naturally occurring facial affect and coordination. Youth with ASD and matched typically developing youth participated in cooperative conversations with their mothers and unfamiliar adults. Time-synchronized videos were analyzed with an open-source computer vision toolkit for automated facial analysis, for the presence and intensity of facial movements associated with positive affect. Both youth and adult conversation partners exhibited less positive affect during conversations when the youth partner had ASD. Youth with ASD also engaged in less affect coordination over the course of conversations. When considered dimensionally across youth with and without ASD, affect coordination significantly predicted scores on rating scales of autism-related social atypicality, adaptive social skills, and empathy. Findings suggest that affect coordination is an important interpersonal process with implications for broader social-emotional functioning. This preliminary study introduces a promising novel method for quantifying moment-to-moment facial expression and emotional reciprocity during natural interactions. LAY SUMMARY: This study introduces a novel, automated method for measuring social-emotional reciprocity during natural conversations, which may improve assessment of this core autism diagnostic behavior. We used computerized methods to measure facial affect and the degree of affect coordination between conversation partners. Youth with autism displayed reduced affect coordination, and reduced affect coordination predicted lower scores on measures of broader social-emotional skills. En ligne : http://dx.doi.org/10.1002/aur.2334 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=434 [article] Computer Vision Analysis of Reduced Interpersonal Affect Coordination in Youth With Autism Spectrum Disorder [texte imprimé] / Casey J. ZAMPELLA, Auteur ; Loisa BENNETTO, Auteur ; John D. HERRINGTON, Auteur . - p.2133-2142. Langues : Anglais (eng) in Autism Research > 13-12 (December 2020) . - p.2133-2142 Mots-clés : affect/emotion  computer vision  facial expression  interpersonal coordination  social-emotional reciprocity  synchrony Index. décimale : PER Périodiques Résumé : Atypical social-emotional reciprocity is a core feature of autism spectrum disorder (ASD) but can be difficult to operationalize. One measurable manifestation of reciprocity may be interpersonal coordination, the tendency to align the form and timing of one's behaviors (including facial affect) with others. Interpersonal affect coordination facilitates sharing and understanding of emotional cues, and there is evidence that it is reduced in ASD. However, most research has not measured this process in true social contexts, due in part to a lack of tools for measuring dynamic facial expressions over the course of an interaction. Automated facial analysis via computer vision provides an efficient, granular, objective method for measuring naturally occurring facial affect and coordination. Youth with ASD and matched typically developing youth participated in cooperative conversations with their mothers and unfamiliar adults. Time-synchronized videos were analyzed with an open-source computer vision toolkit for automated facial analysis, for the presence and intensity of facial movements associated with positive affect. Both youth and adult conversation partners exhibited less positive affect during conversations when the youth partner had ASD. Youth with ASD also engaged in less affect coordination over the course of conversations. When considered dimensionally across youth with and without ASD, affect coordination significantly predicted scores on rating scales of autism-related social atypicality, adaptive social skills, and empathy. Findings suggest that affect coordination is an important interpersonal process with implications for broader social-emotional functioning. This preliminary study introduces a promising novel method for quantifying moment-to-moment facial expression and emotional reciprocity during natural interactions. LAY SUMMARY: This study introduces a novel, automated method for measuring social-emotional reciprocity during natural conversations, which may improve assessment of this core autism diagnostic behavior. We used computerized methods to measure facial affect and the degree of affect coordination between conversation partners. Youth with autism displayed reduced affect coordination, and reduced affect coordination predicted lower scores on measures of broader social-emotional skills. En ligne : http://dx.doi.org/10.1002/aur.2334 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=434

A pilot study to identify autism related traits in spontaneous facial actions using computer vision / Manar D. SAMAD in Research in Autism Spectrum Disorders, 65 (September 2019)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 65 (September 2019) . - p.14-24 Titre : A pilot study to identify autism related traits in spontaneous facial actions using computer vision Type de document : texte imprimé Auteurs : Manar D. SAMAD, Auteur ; Norou DIAWARA, Auteur ; Jonna L. BOBZIEN, Auteur ; Cora M. TAYLOR, Auteur ; John W. HARRINGTON, Auteur ; Khan M. IFTEKHARUDDIN, Auteur Article en page(s) : p.14-24 Langues : Anglais (eng) Mots-clés : ASD  Behavioral marker  Differential traits  Facial action units  Computer vision  Spontaneous expressions Index. décimale : PER Périodiques Résumé : Background Individuals with autism spectrum disorders (ASD) may be differentiated from typically developing controls (TDC) based on phenotypic features in spontaneous facial expressions. Computer vision technology can automatically track subtle facial actions to gain quantitative insights into ASD related behavioral abnormalities. Method This study proposes a novel psychovisual human-study to elicit spontaneous facial expressions in response to a variety of social and emotional contexts. We introduce a markerless facial motion capture and computer vision methods to track spontaneous and subtle activations of facial muscles. The facial muscle activations are encoded into ten representative facial action units (FAU) to gain quantitative, granular, and contextual insights into the psychophysical development of the participating individuals. Statistical tests are performed to identify differential traits in individuals with ASD after comparing those in a cohort of age-matched TDC individuals. Results The proposed framework has revealed significant difference (p < 0.001) in the activation of ten FAU and contrasting activations of FAU between the group with ASD and the TDC group. Unlike the TDC group, the group with ASD has shown unusual prevalence of mouth frown (FAU 15) and low correlations in temporal activations of several FAU pairs: 6–12, 10–12, and 10–20. The interpretation of different FAU activations suggests quantitative evidence of expression bluntness, lack of expression mimicry, incongruent reaction to negative emotions in the group with ASD. Conclusion Our generalized framework may be used to quantify psychophysical traits in individuals with ASD and replicate in similar studies that require quantitative measurements of behavioral responses. En ligne : https://doi.org/10.1016/j.rasd.2019.05.001 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=401 [article] A pilot study to identify autism related traits in spontaneous facial actions using computer vision [texte imprimé] / Manar D. SAMAD, Auteur ; Norou DIAWARA, Auteur ; Jonna L. BOBZIEN, Auteur ; Cora M. TAYLOR, Auteur ; John W. HARRINGTON, Auteur ; Khan M. IFTEKHARUDDIN, Auteur . - p.14-24. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 65 (September 2019) . - p.14-24 Mots-clés : ASD  Behavioral marker  Differential traits  Facial action units  Computer vision  Spontaneous expressions Index. décimale : PER Périodiques Résumé : Background Individuals with autism spectrum disorders (ASD) may be differentiated from typically developing controls (TDC) based on phenotypic features in spontaneous facial expressions. Computer vision technology can automatically track subtle facial actions to gain quantitative insights into ASD related behavioral abnormalities. Method This study proposes a novel psychovisual human-study to elicit spontaneous facial expressions in response to a variety of social and emotional contexts. We introduce a markerless facial motion capture and computer vision methods to track spontaneous and subtle activations of facial muscles. The facial muscle activations are encoded into ten representative facial action units (FAU) to gain quantitative, granular, and contextual insights into the psychophysical development of the participating individuals. Statistical tests are performed to identify differential traits in individuals with ASD after comparing those in a cohort of age-matched TDC individuals. Results The proposed framework has revealed significant difference (p < 0.001) in the activation of ten FAU and contrasting activations of FAU between the group with ASD and the TDC group. Unlike the TDC group, the group with ASD has shown unusual prevalence of mouth frown (FAU 15) and low correlations in temporal activations of several FAU pairs: 6–12, 10–12, and 10–20. The interpretation of different FAU activations suggests quantitative evidence of expression bluntness, lack of expression mimicry, incongruent reaction to negative emotions in the group with ASD. Conclusion Our generalized framework may be used to quantify psychophysical traits in individuals with ASD and replicate in similar studies that require quantitative measurements of behavioral responses. En ligne : https://doi.org/10.1016/j.rasd.2019.05.001 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=401

Autism Digital Phenotyping in Preschool- and School-Age Children / Kimberly L.H. CARPENTER ; Pradeep Raj Krishnappa BABU ; J. Matias DI MARTINO ; Steven ESPINOSA ; Scott N. COMPTON ; Naomi DAVIS ; Lauren FRANZ ; Marina SPANOS ; Guillermo SAPIRO ; Geraldine DAWSON in Autism Research, 18-6 (June 2025)  

Public ISBD [article]  inAutism Research > 18-6 (June 2025) . - p.1217-1233 Titre : Autism Digital Phenotyping in Preschool- and School-Age Children Type de document : texte imprimé Auteurs : Kimberly L.H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Marina SPANOS, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur Article en page(s) : p.1217-1233 Langues : Anglais (eng) Mots-clés : autism  computer vision  digital phenotyping  preschool- and school-age Index. décimale : PER Périodiques Résumé : ABSTRACT There is a critical need for scalable and objective tools for autism screening and outcome monitoring, which can be used alongside traditional caregiver and clinical measures. To address this need, we developed SenseToKnow, a tablet- or smartphone-based digital phenotyping application (app), which uses computer vision and touch data to measure several autism-related behavioral features, such as social attention, facial and head movements, and visual-motor skills. Our previous work demonstrated that the SenseToKnow app can accurately detect and quantify behavioral signs of autism in 18 40-month-old toddlers. In the present study, we administered the SenseToKnow app on an iPad to 149 preschool- and school-age children (45 neurotypical and 104 autistic) between 3 and 8 years of age. Results revealed significant group differences between autistic and neurotypical children in terms of several behavioral features, which remained after controlling for sex and age. Repeat administration with a subgroup demonstrated stability in the individual digital phenotypes. Examining correlations between the Vineland Adaptive Behavior Scales and individual digital phenotypes, we found that autistic children with higher levels of communication, daily living, socialization, motor, and adaptive skills exhibited higher levels of social attention and coordinated gaze with speech, less frequent head movements, higher complexity of facial movements, higher overall attention, lower blink rates, and higher visual motor skills, demonstrating convergent validity between app features and clinical measures. App features were also significantly correlated with ratings on the Social Responsiveness Scale. These results suggest that the SenseToKnow app can be used as an accessible, scalable, and objective digital tool to measure autism-related behaviors in preschool- and school-age children. En ligne : https://doi.org/10.1002/aur.70032 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558 [article] Autism Digital Phenotyping in Preschool- and School-Age Children [texte imprimé] / Kimberly L.H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Marina SPANOS, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.1217-1233. Langues : Anglais (eng) in Autism Research > 18-6 (June 2025) . - p.1217-1233 Mots-clés : autism  computer vision  digital phenotyping  preschool- and school-age Index. décimale : PER Périodiques Résumé : ABSTRACT There is a critical need for scalable and objective tools for autism screening and outcome monitoring, which can be used alongside traditional caregiver and clinical measures. To address this need, we developed SenseToKnow, a tablet- or smartphone-based digital phenotyping application (app), which uses computer vision and touch data to measure several autism-related behavioral features, such as social attention, facial and head movements, and visual-motor skills. Our previous work demonstrated that the SenseToKnow app can accurately detect and quantify behavioral signs of autism in 18 40-month-old toddlers. In the present study, we administered the SenseToKnow app on an iPad to 149 preschool- and school-age children (45 neurotypical and 104 autistic) between 3 and 8 years of age. Results revealed significant group differences between autistic and neurotypical children in terms of several behavioral features, which remained after controlling for sex and age. Repeat administration with a subgroup demonstrated stability in the individual digital phenotypes. Examining correlations between the Vineland Adaptive Behavior Scales and individual digital phenotypes, we found that autistic children with higher levels of communication, daily living, socialization, motor, and adaptive skills exhibited higher levels of social attention and coordinated gaze with speech, less frequent head movements, higher complexity of facial movements, higher overall attention, lower blink rates, and higher visual motor skills, demonstrating convergent validity between app features and clinical measures. App features were also significantly correlated with ratings on the Social Responsiveness Scale. These results suggest that the SenseToKnow app can be used as an accessible, scalable, and objective digital tool to measure autism-related behaviors in preschool- and school-age children. En ligne : https://doi.org/10.1002/aur.70032 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558

Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism / Kimberly L.H. CARPENTER in Autism Research, 14-3 (March 2021)  

Public ISBD [article]  inAutism Research > 14-3 (March 2021) . - p.488-499 Titre : Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism Type de document : texte imprimé Auteurs : Kimberly L.H. CARPENTER, Auteur ; Jordan HAHEMI, Auteur ; Kathleen CAMPBELL, Auteur ; Steven J. LIPPMANN, Auteur ; Jeffrey P. BAKER, Auteur ; Helen Link EGGER, Auteur ; Steven ESPINOSA, Auteur ; Saritha VERMEER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur Article en page(s) : p.488-499 Langues : Anglais (eng) Mots-clés : autism  computer vision  early detection  facial expressions  risk behaviors  Amazon, Google, Cisco, and Microsoft and is a consultant for Apple and Volvo.  Geraldine Dawson is on the Scientific Advisory Boards of Janssen Research and  Development, Akili, Inc., LabCorp, Inc., Tris Pharma, and Roche Pharmaceutical  Company, a consultant for Apple, Inc, Gerson Lehrman Group, Guidepoint, Inc., Teva  Pharmaceuticals, and Axial Ventures, has received grant funding from Janssen  Research and Development, and is CEO of DASIO, LLC (with Guillermo Sapiro). Dawson  receives royalties from Guilford Press, Springer, and Oxford University Press.  Dawson, Sapiro, Carpenter, Hashemi, Campbell, Espinosa, Baker, and Egger helped  develop aspects of the technology that is being used in the study. The technology  has been licensed and Dawson, Sapiro, Carpenter, Hashemi, Espinosa, Baker, Egger,  and Duke University have benefited financially. Index. décimale : PER Périodiques Résumé : Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N = 22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism. En ligne : http://dx.doi.org/10.1002/aur.2391 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=443 [article] Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism [texte imprimé] / Kimberly L.H. CARPENTER, Auteur ; Jordan HAHEMI, Auteur ; Kathleen CAMPBELL, Auteur ; Steven J. LIPPMANN, Auteur ; Jeffrey P. BAKER, Auteur ; Helen Link EGGER, Auteur ; Steven ESPINOSA, Auteur ; Saritha VERMEER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.488-499. Langues : Anglais (eng) in Autism Research > 14-3 (March 2021) . - p.488-499 Mots-clés : autism  computer vision  early detection  facial expressions  risk behaviors  Amazon, Google, Cisco, and Microsoft and is a consultant for Apple and Volvo.  Geraldine Dawson is on the Scientific Advisory Boards of Janssen Research and  Development, Akili, Inc., LabCorp, Inc., Tris Pharma, and Roche Pharmaceutical  Company, a consultant for Apple, Inc, Gerson Lehrman Group, Guidepoint, Inc., Teva  Pharmaceuticals, and Axial Ventures, has received grant funding from Janssen  Research and Development, and is CEO of DASIO, LLC (with Guillermo Sapiro). Dawson  receives royalties from Guilford Press, Springer, and Oxford University Press.  Dawson, Sapiro, Carpenter, Hashemi, Campbell, Espinosa, Baker, and Egger helped  develop aspects of the technology that is being used in the study. The technology  has been licensed and Dawson, Sapiro, Carpenter, Hashemi, Espinosa, Baker, Egger,  and Duke University have benefited financially. Index. décimale : PER Périodiques Résumé : Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N = 22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism. En ligne : http://dx.doi.org/10.1002/aur.2391 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=443

"You should smile more": Population-level sex differences in smiling also exist in autistic people / Casey J. ZAMPELLA in Autism, 29-5 (May 2025)  

Public ISBD [article]  inAutism > 29-5 (May 2025) . - p.1236-1245 Titre : "You should smile more": Population-level sex differences in smiling also exist in autistic people Type de document : texte imprimé Auteurs : Casey J. ZAMPELLA, Auteur ; Julia PARISH-MORRIS, Auteur ; Jessica FOY, Auteur ; Meredith L. COLA, Auteur ; Robert T. SCHULTZ, Auteur ; John D. HERRINGTON, Auteur Article en page(s) : p.1236-1245 Langues : Anglais (eng) Mots-clés : autism  computer vision  facial expression  sex differences  smiling Index. décimale : PER Périodiques Résumé : Societal expectations for social-emotional behavior differ across sexes; however, diagnostic definitions of autism do not account for this when delineating "typical" versus "atypical." This study examines sex differences in autism in one behavior associated with strong gender biases: smiling. Computer vision was used to quantify smiling in 60 autistic (20 female) and 67 neurotypical (25 female) youth during conversations. Effects of sex and diagnosis were examined on degree of smiling, smile prototypicality, changes in smiling, and impact of smiling on interaction quality. Sex differences in smiling persisted across diagnosis groups: females smiled more than males, and their smiles were more prototypical. Autistic youth smiled less, and less prototypically, than neurotypical youth, with no sex by diagnosis interactions. In autism, the association between smile activity and interaction quality approached statistical significance, seemingly driven by autistic males but not females. Findings are consistent with population trends for females to smile more during social exchanges and "display rules" requiring more positive expressivity from females. Autism has historically been defined based on differences between autistic and neurotypical males. Failure to acknowledge sex-based differences in social-emotional behavior may leave some females appearing to have fewer autistic traits, increasing their risk of being under-identified and misunderstood.Lay abstractThis study uses automated computerized methods to measure facial expression, namely smiling, in autistic and neurotypical males and females as they converse naturally with an unfamiliar social partner. Results show that typical population-level sex differences in smiling also exist in autistic youth; females smile more and more prototypically than males. Failure to acknowledge these sex-based differences in social-emotional norms and behavior may leave autistic females at increased risk for being misdiagnosed and misunderstood. En ligne : https://dx.doi.org/10.1177/13623613241301113 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555 [article] "You should smile more": Population-level sex differences in smiling also exist in autistic people [texte imprimé] / Casey J. ZAMPELLA, Auteur ; Julia PARISH-MORRIS, Auteur ; Jessica FOY, Auteur ; Meredith L. COLA, Auteur ; Robert T. SCHULTZ, Auteur ; John D. HERRINGTON, Auteur . - p.1236-1245. Langues : Anglais (eng) in Autism > 29-5 (May 2025) . - p.1236-1245 Mots-clés : autism  computer vision  facial expression  sex differences  smiling Index. décimale : PER Périodiques Résumé : Societal expectations for social-emotional behavior differ across sexes; however, diagnostic definitions of autism do not account for this when delineating "typical" versus "atypical." This study examines sex differences in autism in one behavior associated with strong gender biases: smiling. Computer vision was used to quantify smiling in 60 autistic (20 female) and 67 neurotypical (25 female) youth during conversations. Effects of sex and diagnosis were examined on degree of smiling, smile prototypicality, changes in smiling, and impact of smiling on interaction quality. Sex differences in smiling persisted across diagnosis groups: females smiled more than males, and their smiles were more prototypical. Autistic youth smiled less, and less prototypically, than neurotypical youth, with no sex by diagnosis interactions. In autism, the association between smile activity and interaction quality approached statistical significance, seemingly driven by autistic males but not females. Findings are consistent with population trends for females to smile more during social exchanges and "display rules" requiring more positive expressivity from females. Autism has historically been defined based on differences between autistic and neurotypical males. Failure to acknowledge sex-based differences in social-emotional behavior may leave some females appearing to have fewer autistic traits, increasing their risk of being under-identified and misunderstood.Lay abstractThis study uses automated computerized methods to measure facial expression, namely smiling, in autistic and neurotypical males and females as they converse naturally with an unfamiliar social partner. Results show that typical population-level sex differences in smiling also exist in autistic youth; females smile more and more prototypically than males. Failure to acknowledge these sex-based differences in social-emotional norms and behavior may leave autistic females at increased risk for being misdiagnosed and misunderstood. En ligne : https://dx.doi.org/10.1177/13623613241301113 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555