Social performance–based interventions promote gains in social knowledge in the absence of explicit training for youth with autism spectrum disorder

2019 ◽  
Vol 83 (3) ◽  
pp. 301-325 ◽  
Author(s):  
Bianca M. Marro ◽  
Erin Kang ◽  
Kathryn M. Hauschild ◽  
Karys M. Normansell ◽  
Tamara M. Abu-Ramadan ◽  
...  

Youth with autism spectrum disorder (ASD) experience deficits in social knowledge. It has long been theorized that these youth must learn these skills explicitly, and social skills interventions (SSIs) have followed suit. Recently, performance-based SSIs have emerged, which promote in vivo opportunities for social engagement without explicit instruction. Effects of performance-based SSIs on social knowledge have not been examined. This study employs two discrete samples (one lab-based, one community-based) of youth with ASD to examine the effects of performance-based interventions on social knowledge. Results largely support the efficacy and effectiveness of improving social knowledge by performance-based interventions without explicit teaching. This indicates that youth with ASD may be able to learn these aspects of social cognition implicitly, rather than exclusively explicitly. The results of the current study also suggest that SSI content, dosage, and intensity may relate to these outcomes, which are important considerations in clinical practice and future studies.

2020 ◽  
Vol 5 (1) ◽  
pp. 314-325
Author(s):  
Kimberly F. Frazier ◽  
Jessica Collier ◽  
Rachel Glade

Background The aim of this study was to determine the clinical efficacy of combining self-management strategies and a social thinking approach to address the social performance and executive function of an adolescent female with autism spectrum disorder. Method This research examined the effects of a social knowledge training program, “Think Social,” as well as strategies to improve higher order cognitive abilities. Results and Conclusion Although quantitative improvement was not found, several qualitative gains in behavior were noted for the participants of this study, suggesting a benefit from using structured environmental cues of self-management strategies, as well as improved social understanding through social cognitive training.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lucia Janickova ◽  
Karin Farah Rechberger ◽  
Lucas Wey ◽  
Beat Schwaller

An amendment to this paper has been published and can be accessed via the original article.


Author(s):  
Gazi Azad ◽  
Maryellen Brunson McClain ◽  
Cassity Haverkamp ◽  
Barbara Maxwell ◽  
Jeffrey D. Shahidullah

2021 ◽  
Author(s):  
Maya Varma ◽  
Peter Washington ◽  
Brianna Chrisman ◽  
Aaron Kline ◽  
Emilie Leblanc ◽  
...  

Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Children with ASD exhibit behavioral and social impairments, giving rise to the possibility of utilizing computational techniques to evaluate a child's social phenotype from home videos. Here, we use a mobile health application to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We utilize automated dataset annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns and (2) visual scanning methods. We compare the gaze fixation and visual scanning methods utilized by children during a 90-second gameplay video in order to identify statistically-significant differences between the two cohorts; we then train an LSTM neural network in order to determine if gaze indicators could be predictive of ASD. Our work identifies one statistically significant region of fixation and one significant gaze transition pattern that differ between our two cohorts during gameplay. In addition, our deep learning model demonstrates mild predictive power in identifying ASD based on coarse annotations of gaze fixations. Ultimately, our results demonstrate the utility of game-based mobile health platforms in quantifying visual patterns and providing insights into ASD. We also show the importance of automated labeling techniques in generating large-scale datasets while simultaneously preserving the privacy of participants. Our approaches can generalize to other healthcare needs.


Author(s):  
Derek Sayre Andrews ◽  
Thomas A. Avino ◽  
Maria Gudbrandsen ◽  
Eileen Daly ◽  
Andre Marquand ◽  
...  

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