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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

75
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Operant Conditioning Intervention01:24

Operant Conditioning Intervention

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Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
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Virtual Prism Adaptation Therapy: Protocol for Validation in Healthy Adults
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Exploring Adaptive Virtual Reality Systems Used in Interventions for Children With Autism Spectrum Disorder:

Luna Maddalon1, Maria Eleonora Minissi1, Thomas Parsons2,3

  • 1Laboratory of Immersive Neurotechnologies, Institute Human-Tech, Universitat Politècnica de València, Valencia, Spain.

Journal of Medical Internet Research
|September 18, 2024
PubMed
Summary
This summary is machine-generated.

Adaptive virtual reality (VR) systems show promise for autism spectrum disorder (ASD) interventions. Non-machine learning techniques using implicit signals offer effective, personalized training for young individuals with ASD.

Keywords:
adaptive systemautism spectrum disorderbiosignalchildreninterventionmachine learningtrainingvirtual reality

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Area of Science:

  • Neuroscience
  • Computer Science
  • Developmental Psychology

Background:

  • Adaptive systems personalize interventions based on user needs and performance.
  • Adaptive virtual reality (VR) systems offer efficient data monitoring and real-world learning transfer.
  • There is growing interest in using adaptive VR for autism spectrum disorder (ASD) due to symptom heterogeneity and the need for customized interventions.

Purpose of the Study:

  • To systematically review the application of adaptive VR systems in interventions for young individuals with ASD.
  • To explore the effectiveness of different adaptation techniques and engine types in VR interventions for ASD.

Main Methods:

  • A systematic search was conducted across PubMed Central, Scopus, and Web of Science for studies from the past decade.
  • Inclusion criteria focused on VR delivery, automated adaptation, ASD intervention/training, age range (6-19 years), ASD participant group, and explained adaptation strategies.
  • Data extracted included sample characteristics, objectives, skills trained, devices, adaptive strategies, engine techniques, and adaptation signals.

Main Results:

  • Ten articles involving 129 participants (76% with ASD) were included.
  • Common strategies included level switching (70%) and adaptive feedback (90%).
  • Non-machine learning (ML) engines (80%) were more prevalent than ML engines (30%), utilizing explicit (60%) and implicit (70%) signals for adaptation.

Conclusions:

  • Automated VR systems with real-time progression and feedback, particularly non-ML techniques using implicit signals, show potential for enhancing ASD interventions.
  • Future research should address limitations related to data handling and potential human bias in automated systems.