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Multimodal interaction technologies for training affective social skills

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimodal interaction technologies for training affective social skillsedited by Satoshi Nakamura.
other author:	Nakamura, Satoshi.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	viii, 196 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Social skillsStudy and teaching.
Online resource:	https://doi.org/10.1007/978-981-96-1313-7
ISBN:	9789819613137$q(electronic bk.)

Multimodal interaction technologies for training affective social skills
Multimodal interaction technologies for training affective social skills[electronic resource] /edited by Satoshi Nakamura. - Singapore :Springer Nature Singapore :2025. - viii, 196 p. :ill., digital ;24 cm.

Introduction -- Theoretical Framework in Social Psychology -- Theory of Social Skills Training -- Theory of Cognitive Behavioral Therapy -- Social Performance Rating -- Eye-gaze and Physiological Data Analysis -- Multimodal Behavior Modeling -- Multimodal Behavior Generation -- Virtual Reality and Augmented Reality -- Interaction in Social Skills Training -- Interaction in Cognitive Behavioral Therapy -- Validation of the Developed System.

This book focuses on how interactive, multimodal technology such as virtual agents can be used in training and treatment (social skills training, cognitive behavioral therapy). People with socio-affective deficits have difficulties controlling their social behavior and also suffer from interpreting others' social behavior. Behavioral training, such as social skills training, is used in clinical settings. Patients are trained by a coach to experience social interaction and reduce social stress. In addition to behavioral training, cognitive behavioral therapy is also useful for understanding better and training social-affective interaction. All these methods are effective but expensive and difficult to access. This book describes how multimodal interactive technology can be used in healthcare for measuring and training social-affective interactions. Sensing technology analyzes users' behaviors and eye-gaze, and various machine learning methods can be used for prediction tasks. This bookfocuses on analyzing human behaviors and implementing training methods (e.g., by virtual agents, virtual reality, dialogue modeling, personalized feedback, and evaluations). Target populations include depression, schizophrenia, autism spectrum disorder, and a much larger group of social pathological phenomena.

ISBN: 9789819613137$q(electronic bk.)

Standard No.: 10.1007/978-981-96-1313-7doiSubjects--Topical Terms:

481759
Social skills
--Study and teaching.

LC Class. No.: HQ783

Dewey Class. No.: 646.76

Multimodal interaction technologies for training affective social skills
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505 0 $a Introduction -- Theoretical Framework in Social Psychology -- Theory of Social Skills Training -- Theory of Cognitive Behavioral Therapy -- Social Performance Rating -- Eye-gaze and Physiological Data Analysis -- Multimodal Behavior Modeling -- Multimodal Behavior Generation -- Virtual Reality and Augmented Reality -- Interaction in Social Skills Training -- Interaction in Cognitive Behavioral Therapy -- Validation of the Developed System.
520 $a This book focuses on how interactive, multimodal technology such as virtual agents can be used in training and treatment (social skills training, cognitive behavioral therapy). People with socio-affective deficits have difficulties controlling their social behavior and also suffer from interpreting others' social behavior. Behavioral training, such as social skills training, is used in clinical settings. Patients are trained by a coach to experience social interaction and reduce social stress. In addition to behavioral training, cognitive behavioral therapy is also useful for understanding better and training social-affective interaction. All these methods are effective but expensive and difficult to access. This book describes how multimodal interactive technology can be used in healthcare for measuring and training social-affective interactions. Sensing technology analyzes users' behaviors and eye-gaze, and various machine learning methods can be used for prediction tasks. This bookfocuses on analyzing human behaviors and implementing training methods (e.g., by virtual agents, virtual reality, dialogue modeling, personalized feedback, and evaluations). Target populations include depression, schizophrenia, autism spectrum disorder, and a much larger group of social pathological phenomena.
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