Workshop description:
Social Skill Training is often used in the multimodal Interaction research community as an umbrella term for systems that aim at training social skills: managing appropriately verbal and nonverbal behaviors when interacting with one or more persons, in relation with various communicative functions such as turn taking and emotions.
People with social affective deficits have difficulties controlling their own social behavior and also suffer from interpreting others’ social behavior. Behavioral therapy (eg with a clinician) and Social Skill Training (SST) are used in medical settings. Patients are trained with a coach to experience social interaction and to reduce social stress. SST includes a role-play of a simulation of actual situations [1,2]. In addition to behavioral training, cognitive therapy [3] and motivational interviewing are also useful to better understand and train social-affective interaction. All these methods are effective but expensive and difficult to access.
This workshop is looking for works describing how interactive, multimodal technology such as virtual agents can be used in social skills training for measuring and training social-affective interactions [4]. Sensing technology now enables analyzing user’s behaviors and physiological signals (heart-rate, EEG, etc). Various signal processing and machine learning methods can be used for such prediction tasks. Beyond sensing, it is also important to analyze human behaviors and model and implement training methods (e.g. by virtual agents, social robots, relevant scenarios, design appropriate and personalized feedback about social skills performance). Such social signal processing and tools can be applied to measure and reduce social stress in everyday situations, including public speaking at schools and workplaces. Target populations include depression, Social Anxiety Disorder (SAD), Schizophrenia, Autism Spectrum Disorder (ASD), but also a much larger group of different social pathological phenomena.
In this workshop, we invite participants from academia, industry or clinical settings to present and discuss social-affective design of multimodal training for health.
References:
[1] Tanaka H, Negoro H, Iwasaka H, Nakamura S, Embodied conversational agents for multimodal automated social skills training in people with autism spectrum disorders. Plos one, 12(8) (2017).
[2] Hoque Mohammed, Courgeon Matthieu, Martin Jean-Claude, Mutlu Bilge, Picard Rosalind, MACH: My automated conversation coach. UbiComp 2013, Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (2013).
[3] Fitzpatrick K, Darcy A, Vierhile M, Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial, JMIR Mental Health (2017).
[4] Daniels, J., Schwartz, J.N., Voss, C. et al. Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism. Nature Digital Med 1, 32 (2018).
[5] Bruijnes, M., Linssen, J. & Heylen, D. Special issue editorial: Virtual Agents for Social Skills Training. Journal on Multimodal User Interfaces 13: 1 (2019).
Important dates:
Workshop papers or abstract due: August 23, 2020.
Notification of acceptance: September 4, 2020. September 7, 2020.
Camera-ready paper: September 28, 2020.
Workshop date: October 29, 2020.
Organizers:
Hiroki Tanaka (Nara Institute of Science and Technology, Japan)
contact: hiroki-tan[at]is.naist.jp
Satoshi Nakamura (Nara Institute of Science and Technology, Japan)
Jean-Claude Martin (CNRS-LIMSI, France)
Catherine Pelachaud (CNRS-ISIR, Sorbonne University, France)
Program Committee:
Mohamed Chetouani (University Pierre and Marie Curie)
Mathieu Chollet (IMT Atlantique)
Yuichiro Fujimoto (Nara Institute of Science and Technology)
Shogo Okada (Japan Advanced Institute of Science and Technology)
Shiro Kumano (NTT)
Theodora Chaspari (Texas A&M Univ)
Projects:
ANR-CREST-TAPAS Japan - France project