Analysis of Social Performance and Action Units During Social Skills Training: Focus Group Study of Adults With Autism Spectrum Disorder and Schizophrenia.

Background: Social communication is a crucial factor influencing human social life. Quantifying the degree of difficulty faced in social communication is necessary for understanding developmental and neurological disorders and for creating systems used in automatic symptom screening and assistive methods such as social skills training (SST). SST by a human trainer is a well-established method.

Agency and social affordance shape visual perception.

Research on the sense of agency has shown that being the author of an action changes the way we estimate the timing and the intensity of the action-effect. Yet, there is a dearth of attempts to assess the influence of agency on perception per se. The present study used the Representational Momentum paradigm to measure participants' visual anticipation of movement while manipulating their agency.

Adaptation and validation of two annotation scales for assessing social skills in a corpus of multimodal collaborative interactions.

Context: Behavioral observation scales are important for understanding and assessing social skills. In the context of collaborative problem-solving (CPS) skills, considered essential in the 21st century, there are no validated scales in French that can be adapted to different CPS tasks. The aim of this study is to adapt and validate, by annotating a new video corpus of dyadic interactions that we have collected, two observational scales allowing us to qualitatively assess CPS skills: the Social Performance Rating Scale (SPRS) and the Social Skills of Collaboration Scale (SSC).

Machine Learning of Motion Statistics Reveals the Kinematic Signature of the Identity of a Person in Sign Language.

Sign language (SL) motion contains information about the identity of a signer, as does voice for a speaker or gait for a walker. However, how such information is encoded in the movements of a person remains unclear. In the present study, a machine learning model was trained to extract the motion features allowing for the automatic identification of signers.