Decoding communicative action vitality forms in social contexts

The form of an action conveys important information about the agent performing it. Humans may execute the same action in different ways, e.g., vigorously, gently or rudely. This fundamental affective component of the action has been named vitality forms (VFs) by Stern. To date, despite the fundamental role of VFs in social communication, the kinematic features characterizing them have been rarely studied. The aims of the present study are twofold: to investigate spatiotemporal characteristics of transitive and intransitive actions performed with different VFs; to investigate whether and how it is possible to recognize these aspects of action automatically. For this purpose, we asked two actors to perform seven actions with VFs (gentle and rude) or without VFs (neutral, slow and fast). Thousand repetitions of actions were collected, and their kinematics was recorded by using a motion capture system. Twenty-two kinematic features were identified from kinematic data. Results indicate that VFs are not merely characterized by a modulation of a single motion parameter such as velocity or acceleration, but by a combination of different spatiotemporal properties. Finally, we also demonstrate that automatic recognition of VFs is possible using traditional machine learning methods, with an accuracy of 87.3%. Moreover, this recognition is also feasible for action types do not present in the training set, with an accuracy of 74.2%. These results will have significant implications in the future across various fields, including neuroscience, social robotics, and the development of virtual agents. For instance, it could enable artificial agents to recognize human attitudes and adapt their behavior appropriately to the partner during interactions. Moreover, understanding the VFs features could be useful in designing rehabilitative interventions for conditions involving social and communicative impairments, such as autism.