The sociomotor framework outlines a possible role of social action effects on human action control, suggesting that anticipated partner reactions are a major cue to represent, select, and initiate own body movements. Here, we review studies that elucidate the actual content of social action representations and that explore factors that can distinguish action control processes involving social and inanimate action effects. Specifically, we address two hypotheses on how the social context can influence effect-based action control: first, by providing unique social (...) features such as body-related, anatomical codes, and second, by orienting attention towards any relevant feature dimensions of the action effects. The reviewed empirical work presents a surprisingly mixed picture: while there is indirect evidence for both accounts, previous studies that directly addressed the anatomical account showed no signs of the involvement of genuinely social features in sociomotor action control. Furthermore, several studies show evidence against the differentiation of social and non-social action effect processing, portraying sociomotor action representations as remarkably non-social. A focus on enhancing the social experience in future studies should, therefore, complement the current database to establish whether such settings give rise to the hypothesized influence of social context. (shrink)

The sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions. In the study of joint action, models are even more necessary to understand the (...) mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner’s actions and intentions and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for ‘joint’ and ‘self’ action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the ‘joint’ and ‘self’ motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions. (shrink)