Game theory and partner representation in joint action: toward a computational theory of joint agency

Abstract

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 (comparator model). 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 (a ‘partner model’) 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.

Sense of agency in joint action: a critical review of we-agency

“…In order to explain the observed changes in the sense of agency during joint action, researchers proposed various accounts ( Seemann, 2009 ; Pacherie, 2012 , 2014 ; Salmela and Nagatsu, 2017 ; Salice et al, 2019 ; Silver et al, 2021 ; De Vicariis et al, 2022 ). Firstly, one proposition specifically pertained to hierarchically structured joint actions where some agents take on the role of leaders while others obey.…”

Computational joint action: dynamical models to understand the development of joint coordination

“…Perception is believed to optimally combine prior beliefs and the available observations to minimize the prediction uncertainty [15, 16]. These same Bayesian mechanisms likely mediate the prediction of the actions of our partners [6, 14]. Partial information about the partner likely affects the development of a joint coordination.…”

“…A similar approach can be used to gain more insight on how we develop coordination strategies while we interact with our peers. Few studies have addressed the perceptual and control mechanisms of joint action [4][5][6]14]. Dynamical models rely on optimality principles to account for perception and action selection within a single trial.…”

Automated Design Using Machine Learning in Materials Engineering - An Explicit Forecasts

“…The cognitive game theory [1] (such as computer chess), event forecasting, pattern recognition (such as facial or fingerprint), and bioinformatics have all been influenced by machine learning systems. They are making significant advances in the field of materials science and show great potential for future advancements in the study of and innovation with various materials.…”