Predictive joint-action model: A hierarchical predictive approach to human cooperation

Abstract

Research in a number of related fields has recently begun to focus on the perceptual, cognitive, and motor workings of cooperative behavior. There appears to be enough coherence in these efforts to refer to the study of the mechanisms underlying human cooperative behavior as the field of joint-action (Knoblich, Butterfill, & Sebanz, 2011; Sebanz, Bekkering, & Knoblich, 2006). Yet, the development of theory in this field has not kept pace with the proliferation of research findings. We propose a hierarchical predictive framework for the study of joint-action that we call the predictive joint-action model (PJAM). The presentation of this theoretical framework is organized into three sections. In the first section, we summarize hierarchical predictive principles and discuss their application to joint-action. In the second section, we juxtapose PJAM's assumptions with empirical evidence from the current literature on joint-action. In the third section, we discuss the overall success of the hierarchical predictive approach to account for the burgeoning empirical literature on joint-action research. Finally, we consider the model's capacity to generate novel and testable hypotheses about joint-action. This is done with the larger goal of uncovering the empirical and theoretical pieces that are still missing in a comprehensive understanding of joint action.

Human group coordination in a sensorimotor task with neuron-like decision-making

“…In general, the ability to perform complementary actions in joint tasks and to imitate other actors is particularly advanced in humans, and is developed early in childhood. In line with our findings, a large body of evidence suggests that pairs of humans involved in joint action have internal models and expectations about the consequences of their own actions and about the actions undertaken by their interaction partner [33][34][35][36][37][38][39] . Quantitative models of sensorimotor interactions have so far also focused on the two-player scenario within a game-theoretic setup 18,19,40 .…”

Effect of External Force on Agency in Physical Human-Machine Interaction

“…For instance, past studies (Sato and Yasuda, 2005;Farrer et al, 2008) observed agency can be misattributed to an external source when the outcome of an action is incongruent with own predicted sensory outcome, and attributing the motion to an external source. In reverse, the central nervous system can distinguish self-action and external sources through learning (Synofzik et al, 2006;Novembre et al, 2012;Pesquita et al, 2017). The monitoring of the sensory-motor error in regard with expected outcome may thus be linked to SoA (Bellebaum et al, 2010).…”

When Affective Relation Weighs More Than the Mug Handle: Investigating Affective Affordances

“…Enactive and embodied approaches to cognition are becoming increasingly interested in the affective dimension of human experience (Varela and Depraz, 2005;Colombetti, 2007Colombetti, , 2014Colombetti and Thompson, 2008;Di Paolo and De Jaegher, 2015;Gallagher and Varga, 2015;Gallagher and Allen, 2016;Scorolli, 2019). Consistently, this issue has been addressed in empirical research, which is paying growing attention to the affective quality of social contexts by addressing motor simulations (Bastiaansen et al, 2009;Kuhbandner et al, 2010), joint actions (Godman, 2013;Pesquita et al, 2018), emotional disorders (Gjelsvik et al, 2018), and body psychotherapy (Röhricht et al, 2014).…”