Information-Based Social Coordination Between Players of Different Skill in Doubles Pong

Abstract

We studied how teams of two players of different skill level intercepted approaching balls in the doubles-pong task. In this task, the two players moved their on-screen paddles along a shared interception axis, so that the approaching ball was intercepted by one of the paddles and that the paddles did not collide. Earlier work revealed the presence of a fuzzy division of interception space, with a boundary between interception domains located in the space between the two initial paddle positions. In the present study, using the performance of the players in their individual training sessions, we formed teams of players of varying skill level. We considered two accounts of how this boundary should be understood. In a first account, the players have shared knowledge of this boundary. Based on the side of the boundary at which the approaching ball will cross the interception axis, the players would decide whose paddle is to make the interception. Under this account, we expected that a better-skilled player would take responsibility for a larger interception domain, leading to a boundary closer to the lesser-skilled player. However, our analyses did not reveal any systematic effect of skill difference on the location (or degree of fuzziness) of the boundary: location of boundaries and overlap of interception domains varied over teams but were not systematically related to skill differences between team members. We did find effects of ball speed and approach angle. In a second account, the boundary emerges from (information-driven) player–player–ball interactions. An action-based model consistent with this account was able to capture all the patterns in boundary positions and overlaps that we observed. We conclude that the interception patterns that players demonstrate in the doubles-pong task are best understood as emerging from the unfolding of the dynamics of the system of the two players and the ball, coupled through information.

Interpersonal coordination in joint multiple object tracking.

“…These findings dovetail with results in an earlier study, in which the screen shape systematically influenced whether coactors adopted a left-right (for a landscape display) or top-down (for portrait display) labor division (Wahn et al, 2020). However, these findings conflict with the findings obtained in studies of joint motor control, where the labor division is often dynamically negotiated via visuomotor couplings, and as such it does not follow a strict division of space such as the midline (Benerink et al, 2016; 2018; van Opstal et al, 2018). It is also worth noting that in joint perceptual decision-tasks, no spatial labor devision is employed at all.…”

“…However, again, these findings do not apply to joint perceptual decision-making tasks as feedback alone (composed of individual and team feedback) was found to be insufficient for group benefits (Bahrami et al, 2010). Regarding joint motor tasks, a set of studies investigating a joint pong task (Benerink et al, 2016; van Opstal et al, 2018) highlighted the critical role of visual access to the coactors’ actions, as coordination depends on close visuomotor couplings between coactors. In such a task, team feedback is unlikely to be helpful.…”

“…In a different set of studies, also investigating joint motor control (Benerink et al, 2016; 2018; van Opstal et al, 2018); each coactor has control over her/his own object (so control is no longer shared). Here, for instance, coactors would jointly play a pong game with each coactor controlling her/his own paddle.…”

Optimizing Performative Skills in Social Interaction: Insights From Embodied Cognition, Music Education, and Sport Psychology

“…These studies emphasize the strong recursive interplay between bodies-in-action, internal states, and extrabodily resources 8 . It might therefore be argued that athletes’ actions are properties emerging from the fluid integration of internal and external components (see also Semin and Cacioppo, 2008; Hwang et al, 2018; van Opstal et al, 2018). Importantly, such elements can only be exploited through action, giving rise to continuous loops where athletes shape and are shaped by the various contextual contingencies associated with (the goals of) each performance.…”

Obstacles shape the way we walk at home

“…Speeds above this range are categorized as "fast, " with a maximum speed between 2.0 and 2.3 m/s, while speeds below 1.0 m/s are classified as "slow, " often observed in older adults or individuals with specific pathologies (Bohannon, 1997). In addition to speed, locomotor trajectories are also classically studied through factors such as for instance the kinematics of the lower limbs (Patla and Rietdyk, 1993;Austin et al, 1999;Fink et al, 2007;Van Opstal et al, 2022), kinetic energy (Bertram, 2005;Emken et al, 2007;Selinger et al, 2015), or spatio-temporal walking parameters (Lee and Lishman, 1977;Ondrej et al, 2010;Olivier et al, 2012), as well as their variation with environmental (obstacles, luminosity) (Patla and Rietdyk, 1993;Austin et al, 1999;Fajen and Warren, 2003), psychological (depression, chronic fatigue) (Michalak et al, 2009;Feldman et al, 2019) and physical (injury, aging) factors (Said et al, 2001;Grinberg et al, 2022). Specific metrics such as the distance covered and its associated travel time, step frequency and length, speed changes (acceleration, deceleration), or trajectory radius of curvature have often been used, but always in the laboratory or outdoor, and have never been documented indoor.…”