 Incomplete information about the partner affects the development of collaborative strategies in joint actionVinil T Chackochan and Vittorio Sanguineti PLOS Computational Biology, 2019, vol. 15, issue 12, 1-23 Abstract: Physical interaction with a partner plays an essential role in our life experience and is the basis of many daily activities. When two physically coupled humans have different and partly conflicting goals, they face the challenge of negotiating some type of collaboration. This requires that both participants understand their partner’s state and current actions. But, how would the collaboration be affected if information about their partner were unreliable or incomplete? We designed an experiment in which two players (a dyad) are mechanically connected through a virtual spring, but cannot see each other. They were instructed to perform reaching movements with the same start and end position, but through different via-points. In different groups of dyads we varied the amount of information provided to each player about his/her partner: haptic only (the interaction force perceived through the virtual spring), visuo-haptic (the interaction force is also displayed on the screen), and partner visible (in addition to interaction force, partner position is continuously displayed on the screen). We found that incomplete information about the partner affects not only the speed at which collaboration is achieved (less information, slower learning), but also the actual collaboration strategy. In particular, incomplete or unreliable information leads to an interaction strategy characterized by alternating leader-follower roles. Conversely, more reliable information leads to more synchronous behaviors, in which no specific roles can be identified. Simulations based on a combination of game theory and Bayesian estimation suggested that synchronous behaviors correspond to optimal interaction (Nash equilibrium). Roles emerge as sub-optimal forms of interaction, which minimize the need to account for the partner. These findings suggest that collaborative strategies in joint action are shaped by the trade-off between the task requirements and the uncertainty of the information available about the partner.Author summary: Many activities in daily life involve physical interaction with a partner or opponent. In many situations, they have conflicting goals and need to negotiate some form of collaboration. Although very common, these situations have rarely been studied empirically. In this study, we specifically address what is a ‘optimal’ collaboration and how it can be achieved. We also address how developing a collaboration is affected by uncertainty about partner actions. Through a combination of empirical studies and computer simulations based on game theory, we show that subject pairs (dyads) are capable of developing stable collaborations, but the learned collaboration strategy depends on the reliability of the information about the partner. High-information dyads converge to optimal strategies in a game-theoretic sense. Low-information dyads converge to strategies that minimize the need to know about the partner. These findings are consistent with a game-theoretic learning model which relies on estimates of partner actions, but not partner goals. This similarity sheds some light on the minimal computational machinery which is necessary to an intelligent agent in order to develop stable physical collaborations with a human partner. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006385 DOI: 10.1371/journal.pcbi.1006385 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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