Distributed Cognition and Temporal Knowledge in League of Legends

Introduction

In 2000, Hollan, Hutchins, and Kirsh pointed out that as we passed “through the historical moment when people work in front of a single computer … focused on tasks involving only local information,” networked computers – and the teams people form with them – were changing what it meant to do work. In 2017, if a co-worker cannot access the network, we often assume they cannot work. Computer-based play has experienced the same shift. As it became easier to connect over the internet, titles like League of Legends (Riot Games, 2009), a multiplayer online battle arena (MOBA) in which teams of five compete to destroy their opponent’s base, grew in popularity. With popularity came a higher level of competition and professional competitive gaming, or esports, careers for the best players. At this elite level, coordinating on a split-second timescale, strategizing on a many minute timescale, and tracking durations anywhere in between are all equally essential to winning. The work presented in this paper examines a central aspect of collaboration and elite performance in League of Legends: distributed temporal cognition, or how a team distributes knowledge of time during play.

The focus on timescales on which a League team operates, and how it manages information on those timescales, builds on Chabris’s (2017) argument for the possibility of games to be vital to major advances in other fields, most notably cognitive science. He claims that by virtue of the breadth of timescales on which precisely measurable, complex activity takes place in action video games, “from millisecond-level responses...” (Chabris, 2017) through months of skill and knowledge development, such games could open new avenues for studying human cognition and performance. To examine this claim, the present work details the various timescales on which a League team thinks and acts in a competitive match and the strategies for distributing and using temporal knowledge in one particular elite team.

Wagner (2006), too, contends that studying esports can serve purposes other than understanding and designing games. Learning from how teams operate in the highly structured, high-pressure environment of competitive gaming can, he argues, translate to better understanding of how to train people to collaborate skillfully in a rigid digital structure, how elite performance in virtual environments compares to elite performance in traditional organizational teams, and how people learn through and around information and communication technology (Wagner, 2006).

League is particularly well suited for studying skillful collaboration, because no one player can be solely responsible for keeping track of and performing operations on every temporal parameter of the game. The team has to allocate knowledge where and when it is most helpful. Stored in the digital artifacts on-screen or in the players’ memories, information must be presented at the right moment to be of use. Produce potentially game-saving knowledge too early and it is, at best, lost in the sea of information flowing through the team – too late and a teammate has already died, the enemy has taken an objective, or at worst, the game has been lost. The player is situated in a system of people and artifacts, throughout which cognition is distributed. “Team,” throughout this article, refers to the cognitive system, comprised of screens, mouses, keyboards, headsets, human bodies, human minds, game clocks, countdown animations, chat logs, minimaps, gold and experience counters, and a number of other representational media. As Chen (2010) notes in his analysis of distributed cognition and actor-network theory (Latour, 2005) in World of Warcraft (Blizzard, 2004), “The material resources are not only helping; they are actually doing,” (p. 132) making people and artifacts equal actors in the activity.