The study of expertise is difficult to do in a laboratory environment due to the challenge of finding people at different skill levels and the lack of time for participants to acquire mastery. In this paper, we report on two studies that analyze naturalistic gameplay data using cohort analysis to better understand how skill relates to practice and habit. Two cohorts are analyzed, each from two different games. Our work follows skill progression through 7 months of Halo matches for a (...) holistic perspective, but also explores low-level in-game habits when controlling game units in StarCraft 2. Players who played moderately frequently without long breaks were able to gain skill the most efficiently. What set the highest performers apart was their ability to gain skill more rapidly and without dips compared to other players. At the beginning of matches, top players habitually warmed up by selecting and re-selecting groups of units repeatedly in a meaningless cycle. They exhibited unique routines during their play that aided them when under pressure. (shrink)

This article advances a tentative theory of skill in relation to eSports. This conjectural theory of skill rests on hypothesizes informed by assumptions from watching 100+ hours of eSport events on Twitch, YouTube, and AfreecaTV and is supported by discussions, reflections and evaluations with eSport players. The case material of this article includes the games Clash Royale (CR), StarCraft 2 (SC2), Counter-Strike: Global Offensive (CS:GO), and online battle arenas (mobas) such as League of Legends (LOL) and Defense of the (...) Ancient 2 (DOTA2). This inferred theory of skill is conjoined and composed of seven strands, which provide a framework for explaining how to understand skill in eSport. These seven strands are presented under the headings: (1) knowledge about game objects, (2) insights into game systems, (3) understanding metagaming, (4), yomi: ‘reading’ the opponent, (5) ability to execute, (6) emotional discipline, and finally (7) team coherency (depending upon whether the skill is associated with a one-versus-one or team-versus-team game). In conclusion, the proposed theory of skill will expose its explanatory power and reach by providing an example of how it can both be implemented to create profiles of players’ skill, and how these profiles in turn can serve as guides to help players improve their skill levels in eSport. (shrink)

This article advances a tentative theory of skill in relation to eSports. This conjectural theory of skill rests on hypothesizes informed by assumptions from watching 100+ hours of eSport events on Twitch, YouTube, and AfreecaTV and is supported by discussions, reflections and evaluations with eSport players. The case material of this article includes the games Clash Royale (CR), StarCraft 2 (SC2), Counter-Strike: Global Offensive (CS:GO), and online battle arenas (mobas) such as League of Legends (LOL) and Defense of the (...) Ancient 2 (DOTA2). This inferred theory of skill is conjoined and composed of seven strands, which provide a framework for explaining how to understand skill in eSport. These seven strands are presented under the headings: (1) knowledge about game objects, (2) insights into game systems, (3) understanding metagaming, (4), yomi: ‘reading’ the opponent, (5) ability to execute, (6) emotional discipline, and finally (7) team coherency (depending upon whether the skill is associated with a one-versus-one or team-versus-team game). In conclusion, the proposed theory of skill will expose its explanatory power and reach by providing an example of how it can both be implemented to create profiles of players’ skill, and how these profiles in turn can serve as guides to help players improve their skill levels in eSport. (shrink)

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring (...) processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

Many theories of complex cognitive-motor skill learning are built on the notion that basic cognitive processes group actions into easy-to-perform sequences. The present work examines predictions derived from laboratory-based studies of motor chunking and motor preparation using data collected from the real-time strategy video game StarCraft 2. We examined 996,163 action sequences in the telemetry data of 3,317 players across seven levels of skill. As predicted, the latency to the first action is delayed relative to the other actions in (...) the group. Other predictions, inspired by the memory drum theory of Henry and Rogers, received only weak support. (shrink)

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well (...) as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld. (shrink)

Why games? How could anyone consider action games an experimental paradigm for Cognitive Science? In 1973, as one of three strategies he proposed for advancing Cognitive Science, Allen Newell exhorted us to “accept a single complex task and do all of it.” More specifically, he told us that rather than taking an “experimental psychology as usual approach,” we should “focus on a series of experimental and theoretical studies around a single complex task” so as to demonstrate that our theories of (...) human cognition were powerful enough to explain “a genuine slab of human behavior” with the studies fitting into a detailed theoretical picture. Action games represent the type of experimental paradigm that Newell was advocating and the current state of programming expertise and laboratory equipment, along with the emergence of Big Data and naturally occurring datasets, provide the technologies and data needed to realize his vision. Action games enable us to escape from our field's regrettable focus on novice performance to develop theories that account for the full range of expertise through a twin focus on expertise sampling and longitudinal studies of simple and complex tasks. (shrink)