Zone In Not Out! The Key to Winning High-Level Tetris

Abstract

Automating a perceptual-motor task will not win you a perceptual-motor contest. Despite claims that mindless automaticity is the essence of expertise, the view espoused here is that automaticity is worthwhile only because it enables the expert to plan and strategize. Indeed, the purpose of learning to manually shift gears is to eventually ignore that function to focus instead on actual driving. To perform well, the expert must transition their attention from a task’s low-level components to its high-level nuances. This is best understood in real-world scenarios (e.g. driving, in which performance is dynamic and sometimes competitive). This argument is based on a years-long, longitudinal case study of learning to play the puzzle game, Tetris. Tetris is intensively perceptual-motor with complicated manual routines needed to manage expert game speeds. For this case study, the player began as an advanced novice but successfully transitioned to championship level in the 2020 Classic Tetris World Championship. Initially, the challenge was gaining enough skill to make and execute perceptual-motor decisions in a fraction of a second. However, once that process became automatic, the player could spend those freed mental resources elsewhere. Performance was better for all games when the player was mentally engaged and used their focused attention to plan ahead rather than just automatically respond to the game pieces. We argue that the end goal for automating perceptual-motor skills in competitive, dynamic environments is to free cognitive space in the brain for the user to excel strategically.