ScholarOne - Zone In not Out! The Key to Winning for High-Level Tetris
Abstract
Automating a perceptual-motor task won't win you a perceptual-motor
contest. Despite claims that mindless automaticity is the essence of
expertise, our view is that it 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 part and focus on
actually 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, where
performance is dynamic and sometimes competitive. Our 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 our case study,
the player started out as an advanced novice but successfully
transitioned to competing at the championship level and entered the
Classic Tetris World Championship in 2020. Initially, the challenge was
gaining enough skill to make and execute perceptual-motor decisions in a
fraction of a second. However, once that became automatic the player
could spend those mental resources someplace else. Performance was
better for all games when the player was mentally engaged and used their
focused attention to plan ahead rather than just automatically
responding to the game pieces. We argue that the end goal for automating
perceptual-motor skills in competitive, dynamic environments is to make
space for the user to excel strategically.29 Jan 2024Submitted to Advance 09 Apr 2024Published in Advance