Carnegie Mellon University
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Computational & neural investigation of skill learning across speeds in video games

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posted on 2024-01-30, 19:05 authored by Pierre GianferraraPierre Gianferrara

 There has recently been a growing interest in video games as an opportunity to gain insight into the mechanisms of human psychology and skill learning. While past research has started to uncover effects of video game practice on attention, spatial cognition, and perception, it is not yet clear how motor behavior changes as a function of skill and what the precise neural correlates of sensorimotor learning are over the course of skill acquisition. This dissertation proposes to employ a Game-XP perspective (i.e., video games as experimental paradigms) to shed light on key cognitive, motor, and neural dimensions of skill acquisition across sub-second and supra-second time scales. 

This dissertation is broken down into three main parts: The first part’s objective is to characterize and computationally simulate general patterns of human skill learning in video games. Chapter 1 introduces some terminology pertaining to skill acquisition in the context of video games and provides an overview of some key motor correlates of skill. Chapter 2 introduces the adaptive control of thought rational (ACT-R) architecture and presents an instance of a computational ACT-R model of skill learning in the Auto Orbit game, which was used to investigate skill transfer across slow speeds (keypress rates > 500 ms). 

The second part’s objective is to account for and computationally simulate idiosyncratic motor patterns of behavior across speeds and across groups of subjects. Chapter 3 introduces periodic tapping and proposes a novel fast-paced ACT-R motor mechanism that can reproduce humans’ automatic motor behavior at fast speeds. Chapter 4 shows how one can leverage this ACT-R motor extension to account for individual differences across groups of subjects at fast speeds (keypress rates ≤ 500 ms) in the Auto Orbit video game.

 Finally, the third part’s objective is to investigate the neural mechanisms of sensorimotor learning in a game-inspired laboratory self-paced finger tapping task. Chapter 5 introduces electrophysiological correlates of skill acquisition and feedback processing with electroencephalography (EEG) and electromyography (EMG). Chapter 6 presents the results from neural investigations of sensorimotor learning with functional magnetic resonance imaging (fMRI) in the same self-paced finger tapping task. Overall, this dissertation introduces a number of novel methodological strategies which aid in providing an integrative account of computational and neural mechanisms of skill learning in video games as an example of complex task environments 

History

Date

2023-09-28

Degree Type

  • Dissertation

Department

  • Psychology

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

John R. Anderson

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