Abstract
We examined the detailed behavioral characteristics of transfer of skill and the ability of the adaptive control of thought rational (ACT-R) architecture to account for this with its new Controller module. We employed a simple action video game called Auto Orbit and investigated the control tuning of timing skills across speed perturbations of the environment. In Auto Orbit, players needed to learn to alternate turn and shot actions to blow and burst balloons under time constraints imposed by balloon resets and deflations. Cognitive and motor skill transfer was assessed both in terms of game performance and in terms of the details of their motor actions. We found that skill transfer across speeds necessitated the recalibration of action timing skills. In addition, we found that acquiring skill in Auto Orbit involved a progressive decrease in variability of behavior. Finally, we found that players with higher skill levels tended to be less variable in terms of action chunking and action timing. These findings further shed light on the complex cognitive and motor mechanisms of skill transfer across speeds in complex task environments.
Highlights
The common saying is that “practice makes perfect”
We evaluated a total of eight experimental measures of skill acquisition divided into two sets
The first part will explore how the results can be understood in terms of adaptive control of thought rational (ACT-R) models and the second two parts will further explore effects of skill transfer and inter-individual differences respectively
Summary
Chess players, cooks and sports players have all acquired and perfected their skills through hours spent practicing and training [1,2]. In the context of skill acquisition, researchers often distinguish between lower-skilled and higher-skilled individuals based on objective quantifiable measures of performance [4]. The researcher showed that acquiring a skill involved fast improvements early on, which progressively slowed down as performance asymptotically approached a learning plateau. One common way of describing this learning trajectory is to express it in terms of a power function regardless of the task at hand [6] (but see [7,8,9] for further discussion on the power function)
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