Time-related Effects of Speed on Motor Skill Acquisition
Anderson et al. (2019) present an ACT-R model of how humans learn to play rapid-action video games. To further test this model, we utilized new measures of action timing and sequencing to predict skill acquisition in a controlled motor task named Auto Orbit. Our first goal was to use these measures to capture time-related effects of speed on motor skill acquisition, operationalized as a performance score. Our second goal was to compare human and model motor skill learning. Our results suggest that humans rely on different motor timing systems in the sub- and supra-second time scales. While our model successfully learned to play Auto Orbit, some discrepancies in terms of motor learning were noted as well. Future research is needed to improve the current model parametrization and enable ACT-R’s motor module to engage in rhythmic behavior at fast speeds.
hi Pierre, cool stuff! I noticed that the entropy and the log CV ISI tended to miss trends at the very beginning of the game. I guess this is where humans get their bearing. Do you have any idea on what that process of "getting their bearing" is and would it be possible to model that?
First off, very interesting talk/paper Pierre! I really liked the different measures that you created and thought the use of the mixed effects models to test your hypotheses was really nice. The use of Shannon's Entropy was a neat idea. Do you see within-game trends in terms of entropy, for example, do people 'find a rhythm' as they get closer to...
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