More about my research
Action Sequencing, Timing, and Chunking in the Space Fortress video game.
This project introduces action timing and action sequencing measures of skill acquisition in a variant of the original Space Fortress video game with complex dynamics called YouTurn. We first explore the structure of keypress chunks over forty 3-min games in SF YouTurn. We then investigate skill acquisition and inter-individual skill differences in terms of action sequencing variability and action timing variability. Finally, we fit linear models to see whether measures of action sequencing variability and action timing variability can predict skill in SF YouTurn. This video was recorded for the 20th International Conference on Cognitive Modeling (2022).
Simulating Human Periodic Tapping and Implications for Cognitive Models.
This project’s purpose was to simulate human periodic motor behavior in a simple self-paced tapping task that involved period error correction and feedback processing. We calibrated the adaptive control of thought rational (ACT-R) architecture’s new periodic tapping motor extension based on human experimental results and showed that ACT-R can simulate human motor behavior. This video was recorded for the 19th International Conference on Cognitive Modeling (2021).
Time-related Effects of Speed on Motor Skill Acquisition.
We introduce measures of action timing and action sequencing that predict skill acquisition in a controlled motor task named Auto Orbit, inspired from the Space Fortress video game. The first goal was to use these measures to capture time-related effects of speed on skill, operationalized as a performance score. The second goal was to compare human and model motor skill learning. Results suggest that humans rely on different motor timing systems in the sub- and supra-second time scales. This video was recorded for the 18th International Conference on Cognitive Modeling (2020).