Periodic tapping mechanisms of skill learning in a fast-paced video game.

Timing plays a critical role when building up motor skill. In this study, we investigated and simulated human skill learning in a simplified variant of the Space Fortress video game named Auto Orbit with a strong timing component. Our principal aim was to test whether a computational model designed to simulate keypress actions repeated at rates slower than 500 ms (>500 ms) could also simulate human learning with repeated keypress actions taking place at very fast rates (≤500 ms). The main finding was that increasing speed stress forced human participants to qualitatively switch their behavior from a cognitively controlled strategy to an inherently rhythmic motor strategy. We show how the adaptive control of thought rational architecture's periodic tapping motor extension can replicate such rhythmic patterns of keypresses in two different computational models of human learning. The first model implements streamed motor actions across hands that are temporally decoupled, while the second model implements a coupled motor strategy in which actions from both hands are executed relative to the same periodic motor clock. Different subsets of subjects correspond to these two models. Our modeling simulations integrate previous psychological and motor control findings within a single cognitive architecture, and successfully replicate human behavioral patterns across a range of experimental measures at fast speed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An Integrated Model of Collaborative Skill Acquisition: Anticipation, Control Tuning, and Role Adoption.

We studied collaborative skill acquisition in a dynamic setting with the game Co-op Space Fortress. While gaining expertise, the majority of subjects became increasingly consistent in the role they adopted without being able to communicate. Moreover, they acted in anticipation of the future task state. We constructed a collaborative skill acquisition model in the cognitive architecture ACT-R that reproduced subject skill acquisition trajectory. It modeled role adoption through reinforcement learning and predictive processes through motion extrapolation and learned relevant control parameters using both a reinforcement learning procedure and a new to ACT-R supervised learning procedure. This is the first integrated cognitive model of collaborative skill acquisition and, as such, gives us valuable insights into the multiple cognitive processes that are involved in learning to collaborate.

A decay-based account of learning and adaptation in complex skills.

How do humans adapt to parametric changes in a task without having to learn a new skill from scratch? Many studies of memory and sensorimotor adaptation have proposed theories that incorporate a decay on prior events, which leads the agent to eventually forget old experiences. This study investigates if a similar decay mechanism can account for human adaptation in complex skills that require the simultaneous integration of cognitive, motor, and perceptual processes. In 2 experiments, subjects learned to play a novel racing video game while adapting to parametric changes in the physics of the game's controls. Human learning and performance were modeled using the ACT-R cognitive architecture, which has been used successfully to model learning and fluency across a wide range of skills in prior research. Anderson et al. (2019) introduced the Controller module, a new component of the architecture that learns the setting of control parameters for actions and allows the agent to execute the rapid and precise actions that are necessary for good performance on complex tasks. Model simulations support including a moderate time-based decay on the weight of the experiences that the Controller uses. This is implemented in the Controller module by discounting the influence of older observations which helps the agent to focus on recent experiences that better reflect the current relationship between different settings of a control parameter and the rate of payoff from using that setting. (PsycInfo Database Record (c) 2022 APA, all rights reserved).