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 die 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.