Establishing a paradigm to investigate strategy use in complex skills
Questions of strategy selection have been studied in various contexts such as problem solving, text editing, and even dynamic, fast-paced tasks. One way to model the strategy selection process is as a learning and decision problem: with experience, the agent learns the expected utilities of strategies, and executes a strategy based on what it has learned. However, the strategies studied in most of the past research have relatively stable utilities. Even when the task structure is manipulated to change the utilities of strategies, these changes are relatively infrequent. This contrasts with many real-world skills, such as sports and video gaming, where different strategies are optimal at different points during the learner’s trajectory. As a learner practices a skill, improvements in the learner’s degree of perceptual-motor calibration to the physics of tools and devices interacts with the difficulty of executing a strategy to affect the strategy’s utility. Furthermore, it is often unknown what the maximum utility of any strategy will be, as this is partly determined by the learner’s own general perceptual-motor abilities and prior experiences. How humans learn and select strategies in the face of such variation and uncertainty behooves further investigation. Towards that goal, we present a task and strategy paradigm that captures many of the features of a typical complex skill. We also demonstrate possible interactions between strategy use, perceptual-motor calibration, and task knowledge using past experimental data and model simulations within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture.