RI-Small: Cognitive Modeling of Human Motor Skill Acquisition

直到最近，理论心理学才对人类如何在动态的、多自由度的人工控制领域中学习有了更多的了解。虽然应用方面的关注已经导致了人工控制任务的成功培训项目（例如，大多数人在合理的时间内学会驾驶汽车），但我们目前缺乏预测人们在这些领域能做得多好，或者他们能学得多快的能力。这限制了我们在这些领域培训人员的能力，目前我们依靠昂贵的一对一辅导或类似的强化方法。该项目研究人类在真实和虚拟环境中的表现和感觉运动任务的获得。将对表现出线性表现的高性能和低性能者的人体运动数据和各种技能的表现进行分析，并将其与快速获得技能并表现出非线性表现的受试者的数据进行比较。这些数据将为更准确的感觉运动技能习得模型的开发提供信息，这些模型可以用ACT-R表达，这样做应该会提高对人类运动学习领域训练方法的理解。

Theoretical psychology has not, until recently, been in position to have much to say about how humans learn in dynamic, multidegree-of-freedom manual control domains. While applied concerns have led to successful training programs for manual control tasks (e.g., most people learn to steer automobiles in reasonable time), we currently lack the ability to predict how well people will do in these domains or how rapidly they will learn. This limits our ability to train people in these domains, where we presently rely on expensive one-on-one tutoring or similar intensive methods.The project studies human performance and acquisition of sensorimotor tasks in real and virtual environments. Human motion data and performance of various skills by high performers and low performers, who exhibit linear performance gains, will be analyzed and compared to data for subjects who rapidly acquire skill and exhibit nonlinear performance gains. This data will inform the development of more accurate models of sensorimotor skill acquisition that can be expressed in ACT-R, and doing this should lead to improved understanding of training methods in human motor learning domains.