RI-Small: Cognitive Modeling of Human Motor Skill Acquisition

RI-Small：人类运动技能习得的认知建模

• 批准号: 0812569
• 负责人: Marcia O'Malley
• 金额: $ 43.1万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812569&HistoricalAwards=false
• 关键词: RI; Small; Cognitive; Modeling; Human

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.

直到最近，理论心理学才能够对人类如何在动态、多自由度手动控制领域中学习进行更多的探讨。虽然应用问题已经导致手动控制任务的培训计划取得成功（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的表现或学习速度的能力。这限制了我们在这些领域培训人员的能力，目前我们依赖昂贵的一对一辅导或类似的强化方法。该项目研究真实和虚拟环境中的人类表现和感觉运动任务的获取。将分析表现出线性性能增益的高绩效者和低绩效者的人体运动数据和各种技能的表现，并将其与快速获得技能并表现出非线性性能增益的受试者的数据进行比较。这些数据将为开发更准确的感觉运动技能获取模型提供信息，这些模型可以在 ACT-R 中表达，这样做应该可以提高对人类运动学习领域训练方法的理解。