Production and operations management (POM) uses learning curve (LC) models to determine the length of training sessions for new workers and predicting future task performance. Empirically validated LC parameters provide managers with quantitative information on the effects of the presumed factors behind the learning process. Previous studies considered LC to compose of cognitive and motor curves. Another widely acknowledged but only recently parameterized phenomenon in the POM field is interference, which assumes some loss of information or experience could occur over a learning session. This paper takes a logical step in this line of research by developing an interference-adjusted power LC model, a composite of cognitive and motor elements. This paper accounts for the decay of cognitive and motor memory traces from repetitions to measure the residual (interference-adjusted) experience and capture these phenomena. Three variants of the model are developed that assume power and exponential decay functions and an approximate version of the exponential one. Assembly data representing various forms of an individual learning profile have been used to test the fits of the developed models. In addition to those models, four potential models from the literature were selected for comparison purposes. The results show that the approximate model fits very well exponential learning profile. The findings highlight the confluence of the three phenomena in learning, component (cognitive/motor) learning, interference, and plateauing.

生产和运营管理 (POM) 使用学习曲线 (LC) 模型来确定新员工的培训课程长度并预测未来的任务绩效。经过实证验证的 LC 参数为管理人员提供了有关学习过程背后假定因素影响的定量信息。以前的研究认为 LC 由认知和运动曲线组成。POM 领域中另一个广为人知但最近才被参数化的现象是干扰，它假设在学习过程中可能会发生一些信息或经验的丢失。本文通过开发干扰调整功率 LC 模型，这是认知和运动元素的组合，在这方面的研究中迈出了合乎逻辑的一步。本文解释了从重复中产生的认知和运动记忆痕迹的衰减，以测量残余（干扰调整）经验并捕获这些现象。该模型的三个变体被开发出来，它们假定幂和指数衰减函数以及指数函数的近似版本。代表各种形式的个人学习档案的装配数据已被用于测试开发模型的拟合度。除了这些模型之外，还从文献中选择了四个潜在模型进行比较。结果表明，近似模型非常适合指数学习曲线。研究结果强调了学习、成分（认知/运动）学习、干扰和停滞的三种现象的汇合。该模型的三个变体被开发出来，它们假定幂和指数衰减函数以及指数函数的近似版本。代表各种形式的个人学习档案的装配数据已被用于测试开发模型的拟合度。除了这些模型之外，还从文献中选择了四个潜在模型进行比较。结果表明，近似模型非常适合指数学习曲线。研究结果强调了学习、成分（认知/运动）学习、干扰和停滞的三种现象的汇合。该模型的三个变体被开发出来，它们假定幂和指数衰减函数以及指数函数的近似版本。代表各种形式的个人学习档案的装配数据已被用于测试开发模型的拟合度。除了这些模型之外，还从文献中选择了四个潜在模型进行比较。结果表明，近似模型非常适合指数学习曲线。研究结果强调了学习、成分（认知/运动）学习、干扰和停滞的三种现象的汇合。代表各种形式的个人学习档案的装配数据已被用于测试开发模型的拟合度。除了这些模型之外，还从文献中选择了四个潜在模型进行比较。结果表明，近似模型非常适合指数学习曲线。研究结果强调了学习、成分（认知/运动）学习、干扰和平稳三个现象的汇合。代表各种形式的个人学习档案的装配数据已被用于测试开发模型的拟合度。除了这些模型之外，还从文献中选择了四个潜在模型进行比较。结果表明，近似模型非常适合指数学习曲线。研究结果强调了学习、成分（认知/运动）学习、干扰和停滞的三种现象的汇合。