Models of fatigue and rest in learning. Part II: Motor and cognitive skills

This paper considers the possibilities and examples of using a general learning model with fatigue and rest effects to describe experimental data. A classification of iterative learning models is provided, and the existing datasets on learning from various fields are overviewed. A general algorithm for selecting an appropriate iterative learning model based on available experimental data is proposed. Examples of processing experimental and modeling data are presented for motor and cognitive skills, visual-motor adaptation, and tasks with long breaks. The following hypothesis is formulated and tested: learning models describe the data with deviations representing independent and identically distributed realizations of Gaussian random variables with zero mean. According to the values of statistical criteria, there are no grounds to reject this hypothesis. Based on the modeling results, recommendations on the optimization and management of the learning process are given.