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IntroductionIn this study, the change detection paradigm was used to study the working memory of patterned movements and the relationship of this type of memory with the visuospatial sketchpad in three experiments.MethodsExperiment 1 measured participants’ working memory capacity for patterned movements and explored the influence of stimulus type with indicators such as response time and accuracy rate. Experiments 2 and 3 explored the relationship between patterned movements and the visual and spatial subsystems, respectively.ResultsThe results of Experiment 1 indicated that individuals can store 3–4 patterned movements in working memory; however, a change in stimulus format or an increase in memory load may decrease the speed and efficiency of working memory processing. The results of Experiment 2 showed that working memory and visual working memory are independent when processing patterned movements. The results of Experiment 3 showed that the working memory of patterned movements was affected by spatial working memory.DiscussionChanges in stimulus type and memory load exerted different effects on the working memory capacity of participants. These results provide behavioral evidence that the storage of patterned movement information is independent of the visual subsystem but requires the spatial subsystem of the visuospatial sketchpad.

Optimization was carried out on the electric adjustment mechanism for transplanter by using the multidisciplinary design optimization method. According to statics, kinematics, dynamics, and control, disciplinary optimization models were established, with weight, transmission efficiency, vibration frequency, and control error as the optimization goals. Then, a collaborative optimization model for the multidisciplinary design of a mechanism system was constructed. Based on ISIGHT software, the multidisciplinary design integration platform for the electric adjustment mechanism was built. A hybrid algorithm comprising the dual sequential quadratic programming method and the multi-island genetic algorithm was used to calculate the model. Optimization results show that the weight of the electric adjustment mechanism drops by 13.10%, its vibration frequency decreases by 27.71%, its transmission efficiency increases by 20.26%, and the control error decreases by 36.98%. Under the mutual coordination and balance of all discipline goals, the optimal values of the design variables of the electric adjustment mechanism indicate overall optimal performance.