Amount of mental practice and performance of a simple motor task

The effects of different amounts of mental practice on the performance of a motor skill were studied. Research supports the effectiveness of mental practice on performance; however, little is known about how much practice is needed and whether there is an optimal amount for these practice effects. Participants, 209 students ages 18 to 44 years (M = 20.5, SD = 2.9), completed a pre- and posttest of dart throwing with the nonpreferred hand. In the practice phase, participants completed either 25 (Mental Practice 25), 50 (Mental Practice 50), or 100 (Mental Practice 100) trials of the darts task or 50 trials of a catching task (Catching Task). Performance for all groups improved from pre- to posttest. Improvements for the three mental practice groups were greater than for the Catching Task group; however, there were no differences for the three Mental Practice groups. The findings support the positive effect of mental practice over a control condition and suggest that small amounts of mental practice may be sufficient for performance improvements, at least for a simple motor skill.

Predicting amount of saleable products using neural network metamodels of casthouses

This study aims at developing abstract metamodels for approximating highly nonlinear relationships within a metal casting plant. Metal casting product quality nonlinearly depends on many controllable and uncontrollable factors. For improving the productivity of the system, it is vital for operation planners to predict in advance the amount of high quality products. Neural networks metamodels are developed and applied in this study for predicting the amount of saleable products. Training of metamodels is done using the Levenberg-Marquardt and Bayesian learning methods. Statistical measures are calculated for the developed metamodels over a grid of neural network structures. Demonstrated results indicate that Bayesian-based neural network metamodels outperform the Levenberg-Marquardt-based metamodels in terms of both prediction accuracy and robustness to the metamodel complexity. In contrast, the latter metamodels are computationally less expensive and generate the results more quickly.