Artificial Neural Networks Predicting the Outcome of a Throwing Task – Effects of Input Quantity and Quality
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Internal forward models are used to explain motor prediction processes in motor control and learning e.g. predicting an upcoming miss in a throwing task before the knowledge of results is available. In this study we used artificial neural networks (ANN) to model such movement outcome prediction processes. Additionally, we varied the inputs of four different multilayer perceptrons (MLP) with respect to the quantity and the reliability (quality) of input parameters to account for perceptual noise. The results show that ANNs are able to learn the non-linear input-output mapping underlying the throwing task even with few input variables (velocity and angle at ball release). Results improve when providing additional information about the ball flight (prediction accuracy increases from RMSE = 7.9 mm to RMSE = 3.9 mm). However, when a model is provided with noisy inputs only, model training and prediction suffers substantially (RMSE = 53.8 mm). Yet, additional reliable information about the ball flight (in addition to noisy velocity and angle) leads to very high model prediction accuracy again (RMSE = 4.1 mm). In a nutshell, ANNs can be used to model internal forward model predictions, but the availability of reliable input information is essential at least to some extent.
KeywordsSkittles Multilayer perceptron Task performance prediction Forward model Motor control
This research was supported by the Deutsche Forschungsgemeinschaft funded Collaborative Research Center on “Cardinal Mechanisms of Perception” Grant SFB-TRR 135 and research project MU 1374/3-1.
- 3.Schmidt, R., Lee, T.: Motor control and learning: a behavioral emphasis. Human Kinetics, Champaign, IL (2011)Google Scholar
- 9.Welch, R., Warren, D.: Intersensory interactions. In: Boff, K.R., Kaufman, L., Thomas, J.P. (eds.) Handbook of Perception and Human Performance, pp. 25.1–25.36. Wiley, New York (1986)Google Scholar
- 14.Schöllhorn, W.I., Jäger, J.M., Janssen, D.: Artificial neural network models of sports motions. In: Handbook of Biomechanics and Human Movement Science, pp. 50–64. Routledge, New York City (2008)Google Scholar
- 16.Sternad, D., Huber, M.E., Kuznetsov, N.: Acquisition of novel and complex motor skills: stable solutions where intrinsic noise matters less. In: Levin, M.F. (ed.) Progress in Motor Control. Skill Learning, Performance, Health, and Injury, pp. 101–124. Springer, New York (2014)Google Scholar
- 18.Adams, L.: Bestimmung der Wahrnehmungsschwelle des motorischen Rauschens bei Zielwurfbewegungen. Unpublished thesis Justus-Liebig-University Giessen (2008)Google Scholar
- 20.Günther, F., Fritsch, S.: neuralnet: training of neural networks. R J. 2, 30–38 (2010)Google Scholar