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. 2019 Jun 20;9:8881. doi: 10.1038/s41598-019-44166-7

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The PLM can predict the effect of temporal smoothing on online performance. Results are shown for an example session with participant T8. (A) Observed cursor movements made during a single session under different smoothing settings (α) are shown next to simulated trajectories predicted by the model. The model parameters were fit using data only from a single condition (α = 0.95, indicated in gray) and held fixed when simulating movements under different smoothing settings. (B) The data from (A) is quantified using four movement performance metrics (error bars represent 95% confidence intervals). Online performance is well predicted by the model (the red lines lie close to the black lines). Confidence intervals for model predictions were generated using bootstrap resampling (trials were resampled from each condition with replacement); the confidence intervals represent uncertainty in the predictions due to limited training data.