Transfer of complex skill learning from virtual to real rowing

PLoS One. 2013 Dec 20;8(12):e82145. doi: 10.1371/journal.pone.0082145. eCollection 2013.


Simulators are commonly used to train complex tasks. In particular, simulators are applied to train dangerous tasks, to save costs, and to investigate the impact of different factors on task performance. However, in most cases, the transfer of simulator training to the real task has not been investigated. Without a proof for successful skill transfer, simulators might not be helpful at all or even counter-productive for learning the real task. In this paper, the skill transfer of complex technical aspects trained on a scull rowing simulator to sculling on water was investigated. We assume if a simulator provides high fidelity rendering of the interactions with the environment even without augmented feedback, training on such a realistic simulator would allow similar skill gains as training in the real environment. These learned skills were expected to transfer to the real environment. Two groups of four recreational rowers participated. One group trained on water, the other group trained on a simulator. Within two weeks, both groups performed four training sessions with the same licensed rowing trainer. The development in performance was assessed by quantitative biomechanical performance measures and by a qualitative video evaluation of an independent, blinded trainer. In general, both groups could improve their performance on water. The used biomechanical measures seem to allow only a limited insight into the rowers' development, while the independent trainer could also rate the rowers' overall impression. The simulator quality and naturalism was confirmed by the participants in a questionnaire. In conclusion, realistic simulator training fostered skill gains to a similar extent as training in the real environment and enabled skill transfer to the real environment. In combination with augmented feedback, simulator training can be further exploited to foster motor learning even to a higher extent, which is subject to future work.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Biomechanical Phenomena
  • Computer Simulation*
  • Female
  • Humans
  • Learning*
  • Male
  • Middle Aged
  • Ships*
  • Surveys and Questionnaires
  • Task Performance and Analysis*
  • User-Computer Interface*
  • Video Recording

Grant support

This work was supported by ETH Zurich and the Swiss National Science Foundation (Grant: “:“Impact of Different Feedback Modalities on Complex Skill Learning”, fund number: CR22I2_135105). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.