Dealing with uncertainty in sensorimotor control in sports: A systematic review

  • Damian Beck Institute of Sport Science, University of Bern, Switzerland
  • Stephan Zahno Institute of Sport Science, University of Bern, Switzerland
  • Ernst-Joachim Hossner Institute of Sport Science, University of Bern, Switzerland
Keywords: uncertainty, sensorimotor control, sports, Bayesian decision theory



In complex sensorimotor behavior, uncertainty arises from ambiguity in the sensory inputs of the environment (e.g. Kersten et al., 2004) as well as from noise in sensory detection and motor execution (e.g. Todorov & Jordan, 2002). Moreover, the observable sensory inputs are delayed and there are typically multiple solutions to solve a motor task (e.g. Franklin & Wolpert, 2011). In sports, where human capacity reaches its limit with these challenges, dealing with uncertainty is crucial. Thus, the question arises how humans are able to deal with uncertainty in sensorimotor control in naturalistic situations such as sports.


A systematic search for original articles was conducted in six scientific databases with the following terms: (uncertainty OR noise) AND (sensor* OR motor) AND control AND sport AND movement. After independently screening 4,309 articles and additional reference lists by two raters, 70 articles remained for the review.


There is clear evidence that prior knowledge (or similar contextual information) and multiple sensory information affect perception, action and performance. Moreover, there is considerable evidence that prior knowledge and multiple sensory information get integrated according to their reliability in order to reduce uncertainty. Furthermore, it has been reported that gaze behavior is close to the optimal compared to an optimal Bayesian observer in an expected and unexpected uncertain environment. There is also considerable evidence that not all variables have to be controlled and rather uncertainty is accepted in variables that do not impair overall performance. Some findings show that inherent noise is taken into account when planning motor execution so that the potential costs and rewards of the movement outcome are optimized. Finally, some findings show that unforeseen perturbation can be absorbed by means of higher muscle stiffness resulting from muscular co-contraction.


The strategies to deal with uncertainty mentioned in the results section can be summarized under the umbrella of Bayesian decision theory (Franklin & Wolpert, 2011). Only a few alternative models were referenced that, however, were conceptually quite similar to Bayesian models. Therefore, Bayesian decision theory seems to offer a promising framework for future research on uncertainty in sensorimotor control in sport science.


Franklin, D. W., & Wolpert, D. M. (2011). Computational mechanisms of sensorimotor control. Neuron, 72(3), 425-442.

Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271-304.

Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5(11), 1226-1235.

How to Cite
Beck, D., Zahno, S., & Hossner, E.-J. (2023). Dealing with uncertainty in sensorimotor control in sports: A systematic review. Current Issues in Sport Science (CISS), 8(2), 092.