Target Uncertainty Mediates Sensorimotor Error Correction

PLoS One. 2017 Jan 27;12(1):e0170466. doi: 10.1371/journal.pone.0170466. eCollection 2017.


Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects' scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one's response. By suggesting that subjects' decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated.

MeSH terms

  • Adult
  • Bayes Theorem
  • Feedback, Sensory / physiology*
  • Female
  • Humans
  • Male
  • Movement / physiology*
  • Psychomotor Performance / physiology*

Grants and funding

This work was supported in part by grants EP/F500385/1 and BB/F529254/1 for the University of Edinburgh School of Informatics Doctoral Training Centre in Neuroinformatics and Computational Neuroscience from the UK Engineering and Physical Sciences Research Council (EPSRC), UK Biotechnology and Biological Sciences Research Council (BBSRC), and the UK Medical Research Council (MRC) to L. Acerbi. This work was also supported by the Wellcome Trust, the Human Frontiers Science Program, and the Royal Society Noreen Murray Professorship in Neurobiology (D. M. Wolpert). S. Vijayakumar is supported through grants from Microsoft Research, Royal Academy of Engineering and EU FP7 programs. The work has made use of resources provided by the Edinburgh Compute and Data Facility, which has support from the eDIKT initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.