Experimental Brain Research

, Volume 208, Issue 1, pp 73–87 | Cite as

Target switching in curved human arm movements is predicted by changing a single control parameter

  • Heiko Hoffmann
Research Article


Straight-line movements have been studied extensively in the human motor-control literature, but little is known about how to generate curved movements and how to adjust them in a dynamic environment. The present work studied, for the first time to my knowledge, how humans adjust curved hand movements to a target that switches location. Subjects (n = 8) sat in front of a drawing tablet and looked at a screen. They moved a cursor on a curved trajectory (spiral or oval shaped) toward a goal point. In half of the trials, this goal switched 200 ms after movement onset to either one of two alternative positions, and subjects smoothly adjusted their movements to the new goal. To explain this adjustment, we compared three computational models: a superposition of curved and minimum-jerk movements (Flash and Henis in J Cogn Neurosci 3(3):220–230, 1991), Vector Planning (Gordon et al. in Exp Brain Res 99(1):97–111, 1994) adapted to curved movements (Rescale), and a nonlinear dynamical system, which could generate arbitrarily curved smooth movements and had a point attractor at the goal. For each model, we predicted the trajectory adjustment to the target switch by changing only the goal position in the model. As result, the dynamical model could explain the observed switch behavior significantly better than the two alternative models (spiral: P = 0.0002 vs. Flash, P = 0.002 vs. Rescale; oval: P = 0.04 vs. Flash; P values obtained from Wilcoxon test on R 2 values). We conclude that generalizing arbitrary hand trajectories to new targets may be explained by switching a single control command, without the need to re-plan or re-optimize the whole movement or superimpose movements.


Behavioral experiment Target switch Curved movement Computational model Dynamical system Convergent force field 



I am grateful to Drs Stefan Schaal, Simon Giszter, and Francisco Valero-Cuevas for helpful discussions, particularly, about the interpretation and presentation of the results. Furthermore, I like to thank Dr John Krakauer for suggesting to extract the transition function from data and Dr Scott Young and the anonymous reviewers for their comments which helped to improve the manuscript. The experiments were performed in the Computational Learning & Motor Control Lab of Dr Schaal. This work was supported by grant HO-3771-1 from the German Research Foundation (DFG).


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.HRL Laboratories, LLCMalibuUSA

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