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Recent findings have demonstrated that reward feedback alone can drive motor learning. However, it is not yet clear whether reward feedback alone can lead to learning when a perturbation is introduced abruptly, or how a reward gradient can modulate learning. In this study, we provide reward feedback that decays continuously with increasing error. We asked whether it is possible to learn an abrupt visuomotor rotation by reward alone, and if the learning process could be modulated by combining reward and sensory feedback and/or by using different reward landscapes. We designed a novel visuomotor learning protocol during which subjects experienced an abruptly introduced rotational perturbation. Subjects received either visual feedback or reward feedback, or a combination of the two. Two different reward landscapes, where the reward decayed either linearly or cubically with distance from the target, were tested. Results demonstrate that it is possible to learn from reward feedback alone and that the combination of reward and sensory feedback accelerates learning. An analysis of the underlying mechanisms reveals that although reward feedback alone does not allow for sensorimotor remapping, it can nonetheless lead to broad generalization, highlighting a dissociation between remapping and generalization. Also, the combination of reward and sensory feedback accelerates learning without compromising sensorimotor remapping. These findings suggest that the use of reward feedback is a promising approach to either supplement or substitute sensory feedback in the development of improved neurorehabilitation techniques. More generally, they point to an important role played by reward in the motor learning process.

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The Delft Shoulder and Elbow Model (DSEM), a musculoskeletal model of the shoulder and elbow has been extensively developed since its introduction in 1994. Extensions cover both model structures and anatomical data focusing on the addition of an elbow part and muscle architecture parameters. The model was also extended with a new inverse-dynamics optimization cost function and combined inverse-forward-dynamics models. This study is an update on the developments of the model over the last decade including a qualitative validation of the different simulation architectures available in the DSEM. To validate the model, a dynamic forward flexion motion was performed by one subject, of which the motion data and surface EMG-signals of 12 superficial muscles were measured. Patterns of the model-predicted relative muscle forces were compared with their normalized EMG-signals. Results showed relatively good agreement between forces and EMG (mean correlation coefficient of 0.66). However, for some cases, no force was predicted while EMG activity had been measured (false-negatives). The DSEM has been used and has the potential to be used in a variety of clinical and biomechanical applications.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-011-0839-7) contains supplementary material, which is available to authorized users.

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